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.

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.

NiFe composites have been regarded as promising candidates to replace commercial noble-based electrocatalysts for the oxygen evolution reaction (OER). However, their practical applications still suffer from poor conductivity, limited activity, and durability. To address these issues, herein, by utilizing three-dimensional covalent organic framework (3D-COF) with porous confined structures and abundant coordinate N sites as the precursor, the partially oxidized Ni₃Fe nanoalloys wrapped by N-doped carbon (N-C) layers are constructed via simple pyrolysis and subsequent oxidization. Benefiting from the 3D curved hierarchical structure, high-conductivity of Ni₃Fe and N-C layers, and well-distributed active sites, the as-synthesized O-Ni₃Fe-N-C catalyst demonstrates excellent activity and durability for catalyzing OER. Experimental and theoretical analyses disclose that both high-temperature oxidization and the OER process greatly promote the formation and exposure of the Ni(Fe)OOH active species as well as lower charge transfer resistance, inducing its optimized OER activity. The robust graphitized N-C layers with superior conductivity and their couplings with oxidized Ni₃Fe nanoalloys are beneficial for stabilizing catalytic centers, thereby imparting O-Ni₃Fe-N-C with such outstanding stability. This work not only provides a rational guidance for enriching and stabilizing high-activity catalytic sites towards OER but also offers more insights into the structural evolution of NiFe-based OER catalysts.