【摘要】 随着电动汽车产业的快速发展，动力电池在能量密度和安全性方面亟需进一步提升。全固态电池具有安全性好、能量密度高的优点，被认为是下一代电池关键技术之一，其中硫化物固态电解质以其高离子电导率和优异的可加工性而备受关注。硫化物基全固态电池的健康状态估计对探究电池的老化特性至关重要，然而相关研究仍处于早期阶段。基于电化学模型，利用其能够描述电池内部反应机理并提供高精度仿真结果的优点，结合无迹卡尔曼滤波算法，进行了硫化物基全固态电池的健康状态估计研究，并通过实验验证了所提出方法的可行性，结果表明：模型能够快速收敛，并且后续健康状态估计平均误差在1%以内，最大误差在2%以内。更多还原

【Abstract】 With the rapid advancement of the electric vehicle industry, there is an imperative need to enhance power batteries in terms of energy density and safety. All-solid-state batteries are considered a key technology for next generation batteries due to their superior safety and high energy density.Among these, sulfide solid-state electrolytes have garnered significant attention for their high ionic conductivity and excellent processability. Estimating the state of health of sulfide-based all-solid-state batteries is crucial for understanding the aging characteristics of batteries, yet related studies are still in their early stages. In this paper, we investigate the state of health estimation of sulfide-based allsolid-state batteries using an electrochemical model that elucidates the internal reaction mechanisms of the batteries and provides high precision simulation results, integrating with the unscented Kalman filter algorithm, and the feasibility of the proposed method is experimentally validated. The model demonstrates rapid convergence, and the subsequent state of health estimation achieves the average error within 1% and the maximum error within 2%.更多还原