针对锂离子电池在老化和衰退过程中容量的非线性和非平稳特性问题，结合充放电特性的变化，提出了一种基于粒子群优化长短期记忆神经网络（particle swarm optimization long short-term memory neural network, PSO-LSTM）的电池容量预测模型。该方法详细描述了锂离子电池充放电过程中的关键环节，包括恒流充电时间、恒压充电时间及放电过程中的电压变化，并明确了这些特征与老化之间的相关性。通过数据挖掘技术，系统地提取了这些特征在电池生命周期中的变化规律，从而为电池性能评估提供了可靠的基础。为验证所提出方法的有效性，进行了多组实验，通过与传统预测模型进行比较，实验结果表明，该模型在容量估计中的最大均方根误差（root mean squared error, RMSE）仅为2.417 6，最大平均绝对误差（mean absolute error, MAE）为1.984 3，展示了良好的适应性与鲁棒性，能够有效反映电池的实际性能衰退情况，为电池管理系统的优化提供了重要的理论支持。

To address the nonlinear and non-stationary characteristics of capacity during the aging and degradation process of lithium-ion batteries, a battery capacity prediction model based on particle swarm optimization long short-term memory neural network(PSO-LSTM) was proposed, incorporating changes in charge and discharge characteristics. The key links in the charging and discharging process of lithium-ion batteries were described in detail, including constant current charging time, constant voltage charging time and voltage changes during the discharging process, and the correlation between these characteristics and aging was clarified. Through data mining technology, the variation patterns of these features throughout the battery life cycle were systematically extracted, thereby providing a reliable basis for battery performance evaluation. To verify the effectiveness of the proposed method, multiple sets of experiments were conducted. By comparing with the traditional prediction model, the experimental results show that the maximum root mean squared error (RMSE) of this model in capacity estimation is only 2.417 6. The mean absolute error (MAE) is 1.984 3, demonstrating good adaptability and robustness. It can effectively reflect the actual performance degradation of the battery and provide important theoretical support for the optimization of the battery management system.