基于PSO-PID算法的AUV动力电池组的活性平衡策略
Shaowei Zhang1, Yuli Hu1, Silun Luo1
1School of Marine Science and Technology, Northwestern Polytechnical University, Xian, 710072, China.
Heliyon
|October 9, 2024
概括
一种新的电池均等化策略使用融合等效电路模型,以获得更高的准确性. 这种方法有效地平衡了多个电池,提高了性能,增加了电池不一致性.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
背景情况:
- 电池管理系统需要准确的状态估计.
- 不一致的电池细胞降低了整体电池组的性能和寿命.
- 现有的同等电路模型在准确性上有局限性.
研究的目的:
- 开发一种新的电池均等化策略.
- 为增强电池状态估计提出融合模型.
- 实施和验证一个活性电荷均衡系统.
主要方法:
- 使用BP神经网络结合1RC,2RC和PNGV等效电路模型的融合模型.
- 利用开源的DST动态运行测试数据进行模型验证.
- 开发一个由PSO-PID战略控制的活跃均等化系统.
主要成果:
- 拟议的融合模型实现了最高的估计准确性 (最大误差为0.00947,RMSE为0.00217),优于单个模型.
- 积极的等分系统有效地减少了电池组中的细胞间变异性,最初的SOC不一致性.
- 该系统表现出对动态干扰的稳定性,保持低方差 (平均值为0.0016).
结论:
- 新的融合模型显著提高了电池状态估计的准确性.
- 控制PSO-PID的活跃均等系统简单,有效,优于传统方法,特别是在增加细胞不一致的情况下.
- 这种方法通过高效的充电均等,提高了电池组的性能和寿命.
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