针对电动船舶动力电池组荷电状态（SOC）估计困难，导致电池容量利用率难以提高以及缩短电池寿命等问题，提出一种基于改进粒子滤波的动力电池SOC估计方法。在模型参数辨识阶段，利用递推最小二乘法实时估计电池参数并引入多时间尺度辨识策略优化算法适应性。在SOC估计阶段，利用适合非线性系统的粒子滤波（PF）修正传感器观测误差，并以轻量化的果蝇优化算法（FOA）改进重采样过程，提高估计精度，优化算力资源消耗。最后以280 Ah磷酸铁锂电池为实验对象，模拟船舶运行工况，结果表明估计方法均方根误差为0.0058，CPU利用率相较PF算法下降了17%，有效提高了电池SOC估计精度与稳定性，具备在实际环境中的应用价值。
Aiming at the difficulty in State of charge (SOC) estimation of electric ship power battery pack, which leads to problems in improving battery capacity utilization and shortening battery life, a battery SOC estimation method based on improved particle filter is proposed. For model parameter identification, the recursive least squares method is used to estimate the battery parameters, and the multi-time scale identification strategy is introduced to optimize the adaptability of the algorithm. For SOC estimation, the particle filter (PF) suitable for nonlinear systems is used to correct the sensor observation error, and the lightweight Fruit Fly Optimization Algorithm (FOA) is used to improve the resampling process, improve the estimation accuracy, and optimize the computing resource consumption. Finally, a 280 Ah lithium iron phosphate battery was used as the experimental object to simulate the ship's operating conditions. The results showed that the root mean square error of the estimation method was 0.0058, and the CPU occupancy rate was reduced by 17% compared with the PF algorithm, which effectively improved the battery SOC estimation accuracy and stability, and has application value in practicalenvironments.
2025,47(9): 120-126 收稿日期：2024-7-12
DOI：10.3404/j.issn.1672-7649.2025.09.021
分类号：TK012
基金项目：绿色智能内河船舶创新专项（工信部装函[2019]358号）
作者简介：吴林翼（2000-），男，硕士研究生，研究方向为船舶新能源与节能减排技术
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