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Neural Inverse Optimal Control of a Regenerative Braking System for Electric Vehicles

Author

Listed:
  • Jose A. Ruz-Hernandez

    (Faculty of Engineering, Universidad Autonoma del Carmen, Campeche 24180, Campeche, Mexico)

  • Larbi Djilali

    (Faculty of Engineering, Universidad Autonoma del Carmen, Campeche 24180, Campeche, Mexico)

  • Mario Antonio Ruz Canul

    (Faculty of Engineering, Universidad Autonoma del Carmen, Campeche 24180, Campeche, Mexico)

  • Moussa Boukhnifer

    (Université de Lorraine, LCOMS, 57000 Metz, France)

  • Edgar N. Sanchez

    (Department of Electrical Engineering, Cinvestav Guadalajara, Av. del Bosque 1145, Col. El Bajío, Zapopan 45019, Jalisco, Mexico)

Abstract

This paper presents the development of a neural inverse optimal control (NIOC) for a regenerative braking system installed in electric vehicles (EVs), which is composed of a main energy system (MES) including a storage system and an auxiliary energy system (AES). This last one is composed of a supercapacitor and a buck–boost converter. The AES aims to recover the energy generated during braking that the MES is incapable of saving and using later during the speed increase. To build up the NIOC, a neural identifier has been trained with an extended Kalman filter (EKF) to estimate the real dynamics of the buck–boost converter. The NIOC is implemented to regulate the voltage and current dynamics in the AES. For testing the drive system of the EV, a DC motor is considered where the speed is controlled using a PID controller to regulate the tracking source in the regenerative braking. Simulation results illustrate the efficiency of the proposed control scheme to track time-varying references of the AES voltage and current dynamics measured at the buck–boost converter and to guarantee the charging and discharging operation modes of the supercapacitor. In addition, it is demonstrated that the proposed control scheme enhances the EV storage system’s efficacy and performance when the regenerative braking system is working. Furthermore, the mean squared error is calculated to prove and compare the proposed control scheme with the mean squared error for a PID controller.

Suggested Citation

  • Jose A. Ruz-Hernandez & Larbi Djilali & Mario Antonio Ruz Canul & Moussa Boukhnifer & Edgar N. Sanchez, 2022. "Neural Inverse Optimal Control of a Regenerative Braking System for Electric Vehicles," Energies, MDPI, vol. 15(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8975-:d:985965
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    References listed on IDEAS

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    1. Jingang Guo & Xiaoping Jian & Guangyu Lin, 2014. "Performance Evaluation of an Anti-Lock Braking System for Electric Vehicles with a Fuzzy Sliding Mode Controller," Energies, MDPI, vol. 7(10), pages 1-18, October.
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    Cited by:

    1. Marcin Kaminski & Tomasz Tarczewski, 2023. "Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction," Energies, MDPI, vol. 16(11), pages 1-25, May.
    2. Humam Al-Baidhani & Abdullah Sahib & Marian K. Kazimierczuk, 2023. "State Feedback with Integral Control Circuit Design of DC-DC Buck-Boost Converter," Mathematics, MDPI, vol. 11(9), pages 1-18, May.

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