Combining the characteristics of the high precision of inductive energy storage equalization and the fast speed of capacitive energy storage equalization, an active equalization method is proposed for a series battery pack based on an inductor and capacitor. The energy storage devices responsible for energy transfer have only one inductor and one capacitor. First, we propose the topology, analyze the working principle, and elaborate on its parameter design process. Second, an adaptive equalization control strategy matching the topology is designed. Third, based on the simulation models, the speed and efficiency characteristics of the new equalization method are verified, and the significant cost and volume advantages and the simple control characteristics are analyzed in detail. Finally, an experimental platform was built for a four-cell series battery pack to verify the effectiveness of the new equalization method for the battery pack charging and discharging process. The voltage difference is less than 80 mV.

To eliminate the influence of the inconsistency on the cycle life and the available capacity of battery packs, and improve the balancing speed, a novel inductor-based layered bidirectional equalizer (IBLBE) is proposed. The equalizer is composed of two layers of balancing circuits connected in parallel. Each layer contains multiple balancing sub-circuits based on buck-boost converters. These balancing sub-circuits can equalize the corresponding cells simultaneously, and allow the dynamic adjustment of equalization path and equalization threshold. Analysis and simulation results demonstrate the IBLBE has a higher level balancing speed than other equalizers based on switched-capacitor or switched-inductor converters, and reduces the balancing time by 30% compared to existing inductor-based parallel architecture equalizers (PAEs). Experimental results are presented to validate the analysis and effectiveness of the proposed equalizer.

State of charge (SOC) estimation is generally acknowledged to be one of the most important functions of the battery management system (BMS) and is thus widely studied in academia and industry. Based on an accurate SOC estimation, the BMS can optimize energy efficiency and protect the battery from being over-charged or over-discharged. The accurate online estimation of the SOC is studied in this paper. First, it is proved that the second-order resistance capacitance (RC) model is the most suitable equivalent circuit model compared with the Thevenin and multi-order models. The second-order RC equivalent circuit model is established, and the model parameters are identified. Second, the reasonable optimization of model parameters is studied, and a reasonable optimization method is proposed to improve the accuracy of SOC estimation. Finally, the SOC is estimated online based on the adaptive unscented Kalman filter (AUKF) with optimized model parameters, and the results are compared with the results of an estimation based on pre-optimization model parameters. Simulation experiments show that, without affecting the convergence of the initial error of the AUKF, the model after parameter optimization has a higher online SOC estimation accuracy.

Inconsistencies are inevitable in the practical application of battery packs of new energy vehicles, which will reduce the energy utilisation rate and service life and even endanger the safety of the battery system. To reduce the inconsistency of battery packs, this study innovatively proposes an integrated active balancing method for series‐parallel battery packs based on LC energy storage. Only one inductor and one capacitor are used to store energy to achieve the balance of each cell in a series‐parallel battery pack. This design has the characteristics of simple structure, small volume, fast balancing speed and easy expansion. By building an experimental platform, the effectiveness of this novel integrated balancing method is verified.

Accurate state of health (SOH) estimation is critical for Lithium-ion battery maintenance and safety. Currently, numerous time series models based on deep learning have been applied to online prediction of SOH. However, the strong nonlinear coupling between different sample data seriously interferes with the estimation accuracy of existing models. Rational network expansion can alleviate the issue, but limited samples pose a significant challenge to model training. Therefore, how to effectively reduce the strong nonlinear coupling between data is the key to achieve high accuracy prediction of SOH. In view of this, this paper proposes an SOH estimation method based on frequency-domain assisted data decoupling and time-frequency features compensation learning network (FA2D-TFCLNet). Specifically, this paper proposes a Mel filtering algorithm to achieve preliminary nonlinear decoupling of the discharge data. Subsequently, time-domain features and frequency-domain features are fed in parallel to TFCLNet, which consists of a parallel multi-scale convolutional attention-based local receptor (CPMCA) and Transformer. By organically combining the refined local aging features mined by the CPMCA and the discriminative global semantic relations captured by the Transformer, the proposed TFCLNet effectively realizes the comprehensive perception and deep decoupling of discharge data, thereby obtaining competitive SOH estimation accuracy. To validate the effectiveness of the proposed method, relevant experiments are conducted on NASA PCoE and Oxford data sets, and the results prove that the method has a high accuracy in estimating battery SOH.