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Regenerative Braking Strategy for Electric Vehicles Based on Grey Wolf Optimizer-Optimized Fuzzy Control
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DOI：10.3390/wevj17080399.png)
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Abstract
En 中文
To improve braking energy recovery in pure electric vehicles while maintaining a reasonable braking-force distribution, this study proposes a regenerative braking strategy based on a grey wolf optimizer (GWO)-optimized fuzzy control. A single-motor front-wheel-drive pure electric vehicle is modelled in terms of vehicle longitudinal dynamics, motor characteristics, and battery state of charge (SOC). A front–rear braking-force distribution strategy is developed based on the ideal braking-force distribution I-curve and ECE regulation constraints. A Mamdani fuzzy controller is then designed with braking intensity z and battery SOC as inputs and the front-axle regenerative braking-force distribution coefficient k as the output, enabling coordinated allocation between front-axle regenerative braking and mechanical braking. To reduce the dependence of fuzzy rules on expert experience, the GWO is used to optimize 25 fuzzy rules, and the proposed strategy is verified in MATLAB R2023b under a typical urban driving cycle. The results show that all strategies satisfy the braking demand. Compared with the unoptimized fuzzy control strategy, the optimized strategy reduces SOC consumption by 5.15% and increases recovered braking energy by 59.56%, indicating improved regenerative braking performance.
Keywords:
pure electric vehicle
regenerative braking
fuzzy control
grey wolf optimizer
braking-force distribution
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