Research on particle swarm optimization algorithm of electromechanical hybrid braking control strategy based on road surface recognition

ABSTRACT

Concerning the issue of low energy recovery efficiency of compound braking in Battery electric vehicles, an electromechanical composite braking control strategy based on road recognition and particle swarm optimization algorithm is proposed, which is different from the braking force control strategy of front and rear wheels with fixed ratio distribution before optimization. By analyzing the structure and working principle of the compound braking system of electric vehicle, a road surface identifier based on fuzzy algorithm is designed to track the peak adhesion coefficient of the road surface to obtain the maximum braking force of the brake. In addition, particle swarm optimization (PSO) is used to modify the motor braking force, so as to maximize the efficiency of energy recovery, and CRUISE and MATLAB are used in simulation environment to carry out joint simulation analysis. The results show that compared with the control strategy before optimization, the proposed control strategy not only ensures the vehicle braking stability but also has shorter braking distance, shorter braking time, larger motor braking torque, and slower decrease of battery State of Charge (SOC) value. Under the cycle condition of New European Driving Cycle (NEDC), Federal Test Procedure (FTP) and Extra Urban Driving Cycle (EUDC), the State of Charge of the battery increased by 1.98%, 2.1%, and 0.5%, respectively.

KEYWORDS:

• Compound braking
• Energy recovery
• fuzzy algorithm
• particle swarm optimization algorithm
• road recognition

Acknowledgments

This work was supported by the National Natural Science Foundation of China (51875494), the Natural Science Research Program of Jiangsu Province Colleges and Universities (19KJB580019), and the Jiangsu Normal University Graduate Innovation Program Project (SJCX20_1353).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author Contribution

Methodology, Z.Z.; software,C.L.;formal analysis,C.L.;resources, Z.Z.;data curation, C.L.;writing-original draft preparation, C.L.;writing-review and editing, Z.Z.;project administration, C.L.;funding acquisition, Z.Z.All authors have read and agreed to the published version of the manuscript

Additional information

Funding

This research was funded by the National Natural Science Foundation of China (51875494), the Natural Science Research Program of Jiangsu Province Colleges and Universities (19KJB580019), and the Jiangsu Normal University Graduate Innovation Program Project (SJCX20_1353).