In the secondary utilization of retired batteries in energy storage systems, there exists a significant disparity between batteries, rendering the storage battery pack susceptible to issues such as overcharging and overdischarging during the charging and discharging process. These challenges impose limitations on the available capacity of the storage battery system and may even give rise to safety hazards. To tackle these issues, this study proposes the design of a battery equalization system specifically tailored for retired batteries employed in energy storage systems. A multilayer equalization topology based on a buck-boost chopper circuit is introduced, along with a multi-objective adaptive fuzzy control equalization strategy. This strategy integrates the open-circuit voltage-state of charge (OCV-SOC) characteristic curve of the battery and utilizes voltage and SOC as equalization variables. It governs the magnitude of equalization current based on the battery pack’s SOC state, enabling effective equalization control of the retired battery pack. Simulation results demonstrate that the proposed equalization strategy does not rely on the precise mathematical model of the battery system. When compared with the mean-difference equalization strategy or the single-variable equalization strategy, it exhibits an enhanced equalization rate. Consequently, it fulfills the practical application requirements for equalizing retired battery packs.

Battery state of power (SOP) estimation is an important parameter index for electric vehicles to improve battery utilization efficiency and maximize battery safety. Most of the current studies on the SOP estimation of lithium–ion batteries consider only a single constraint and rarely pay attention to the estimation of battery state on different time scales, which can reduce the accuracy of SOP estimation and even cause safety problems. In view of this, this paper proposes a multi-time scale and multi-constraint SOP estimation method for lithium–ion batteries based on H∞ filtering. Firstly, a second-order RC equivalent circuit model is established with a ternary lithium–ion monolithic battery as the research object, and parameter identification is performed by using the recursive least squares method with a forgetting factor. Secondly, the H∞ filtering algorithm is applied to estimate the state of charge (SOC), and then the joint multi-time scale multi-constrained SOC-SOP estimation is performed. Finally, the joint estimation algorithm is validated under UDDS conditions. The mean absolute value relative error (MARE) of SOC estimation is 1.17%, and the MARE of SOP estimation at different time scales is less than 1.6%. The results indicate the high accuracy and strong robustness of the joint estimation method.

Achieving accurate retired battery state of health (SOH) and state of charge (SOC) estimation is a safe prerequisite for securing the battery secondary utilization and thus effectively improving the energy utilization efficiency. The data-driven approach is efficient and accurate, and does not rely on accurate battery models, which is a hot direction in battery state estimation research. However, the huge number of retired batteries and obvious consistency differences bring bottleneck problems such as long learning time and low model updating efficiency to the traditional data-driven algorithm. In view of this, this paper proposes an integrated learning algorithm based on AdaBoost. Rt-LSTM to realize the joint estimation of SOC and SOH of retired lithium batteries, which relies on the LSTM neural network model and completes the correlation adaption in the spatio-temporal dimension of the whole life cycle sample data. The LSTM model is used as the base learner to construct the AdaBoost. Rt-LSTM strong learning model. The LSTM weak predictor is combined with weights to form a strong predictor, which greatly solves the problem of low accuracy of state estimation due to the large number and variability of retired batteries. Simulation and experimental comparison show that the integrated algorithm proposed in this paper is suitable for improving the SOC and SOH prediction accuracy and the generalization performance of the model.