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Insulation is the foundation for the safe operation of battery systems. However, the working condition of the battery system is complex, which challenges insulation fault detection. This article presents an online estimation algorithm of insulation resistance based on an adaptive filtering algorithm for a battery energy storage system (BESS). Specifically, the insulation detection model is developed based on the Thevenin model. Aiming at the problem of system noise, a joint estimation algorithm for battery parameters and voltage is proposed based on the recursive least square and unscented Kalman filter (RLS-UKF) algorithm. The full climate models of capacity and temperature are developed. Furthermore, an insulation resistance estimation algorithm is proposed based on the UKF algorithm. The proposed method is verified by different dynamic experiments. Experimental results show that the RLS-UKF algorithm has a better voltage filtering effect than the UKF algorithm with fixed model parameters. The proposed insulation resistance algorithm can accurately estimate the system’s insulation resistance under dynamic and static conditions.

Charging control is one of the essential functions of battery management systems. Battery charging involves behavioral changes such as electricity, heat, and aging. Balancing these factors is crucial for the safe and efficient operation of batteries. This work proposes an intelligent charging scheme for lithium-ion batteries that considers charging time, temperature rise, and health losses. First, charging aging experiments are conducted to investigate the effect of charging rate on battery aging. Specifically, half-cell experiments are carried out to construct an electrode open circuit voltage model to explore battery aging modes. The aging mode attenuation model is proposed based on empirical formulas. Then, the charging multiobjective function is established based on the equivalent circuit, thermal, and aging empirical models (AEMs). A multistage constant current (CC) charging optimization scheme has been developed based on the multiobjective gray wolf optimizer (MOGWO). Finally, the feasibility of three targets and dual targets fast charging schemes is verified through simulation and experiments.

The grouping and large-scale of battery energy storage systems lead to the problem of inconsistency. Practical consistency evaluation is significant for the management, equalization and maintenance of the battery system. Various evaluation methods have been developed over the past decades to better assess battery pack consistency. In these research efforts, the accuracy of the assessment results is often of paramount importance. In this work, a battery pack consistency evaluation approach is proposed based on multi-feature information fusion. Ohmic resistance, polarization resistance and open circuit voltage are identified as feature parameters from electric vehicle operation data. An adaptive forgetting factor recursive least squares (AFFRLS) algorithm is developed using fuzzy logic to modify the forgetting factor for parameter identification. Grey correlation analysis is applied to calculate the dispersion of features (DF). The DF is weighted to evaluate the inconsistency of the battery pack. Further, the weights are assigned through the CRITIC-G1 method. Moreover, a mapping model between the extracted voltage features and the DF is established through a cost-sensitive support vector machine (CS-SVM) algorithm, which is used to evaluate and predict the consistency distribution of battery parameters. Finally, the proposed algorithm is verified by experimental data. The results indicate that the proposed parameter identification, consistency evaluation and prediction methods have high accuracy.