This paper investigates the state-of-charge (SOC) estimation problem for lithium-ion batteries in balancing systems. Currently, most research focuses on the SOC estimation of individual lithium-ion cell. However, in practical applications, lithium-ion batteries are often connected to balancing circuits to eliminate imbalances within the battery pack. When the balancing circuit is activated, the dynamic characteristics of the battery system change considerably, and existing estimation methods often fail to effectively capture this dynamic transition. To address this issue, this paper proposes a switched Gaussian process regression(GPR) method. First, we conduct a qualitative analysis of the lithium-ion battery balancing circuit switched system based on a switch resistor balancing circuit. Then, building on this analysis, we develop a switched GPR method that adapts to the on/off state of the balancing circuit. Finally, we construct an experimental platform and validate the proposed switched model through comparative experiments with traditional methods. The results demonstrate that the proposed switched model exhibits significant advantages in terms of SOC estimation accuracy and adaptability, effectively handling the complex dynamic changes in the system after the balancing circuit is activated.

Contemporary battery management systems pre-dominantly employ machine learning frameworks as the principal methodology for lithium-ion batteries degradation assessment. However, this approach suffers from the drawback of relying on a large amount of labeled data, which is not applicable when estimating the state of health (SOH) of real vehicle batteries. To address this challenge, this paper proposes a Physics-Informed Neural Network -based SOH estimation method for lithium-ion batteries. Our approach integrates the physical mechanisms of battery aging into a deep learning framework, taking into account the model’s interpretability, adaptability, and robustness under complex conditions. Experimental validation with multi-year real-vehicle datasets demonstrates that in comparison to existing methods, the PINN approach achieves mean absolute errors below 2.5% and approximately 30% higher accuracy, which realizes effective SOH estimation in practical vehicular applications.

Accurate prediction of lithium-ion battery capacity degradation and remaining useful life (RUL) is crucial for battery health management and the safe operation of equipment. However, the diversity of battery types and variations in usage environments pose challenges to data-driven predictive models. Traditional machine learning models often exhibit poor performance in terms of prediction and generalization capabilities. This paper introduces a time-series large model: ANVMD-Llama. The model employs Adaptive Noise Variational Mode Decomposition (ANVMD) to process battery aging data for RUL prediction. Initially, the adaptive noise variational mode decomposition optimizes the tokenization scheme of Lag-Llama, decomposing battery degradation data into multiscale modal components with distinct features to characterize degradation trends and fluctuation properties, aiding the model in understanding fluctuation patterns. Subsequently, ANVMD-Llama is pre-trained on a large dataset of diverse lithium-ion battery degradation data to learn capacity degradation patterns. The model is then fine-tuned using a small amount of data to update the top-level modules, achieving more accurate predictions. Finally, the experimental results demonstrate that the proposed model achieves accurate RUL prediction and exhibits strong transfer capability.

The estimation of the state-of-charge (SOC) in lithium-ion batteries has garnered significant attention, with current research primarily concentrating on individual batteries. In practice, however, lithium-ion batteries often require connection to a balancing circuit to correct battery imbalance. In such cases, the system topology differs from that of an individual battery. Therefore, it is essential to account for this change. This article proposes a method for estimating the SOC of lithium-ion battery cells within reconfigurable circuits. We established a switching system model for lithium-ion batteries in reconfigurable circuits. We then design a nonlinear switching observer and examine its stability. Additionally, we conducted extensive experiments to evaluate the proposed observer’s performance and compared it with other observers.

State of charge (SOC) estimation of lithium-ion batteries has been extensively studied, and most of the existing research focuses on SOC estimation of individual lithium-ion battery. In practical applications, however, lithium-ion batteries are connected to a balancing circuit to eliminate imbalances between batteries. When a balancing circuit is activated, the state space equation of its equivalent circuit will change. In this paper, we propose a switched extended Kalman filter method for SOC estimation of lithium-ion battery balance systems. The switching system model is established by combining the Li-ion battery equivalent circuit model and the switching resistance balance circuit. A switching extended Kalman filter is designed to estimate the SOC of a switching system.

Batteries are pivotal in electric vehicles (EVs), serving as the primary source of power. To ensure the safe and efficient operation of EVs, it is essential to accurately predict the state of health (SOH) of the battery, typically achieved through a battery management system (BMS). However, the complex coupling reactions and nonlinear degradation processes inherent in lithium-ion batteries (LIBs) present significant challenges in SOH prediction. Current data-driven models often require extensive datasets and prolonged training periods, while also exhibiting limited generalization capabilities. To address these challenges, this paper proposes a novel method based on a foundation model which named Lag-Llama for predicting SOH and other critical battery characteristics synchronously, such as temperature, internal resistance. Remarkably, under zero-sample conditions, our approach achieves a Continuous Ranked Probability Score (CRPS) of 0.0079, demonstrating robust zero-shot generalization capabilities. Furthermore, the model’s predictive performance is significantly enhanced following fine-tuning.

To ensure optimal functionality of the super-capacitor management system in practical applications, the accurate and robust state of charge (SOC) estimation is crucial, particularly to account for aging effects and varying operating conditions. This paper proposes a switched system-based approach for the co-estimation of SOC and parameters of supercapacitors coupled with balancing resistor circuits. A switched model incorporating an equivalent circuit model is developed to accommodate the activation of equalization within a series-connected supercapacitor pack. The method combines a modified recursive least squares (RLS) algorithm with a switching sliding mode observer (SMO) for real-time parameter adaptation and SOC estimation. The experimental verification under a multi-balancing charging scenario demonstrates sig-nificant enhancements in accuracy and robustness compared to traditional methods employing fixed model configurations and parameters.