The state of charge (SOC) of lithium-ion batteries is the main parameter of the battery management system. To improve the accuracy of lithium-ion battery SOC estimation, this paper uses a double RC physical characteristic circuit network to model the polarization reaction inside the battery and realizes the full parameter identification of the model based on the double-exponential fitting strategy. Then, using the spherical unscented transform (SUT) to realize the selection of sigma points and the calculation of weight coefficients, at the same time, the adaptive factor is introduced to correct the error covariance matrix in real time and an adaptive spherical unscented Kalman filter (AS-UKF) algorithm. Finally, the algorithm is compared with the unscented Kalman filter (UKF) and adaptive unscented Kalman filter (AUKF) algorithms through simulation. The results show that the average error of the AS-UKF algorithm is reduced by 0.5% and 1.18% under the Hybrid Pulse Power Characterization (HPPC) and the Beijing Bus Dynamic Street Test (BBDST) conditions. The AS-UKF algorithm not only improves the accuracy but also is more stable.

Accurate identification of model parameters is a key aspect of lithium battery state estimation. To accurately identify battery model parameters, this paper establishes hysteresis characteristic-electrical equivalent circuit (HC-EEC) modeling by analyzing the influence of the hysteresis effect on battery state of charge. For the high-precision identification of battery model parameters, an online multi-time scale adaptive parameter identification strategy (OM-TSAPIS) is proposed. According to the different dynamic response links in the HC-EEC model, the strategy performs parameter identification through different time scale links, and uses the adaptive step size as the starting identification condition for the multi-time scale links, thereby improving the parameter identification accuracy of the HC-EEC model. The absolute average error of OM-TSAPIS was 0.0437 and 0.298 mV under the urban dynamometer driving schedule and Beijing bus dynamic street test conditions, respectively. Simulation results show that the identification accuracy of the proposed algorithm is high.

The accuracy of the peak power is influenced by the accurate battery model, the results of the parameter identification, and the state of charge (SOC). First, to accurately predict the peak power of lithium-ion batteries, we propose an improved Thevenin model to describe the operating state of lithium-ion batteries by introducing model noise into the Thevenin model. Second, to achieve accurate online parameter identification, a forgetting factor recursive extended least squares (FFRELS) method is proposed to identify the parameters of the improved model. To optimize the effect of noise on SOC estimation, an improved adaptive extended Kalman filtering (AEKF) algorithm is proposed. Finally, to obtain higher accuracy of peak power estimation, a multi-constrained peak power prediction method based on state-recursive estimation is used in this paper. Experimental results show that the maximum error of the FFRELS algorithm under different working conditions is 34.35 mV, and the SOC estimation error of the improved AEKF algorithm is less than 0.53%. The improved multi-constraint peak power estimation algorithm has high estimation accuracy under two complex working conditions, and can accurately predict the power input and output capability of the battery.

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The precise assessment of the state of charge (SOC) of lithium-ion batteries (LIBs) is critical in battery management systems. This work offers a comprehensive learning particle swarm optimization (CLPSO) and extended Kalman filter (EKF) technique to forecast the SOC of LIBs in order to obtain an accurate SOC estimate for power batteries. First, to address the challenge of identifying various parameters of the battery model, the bilinear transformation technique is employed to determine the parameters of the second-order RC equivalent circuit model. Second, to improve the fitness values for the conventional PSO algorithm, which is prone to entering local optimality, a learning strategy (f_i) is added to the particle velocity update method. The optimized PSO and EKF algorithms are integrated to perform online prediction of the SOC of LIBs. The experimental results demonstrate that under the conditions of the Beijing Bus Dynamic Stress Test (BBDST), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization Test (HPPC), the parameter identification inaccuracy of CLPSO is restricted to 1%. After multi-metric evaluation, the maximum error and mean absolute error of the CLPSO-EKF algorithm in SOC estimation are 0.32% and 0.0652%, respectively, demonstrating a higher robustness and accuracy advantage over other versions.