Energy Management Strategy for Hybrid Energy Storage System using Optimized Velocity Predictor and Model Predictive Control

Abstract

Reasonable power distribution between battery and supercapacitor in electric vehicles is a crucial problem to improve energy consumption and economy. An online energy management strategy based on model predictive control (MPC) is proposed in this paper. Firstly, a radial basis function neural network optimized by particle swarm algorithm is presented to generate the short-term future velocity, i.e., the reference trajectory of the MPC. Then, a cost function considering the battery degradation cost and the electricity cost is constructed and optimized within each prediction horizon while maintaining the state of charge of the supercapacitor. Simulation results on the UDDS driving cycle show that the total cost of the proposed strategy is reduced by 6.3% and 3.9% compared with the near-optimal rule-based strategy and the none optimized velocity predictor-MPC, respectively, indicating that the velocity prediction accuracy has a significant impact on the performance of real-time energy management.