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Summary In developing an efficient battery management system (BMS), an accurate and computationally efficient battery states estimation algorithm is always required. In this work, the highly accurate and computationally efficient model‐based state of X (SOX) estimation method is proposed to concurrently estimate the different battery states such as state of charge (SOC), state of energy (SOE), state of power (SOP), and state of health (SOH). First, the SOC and SOE estimation is performed using a new joint SOC and SOE estimation method, developed using a multi‐time scale dual extended Kalman filter (DEKF). Then, the SOP estimation using T‐method and 2RC battery model is performed to evaluate the non‐instantaneous peak power during charge/discharge. Finally, the battery current capacity estimation is performed using a simple coulomb counting method (CCM)‐based capacity estimation with a sliding window. The performance of the proposed SOX estimation method is compared and analyzed. The experimental results show that the estimated SOC and SOE error is less than 1% under considered dynamic load profile at three different temperatures. After the final convergence, the estimated capacity maximum value absolute error is ±0.08 Ah. In addition, the low value of evaluated mean execution time (MET) justifies the high computational efficiency of the proposed method.

Hybrid energy storage system (HESS) has emerged as the solution to achieve the desired performance of an electric vehicle (EV) by combining the appropriate features of different technologies. In recent years, lithium-ion battery (LIB) and a supercapacitor (SC)-based HESS (LIB-SC HESS) is gaining popularity owing to its prominent features. However, the implementation of optimal-sized HESS for EV applications is a challenging task due to the complex behavior of LIB and SC under different driving behaviors. Besides, the power electronics (PE) converter configurations and system-level optimizations, include component sizing (CS) and power-energy management strategy (PEMS), are essential for developing efficient HESS. Therefore, this paper reviews existing LIB-SC HESS, different possible combinations of CS and PEMS, generalized algorithm formulation, and algorithms used for both CS and PEMS. The current issues of LIB-SC HESS regarding the performance in EV applications, PE converters, and optimization algorithms are also analyzed. In addition, future recommendations for the development of efficient LIB-SC HESS are provided to inspire researchers for further studies. Highlights• Lithium-ion battery (LIB) and supercapacitor (SC)-based hybrid energy storage system (LIB-SC HESS) suitable for EV applications is analyzed comprehensively.

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With an accurate state of charge (SOC) estimation, lithium-ion batteries (LIBs) can be protected from overcharge, deep discharge, and thermal runaway. However, selecting appropriate algorithms to maintain the trade-off between accuracy and computational efficiency is challenging, especially under dynamic load profiles such as electric vehicles. In this study, seven different widely utilized online SOC estimation algorithms were considered with the following goals: (a) to compare the accuracy of the different algorithms; (b) to compare the computational time in the simulation. Since the 2-RC battery model is highly accurate and not very computationally complex, it was selected for implementing the considered algorithms for the model-based SOC estimation. The considered online SOC estimation performance was evaluated using measurement data obtained from experimental tests on commercial lithium manganese cobalt oxide batteries. The experimental analysis consisted of a dynamic current profile comprising a worldwide harmonized light vehicle test procedure (WLTP) cycle and constant current discharging pulses. In addition, the performance of the considered different algorithms was compared in terms of estimation error and computational time to understand the challenges of each algorithm. The results indicated that the extended Kalman filter (EKF) and sliding mode observer (SMO) were the best choices because of their estimation accuracy and computation time. However, achieving the SOC estimation accuracy depended on the battery modeling. On the other hand, the estimated SOC root means square error (RMSE) using a backpropagation neural network (BPNN) was less than that using a Luenberger observer (LO). Moreover, with the advantages of BPNNs, such as no need for battery modeling, the estimation error could be further reduced using a large size dataset.