Battery-ultracapacitor hybrid energy storage system (HESS) is a reliable candidate to overcome the drawbacks of single power source system for its complementary features of power and energy in plug-in hybrid electric vehicle (PHEV) application. In this paper, an integrated energy management strategy procedure, which consists of three layers, is presented to distribute the power of the battery packs and the ultracapacitor packs. First, a HESS parameter match method is developed under different driving cycles with optimization objectives of cost and weight. Second, three-level wavelet transform (WT) algorithm is employed to isolate the different frequency components of power demand. The low frequency components with less transients and sharps in power demand is provided by extending the battery lifetime, while ultracapacitor satisfies the high frequency components for its nice features of high specific power. Finally, fuzzy logic control (FLC) is used to manage power flows between various components for guaranteeing the HESS performance. The simulation results show that compared with WT-based-only energy management strategy, the proposed procedure can always control the battery currents within 2C rate and reduce the energy consumption of about 6.54 %.

To satisfy the high-rate power demand fluctuations in the complicated driving cycle, electric vehicle (EV) energy storage systems should have both high power density and high energy density. In order to obtain better energy and power performances, a combination of battery and supercapacitor are utilized in this work to form a semi-active hybrid energy storage system (HESS). A parameter matching method of battery-supercapacitor HESS for electric vehicles (EVs) is proposed. This method can meet the performance indicators of EVs in terms of power and energy for parameter matching. The result shows that optimized parameter matching is obtained by reducing the weight and cost.

The state of charge (SOC) of lithium-ion batteries (LIBs) is a pivotal metric within the battery management system (BMS) of electric vehicles (EVs). An accurate SOC is crucial to ensuring both the safety and the operational efficiency of a battery. The unscented Kalman filter (UKF) is a classic and commonly used method among the various SOC estimation algorithms. However, an unscented transform (UT) utilized in the algorithm struggles to completely simulate the probability density function of actual data. Additionally, inaccuracies in the identification of battery model parameters can lead to performance degradation or even the divergence of the algorithm in SOC estimation. To address these challenges, this study introduces a combined UKF-LSTM algorithm that integrates a long short-term memory (LSTM) network with the UKF for the precise SOC estimation of LIBs. Firstly, the particle swarm optimization (PSO) algorithm was utilized to accurately identify the parameters of the battery model. Secondly, feature parameters that exhibited a high correlation with the estimation error of the UKF were selected to train an LSTM network, which was then combined with the UKF to establish the joint algorithm. Lastly, the effectiveness of the UKF-LSTM was confirmed under various conditions. The outcomes demonstrate that the average absolute error (MAE) and the root mean square error (RMSE) for the SOC estimation by the algorithm were less than 0.7%, indicating remarkable estimation accuracy and robustness.