To address the limitations of traditional energy management strategies in hybrid energy storage systems for electric vehicles, including poor adaptability to dynamic conditions and safety risks in online reinforcement learning, this paper proposes an offline reinforcement learning framework integrating a time-aware variational autoencoder and Decision Transformer. Initially, high-quality expert trajectories are generated by a dynamic programming-based energy management strategy. Subsequently, a bidirectional long short-term memory network extracts temporal features from state sequences, while variational autoencoder synthesizes physics-constrained trajectories to mitigate distribution shift. Finally, Decision Transformer employs a self-attention mechanism to conduct multiscale temporal modeling of historical state-action sequences, establishing implicit policy mapping. Experimental results under the Dallas5 driving cycle demonstrate that the energy management strategy trained with a mixed dataset D1 outperforms the strategy trained with a pure expert dataset D2: battery capacity loss is reduced by 5.5%, and the final state of charge of the supercapacitor is stably maintained at 0.7320. This highlights the critical role of data diversity in enhancing generalization, offering a novel pathway for robust EMS design in real-world vehicular applications.

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 and effective prediction of battery remaining useful life (RUL) is crucial for the retirement planning of electric buses and the secondary utilization of battery packs. This study utilizes four years of operational data from nine electric buses to achieve precise RUL prediction for power batteries. First, considering the real-world operating characteristics of electric buses, this paper introduces a new RUL definition based on remaining mileage (RML) and remaining days of life (RDL) to characterize the remaining lifespan of battery packs. Subsequently, SOH labeling is conducted using charging data and filtering algorithms, followed by determining the end-of-life point of battery packs from SOH degradation trajectories. Finally, multi-time-scale features—including battery features, historical features, seasonal features, and discharging features are extracted from raw data, and the predictive performance of multiple ensemble learning models is compared. The results indicate that the AdaBoost model achieves the best performance in predicting RML and RDL, with a mean absolute error of 98 days and 16,852 km, respectively.

With the rapid advancement of technologies such as electric vehicles, the demand for energy storage devices has surged, leading to the widespread adoption of supercapacitors due to their numerous advantages. In practical applications, supercapacitors are often arranged in series or parallel configurations to form capacitor banks, which cater to higher voltage or capacity requirements. However, inconsistencies in the manufacturing processes and materials can lead to variations in the electrical performance of individual supercapacitors, necessitating effective balance management. Existing balancing methods are generally categorized into passive and active approaches. While passive balancing circuits are simple and cost-effective, they tend to be inefficient. On the other hand, active balancing methods, although offering high control precision, are typically more complex and expensive. This paper introduces a collaborative balancing strategy based on Deep Deterministic Policy Gradient (DDPG) using a switch resistor circuit, which serves as an intermediate approach between passive and active methods by combining their respective advantages. By integrating deep reinforcement learning with the switch resistor circuit for supercapacitor balancing, the proposed method addresses the slow balancing speed of traditional circuits under significant voltage disparities, enhancing the robustness of the balancing process and achieving superior performance. A simulation environment is established in Simulink to evaluate the effectiveness of the proposed method under various initial voltage conditions. The results demonstrate that the supercapacitor bank achieves balance within a short time frame. Moreover, comparative experiments indicate that the collaborative strategy significantly reduces overshoot and improves the robustness of supercapacitor balancing compared to noncollaborative approaches.

In the current era, a Li-ion battery pack, typically comprised of multiple cells, can offer higher voltage and output power. This plays a crucial role in various applications, including electric vehicles and energy storage. Accurate estimating the battery pack's state of charge (SOC) is crucial to offer users a clearer understanding of the battery status and to alleviate range anxiety. In the industry, it's common practice to precisely estimate the SOC for each cell, enabling an accurate assessment of the battery pack's overall SOC. However, most current methods for estimating the SOC in battery packs are centralized. In such cases, a problem with estimating the SOC of a single cell can greatly impact the overall SOC estimation of the entire battery pack. Likewise, if centralized equipment encounters issues, the SOC estimation for the entire battery pack is likely to be interrupted. This paper presents a distributed method for estimating battery pack SOC, utilizing a digital twin-based simulation platform. In the following, the each node that measures the SOC of cell is regarded as an agent that can communicate. Through communication among agents, each agent can converge to a reliable battery pack SOC estimation. In the event of a sudden issue arising in the SOC estimation of a cell, the proposed method can still uphold a dependable estimate of the battery pack's SOC, thereby bolstering the overall robustness of the SOC estimation system for the entire battery pack.

Supercapacitors, leveraging their distinctive characteristics and advantages, have evolved into efficient energy storage solutions. State of Charge (SOC) is a crucial parameter for supercapacitors, and the estimation of SOC for individual supercapacitor cells has been extensively researched. In practical applications, it is common to assemble hundreds or even thousands of cells to form a supercapacitor pack, particularly in fields like electric vehicles a nd electric b uses. Therefore, estimating the SOC for the supercapacitor pack becomes imperative. In response to the demands for supercapacitor pack SOC$(SOC_{pack})$estimation and wireless management, this paper proposes a distributed method. After modeling the supercapacitor cells, the definition of$SOC_{pack}$is introduced. The SOC of a cell in the definition is estimated based on Kalman filter. The proposed distributed method relies on wireless communication and computational updates between cells. Through iterative processes, it ultimately converges to the estimated$SOC_{pack}$. Finally, we conducted simulation experiments to analyze the performance of the proposed method under various communication conditions, thereby validating its effectiveness and robustness.