Optimization of Regenerative Braking Control Strategy in Single-Pedal Mode Based on Electro-Mechanical Braking
Xiaobin Ning, Zhenghao Wang, Yong Lin, Yuming Yin, Jiazheng Wang, Yihao Hong
Abstract
To enhance the braking stability of electric vehicles and maximize braking energy recovery, this paper proposes a regenerative braking force distribution strategy based on Electro-Mechanical Braking (EMB) in a single-pedal mode. The braking intention during single-pedal operation is identified using an Adaptive Neuro-Fuzzy Inference System (ANFIS), with the effectiveness of this method validated through data collection and analysis on a six-degree-of-freedom test rig, showing significant improvement in intention recognition accuracy. An innovative distribution method for front and rear axle braking forces is developed, and a fuzzy controller is designed with battery State of Charge (SOC), vehicle velocity (v), braking intensity (z), and braking intention (I) as inputs, and the regenerative braking ratio coefficient (k) as the output. The controller is optimized using the Sparrow Search Algorithm (SSA), further enhancing braking energy recovery efficiency. Co-simulation with Simulink and AVL Cruise software demonstrates the strategy’s effectiveness. Results indicate that under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and China Light-Duty Vehicle Test Cycle (CLTC) conditions, the braking energy recovery efficiency of the proposed strategy reaches 21.54% and 25.39%, respectively. These findings confirm that the EMB-based single-pedal regenerative braking force distribution strategy significantly improves both braking stability and energy recovery efficiency in electric vehicles, offering valuable insights for future braking strategy development.