An adaptive fuzzy sliding‐mode control for regenerative braking system of electric vehicles

This article proposes a novel fuzzy sliding‐mode control scheme under an adaptive control strategy for energy management mechanism in electric vehicles that are subject to a regenerative braking system. The effectiveness of the fuzzy logic controller is to adjust the sliding mode parameters according to the slip ratio tracking error between the optimal slip ratio and the actual slip ratio. Specifically, the proposed torque distribution strategy can integrate the best battery condition and energy recovery efficiency under the practical constraints through this control method by fixing the pneumatic braking torque and motor torque. Finally, an electric vehicle model is established in the Simulink environment to verify the applicability of the proposed control algorithm.

[1]  Zhengyou He,et al.  An Energy Storage System for Recycling Regenerative Braking Energy in High-Speed Railway , 2021, IEEE Transactions on Power Delivery.

[2]  Changle Xiang,et al.  A swarm intelligence-based predictive regenerative braking control strategy for hybrid electric vehicle , 2020, Vehicle System Dynamics.

[3]  Cong Huang,et al.  Event-triggering robust fusion estimation for a class of multi-rate systems subject to censored observations. , 2020, ISA transactions.

[4]  Ammu Susanna Jacob,et al.  Trade-off between end of life of battery and reliability in a photovoltaic system , 2020 .

[5]  Yong Jiang,et al.  Study on a segmented electro-pneumatic braking system for heavy-haul trains , 2020, Transportation Safety and Environment.

[6]  Kumar Alok,et al.  Adoption of electric vehicle: A literature review and prospects for sustainability , 2020 .

[7]  B. Dias,et al.  Regenerative Braking for Energy Recovering in Diesel-Electric Freight Trains: A Technical and Economic Evaluation , 2020, Energies.

[8]  Qi Zhang,et al.  Energy recovery based on pedal situation for regenerative braking system of electric vehicle , 2020, Vehicle System Dynamics.

[9]  Steffen Junginger,et al.  Transportation robot battery power forecasting based on bidirectional deep-learning method , 2019, Transportation Safety and Environment.

[10]  Xu Han,et al.  An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle , 2019, International Journal of Adaptive Control and Signal Processing.

[11]  Enrique Rosales-Asensio,et al.  Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario , 2019, Energy.

[12]  Kyongsu Yi,et al.  Dynamic Handling Characteristics Control of an in-Wheel-Motor Driven Electric Vehicle Based on Multiple Sliding Mode Control Approach , 2019, IEEE Access.

[13]  A. Khajepour,et al.  A comprehensive review of the key technologies for pure electric vehicles , 2019, Energy.

[14]  Neil Bose,et al.  A Fuzzy‐Based Risk Assessment Framework for Autonomous Underwater Vehicle Under‐Ice Missions , 2019, Risk analysis : an official publication of the Society for Risk Analysis.

[15]  Fouad Giri,et al.  Output feedback control of supercapacitors parallel charging system for EV applications: Theoretical design and experimental validation , 2019, International Journal of Adaptive Control and Signal Processing.

[16]  Wanzhong Zhao,et al.  Energy transfer and utilization efficiency of regenerative braking with hybrid energy storage system , 2019, Journal of Power Sources.

[17]  Zhong-Ping Jiang,et al.  An adaptive learning and control architecture for mitigating sensor and actuator attacks in connected autonomous vehicle platoons , 2019, International Journal of Adaptive Control and Signal Processing.

[18]  Wei Xu,et al.  Torque optimization control for electric vehicles with four in-wheel motors equipped with regenerative braking system , 2019, Mechatronics.

[19]  Weihua Li,et al.  Driver intention based coordinate control of regenerative and plugging braking for electric vehicles with in‐wheel PMSMs , 2018, IET Intelligent Transport Systems.

[20]  M. Shakarami,et al.  Power sharing adaptive control strategy for a microgrid with multiple storage and renewable energy sources , 2018, International Journal of Adaptive Control and Signal Processing.

[21]  Guodong Yin,et al.  Improving Vehicle Handling Stability Based on Combined AFS and DYC System via Robust Takagi-Sugeno Fuzzy Control , 2018, IEEE Transactions on Intelligent Transportation Systems.

[22]  P. Venkatesh,et al.  Simultaneous coordination of distinct plug-in Hybrid Electric Vehicle charging stations: A modified Particle Swarm Optimization approach , 2017 .

[23]  Xiao-gang Wu,et al.  Contrastive Study on Torque Distribution of Distributed Drive Electric Vehicle under Different Control Methods , 2017, J. Control. Sci. Eng..

[24]  Seibum B. Choi,et al.  Adaptive Scheme for the Real-Time Estimation of Tire-Road Friction Coefficient and Vehicle Velocity , 2017, IEEE/ASME Transactions on Mechatronics.

[25]  Lianjie Liu,et al.  Investigation and Control of VIVs with Multi-Lock-in Regions on Wide Flat Box Girders , 2017, J. Control. Sci. Eng..

[26]  Dongpu Cao,et al.  Optimal $\mu $ -Estimation-Based Regenerative Braking Strategy for an AWD HEV , 2017, IEEE Transactions on Transportation Electrification.

[27]  Kai He,et al.  AMT downshifting strategy design of HEV during regenerative braking process for energy conservation , 2016 .

[28]  Zoubir Khatir,et al.  Regenerative Braking Modeling, Control, and Simulation of a Hybrid Energy Storage System for an Electric Vehicle in Extreme Conditions , 2016, IEEE Transactions on Transportation Electrification.

[29]  Hamid Reza Karimi,et al.  A Robust Observer-Based Sensor Fault-Tolerant Control for PMSM in Electric Vehicles , 2016, IEEE Transactions on Industrial Electronics.

[30]  Chao Yang,et al.  Model Predictive Control-based Efficient Energy Recovery Control Strategy for Regenerative Braking System of Hybrid Electric Bus , 2016 .

[31]  Jie Wang,et al.  Electronic Stability Control Based on Motor Driving and Braking Torque Distribution for a Four In-Wheel Motor Drive Electric Vehicle , 2016, IEEE Transactions on Vehicular Technology.

[32]  Liang Li,et al.  Transient switching control strategy from regenerative braking to anti-lock braking with a semi-brake-by-wire system , 2016 .

[33]  Urbano Nunes,et al.  Electrical vehicle modeling: A fuzzy logic model for regenerative braking , 2015, Expert Syst. Appl..

[34]  Bin Wang,et al.  A robust wheel slip ratio control design combining hydraulic and regenerative braking systems for in-wheel-motors-driven electric Vehicles , 2015, J. Frankl. Inst..

[35]  Yong Xu,et al.  A vehicle ABS adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations , 2014 .

[36]  Zhuoping Yu,et al.  Vehicle dynamics control of four in-wheel motor drive electric vehicle using gain scheduling based on tyre cornering stiffness estimation , 2012 .

[37]  Hao Ying,et al.  Derivation and Experimental Validation of a Power-Split Hybrid Electric Vehicle Model , 2006, IEEE Transactions on Vehicular Technology.

[38]  Deliang Yu,et al.  Research on Anti-Lock Braking Control Strategy of Distributed-Driven Electric Vehicle , 2020, IEEE Access.

[39]  Jian Chen,et al.  Adaptive Fuzzy Logic Control of Fuel-Cell-Battery Hybrid Systems for Electric Vehicles , 2018, IEEE Transactions on Industrial Informatics.