Multi-Parameter Collaborative Power Prediction to Improve the Efficiency of Supercapacitor-Based Regenerative Braking System

Abstract

The supercapacitor-based regenerative braking system heightens energy utilization efficiency of the motor. However, the power mismatch between the energy storage unit and the motor would cause the supercapacitor to absorb energy from the power grid and unnecessary energy loss. In this paper, a multi-parameter collaborative power prediction control of the motor-driven system is proposed to solve the power mismatch. Mechanism analysis of energy flow is investigated. Afterward, a power prediction model of the supercapacitor is established based on various signals of certain time sequences. The modeling is carried out by discrete forms, and the collaboration among multi-parameters is considered. The performance optimization function is deduced from the perspective of the minimum energy consumption principle. The storage/release energy of the supercapacitor can be fully coordinated with the energy of the power grid and the power of the motor to maintain the dynamic supply-demand power balance. Simulation and experimental results verify the effectiveness of the proposed control scheme. The energy utilization efficiency is increased by 20% in comparison with the typical control strategy under the recommended operating condition.