An Optimized Prediction Horizon Energy Management Method for Hybrid Energy Storage Systems of Electric Vehicles

Abstract

Model predictive control is a real-time energy management method for hybrid energy storage systems, whose performance is closely related to the prediction horizon. However, a longer prediction horizon also means a higher computation burden and more predictive uncertainties. This paper proposed a predictive energy management strategy with an optimized prediction horizon for the hybrid energy storage system of electric vehicles. Firstly, the receding horizon optimization problem is formulated to minimize the battery degradation cost and traction electricity cost for the electric vehicle operation. Then, the optimal control sequence is solved to obtain the power allocation between the battery and the supercapacitor. Furthermore, the effect of different horizons on the optimization results is analyzed under diverse operating conditions, determining the optimal horizon to balance the system costs and computation burden. Compared with the short horizon, the optimal horizon can achieve 5.2% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 8.5% performance improvement with the acceptable computation time approaching 1 s.