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%~8.5% performance improvement with the acceptable computation time approaching 1s.
An Optimized Prediction Horizon Energy Management Method for Hybrid Energy Storage Systems of Electric Vehicles
IEEE Transactions on Intelligent Transportation Systems ; 25 , 5 ; 4540-4551
2024-05-01
5885630 byte
Article (Journal)
Electronic Resource
English
Commuter Route Optimized Energy Management of Hybrid Electric Vehicles
Online Contents | 2014
|Reinforcement Learning Based Energy Management of Hybrid Energy Storage Systems in Electric Vehicles
SAE Technical Papers | 2021
|