Regenerative Braking Control Strategy for Distributed Drive Electric Vehicles Based on Slope and Mass Co-Estimation

The regenerative braking control strategy of distributed drive electric vehicles (DDEVs) under the varying road slope is investigated in this study. Firstly, vehicle dynamic characteristics at the downhill driving condition are analyzed based on a vehicle dynamics model, and the specific impacts of the road slope on the braking control problem are disclosed. Since the estimate of the slope is related to the vehicle mass, an online co-estimation of the road slope and vehicle mass is proposed based on neural network and least square algorithm. The control lines are adjusted according to the estimation results, and the optimization of power allocation is conducted to achieve the optimal braking torque split among the front motor, rear motor, and hydraulic braking system. Finally, the control scheme of regenerative braking is proposed and evaluated by comparing with the Economic Commission of Europe (ECE)-based strategy and the I-curve strategy. The presented strategy provides better braking performance and higher energy recovery compared with that the traditional methods. The results indicate that energy recovery can be improved by up to 9.62% under certain driving conditions.