A predictive energy management system for hybrid energy storage systems in electric vehicles

Energy management system plays a vital role in exploiting advantages of battery and supercapacitor hybrid energy storage systems in electric vehicles. Various energy management systems have been reported in the literature, of which the model predictive control is attracting more attentions due to its advantage in deal with system constraints. In this paper, a predictive energy management system is proposed based on a combination of Haar wavelet transform and model predictive control. Different from prior publications, the main contribution of this study is that the wavelet transform algorithm is introduced for power demand decomposition. At the same time, the power errors of the model predictive controllers are also fed to the wavelet transform algorithm for coefficient regulation. In this way, the power components distributed to the battery and supercapacitor can better match to their individual characteristics. The proposed method can reduce the maximum voltage drop of the battery up to 10.53%, 9.09% and 23.53%, the battery life cost up to 9.09%, 6.52% and 2.82%, respectively, as compared with the sole model predictive controller without wavelet transform based on NYCC, UDDS and NurembergR36 three driving cycles.