To solve the problem of the slow convergence speed for the battery stateof-charge estimation of cubature Kalman filter algorithm, the ternary lithium-ion battery is taken as the research object, and an algorithm combining the fuzzy self-adaptation and singular value decomposition cubature Kalman filtering is proposed. The algorithm takes the system innovation and its change rate as the fuzzy input and the output as the adjustment factor, which is used to adjust the process noise covariance matrix R. The Kalman gain is adjusted through the fuzzy control of R. To ensure the stability of the algorithm in the calculation process, the singular value decomposition is applied to cubature Kalman algorithm. Then, a second-order RC equivalent circuit model with double internal resistance is built and tested under different conditions to verify the rationality of the improved algorithm. The verification results show that under the simple condition, the convergence speed of the proposed algorithm in the different initial state-of-charge values increased by 40.00% and 25.00%, the maximum estimation error of the state-of-charge is 2.52% and 2.51%, the Mean Absolute Error is 0.816% and 0.880%, and the Root Mean Square Error is 1.276% and 1.380%. When the initial state-of-charge value is 0.8, the convergence speed in the complex condition is increased by about 30.00%; the maximum estimation result error, Mean Absolute Error, and Root Mean Square Error are 2.21%, 0.222%, and 1.327%, respectively. When the initial state-of-charge value is 0.6, the convergence speed in the complex condition is increased by about 10.00%; the maximum estimation result error, Mean Absolute Error, and Root Mean Square Error are 2.72%, 0.941%, and 1.327%, respectively. Without reducing the estimation accuracy, the improved algorithm can significantly increase the convergence speed of predictive value tracking, which provides a theoretical basis for the wide application of lithium-ion batteries.

State‐of‐charge and state‐of‐health of power lithium‐ion batteries are two important state parameters for battery management system monitoring. To accurately estimate the state‐of‐charge and state‐of‐health of in real time, the ternary lithium‐ion battery is taken as the research object, and a novel bias compensation recursive least square‐multiple weighted dual extended Kalman filtering method is proposed innovatively. The noise variance estimation is introduced to compensate the parameters identified by the general least square method to realize the accurate identification. The estimation value is corrected by using the residual and Kalman gain at multiple times, and different weights are configured for each residual according to the amount of information contained. The data of different complex conditions are used to verify the feasibility of the proposed algorithm, the results show that the root‐mean‐square error of bias compensation recursive least square‐multiple weighted dual extended Kalman filtering under dynamic stress test and Beijing bus dynamic stress test condition can be controlled within 1.62% and 2.70% in state‐of‐charge estimation, 0.17% and 0.81% in state‐of‐health estimation, which verifies that the proposed algorithm in this research has good running effect. The novel bias compensation recursive least square‐multiple weighted dual extended Kalman filtering method lays a theoretical foundation for the safe operation of electric vehicles.

Accurate state of charge (SOC) for the lithium-ion battery is not only related to user experience but also the top target to avoid overcharge and overdischarge and to use it safely. The back propagation (BP) neural network is widely used in SOC estimation, but there exist some issues, such as easily falling local extreme value, converging slowly, or even unable to converge and even overfitting. The Drosophila algorithm has a simple algorithm and strong global optimization ability, but there is also a problem of direct inheritance to reduce the optimization ability. To solve these problems, an individual migration dynamic step Drosophila (Improved Drosophila) algorithm combined with the BP neural network is proposed to estimate the SOC of lithium-ion batteries and improve estimation accuracy. In addition, the performance of the proposed method is compared with that of its traditional algorithms and other commonly used functions. The experiments are carried out to verify the ternary lithium-ion battery under DST and BBDST conditions., the mean absolute error is less than 0.8%, and the root mean square error is less than 1.4%. The SOC estimation is carried out when the current data under the DST condition are missing, which also has good estimation performance, which shows the robustness of the algorithm. Compared to other algorithms, there is good estimation accuracy.

Accurate state-of-charge estimation plays an extremely crucial role in battery management systems. To realize the real-time and precise state-of-charge esti-

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