Research on Energy-Saving Optimization Control Strategy for Distributed Hub Motor-Driven Vehicles

Bin Huang, Jinyu Wei, Minrui Ma and Xu Yang ()
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Bin Huang: Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
Jinyu Wei: Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
Minrui Ma: Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
Xu Yang: School of Automation, Wuhan University of Technology, Wuhan 430070, China

Energies, 2025, vol. 18, issue 12, 1-25

Abstract: Aiming at the problems of energy utilization efficiency and braking stability in electric vehicles, a high-efficiency and energy-saving control strategy that takes both driving and braking into account is proposed with the distributed hub motor-driven vehicle as the research object. Under regular driving and braking conditions, the front and rear axle torque distribution coefficients are optimized by an adaptive particle swarm algorithm based on simulated annealing and a multi-objective co-optimization strategy based on variable weight coefficients, respectively. During emergency braking, the anti-lock braking strategy (ABS) based on sliding mode control realizes the independent distribution of torque among four wheels. The joint simulation verification based on MATLAB R2023a/Simulink-Carsim 2020.0 shows that under World Light Vehicle Test Cycle (WLTC) conditions, the optimization strategy reduces the driving energy consumption by 3.20% and 2.00%, respectively, compared with the average allocation and the traditional strategy. The braking recovery energy increases by 4.07% compared with the fixed proportion allocation, improving the energy utilization rate of the entire vehicle. The wheel slip rate can be quickly stabilized near the optimal value during emergency braking under different adhesion coefficients, which ensures the braking stability of the vehicle. The effectiveness of the strategy is verified.

Keywords: distributed hub motor-driven vehicle; particle swarm algorithm; genetic algorithm; sliding mode control; anti-lock braking strategy (ABS); optimization of drive torque; brake energy recovery (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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