Stochastic Control of Predictive Power Management for Battery/Supercapacitor Hybrid Energy Storage Systems of Electric Vehicles

Abstract

This paper presents a neural network (NN) based methodology for power demand prediction and a power distribution strategy for battery/supercapacitor hybrid energy storage systems of pure electric vehicles. To develop an efficient prediction model, driving cycles are first grouped and distinguished as three different driving patterns. For each driving pattern, characteristic parameter data that could better featured driving cycles are extracted effectively and used to train NN. The predictive information combined with its error is subsequently used for power distribution. Then, to deal with different dynamics of battery and supercapacitor systems, a frequency splitter is used and its frequency is further optimized by a particle swarm optimization algorithm to minimize the total cost including battery degradation and system energy for each driving pattern. Based on these efforts, a real-time predictive power management control strategy is finally proposed. To verify its effectiveness, simulation has been conducted to compare with the state-of-the-art control strategy under a speed profile composing of five standard driving cycles. Results show that obviously enhanced performance can be achieved by the proposed control strategy.