Hybrid co-simulation: it’s about time

  • Fabio Cremona
  • Marten Lohstroh
  • David Broman
  • Edward A. Lee
  • Michael Masin
  • Stavros Tripakis
Open Access
Special Section Paper


Model-based design methodologies are commonly used in industry for the development of complex cyber-physical systems (CPSs). There are many different languages, tools, and formalisms for model-based design, each with its strengths and weaknesses. Instead of accepting some weaknesses of a particular tool, an alternative is to embrace heterogeneity, and to develop tool integration platforms and protocols to leverage the strengths from different environments. A fairly recent attempt in this direction is the functional mock-up interface (FMI) standard that includes support for co-simulation. Although this standard has reached acceptance in industry, it provides only limited support for simulating systems that mix continuous and discrete behavior, which are typical of CPS. This paper identifies the representation of time as a key problem, because the FMI representation does not support well the discrete events that typically occur at the cyber-physical boundary. We analyze alternatives for representing time in hybrid co-simulation and conclude that a superdense model of time using integers only solves many of these problems. We show how an execution engine can pick an adequate time resolution, and how disparities between time representations internal to co-simulated components and the resulting effects of time quantization can be managed. We propose a concrete extension to the FMI standard for supporting hybrid co-simulation that includes integer time, automatic choice of time resolution, and the use of absent signals. We explain how these extensions can be implemented modularly within the frameworks of existing simulation environments.


Co-simulation Functional mock-up interface Time 

1 Introduction

Model-based design of cyber-physical systems (CPS) requires modeling techniques that embrace both the cyber and the physical parts of a system [24]. There is a long history of modeling languages and tools that integrate techniques that were originally developed independently, and on different sides of the border that separates the cyber and the physical. Modelica [20, 39], for example, integrates object-oriented design (a cyber modeling technique) with differential-algebraic equations (DAEs, a physical modeling technique). Languages and tools for hybrid systems design [11] integrate finite state automata (cyber) with ordinary differential equations (ODEs, physical). Discrete-event (DE) modeling tools [12, 18, 29, 49] integrate a model of a time continuum (physical) with discrete, instantaneous events (cyber). Such simulation tools are capable, in principle, of simulating both cyber components (software and networks) and physical components (mechanical, electrical, fluid flows, etc.).

In spite of the power and utility of existing tools, we should not be sanguine about CPS modeling. All of the above integrations have pitfalls, limitations, and corner cases where a model that can be easily handled in one tool cannot be easily handled in another. Modelica-based tools, for example, have difficulty with some discrete phenomena, even purely physical ones, forcing model builders to sometimes model discrete behaviors as rapid continuous dynamics [41]. Conversely, tools that handle discrete events well, such as DE tools, may have difficulty with continuous dynamics [36], forcing model builders into brute-force methods such as sampled-data models with high sampling frequencies.

One possible solution is to embrace the heterogeneity of tools and to provide tool integration platforms and protocols that enable co-simulation using a multiplicity of tools [23]. There is a long history of tool integration platforms (sometimes called “simulation backplanes”) for DE modeling and a well-established standard called the high-level architecture (HLA) for tool interoperability [27]. A more recent development is the functional mock-up interface (FMI), a standard initiated by Daimler AG within the ITEA2 MODELISAR project [5, 40], now maintained by the Modelica Association. It has been designed to enable the exchange or co-simulation of model components, functional mock-up units (FMUs), designed with different modeling tools. The standard consists of a C application program interface (API) for simulation components and an XML schema for describing components. Largely unspecified is the algorithm that coordinates the execution of a collection of FMUs, the master algorithm (MA). The idea is that the standard should be flexible enough to accommodate the inevitable differences between execution engines in different tools. FMI provides two distinct mechanisms for interaction between an FMU and a host simulator: (i) model exchange (FMI-ME), where the host simulator is responsible for all numerical integration methods, and (ii) co-simulation (FMI-CS), where the FMU implements its own mechanisms for advancing the values of state variables. FMI for co-simulation is more focused on tool interoperability; the host simulator provides input values to the FMU, requests that the FMU advance its state variables and output values in time, and then queries for the updated output values.

The current standard for co-simulation (version 2.0 [38]), however, is unable to correctly simulate many mixed discrete and continuous behaviors, limiting its utility in current form for model-based design for CPS [8]. As a consequence, the community-driven standardization process is considering another mechanism called hybrid co-simulation that strives for the loose coupling of co-simulation, but with support for discrete and discontinuous signals and instantaneous events. The intent of this mechanism is to support hybrid systems [1, 11, 34, 42, 46], where continuous dynamics are combined with discrete mode changes and discrete events. Hybrid co-simulation promises better interoperability between models of the cyber and the physical sides of the CPS problem.

In this article, we focus on a particular issue with hybrid co-simulation that has proved central to the problem, namely the modeling of time. Time is a central concept in reasoning about the physical world, but is largely abstracted away when reasoning about the cyber world. As a result, the engineering methods that CPS builds on have misaligned abstractions between the physics domain, the mathematical domain used to model physics, the computational domain used to implement these mathematical abstractions for simulation, and the computational domain used on the cyber side of CPS. The most egregious misaligned abstractions concern time, where all four domains routinely use mutually incompatible models of time.

The most common resolution for this conundrum is to adopt the naive Newtonian ideal model of time, where time is a real number known everywhere and advancing uniformly, and the real number is approximated in software as a floating-point number. In this paper, we show that floating-point numbers are inadequate for hybrid co-simulation. We describe instead a representation of time that eliminates the problems associated with floating-point representations, allows for multiplicity of time resolutions, and allows for cyber abstractions where events can occur discretely in time and in sequences without time advancing. For the latter property, we adopt a form of superdense time [33, 34] (see Sect. 2.2). Our solution satisfies all of the requirements for hybrid co-simulation stated in [8].
Fig. 1

A zero-delay feedback model

Although the approach presented in this paper is general and could potentially be applicable to many different hybrid co-simulation environments, we have chosen to illustrate the concept concretely, by applying it to FMI and showing that only modest extensions to the FMI standard are needed to follow our recommendations. Within this framework, we show how to perform hybrid co-simulation with heterogeneous time models and accommodate mixtures of components that may internally represent time differently. Specifically, our solution supports hybrid co-simulation of FMUs that use floating-point time together with integer-time FMUs, even if those integer-time FMUs internally use a different resolutions.

In summary, we make the following contributions:
  • We analyze and compare alternatives for representing time, including floating-point numbers, rational numbers, and integers. We discuss superdense time and the concepts of time resolution. We also propose a model of time that supports a multiplicity of time resolutions, differing even within the same simulation, supports discrete events with an exact notion of simultaneity, invulnerable to quantization errors, and is efficiently converted to and from legacy floating-point representations of time to accommodate legacy simulators within a co-simulation environment. It also supports abstractions of time such as sequences of events where time does not elapse, enabling better integration of cyber models with physical ones (Sect. 2).

  • We present a concrete proposal for a new FMI standard for hybrid co-simulation, FMI-HC. The three main parts of the proposal are: (i) the use of integer time, (ii) the capability of FMUs to negotiate the resolution of time, (iii) the use of absent signals for handling discrete events (Sect. 3).

  • We describe how a master algorithm can use the FMI-HC extensions and support co-simulation of components that operate at different time resolutions. The algorithm finds a suitable global time resolution for the simulation based on the FMUs’ preferences and is able to handle disparities between the time resolutions of co-simulated FMUs. Our modular implementation using wrappers demonstrates that it is easy to add support for hybrid co-simulation to existing master algorithms (Sect. 3).

  • We give a detailed analysis and a solution to the time conversion and quantization problem, an unavoidable consequence when different components operate at different time resolutions (Sect. 4).

And finally, our implementation of the proposed FMU wrappers, a simple method to achieve compatibility with FMI-HC, is explained in detail in “Appendix”.

1.1 A motivating example

Broman et al. [8] give the model shown in Fig. 1 as a “simplest possible” nontrivial illustration of the challenges of hybrid co-simulation. This model includes a simplest-possible physical side, an integrator integrating a constant and the result being offset by a constant via an Adder. This model detects a zero-crossing of the offset output of an integrator, providing a simplest-possible nontrivial interface from the physical side to the cyber side. This detector produces a discrete event that is then processed by the simplest-possible cyber component, labeled “Microstep Delay.” The output of this component then resets the integrator, creating a simplest-possible closed-loop cyber-physical control system. In this model, the cyber component is abstracted as instantaneous, and the ensuing potential causality loop is averted by using superdense time, introducing an infinitesimal delay that makes the model constructive (for subtleties surrounding such models, see [30]). In this model, the resetting of the continuous output of the integrator is required to occur as a discrete, zero-duration discontinuity, but to ensure consistent semantics for the physical models, all continuous-time signals are required to be piecewise continuous, and continuous from both the left and the right at all points of discontinuity. These requirements imply that some form of superdense time is required.
Fig. 2

Output of Integrator

Figure 2 shows the output of the Integrator assuming the constant values 1 and \(-\,0.001\) shown in Fig. 1. An essential feature of this output is that discontinuities occur at precise times, that they take zero time to transition, and that between the discontinuities, signals are continuous.

Our goal in this paper is to provide a model of time for the interactions between these components that is semantically well defined and exact. If the interactions between the components are well defined, then the components themselves can be made much more complex, with predictable results. The integrator FMU could be replaced with a sophisticated ordinary differential equation (ODE) solver with a much more complicated internal model, the adder could be replaced with some continuous-time simulation engine, the zero-crossing detector could be replaced with more elaborate discrete-event processing, and the microstep delay could be replaced with some software engineering model of the control strategies.

1.2 Related work

This paper follows the line of research on FMI initiated in [7], where the FMI standard was formalized, and two co-simulation algorithms were proposed and proven to be determinate. In that same paper, small extensions to the standard were proposed with the goal of enhancing the standard’s ability to handle mixed discrete and continuous behaviors. Some of these extensions are reminiscent of functions included in the actor interface in the modular formal semantics of the Ptolemy tool [48]. For instance, the “getMaxStepSize” function of [7] is similar to the D (“deadline”) function of [48].

Followup work includes [8], which proposes a collection of test cases together with acceptance criteria that can be used to determine whether a hybrid co-simulation technique is acceptable. Tripakis [47] investigates techniques to bridge the semantic gap between various formalisms (state machines, discrete-event models, dataflow, etc.) and FMI. Cremona et al. [14] propose a new master algorithm that uses step size refinement to enable state event detection with FMI. An implementation of the FMI extensions on top of Ptolemy II is described in [15]. This implementation has been used in [6] to connect via co-simulation the model-checkers Uppaal [28] and SpaceEx [19]. Several authors have also described approaches to implement FMI master algorithms [2, 45], and ways to implement FMUs in the currently available FMI standard [17, 43], without considering the time aspects for hybrid co-simulation.

The topics addressed in this paper are relevant to modeling of hybrid and cyber-physical systems at large, but we choose to present our ideas on the basis of a concrete framework, namely the FMI standard. They could equally well be applied to other frameworks, such as HLA. Several papers exist in the literature that address the problem of formal modeling of cyber-physical systems, in particular, using hybrid automata with an emphasis on verification [1, 22, 26, 46]. The focus of this paper, however, is not formal verification, but rather (co-)simulation, with an emphasis on the practicalities, and in particular the representation of time.

The list of modeling languages and tools for CPS and hybrid systems design is long, and beyond the scope of this paper to cover exhaustively. A survey dating from 2006 can be found in [11], and a description of the mapping between formalisms, languages, and tools can be found in [9]. There have been numerous developments in the field, including many others, e.g., [3, 4, 10, 50, 51].

A side benefit of our proposal in this paper is that it potentially enables co-simulation between classical ODE simulators and a relatively newer way of modeling continuous dynamics called “quantized-state systems” (QSS) [25]. QSS simulators model continuous dynamics using discrete events, and sometimes the resulting simulations are more accurate (because of the use of symbolic computation) and more efficient than classical ODE solvers [31]. Since our proposed technique facilitates interoperability between continuous-time solvers and discrete-event systems, and QSS is based on discrete-event systems, it potentially enables interesting hybrid simulation techniques, where QSS can be used where it is most beneficial.

2 Representing time

A major challenge in the design of cyber-physical systems is that time is almost completely absent from models used on the cyber side, while time is central on the physical side. In order for hybrid simulators to interact in predictable and controllable ways, we will need a semantic notion of time that can be used to model both continuous physical dynamics and discrete events. It is naive to assume that we can just use the Newtonian ideal, where time is absolute, a real number t, visible everywhere, and advancing uniformly. We begin in this section by reviewing a set of requirements that any useful model of time must satisfy. We then elaborate with an analysis of practical, realizable alternatives that meet these requirements, at least partially.

2.1 Models of time

Like the Newtonian ideal, any useful semantic notion of time has to provide a clear ordering of events. Specifically, each component in a system must be able to distinguish past, present, and future. The state of a component at a “present” is a summary of the past, and it contains everything the component needs to react to further stimulus in the future. A component changes state as time advances, and every observer of this component should see state changes in the same order.

We also require a semantic notion of time to respect an intuitive notion of causality. If one event A causes another B, then every observer should see A ordered before B.

In order to cleanly support discrete events, we also require a semantic notion of simultaneity. Under such a notion, two events are simultaneous if all observers see them occurring at the same time. We need to avoid models where one observer deems two events to be simultaneous and another does not.

We could easily now digress into philosophy or modern physics. For example, how could a notion of simultaneity be justifiable, given relativity and the uncertainty principles of quantum mechanics? We resist the temptation to digress, and appeal instead to practicality. We need models that are useful for co-simulation. The goal is be able to design and build better simulators, not to unlock the secrets of the universe. Even after the development of relativity and quantum mechanics, Newtonian ideal time is a practical choice for studying many macroscopic systems.

But ironically, Newtonian time proves not so practical for hybrid co-simulation. The most obvious reason is that digital computers do not work with real numbers. Computer programs typically approximate real numbers using floating-point numbers, which can create problems. While real numbers have infinite precision, their floating-point representation does not. This discrepancy leads to quantization errors. Quantization errors may accumulate. Although real numbers can be compared for equality (e.g. to define “simultaneity”), it rarely makes sense to do so for floating-point numbers. In fact, some software bug finders, such as Coverity, report equality tests of floating-point numbers as potential bugs.

Consider a model where two components produce periodic events with the same period starting at the same time. The modeling paradigm should assure that those events will appear simultaneously to any other component that observes them. Without such a notion of simultaneity, the order of these events will be arbitrary, and changing the order of discrete events can have a much bigger effect than perturbing their timing, and a much bigger effect than perturbing samples of a continuous signals. Periods that are simple multiples of one another should also yield simultaneous events. Quantization errors should not be permitted to weaken this property.

Broman et al. [8] list three requirements for a model of time:
  1. 1.

    The precision with which time is represented is finite and should be the same for all observers in a model. Infinite precision (as provided by real numbers) is not practically realizable in computers, and if precision differs between observers, then they will not agree on which events are simultaneous.

  2. 2.

    The precision with which time is represented should be independent of the absolute magnitude of the time. In other words, the time origin (the choice for the meaning of time zero) should not affect the precision.

  3. 3.
    Addition of time should be associative. That is, for any three time intervals \(t_1\), \(t_2\), and \(t_3\),
    $$\begin{aligned} (t_1 + t_2) + t_3 = t_1 + (t_2 + t_3) . \end{aligned}$$

In contrast to the above quote from Broman et al. [8], and to avoid confusion with the term precision in measurement theory, henceforth we will in this article use the term resolution instead of precision to denote the grain at which we can tell apart two distinct time stamps.

Definition 1

(Time resolution) Time resolution is the smallest representable time difference between two time stamps.

For instance, if we state that “a model has a time resolution of one millisecond,” or for short, “the time resolution is milliseconds,” it means that the time points 0.001, 0.002, 0.003 s, \(\ldots \) are representable, but the time points in between are not. No values can be defined at unrepresentable time points.

Note that properties 2 and 3 are not satisfied by floating-point numbers due to rounding errors [21]. For instance, consider the following C code that adds double-precision floating-point numbers.

The output of this program is 0.800000,0.800000,0. Both r and k appear to have value 0.800000, but due to rounding errors, the test for equality r==k evaluates to false, which is represented as a 0-value integer in C. Hence, floating-point numbers should not be used as the primary representation for time if there is to be a clean notion of simultaneity. Unfortunately, in FMI 2.0 and many other simulation frameworks, it is exactly the representation that is used. This is problematic.

2.2 Superdense time

A model of time that is particularly useful for hybrid co-simulation is superdense time [13, 33, 34, 35]. Superdense time is supported by FMI-ME, but not by FMI-CS. Fundamentally, superdense time allows two distinct ordered events to occur in the same signal without time elapsing between them.

A superdense time value can be represented as a pair (tn), called a time stamp, where t is the model time and n is a microstep (also called an index). The model time represents the time at which some event occurs, and the microstep represents the sequencing of events that occur at the same model time. Two time stamps \((t, n_1)\) and \((t,n_2)\) can be interpreted as being simultaneous (in a weak sense) even if \(n_1 \ne n_2\). A stronger notion of simultaneity would require the time stamps to be equal (both in model time and microstep).

Superdense time is ordered lexicographically (like a dictionary), which means that \((t_1, n_1) < (t_2, n_2)\) if either \(t_1 < t_2\), or \(t_1 = t_2\) and \(n_1 < n_2\). Thus, an event is considered to occur before another if its model time is less or, if the model times are the same, if its microstep is lower.

An event is value with a time stamp. Time stamps are a particular realization of tags in the tagged-signal model of [32]. They provide a semantic ordering relationship between events that can be used in software simulations of physical phenomena and also in the programming logic on the cyber side of a cyber-physical system. But computers cannot perfectly represent real numbers, so a time stamp of form \((t,n) \in \mathbb {R}\times \mathbb {N}\) is not realizable in software. Many software systems approximate a time t using a double-precision floating-point number. But as we noted above, this is not a good choice. We examine alternatives below.

The microstep can also be problematic for software, because in theory, it has no bound. But computers can represent unbounded integers (assuming that memory is unbounded), although the implementation cost of doing so may be high, and the benefit may not justify the cost. The microstep, therefore, should either be represented using a bounded integer (such as a 32-bit integer), or not represented at all. With some care in simulator design, it may be possible to never construct an explicit representation of the microstep, and instead rely only on well-defined ordering of time stamped values. Microsteps can implicitly begin at zero and increment until a signal stabilizes. This is the approach used in FMI-ME, where there is no explicit microstep, and yet, superdense time is supported. By contrast, in FMI-CS version 2.0, microsteps are explicitly disallowed [40].

2.3 Integer time

Given that floating-point numbers are a problematic representation of time, what should we use? An obvious alternative is integers. We postulate that a hybrid co-simulation extension must use integer numbers in some way to represent the progress of time for coordinating FMUs. But how, exactly? And at what cost?

Integers are typically represented in a computer using a fixed number of bits. E.g., a C int32_t is a 32-bit, two’s-complement integer. A uint32_t in C is a 32-bit unsigned integer. Note that the integer values can be interpreted as representing a time value with some arbitrary units. For example, we might interpret an integer value as having units of microseconds, in which case a value 100, for example, represents 0.0001 s.

Integers can be added and subtracted without quantization errors, a key property enabling a clean semantic notion of simultaneity. For example, suppose that one discrete-event signal has regularly spaced events with a period of \(p_1 = 3\), and another has regularly spaced events with a period of \(p_2 = 1\), both beginning at time 0. The times of the events in the first signal are \(0, p_1, p_1 + p_1, p_1 + p_1 + p_1, \ldots \), and the times of the events in the second signal are \(0, p_2, p_2 + p_2, p_2 + p_2 + p_2, \ldots \). Then no matter how these additions are performed, every third event in the second signal will be simultaneous with an event in the first signal.

Again, we have no such assurance with floating-point numbers. For example, suppose that we are using the IEEE 754 double-precision floating-point standard, and we let \(p_1 = 0.000003\) (3 ms). If we add \(p_1\) to itself 12 times, performing \((\cdots ((p_1 + p_1) + p_1) + p_1 \cdots )\), then the result is 0.000003599999999999999. On the other hand, if we let \(q = ((p_1 + p_1) + p_1)\), then \((((q + q)+q)+q)\) yields 0.0000036. The results are not equal.

If signals are continuous, then such small differences in time have very little effect on system behavior. But if signals are discrete, then any difference in time can change the order in which events occur, and the potential effects on system behavior are not bounded.

One possible solution is to explicitly use an error tolerance when comparing two floating-point numbers. For example, suppose we assume an error tolerance of 100 ns. That is, we consider two times to be simultaneous if their difference is less than 100 ns. Then the above two times are simultaneous. But now consider three times, \(t_1 = 0.0000036\), \(t_2 = 0.00000367\), and \(t_3 = 0.00000374\). Then \(t_1\) is simultaneous with \(t_2\), and \(t_2\) is simultaneous with \(t_3\), but \(t_1\) is not simultaneous with \(t_3\). Surely we would want simultaneity to be a transitive property!

An alternative to floating-point numbers is rational numbers. A time value could be given by two unsigned integers, a numerator and denominator. Addition of two such numbers will require first finding the least common multiple M of the denominators, then scaling all four numbers so that the two denominators equal M. Then the numerators can be added, and the denominator of the result will be M. However, this makes addition a relatively expensive operation, unless measures are taken to ensure that denominators are equal. Such measures, however, are equivalent to reaching agreement across a model on a time resolution, so we believe that a simpler solution uses an integer representation of time with an agreed resolution. It is also much more difficult to determine when overflow will occur with rational numbers. For example, if denominators are represented using 32-bit unsigned integers, and two times with denominators 100,000 and 100,001 are added, will overflow occur?

Suppose we adopt an integer representation of time. What units should we choose? We could start by considering existing integer representations of time. For example, VHDL, a widely used hardware simulation language, uses integer time with units of femtoseconds. Another example is the network time protocol (NTP) [37], a widely used clock synchronization protocol that sets the current time of day on most computers today. NTP represents time using two 32 bit integers, one counting seconds, one counting fractions of a second (with units of \(2^{-32}\) s). This can be treated as an ordinary 64-bit integer with units of \(2^{-32}\) s (about 0.23 ns). IEEE 1588 [16], a more recent clock synchronization protocol, is designed to deliver higher precision clock synchronization on local area networks. A time value in IEEE 1588 is represented using two integers, a 32-bit integer that counts nanoseconds, and a 48-bit integer that counts seconds.

NTP and IEEE 1588 are designed to coordinate notions of time across a network. All participants in such a network agree to a time resolution (based on a resolution of \(2^{-32}\) s for NTP, 1 nanosecond for 1588). The first requirement in Broman et al. [8] stipulates simply that the time resolution should be the same for all observers in a model. It need not be the same across models. In fact, simulation models tend to have very different time scales; high-speed circuits require femtoseconds while astronomy may only require years. Co-simulation involves the coupling of independent models that are coordinated in a black-box manner, each of which can operate at a different time resolution. From the perspective of the master that coordinates the exchange of data between components, however, all components must be understood to progress in increments that are multiples of the time resolution used by the master.

In Ptolemy II [44], the time resolution is a single global property of a simulation model, shared by all components. The resolution is given as a floating-point number, but the time itself is given as an integer, representing a multiple of that resolution. All arithmetic is done on the integer representation, and the unit is only used when rendering the resulting times for human observation. For example, if the resolution is given by the floating-point number 1E-10, which in units of seconds denotes 0.1 ns, then the integer 10,000,000 will be presented to the user as 0.001 s.

Integers are, of course, vulnerable to overflow. Adding two integers can result in an integer that is no longer representable in the same bit format. Subtracting two unsigned integers can result in a negative number, which is not representable using an unsigned integer.

Whether and when an overflow occurs depends on the resolution, but also on the origin (what time is zero time). NTP and IEEE 1588 both set time relative to a fixed zero time, which in the case of NTP is 0h January 1, 1900, and in the case of 1588 is 0h January 1, 1970, TAI (international atomic time). Sometime in the year 2036, \(2^{32}\) s will have elapsed since January 1, 1970, and all NTP clocks will overflow. IEEE 1588 uses 48 bits, so the first overflow will not occur for approximately 9.1 million years. If we define the time origin to be, say, the start time of a simulation, then the NTP representation will be able to simulate approximated 62 years before its representation of time overflows. A VHDL simulator using a 64 bit integer representation of time with units of femtoseconds can simulate approximately 2.56 h of operation before overflow occurs, so clearly choosing the origin to be January 1, 1900, would not be reasonable. In Ptolemy II, overflow cannot occur, because the integer representation of time uses an unbounded data structure to represent an arbitrarily large integer.1 And the resolution is a parameter of the model, so Ptolemy II simulations can handle high-speed circuits as well as astronomical simulations.

In computers, addition and subtraction of integers is extremely efficient. In the IEEE 1588 representation, however, the two numbers cannot be conjoined into a single number, and arithmetic on the numbers must account for carried digits from the nanoseconds representation (32 bits) to the seconds representation (48 bits). Since computers do not have hardware support for such arithmetic, such a representation will be more computationally expensive to support. Adding two IEEE 1588 times takes quite a few steps in software. For the Ptolemy II unbounded integers, addition and subtraction are also potentially more expensive than addition on ordinary 32 or 64-bit integers, but the cost is not as high as for IEEE 1588 because overflow is more easily detected in the hardware.

In modern computers, addition and subtraction of 32 and 64-bit integers is at least as fast as addition and subtraction of floating-point numbers, and it requires significantly less energy. Multiplication, however, is a more complicated story. The problem with multiplication of integers lies in the units. Consider two integers with units of microseconds. If we multiply the two times, the units of the result will be microseconds squared. First, this is not a time, and hence there is no reason to insist that this result be represented the same way times are represented. Second, whether the result is representable in a 32 or 64-bit integer will depend on the origin and resolution of the times.

Multiplication of two times, however, is a relatively rare operation. A more common operation is multiplication of a time by a unitless scalar. For the example above, instead of adding \(p_1\) to itself 12 times, we might have multiplied \(12*p_1\). As long as there is no overflow, such multiplication will typically be performed without quantization error and reasonably efficiently in a computer. However, not all processors have hardware support for integer division. And multiplication by a non-integer, as in \(0.1 * p_1\), will yield a floating-point representation of the result, not an integer representation. Hence, it will be vulnerable to quantization errors.

We claim that for the purposes of coordinating FMUs, addition, subtraction, and multiplication by integers are mostly sufficient, and hence an integer representation of time can be very efficient. Within an FMU, however, there may be more complex operations involving time, and the FMU may include legacy software or ODE solvers that use floating-point representations of time. Such FMUs will suffer a (hopefully small) cost of conversion of time values at the interface. Presumably, since such FMUs already tolerate quantization errors inherent in a floating-point representation, any errors that are introduced in the conversion process will also be tolerated. For example, such FMUs should never compare two times for equality, because if they are using a floating-point representation of time, such a comparison is meaningless. They should also not have behavior that depends strongly on the relative ordering of two time values.

We choose to represent time with a 64-bit unsigned integer with arbitrary resolution, where the resolution is a parameter of the model, and origin equal to the simulation start time. It is computationally efficient on modern machines. And for well-chosen resolutions, will tolerate very long simulations without overflow. It is also easily converted to and from floating-point representations (with losses, of course). Also, given the enormous range of time scales that might be encountered in different simulation models, choosing a fixed universal resolution that applies to all models probably does not make sense. We believe further that all the acceptance criteria of [8] can be met without an explicit representation of the microstep.

2.4 The choice of resolution

The only remaining issue is how to choose the resolution. There are two questions here. First, what data type should be used to represent the resolution? Second, should an FMU be able to constrain the selected resolution?

The latter question seems relatively easier to answer. In hybrid co-simulation, an FMU may encapsulate considerable expertise about a system that it models, and the FMU’s model may only be valid over a range of time scales. It seems reasonable, therefore, that an FMU should be able to insist on a resolution. On the other hand, to be composable with other FMUs, the FMU should be capable of adapting to a finer resolution than the one it requests. If two FMUs provide different resolutions, or if their resolution differ from the default resolution of the simulation, then how should the differences be reconciled?

We see two possibilities:
  1. (i)

    The selected resolution for the model is the finest of all specified resolutions.

  2. (ii)

    The selected resolution for the model is the greatest common divisor (GCD) of all specified resolutions.

Whether the second option is even possible depends on the data type used for the resolution. Here, we see several possibilities:
  1. (a)

    Double. In Ptolemy II, all components share a single double-precision floating-point number, the unit, which specifies the resolution of the model. All timestamps are interpreted as an integer multiple of this value.

  2. (b)

    Rational. Alternatively, resolution can be specified given as a pair of integers, a numerator and a denominator. In this case, it is always possible in theory to find a GCD, although there is risk of overflow if the numerator and denominator are represented with a bounded number of bits. In addition, conversion to and from a floating-point representation, which is often needed internally by an FMU, may be costly.

  3. (c)

    Decimal. Finally, the resolution can also be specified using integer exponent n that stipulates a resolution of \(10^{n}\) s. For instance, IEEE 1588 resolution is achieved with \(n = -9\), VHDL resolution is achieved with \(n = -12\). Using a decimal resolution, the finest resolution is always the same as the GCD and is always precise.

Defining resolution as a power of ten has the very nice feature that any parameter that is specified using a decimal representation, such as 0.3332, is exactly representable, with no quantization error, as long as the resolution is sufficient, for example \(n=-4\) for 0.3332. In contrast, when such a decimal number is converted to a binary floating-point representation, errors may be introduced. Since it is extremely common to give parameter values in decimal, this advantage cannot be ignored.

Also, since parameters are often related to one another, the ability to, for example, calculate the difference between two parameter values without quantization error can be important. For instance, if a component specifies using parameter values that it produces events at times \(t_1\) and \(t_2\), given in decimal, then the time interval \(t_2-t_1\) can be calculated without error.

If parameter values are not given in decimal, for example “1/3,” then decimal resolution is not sufficient to avoid quantization errors. Such errors can be avoided by selecting rational resolution instead. A rational resolution has the advantage that if an FMU internally performs computation according to its specified resolution, then simultaneity of any events it generates compared to events generated by other similar FMUs is well defined. The simultaneity of such events will not be subject to quantization errors. However, this choice comes at a possibly considerable cost in converting time values to and from floating-point numbers. And, as we have noted, this choice has a more complicated overflow risk.

For these reasons, we prefer option (i)c, which also implies option (ii)c. An FMU can stipulate a minimum required resolution and be guaranteed that resolution or a resolution that divides its resolution by some power of ten:
$$\begin{aligned} r = 10^n \end{aligned}$$
However, not every FMU may be prepared to adapt to a finer resolution than the preferred resolution it declares. For instance, an FMU may generate events exclusively at time instants that are multiples of its declared time resolution. For such an FMU there is no reason to adopt a finer resolution than the one it specifies; it would merely complicate the design of the FMU. For that reason, we believe that this capability should not be mandatory, rather it should be optional. In the next section, we describe an architecture that accommodates such flexibility.

3 Hybrid co-simulation with integer time

In order to examine the effects of using integer time in hybrid co-simulation, we need a practical framework for our analysis. Instead of defining our own, we leverage the existing FMI-CS 2.0 standard, and extend it to use integer time. In addition, in order to support discrete events, we enrich the interface to encode the absence of an event and allow FMUs to react instantaneously, i.e., without moving forward in time. We call this framework FMI-HC (FMI for hybrid co-simulation).

3.1 Extensions to the FMI standard

As a consequence of using integer time, the FMUs and MA need to agree on a resolution before the simulation starts. Two new functions are introduced for this: getPreferredResolution and setResolution. In addition, we introduce a hybrid step function doStep Hybrid that uses integer time instead of doubles, a function getMaxStepSizeHybrid that returns the maximal allowed communication step size, and the functions getHybrid and setHybrid that in addition to the exchange of regular signal values can also communicate “absent,” to indicate that there is no value present at the corresponding time instant.

3.1.1 Advancing time

In FMI, simulation is driven by a master that keeps time and instructs FMUs to advance their time in increments called “steps.” Once all participating FMUs have advanced their time by some delta, an iteration has finished. In each iteration, FMUs exchange data, the master proposes a new step, and so forth. The specifics of this sequence are encoded in a master algorithm. Only the interface of the FMU is standardized. FMUs are prescribed to advance time through calls to the function doStep. In the FMI-CS 2.0 standard, this function has the following signature:

The first parameter points to a particular FMU. The second parameter states the current time, using a double-precision floating-point value (named fmi2Real in the standard). The third parameter states the communication step size, which is the time interval over which the master requests the FMU to advance. Finally, the fourth parameter provides information as to whether any rollbacks can occur, which allows the FMU to abandon any kept state if this parameter is true.

Clearly, this function is not suitable for communicating an integer step size to the FMU. For backward compatibility, we preserve the original fmi2doStep function and add a new function that is used to advance hybrid co-simulation FMUs using integer time steps2:

Instead of using fmi2Real data type, we use the type definition fmiXIntegerTime, a 64-bit unsigned integer type. The additional parameter performedStepSize is used for communicating back to the master the size of the performed step, which could be smaller than the requested step, communicationStepSize. If the performed step size is equal to the requested step, then the FMU has accepted the requested step. If the performed step is smaller than the requested step, then the FMU has rejected the requested step, but nevertheless advanced to time currentCommunicationPoint \(+\) performedStepSize.

As observed by Broman et al. [7], adding a function fmiGetMaxStepSize makes it possible for an FMU to state a predictable step size. Such function with integer time can be defined to have the following signature:

This function returns an upper bound of the step size that the FMU will accept on the next invocation of doStepHybrid. The master algorithm should query this function before calling doStepHybrid.

3.1.2 Negotiating the resolution of time

In the previous section, we explained that FMUs may be designed to preferably (or exclusively) operate at some specific time scale. To accommodate this, an FMU must be able to inform the master of its preferred resolution, which can be done by extending FMI for hybrid co-simulation with:

The preferred resolution is returned as an integer using the second parameter. As described in the previous section, the resolution represents an integer n, that stipulates a resolution of \(10^{n}\) s.

Although it is important that an FMU can express its preferred resolution, we will also show the need for the master to explicitly state that the FMU should use a specific resolution. We define the following function to enforce this behavior:

3.1.3 Discrete events

The aforementioned functions are introduced to offer support for integer time. In order to support discrete signals, an FMU must be able to output or take in discrete events, which are present only for a duration of zero time (one microstep in superdense time) and absent otherwise.

The FMI standard defines two kinds of functions for setting and getting input and output signal values: fmi2SetXXX and fmi2GetXXX. There are different functions for different variable types. The substring XXX is a placeholder for the type.3 For instance, fmiSetReal is the function that is used to set input signal values of type fmi2Real, which is implemented using double-precision floating-point numbers.

In the FMI-CS 2.0 standard, values exchanged between FMUs are always present. This means that the current co-simulation standard does not yet have the support for discrete events. To make it possible to express discrete events, FMI needs to have functions for setting and getting values, where the values can be stated to be either present or absent. By extending the current standard get and set functions, we obtain the following signatures:
The first argument points to the FMU to set values for or get values from. The second argument, vr, is an array of identifiers that refer to specific variables. Furthermore, nvr is the size of that array, and the array value (also of length nvr) specifies the values that should be set or gotten. The \(i_{th}\) element from value is assigned to or read from the \(i_{th}\) variable declared in vr. These function arguments are the same as in the original fmi2Get and fmi2Set functions in the FMI-CS 2.0 standard. The “hybrid” get and set functions introduce an additional argument, fmiXSignalStatus, which is defined as:

If flag[i] == present, the signal is considered to be present and the value of the variable vr[i] is value[i]. In case flag[i] == absent, the signal is not present and the value[i] of variable vr[i] should be ignored. Note that there are many alternative ways of extending the standard with capabilities of expressing absent and present signals. For instance, instead of creating new get and set functions, separate functions can be used for indicating if a signal is present or absent. However, these implementation details are outside the scope of this paper and would be a decision for the FMI steering committee.

3.2 Categories of FMUs in FMI-HC

Hybrid co-simulation should be able to work with “legacy” FMUs that do not implement any of the FMI-HC extensions. Moreover, to make it easy to write FMI-HC FMUs, we do not wish to require that every FMU implement every extension. We can divide FMUs into different categories based on the extensions they implement. It is useful to organize FMUs in a taxonomy based on this criterion because different category FMUs require different handling by the master. We use the taxonomy presented in Fig. 3 throughout the remainder of the paper to identify and refer to FMUs in terms of their category.
Fig. 3

A taxonomy for different categories of hybrid co-simulation FMUs

  • Category 0: An FMU in this category internally uses floating-point numbers to represent time (see the top of Fig. 3). This category can be further refined into two sub categories. Category \(0_A\) denotes an FMU that is compatible with existing FMI 2.0 co-simulation FMUs. Such FMUs are not suitable for hybrid co-simulation because the standard disallows zero step sizes and insists on always calling \(\texttt {doStep}\) (advance time) in between \(\texttt {set}\) (providing inputs) and \(\texttt {get}\) (retrieving outputs). The former rules out the use of superdense time while the latter prohibits the handling of direct feedthrough loops. Therefore, we do not further discuss category \(0_A\) FMUs in this paper. In contrast, Category \(0_B\) follows the assumptions in [7], which allows a zero step size and getting and setting values (multiple times) without having to advance time.

  • Category 1: This is the first out of four possible categories of FMUs that use integers to represent time. Note that categories 1–4 represent the exhaustive combinations of getPreferredResolution and set Resolution. In category 1, neither of these functions is supported. The operation of an FMU in this category is time-invariant; it does not use time to determine its outputs or state updates. Such a component can implement, for example, a time-invariant memoryless function such as addition.

  • Category 2: In category 2, function getPreferred Resolution is supported, but setResolution is not. This means that the FMU states which resolution it will use, but does not allow the master to change its resolution. That is, the resolution is actually required, not just preferred. A composition of multiple category 2 FMUs may result in a heterogeneous model with respect to the resolution of time. Category 2 FMUs are natural to use in cases where the FMU should output data at periodic time internals, e.g., periodic samplers or signal generators. Tools that have a fixed time resolution, such as Rhapsody from IBM or VHDL programs, would produce FMUs of this category.

  • Category 3: FMUs in this category support set Resolution, but do not support getPreferred Resolution. This means that the FMU is using the integer notation of time (in contrast to category 1), but any resolution is acceptable. For instance, a zero-crossing detector would fall into to this category.

  • Category 4: An FMU in this category supports both getPreferredResolution and setResolution. This means that the FMU may first communicate to the master the resolution that it prefers, and be followed by the master telling the FMU what resolution it should use. An ODE solver FMU would belong to this category.

3.3 Modular support for FMI-HC

Technically, an FMU developer can choose whether or not to support functions like getPreferredResolution or setResolution and notify the master of the functions it supports through so-called capability flags in the FMU’s accompanying XML-file, as prescribed by the FMI standard. With this approach, the master algorithm must accommodate the use of all the different categories of FMUs in Fig. 3.
Fig. 4

A generic architecture for supporting hybrid co-simulation using wrappers. The left side of the figure shows an (arbitrary) internal interface between the wrappers and the simulation tool, the right part shows the new FMI-HC extensions used by the wrappers

Figure 4 depicts an architectural view of how an FMI simulation tool can interact with different category FMUs modularly, without drastic changes to its simulation engine.

In the left part of the figure, the dashed line represent an FMI simulation tool that takes a set of connected FMUs as input and produces a simulation result as output. The basic idea is to separate the concerns of the master algorithm from the logic that handles the translation between different resolutions for different categories of FMUs. This logic is instead encoded into wrappers, that is, software components that translate function calls from the master algorithm to the FMUs. Both the implementation of the master algorithm component and the wrapper components are internal to a specific tool implementation. The design discussed in this article does not assume any specific implementation language. Hence, the wrapper interface functions (with prefix “_”) are arbitrary; tool vendors can choose the specifics of their wrapper interface as they see fit and are not tied to a specific programming language for the implementation of their execution engine.

The right part of the figure depicts all different categories of FMUs along with the particular FMI-HC extensions they implement. The wrapper treats the FMU as a black box and performs the conversion between the model of time used by the master and the model of time used by the FMU. This can either be a conversion between integer time resolutions (for a category 2 FMU) or a conversion between integer and floating-point time (for a category \(0_B\) FMU).
Fig. 5

Schematic description of a variant of the deterministic master algorithm by Broman et al. [7], extended to accommodate integer time. The figure and implementation are based on Cremona et al. [15]

3.4 An implementation of FMI-HC

The interface extensions described in this paper can be used by making relatively small adaptations to existing master algorithms. In a nutshell, it requires adopting our integer-based representation time, using specific wrappers for FMUs based on their category, and letting the master negotiate a time resolution as part of its initialization procedure.

In the following, we outline how to make these adjustments based on our own reference implementation called FIDE [15], an FMI Integrated Development Environment. FIDE implements a master algorithm based on the work of Broman et al. [7]. It is capable of deterministically simulating mixtures of continuous-time and discrete-event dynamics, has a superdense model of time [33, 34], and features extended data types to support an explicit notion of absent. Superdense time is modeled by allowing the master to take zero-size steps, allowing the simulation to iterate over a number of lexicographically ordered indexes before it advances to the next Newtonian time instant. The absence of events in signals is enabled through the FMI-HC functions getHybrid and setHybrid described in Sect. 3.1.3.

Figure 5 provides a schematic description of the MA implemented in FIDE. Any tool that supports FMI will feature an execution engine much like the one in FIDE. The simulation tool reads a model description that describes how a set of FMUs is connected. It loads each FMU by reading the FMI XML-file and dynamically linking the required C libraries as it normally would. However, in order to accommodate FMI-HC, each FMU is now identified by category and a matching wrapper object is instantiated for every FMU. The wrapper is specifically designed to interface with FMUs of a particular category. Since all wrappers are using the same interface (all are using integer time), the logic of the execution engine is not complicated by the different ways that FMUs may interpret time: all the necessary conversions are performed by the wrappers. For instance, if a category \(0_B\) FMU is used, the wrapper is handling the correct conversion between integer time and floating-point time.

During initialization, the master calls the function _ determineResolution() that determines the time resolution for the simulation. This function iterates over all the wrappers and queries them for the time resolution exponent using \(\texttt { \_getPreferredResolution}\). The time resolution exponent of the simulation is computed as the minimum among a default value and the resolution exponents obtained from all the FMUs that partake in the simulation. The chosen resolution exponent is then communicated to the wrappers using \(\texttt { \_setResolution}\). The wrappers will eventually use it to convert the integer time stamps used by the master to whichever model of time is used internally by the wrapped FMU, if necessary.

FIDE must keep track of the global time of the master and the current step size using integers. Hence, it cannot use the fmi2Real data type that is prescribed by FMI-CS 2.0 for this purpose. We use a new data type, fmiXIntegerTime, instead. Finally, all direct calls from the master to the FMU functions \(\texttt {fmi2DoStep}\), \(\texttt {getMaxStepSize}\), get, and set have to be removed and replaced by function calls to the corresponding intermediate functions provided by the wrappers.

4 Time conversion and quantization

Using integers for representing time does not completely remove time quantization errors: an FMU may still use a floating-point number internally for time keeping. In such case, conversion from the floating-point representation of the time kept inside of the FMU to the integer time used by the master comes with a loss of precision. Specifically, the effects of time quantization come into play when a category \(0_B\) FMU rejects a proposed step size and makes partial progress over an interval of which the length (a floating-point number) cannot be losslessly converted into a corresponding fixed-resolution integer time for the master to interpret. Similar problems arise when there is a mismatch in time resolution between the master and a category 2 FMU. Quantization errors also result when a higher-resolution integer time is converted to a lower-resolution integer time. For instance, the master may instruct a category 2 FMU to take a step that is too small to represent with the resolution that the FMU uses internally.

The key insight here is that in co-simulation participants are treated as a black box; each component has its own isolated understanding of time that is based on the characteristics of its local clock. Each level of hierarchy in a co-simulation gives rise to a different clock domain in which the passing of time may register differently from another clock domain. The degree to which two components can be synchronized therefore depends on compatibility of their clock domains. The issue of translating time across different clock domains gets complicated by corner cases, which, if not handled appropriately, may lead to Zeno behavior or may cause discrete events emitted by one component to be missed by another. These kinds of issues play a role only in the interaction with category \(0_B\) and category 2 FMUs. The former does not admit integer time and hence requires an conversion from and to integer time, and the latter cannot adapt its resolution to its environment, which requires a conversion between integer times. On the other hand, category 1 FMUs have no time resolution at all, and therefore their behavior must be time-invariant, while categories 3 and 4 can adapt their resolution and therefore synchronize perfectly with their environment. Hence, category \(0_B\) and category 2 FMUs are the main focus of the remainder of this section. A full C implementation of wrappers sufficient to co-simulate any combination of FMUs of any of the aforementioned categories is given in “Appendix”.

4.1 Converting from integer to real-valued time

In the following, we will show the relationship between a \(0_B\) FMU’s internal notion of time and its environment’s integer representation, with the assumption that the FMU internally represents time as a real number with no quantization errors. Please recognize that this is impossible in a computer, and the FMU will internally encode these real numbers as double-precision floating-point numbers. The environment’s time resolution is given as a power of ten, \(r = 10^n\), measured in seconds, where n is an integer. An integer time index, i, once scaled by a resolution, denotes a real-valued time:
$$\begin{aligned} t = i \cdot r, \end{aligned}$$
also measured in seconds.
We express the step size for the master and the FMU separately, each in terms of a relative increment with respect to their local time representation. For the master, we define the new time index \(i'\) after having taken a step \(\varDelta i\) with respect to the previous time index i to be \(i' = i + \varDelta i\), where \(i'\) corresponds to the time \(i' \cdot r\). Similarly, for the FMU, we define the time after a step to be \(t' = t + \varDelta t\), where t is the current time in the clock domain of the FMU and \(\varDelta t\) is the time step. When the master and the FMU agree on the next time step, we have have \(t' = i' \cdot r\), and therefore \(t + \varDelta t = (i + \varDelta i) \cdot r\). We derive the size of the time step of the FMU as follows:
$$\begin{aligned} \varDelta t = (i + \varDelta i) \cdot r - t. \end{aligned}$$

4.2 Converting from real-valued to integer time

No matter how fine a time resolution we choose, an arbitrary real-valued time instant is unlikely to align perfectly with some integer-time instant. As a result, conversion from a real-valued time to integer time can introduce a time quantization error up to one unit of the integer time resolution. Notice that this quantization error is controlled by the user, modeler, or tool integrator through the simulation parameter r, the time resolution. The finer the resolution, the smaller the quantization error.

Ideally, according to (3), the FMU’s step size in the clock domain of the master should be:
$$\begin{aligned} \varDelta i = \frac{t + \varDelta t}{r} - i . \end{aligned}$$
But in general, this will not yield an integer \(\varDelta i\), and we require \(\varDelta i\) to be an integer. We have three alternatives to obtain an integer step from a real-valued time step: (i) take the floor (the largest smaller integer), (ii) take the ceiling (the smallest larger integer), or (iii) round (the nearest integer). Our implementation uses the ceiling operator, rounding to the next larger integer:
$$\begin{aligned} \varDelta i = \Bigl \lceil \frac{t + \varDelta t}{r}\Bigr \rceil - i . \end{aligned}$$
This choice prevents quantization errors from blocking the progress of time. To see this, consider the model depicted in Fig. 6. This model consists of two category \(0_B\) FMUs composed in parallel. FMU A outputs a piecewise-constant signal with a discontinuity at \(t =0.4\), while FMU B outputs a discrete event at \(t=0.8\). Assume that these FMUs are coordinated by an integer-time master and interfaced through wrappers. Both FMUs use floating-point time internally, and the wrapper provides the glue code between the master and the FMU. Specifically, the wrapper’s _getMaxStepSize and _doStep functions implement the conversions between integer time and floating-point time in accordance with Eqs. (3) and (5), and determine when and how the FMU is allowed to advance time.
Fig. 6

The outputs of two FMUs composed in parallel. The signal x shows a discontinuity that registers at \(t=0.4\) in the clock domain of FMU A. The signal y shows a discrete event that registers at \(t=0.8\) in the clock domain of FMU B. The discontinuity and the discrete event, however, occur at the same time, \(t=1\), in the clock domain of the master

For simplicity, we assume the master adopted a time resolution of 1 second (\(n = 0\)). At time \(t=0\) the master calls \(\texttt { \_getMaxStepSize}\) of the FMU A wrapper (FMU B does not implement \(\texttt { \_getMaxStepSize}\), and hence gives no indication as to what step size it will accept). The FMU A wrapper then calls \(\texttt {getMaxStepSize}\) which returns \(\varDelta t = 0.4\). Because of the master’s time resolution \(r=1\), \(\varDelta t\) cannot be represented exactly in terms of multiples of r. Therefore, using the ceiling operator as its quantization method, the wrapper reports back 1, indicating to the master that the FMU will accept a step size of size 1. Notice that had we used the floor operator instead, the wrapper would have returned 0, and the simulation would have gotten stuck forever at \(t=0\) because the master will proceed with the smallest of the step sizes that the wrappers return through \(\texttt { \_getMaxStepSize}\). Similarly, if we had used rounding, and rounding of 0.4 returns 0, then again, the simulation would get stuck.

The master will next invoke _doStep with a proposed step size of 1. It can invoke this function in either order, first for FMU A, or first for FMU B. Assume FMU B goes first. It will reject the step and indicate that it has made progress up to time 0.8. But that time is not representable in integer time either, so the wrapper rounds it up using the ceiling function. Since \(\lceil 0.8 \rceil = 1\), the wrapper accepts the step, but makes an internal annotation that the FMU only progressed to time 0.8. The next invocations of get and set will provide inputs and retrieve outputs that for the master will appear to occur at time 1, but will look to FMU B as if they occur at time 0.8.

The procedure for FMU A is similar, but the wrapper has a bit more information to work with, since the FMU has previously indicated that it would accept a maximum step of 0.4. Hence, when the master proposes a step of size 1, the wrapper can propose a step of 0.4 to the FMU. The next invocations of get and set will provide inputs and retrieve outputs that, again, for the master appear to occur at time 1, but will look to FMU A as if they occur at time 0.4.

Assume further that the output of FMU A has a discontinuity at time 0.4. This means that the FMU requires that the next invocation of doStep has a step size of 0. It indicates this by returning 0 when \(\texttt {getMaxStepSize}\) is called. The wrapper passes this on to the master by returning 0 in \(\texttt { \_getMaxStepSize}\). The master now has no choice but to propose a zero step size. Upon invocation of this zero step, FMU A advances in superdense time because of the discontinuity, so its local time remains at 0.4. Since FMU B produces a discrete event at this time, its local time will remain at 0.8. The outputs of both FMUs will appear to the master to occur at time 1.

Suppose that after this neither FMU has any anticipated events and therefore will accept any step size. Suppose the master proposes a step of size 10 to the wrappers. The wrappers will need to compensate for the lag of their FMUs, and instead propose a step of 10.6 and 10.2, respectively, to FMU A and FMU B.

It may be possible to design other wrappers with a different API that use the floor or other rounding functions instead of the ceiling function, but the our solution appears to be simple, to work well, to preserve causality, and to ensure that time continues to advance. We observe that using the floor in \(\texttt { \_getMaxStepSize}\) will always make the FMU lag behind with respect to the master. For \(0_B\) FMUs, the quantization effects due to the use of integer time only play a part in the conversion of time steps in the FMU’s clock domain to time steps in the master’s clock domain, not vice versa. Conversion from master time to FMU time suffers only from ordinary rounding that is a consequence of the floating-point representation of time inside the FMU.

4.3 Converting between different-resolution integer times

Conversions between times expressed in different-resolution clock domains can be derived in a similar fashion as shown in Sect. 4.2. These conversions are necessary for the support of fixed-resolution integer-time FMUs; the master might choose to operate using a different time resolution than a category 2 FMU. In such scenario, each time the master proposes a time step \(\varDelta i\), expressed as a multiple of the master’s resolution, \(10^{n}\), this step must be converted into a time step \(\varDelta j\) that is interpreted as a multiple of the FMU’s resolution, \(10^{k}\). Assuming that the FMU has accepted the time step, \(\varDelta i\) in clock domain of the master, \(\varDelta j\) in the clock domain of the FMU, then \(j' = j + \varDelta j\) is the future time index of the FMU and \(i' = i + \varDelta i\) is the target time index of the master. After the step is completed, we assume master and FMU have reached the same point in time, hence: \(i' \cdot 10^{n} = j' \cdot 10^{k}\). We obtain \(\varDelta j\), the step to be taken by the FMU, as follows:
$$\begin{aligned} \varDelta j = (i + \varDelta i) \cdot 10^{n - k} - j . \end{aligned}$$
Observe that the term \(10^{n - k}\) in Eq. (6) could be a fractional number (specifically, it is fractional when \(n - k < 0\)). This is possible because n shall always be smaller than or equal to k (as per the resolution negotiation procedure in Fig. 5, the resolution of the master must be at least as fine as the resolution of the FMU). Therefore, since \(\varDelta j\) must be an integer, quantization may be necessary. Just as we did for the time conversion method for category \(0_B\) FMUs, we need to pick a quantization method for category 2 FMUs.

It should be noted that we can compute the floor or ceiling of \(\varDelta j\) using solely integer arithmetic; there is no need for floating-point arithmetic for either of them. The floor can be implemented using an integer division that truncates toward zero, which is standard in C99 and most other contemporary programming languages. The ceiling function can also be implemented using integer division: if the division truncates to zero then \(\lceil \frac{x}{y} \rceil \) can be computed using the following expression: (x+y-1)/y.

Using the ceiling operator as our quantization method allows the FMU to move ahead of the master (and any other higher-resolution FMUs), while using the floor operator would let the FMU lag behind. Neither of the two solutions is more accurate than the other, but given that we quantize time for \(0_B\) FMUs such that they lag behind with respect to the master’s clock, it would make sense to adopt the same policy for category 2 FMUs and therefore select the floor operator; to round to the previous smaller integer. Hence, we obtain:
$$\begin{aligned} \varDelta j = \lfloor (i + \varDelta i) \cdot 10^{n - k} \rfloor - j. \end{aligned}$$
To convert a step in the clock domain of the FMU to a step in the clock domain of the master, we essentially use Eq. 7, except here, we can omit the rounding. Because the master operates at a time resolution greater than or equal to the time resolution of the FMU, it must be that \(k - n \ge 0\), so no rounding is ever needed. Hence, we compute a step in the master’s clock domain based on a step in the FMU’s clock domain as follows:
$$\begin{aligned} \varDelta i = (j + \varDelta j) \cdot 10^{k - n} - i . \end{aligned}$$
Fig. 7

Two category 2 FMUs in cascade composition. Each FMU can only update their input and output signal at times that are multiples of their resolution. The discrete event produced by FMU A at local time 15 is received at by FMU B at local time 10. No time quantization occurs with the discrete events produced at times 20 and 30

It is important to emphasize that time quantization plays a different role for category 2 FMUs than it does for category \(0_B\) FMUs. The former experience quantization only in the conversion from master time to time FMU time, while the latter experience quantization only in the conversion from FMU time to master time; the directionality of time quantization is opposite in comparison between the two. This observation also explains why it is no issue to use ceiling quantization for category 2 FMUs, because the Zeno condition described in Sect. 4.1 is due to loss of precision in the conversion from FMU time to master time, which for category 2 FMUs is lossless. A detailed description of the application of Eqs. (7) and (8) in the wrapper for category 2 FMUs can be found in “Appendix”.

Finally, let us examine the effects of time quantization using an example. Consider the model in Fig. 7 that depicts two category 2 FMUs. The master, along with FMU A, uses a resolution of 1 s, while FMU B uses a resolution of 10 s. In other words, a step of size 1 in the clock domain of FMU B represents a step size of 10 in the clock domain of FMU A. Conversion the other way around, dividing by \(10^1\), may not yield a whole number and therefore incurs a quantization error.

Interestingly, the event emitted by FMU A at internal time index \(j_A=15\), which corresponds to time \(t=15\), will appear on the input of FMU B (due the use of the floor function) when it is at internal time index \(j_B=1\) which corresponds to time \(t=10\). Superficially, this may look like a causality violation, but it is not, because the two internal clock domains are completely isolated from each other. They are analogous to two people having a phone conversation, but where one is looking at a clock that is ahead compared to a clock the other is looking at. They cannot see each other’s clocks. An outside observer (the master) has its own clock, which may differ from both the internal clocks (although in this particular example it is perfectly synchronized with FMU A because they use the same resolution). In all three clock domains, causality is preserved.

5 Conclusions

Although we all harbor a simple intuitive notion of time, how it is measured, how it progresses, and what it means for two events to be simultaneous, a deeper examination of the notion, both in models and in physics, reveals considerable subtleties. Cyber-physical systems pose particularly interesting challenges, because they marry a world, the cyber side, where time is largely irrelevant and is replaced by sequences and precedence relations, with a physical world, where even the classical Newtonian idealization of time stumbles on discrete, instantaneous behaviors and notions of causality and simultaneity. Since CPS entails both the smooth continuous dynamics of classical Newtonian physics, and the discrete, algorithmic dynamics of computation, it becomes impossible to ignore these subtleties.

We have shown that the approach taken in FMI (and many other modeling frameworks) that embraces a naive Newtonian physical model of time, and a cyber-approximation of this model using floating-point numbers, is inadequate for CPS. It is suitable only for modeling continuous dynamics without discrete behaviors. Using this unfortunate choice for CPS results in models with unnecessarily inexplicable, nondeterministic, and complex behaviors. Moreover, we have shown that these problems are solvable in a very practical way, resulting in CPS models with clear semantics that are invulnerable to the pragmatics of limited-precision arithmetic in computers. To accomplish this, our solution requires an explicit choice of time resolution that quantizes time so that arithmetic on time values is performed on integers only, something that modern computers can do exactly, without quantization errors. Moreover, we have shown that such an integer model of time can be used in a practical co-simulation environment, and that this environment can even embrace components that internally use floating-point representations of Newtonian time, for example to model continuous dynamics without discrete behaviors.

We have gone to considerable effort in this paper to show that choosing a better model of time does not complicate a co-simulation framework such as FMI by much. A small number of very simple extensions to the existing standard are sufficient, and these extensions can be realized in a way that efficiently supports legacy simulation environments that use floating-point Newtonian time. But while supporting such legacy simulators, it also admits integration of a new class of simulators, including discrete-event simulators, software engineering models, hybrid systems modelers, and even the new QSS classes of simulators for continuous dynamics. Such a co-simulation framework has the potential for offering a clean and universal modeling framework for CPS. And although we have only worked out the details for FMI, we are convinced that the same principles can be applied to other co-simulation frameworks such as HLA and to simulators that directly embrace mixed discrete and continuous behaviors such as Simulink/Stateflow. We hope that our readers include the people who can make this happen.


  1. 1.

    Strictly speaking, overflow can occur in the sense that the machine may run out of memory to represent the integer times. But this would occur at such absurdly large times, beyond the age of the universe with any imaginable resolution, that it is simply not worth worrying about.

  2. 2.

    Our proposed extension does not target a specific version of FMI. For this reason (and for brevity), we have removed the prefix “\(\texttt {fmi2}\)” from all newly proposed functions. Newly introduced datatypes honor the naming convention but have the FMI version number replaced by a wildcard, “X.”

  3. 3.

    For simplicity, we omit this implementation detail from the remainder of the discussion and refer to these functions without the “XXX” wildcard suffix.


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© The Author(s) 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Fabio Cremona
    • 1
  • Marten Lohstroh
    • 1
  • David Broman
    • 2
  • Edward A. Lee
    • 1
  • Michael Masin
    • 3
  • Stavros Tripakis
    • 1
    • 4
  1. 1.University of California, BerkeleyBerkeleyUSA
  2. 2.KTH Royal Institute of TechnologyStockholmSweden
  3. 3.IBM Research – HaifaHaifaIsrael
  4. 4.Aalto UniversityEspooFinland

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