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Predictive diagnosis with artificial neural network for automated electric vehicle

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Abstract

The requirements and availability of the electric powertrain will be significantly increased with the introduction of automated driving functions. In this case, the mechanical fallback level of the driver must be replaced by a fault-tolerant system. New concepts such as the predictive diagnostic or customized operation strategies ensure the fault tolerance. An essential component to realize the requirements is the electric drive. In the present work, a method for the prediction of the fault condition in permanent magnet synchronous motor (PMSM) is developed based on artificial neural networks (ANN). Not only the failure occurrence is detected, but also the severity of the failure is predicted and classified. For this purpose, a suitable failure indicator is needed, which contradicts the severity of the failure and thus allows both the prediction and degradation (protection) of the system. The prerequisite for the use of machine learning methods, such as artificial neural networks, is the existence of a database. Data is obtained with the help of simulation model of PMSM, which can be corrected with failures. Features from the phase currents and the battery current in the time domain and in the frequency domain are presented as well as classical methods such as the wavelet analysis or the decomposition into symmetrical components. The selection of the features has a great influence on the diagnostic result and on the performance of the algorithm. The failures are represented by the features in the frequency domain. Based on these aspects, several neural networks are formed. To predict the failure, an accuracy of about 95% is achieved and for the classification an accuracy of about 98.5%.

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Abbreviations

AI:

Artificial intelligent

AME:

Adaptive moment estimation

ANN:

Artificial neural networks

EMF:

Electromotive force

EM:

Electric machine

FOC:

Field-oriented control

PMSM:

Permanent-magnet-synchronous-motors

\({\varvec{\omega}}\) :

Angular velocity (rad/s)

\({\varvec{f}}\) :

Frequency (Hz)

\({\varvec{\varphi }}\) :

Angel (rad)

\({\varvec{n}}\) :

Speed (rpm)

\({\varvec{T}}\) :

Torque (Nm)

\({\varvec{i}}_{{{\varvec{abc}}}}\) :

Phase current (A)

\({\varvec{v}}_{{{\varvec{abc}}}}\) :

Phase voltage (V)

\({\varvec{i}}_{{{\varvec{d}},{\varvec{q}}}}\) :

Current (A)

\({\varvec{i}}_{{{\text{bat}}}}\) :

Battery current (A)

\({\varvec{L}}\) :

Self-inductance (H)

\({\varvec{M}}\) :

Mutual inductance (H)

\({\varvec{R}}\) :

Resistance (Ω)

\({\varvec{e}}\) :

Induced voltage (Back-EMF) (V)

\({\varvec{P}}\) :

Power (W)

\({\varvec{R}}_{{\varvec{f}}}\) :

Failure resistance (Ω)

\({\varvec{\eta}}\) :

Number of short-circuited windings

\({\varvec{i}}_{{{\text{circ}}}}\) :

Failure current (A)

\(\Delta {\varvec{P}}_{{{\text{loss}}}}\) :

Differential power (W)

\(\Delta {\varvec{P}}_{{{\text{nom}}}}\) :

Adjusted differential power

\({\varvec{p}}\) :

Number of pole pairs

\({\varvec{\lambda}}\) :

Magnetic flux of permanent magnet (weber)

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  1. Robert Bosch GmbH, Postfach 30 02 40, 70442, Stuttgart, Germany

    Ahmet Kilic

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Kilic, A. Predictive diagnosis with artificial neural network for automated electric vehicle. J Braz. Soc. Mech. Sci. Eng. 44, 544 (2022). https://doi.org/10.1007/s40430-022-03857-5

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