Keywords

1 Introduction

Vehicle development is currently driven and influenced by the topics of electrification, digitalization, automated driving and mobile services. These trends influence each other and impact all aspects of the vehicle and especially the drive system. Drive system electrification is the biggest lever for fuel and energy reduction, especially considering battery electric vehicles (BEV). The savings potentials depend on the drive system layout itself, the operation strategy and also the optimization regarding driving operations. In particular changed driving profiles due to automated and connected driving require a different drive layout, compared to customer driving. Especially cooperative adaptive cruise control (CACC) and adaptive cruise control (ACC) [1,2,3] are subject of many studies and research projects regarding their impact on fuel consumption and improved traffic flow. In a previous investigation [4] the impact of automated driving on the drive system dimensioning and fuel and energy consumption especially for a highway pilot was analyzed. Within this paper we want to elaborate on the impact of connected driving on the drive system and the energetic impact in comparison to customer operation.

2 Methodology and Approach

The overall approach is a connected approach and is shown in Fig. 1. For the concept tool one input is the relevant vehicle and drive data and underlying boundary conditions. By means of an urban traffic simulation representative driving profiles are generated for different approach strategies of an intersection, which are the second input to the tool. Those profiles are the basis for the drive simulation with the developed concept optimization process. The concept optimization process consists in itself of four different modules and is explained in detail in [4]. The processes are a drive dimensioning, where the drives are dimensioned according to the underlying requirements. Furthermore, this module parametrizes the subsequent backwards simulation. The results of the simulation are the performances and efficiencies of each drive system with the corresponding aggregate operation points at each timestep of the cycle. Those results are the input for the evaluation module, where all concepts are assessed in detail. Furthermore, multi-criteria optimizations can be carried out as well.

Fig. 1.
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Cooperative approach for drive system and energy consumption analysis, divided into the inputs to the concept optimization, the cooperative intersection simulation and the input data and requirement-engineering

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The investigation is carried out for a D-segment vehicle, with the corresponding vehicle parameters in Table 1. For the additional connectivity elements an additional auxiliary consumption of 15 W and mass of 2 kg is assumed for each vehicle.

Table 1. Vehicle parameters of the investigated D-segment vehicle
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The investigated drive systems are a one-speed transmission (1G) and a two-speed transmission (2G). The drive layouts are displayed in Fig. 2. The 1G concept has a fixed transmission ratio i1 = 7 and an electric machine (EM) power PEM = 220 kW. The 2G concept has the ratios i1 = 9 and i2 = 4 and an EM power PEM = 180 kW. Both variants are requirement sufficient and allow a maximum velocity vmax = 180 km/h and an acceleration time t0–100,el. Below 6 s.

Fig. 2.
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Drive system layout for one-speed transmission BEV 1G on the left side and two-speed transmission BEV 2G on the right side, both concepts are rear wheel drives (RA)

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3 Result Discussion

In total seven different intersection scenarios are investigated. Three scenarios are fixed time traffic light control (FTTL) with vehicle safety distances of 2 s, 1 s and 0 s. Three other scenarios are a first come first serve (FCFS) with safety distances of 2 s, 1 s and 0 s. The seventh scenario is a cooperative scenario (Coop), where the traffic flow is optimized while having a safety distance of 2 s. In all FCFS scenarios and the cooperative scenario the vehicles are connected to the intersection via Vehicle2X (V2X). For each intersection scenario the trajectories of a total of eight vehicles are simulated. Figure 3 shows the range of energy consumption of the battery EBattery of all eight vehicles with 1G and 2G concept for each investigated scenario.

When assessing the total energy consumption for each scenario, it is clear that the crossing strategies lead to different energy consumption. In particular, the consumption range of all vehicles differs significantly between the scenarios. The differences between the 1G and 2G concepts are small and do not show a significant impact on the energy consumption. Especially due to the low speeds and short distances, the transmission losses in the 1G concept are high and comparable to the 2G concept.

Fig. 3.
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Energy consumption of the Battery EBattery for the investigated scenarios for the 1G and the 2G concept

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The average energy consumption for each scenario is shown in Fig. 4 for each scenario in dependency of the overall scenario execution time. As the total scenario execution time decreases, the energy consumption decreases. This is mainly due to the reduced energy consumption of the auxiliary consumers and also due to the reduced conversion losses especially in the EM from fewer driving operations.

Fig. 4.
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Average energy consumption of each scenario in dependency of the overall scenario execution time for the 1G concept, grey, and the 2G concept, blue, on the left-hand side, right-hand side relative energy consumption of the 1G and 2G concept in the different scenarios (Color figure online)

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The FTTL scenario with a safety distance 0s is an exception in terms of execution time, due to the fact, that one vehicle is stopping. The energy reduction potential caused by the optimized driving profiles result in lower total absolute driving resistances and thus to lower overall drive system energy consumption. The additional energy required to operate the V2X functionality for the cooperative and FCFS scenarios is overcompensated by the reduction of the driving operation and thus also conversion losses. The cooperative scenario allows a 14% reduction in energy consumption compared to a FTTL with the same safety distance of 2 s, while the FCFS scenario leads to a 7% reduction in energy consumption with the same safety distance. The Coop scenario with a safety distance of 2 s achieves a similar consumption to the FTTL scenario with a safety distance of 0 s.

Figure 5 shows the vehicle energy demand and losses for each vehicle in three different scenarios for the 1G concept. The energy demand and losses are divided into driving resistances, transmission losses, EM losses and other energy losses and consumers. These include wheel slip losses, mechanical brake losses, auxiliary energy demand as well as drag and inertia losses. The highest energy demand is caused by the driving resistances. The sum of driving resistance energy demand does not change significantly throughout the different scenarios, although the scenario and driving operations are significantly longer. The energy demand displays the sum of driving resistances, whereby the deceleration phases reduce the overall driving resistances. The higher driving operations lead to increased conversion losses of the EM and also to increased mechanical brakes losses, due to the reached recuperation criterion of a minimum speed of 7 km/h. Vehicle 5 highlights this behavior.

Fig. 5.
figure 5

Energy demand for 1G concept from driving resistances, EM losses, transmission losses and other energy losses during the scenarios FFTL 2s, FCFS 2s and Coop 2s for each of the eight simulated vehicles in each scenario

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Another major share make up the EM losses, which differ significantly between the different scenarios, mainly caused by the extended driving operation and multiple energy conversion, also during recuperation. The transmission losses of all scenarios are mostly similar for each vehicle and scenario. Only in some instances (e.g. vehicle 1) the transmission losses are significantly higher caused by multiple starting maneuvers from standstill. The differences for the remaining losses are mainly attributed to braking losses during deceleration maneuvers and are secondarily attributed to minor changes in wheel slip losses.

4 Conclusion and Outlook

Within this paper we investigated the energetic impact of cooperative driving on the example of intersections. Therefore, seven different intersection scenarios were generated and the resulting profiles were the basis for drive system simulation of a 1G BEV concept and a 2G BEV concept. The assessment of the results showed, that the drive system topology has no significant impact on the energy demand, mainly due to the short scenarios and the low velocity profiles with a high share of acceleration operation. When looking at the overall energy consumption, this investigation showed, that cooperative functions enable an energy consumption reduction of up to 14% in a cooperative scenario and an energy consumption reduction of up to 7% for first come first serve scenarios with a safety distance of 2 s.

In further investigations we want to emphasize two aspects. On the one hand, we want to optimize the drive systems within each scenario in order to identify the optimal drive system parameters for the overall lowest energy consumption. Furthermore, the vehicle individual optimal drive system, thus the lowest overall energy consumption for each scenario would be interesting. On the other hand, further investigations will focus on the scenario definition and expanding the scope of the scenario across the intersection. The definition of a fixed range before the intersection will also allow further scenario investigations when considering the profiles of each vehicle within the defined investigation window.