Research on Modified Genetic Algorithm-based High Efficiency Predictive Regenerative Braking Control Strategy for Hybrid Electric Bus

Abstract

Regenerative braking technology of electric vehicle is one of the main technologies to improve its economy. However, based on the hybrid braking system which integrates traditional mechanical braking system and regenerative braking system, how to reasonably distribute regenerative braking torque and friction mechanical braking torque to ensure the overall optimization of vehicle stability and economy in multiple complex working conditions is still a challenge. To solve this problem, an efficiency predictive regenerative braking control strategy based on modified genetic algorithm is proposed. Firstly, a 7 degree of freedom longitudinal vehicle dynamic model is built according to the braking system mechanical structure and dynamic characteristics. Then, considering the high non-linearity of tire in the unstable region and the multi-objective characteristics of stability, economy and other performance requirements in the braking process, the genetic algorithm is used to solve the optimal braking torque distribution problem in finite time domain, and the rolling optimization method is adopted to achieve the optimal control of the whole braking process. At the same time, in order to prevent that calculation result converges to local optimal solution, some modified methods are designed to improve the genetic algorithm; finally, based on the multi-dimensional table and the nearest point method, the real-time calculation of control strategy is realized, and the simulation and hardware in the loop tests are completed. The test results show that the proposed strategy can not only ensure the stability of the whole vehicle, but also improve the braking energy recovery by 15% compared with the regular control strategy which is used in the real vehicle controller.