Sodium-ion batteries have gained increasing attention due to their advantages, such as abundant raw material reserves and low costs. As a new battery system, the electrochemical and thermal properties of its electrodes and the entire cell, as well as their variations over both short and long periods, still contain many unknowns. Similar to lithium-ion batteries, sodium-ion batteries also experience performance degradation over time. To ensure the long-term, safe, and stable operation of batteries in service, health and safety management are necessary. Modeling and simulation can accurately predict the multi-scale behavior of battery characteristics, and thus, serve as an important theoretical foundation for battery management. Therefore, modeling and simulation of sodium-ion batteries are crucial.

This paper first considers the temperature changes during battery operation and, based on the fundamental working principles of the battery, develops an electrochemical-thermal coupling model by retaining the main physical processes while ignoring secondary processes. Then, to identify and optimize the highly sensitive model parameters, a weighted particle swarm optimization algorithm is used, ensuring that the parameters are valid and reasonable. Finally, to address the differences among individual cells and the uncertainties in the measured data, machine learning algorithms are introduced into battery mechanism modeling. Specifically, a dynamic residual forest model (DRF) for sodium-ion batteries is constructed using random forest and incremental learning algorithms, which iteratively learns from errors to reduce simulation errors in voltage and temperature.

In the DRF model, the random forest algorithm initially performs a preliminary prediction, followed by the use of incremental learning algorithms to correct prediction errors, thereby continuously optimizing the prediction accuracy of battery terminal voltage and temperature. The key feature of this model is its ability to handle real-time data streams, adapt to dynamic changes in data distribution, and reduce the need for retraining on new data, all while maintaining high prediction accuracy. This allows the model to simulate the complex operating conditions during the actual use of the battery. By using the DRF model to correct the outputs of the electrochemical-thermal coupling model, the final predictions of terminal voltage and temperature are obtained. Validation results show that the hybrid model provides better predictions of terminal voltage and temperature for different individual cells with higher accuracy.

Lithium-ion batteries (LIBs) serve as the primary energy source for electric vehicles and smart devices. However, during the usage, the formation of the solid electrolyte interphase (SEI) film is closely related to the capacity decline of the battery, playing a crucial role in the battery performance and lifespan. This study focuses on the growth mechanism of SEI, revealing its evolution during the cycling process of charge and discharge, as well as its impact on the battery’s capacity retention and cycle stability. By establishing a simulation model based on the kinetic Monte Carlo (KMC) dynamics method, the dynamic growth process of the SEI on microsecond timescale under various discharge rates is simulated, achieving a quantitative prediction of SEI growth trends. The experimental part uses 18650 LIBs and validates the accuracy of the KMC model through constant current charge–discharge cycle aging experiments, with the simulation error within 4%. The results indicate that the growth rate of the SEI layer gradually increases during charging and decreases during discharging, with more SEI formed during charging than discharging at the same rate. As the number of aging cycles increases, the proportion of capacity loss caused by the SEI first decreases, then increases, and finally decreases again. This finding provides a new perspective for understanding the growth mechanism of the SEI.

Lithium-ion batteries have been rapidly developed as clean energy sources in many industrial fields, such as new energy vehicles and energy storage. The core issues hindering their further promotion and application are reliability and safety. A digital twin model that maps onto the physical entity of the battery with high simulation accuracy helps to monitor internal states and improve battery safety. This work focuses on developing a digital twin model via a mechanism-data-driven parameter updating algorithm to increase the simulation accuracy of the internal and external characteristics of the full-time domain battery under complex working conditions. An electrochemical model is first developed with the consideration of how electrode particle size impacts battery characteristics. By adding the descriptions of temperature distribution and particle-level stress, a multi-particle size electrochemical-thermal-mechanical coupling model is established. Then, considering the different electrical and thermal effect among individual cells, a model for the battery pack is constructed. A digital twin model construction method is finally developed and verified with battery operating data.

Accurate simulation of characteristics performance and state of health (SOH) estimation for lithium-ion batteries are critical for battery management systems (BMS) in electric vehicles. Battery simplified electrochemical model (SEM) can achieve accurate estimation of battery terminal voltage with less computing resources. To ensure the applicability of life-cycle usage, degradation physics need to be involved in SEM models. This work conducts deep analysis on battery degradation physics and develops an aging-effect coupling model based on an existing improved single particle (ISP) model. Firstly, three mechanisms of solid electrolyte interface (SEI) film growth throughout life cycle are analyzed, and an SEI film growth model of lithium-ion battery is built coupled with the ISP model. Then, a series of identification conditions for individual cells are designed to non-destructively determine model parameters. Finally, battery aging experiment is designed to validate the battery performance simulation method and SOH estimation method. The validation results under different aging rates indicate that this method can accurately estimate characteristics performance and SOH for lithium-ion batteries during the whole life cycle.