Powertrain System Matching Optimization and Regenerative Braking Strategy for Pure Electric Vehicle

Authors

Zhang Qi, 1. School of Control Science and Engineering, Shandong University, Jinan 250061, China;;
Xiaoling Fu, 1. School of Control Science and Engineering, Shandong University, Jinan 250061, China;;2. Department of Physics, Changji University, Changji 831100, China;
Li Ke, 1. School of Control Science and Engineering, Shandong University, Jinan 250061, China;;
Guojing Xing, 1. School of Control Science and Engineering, Shandong University, Jinan 250061, China;;
Chenghui Zhang, 1. School of Control Science and Engineering, Shandong University, Jinan 250061, China;;

Abstract

Abstract: Different matching proposals meeting the constraints were multi-objective optimized with the combined matrix, and a new regenerative braking control strategy for electric vehicle was designed. According to the design targets of pure electric vehicle, the parameters of drive motor, battery pack and reducer were analyzed. Multi-objective optimization function was designed with the linearity weighted aggregation method, considering both power and economy of electric vehicle, and the vehicle performance of different proposals was comparatively measured through Cruise combined matrix simulation. The front and rear braking force distribution control strategy was designed based on the velocity and brake pedal intensity. Simulation results show that the selection and matching of powertrain system has a great impact on the electric vehicle's power and economy, and the regenerative braking control strategy takes into account both energy recovery and braking safety.

Recommended Citation

Qi, Zhang; Fu, Xiaoling; Ke, Li; Xing, Guojing; and Zhang, Chenghui (2020) "Powertrain System Matching Optimization and Regenerative Braking Strategy for Pure Electric Vehicle," Journal of System Simulation: Vol. 28: Iss. 3, Article 13.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss3/13

First Page

600

Revised Date

2015-02-06

DOI Link

https://doi.org/

Last Page

609

CLC

TP391.9;U469.72

Recommended Citation

Zhang Qi, Fu Xiaoling, Li Ke, Xing Guojing, Zhang Chenghui. Powertrain System Matching Optimization and Regenerative Braking Strategy for Pure Electric Vehicle[J]. Journal of System Simulation, 2016, 28(3): 600-609.