使用LSTM神经网络优化基因算法对中高压电池的充电状态估计
Romel Carrera1, Leonidas Quiroz1, Cesar Guevara2
1Universidad de las Fuerzas Armadas ESPE, Departamento de Ciencias de la Energía y Mecánica Sede Latacunga, Av. General Rumiñahui S/N, Sangolquí 171103, Ecuador.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究引入了一种混合方法,将LSTM神经网络和库伦计数结合起来,以准确地估计离子电池的充电状态. 这种新的方法提高了在动态驾驶条件下电动汽车的预测准确度.
科学领域:
- 电池技术 电池技术
- 机器学习 机器学习
- 电动汽车系统 电动汽车系统
背景情况:
- 精确的充电状态 (SOC) 估计对于离子电池管理系统 (BMS) 至关重要.
- 像库伦计数 (CC) 和LSTM神经网络这样的独立方法在动态条件下存在局限性.
- 优化估计算法是电动汽车 (EV) 和第二生命应用中可靠性能的关键.
研究的目的:
- 开发一种混合 SOC 估计方法,将 LSTM 和 CC 整合起来,以提高准确性.
- 使用遗传算法 (GA) 优化混合模型,以提高实时性能.
- 根据中压电池组的标准化驾驶周期 (NEDC,WLTP) 验证拟议的方法.
主要方法:
- 一种混合方法,将LSTM神经网络与库伦计数 (CC) 结合起来用于SOC估计.
- 使用遗传算法 (GA) 优化LSTM.
- 通过参数α进行重新校准,LSTM预测与CC估计的动态融合.
主要成果:
- 混合型号实现了0.181%的低平均绝对误差 (MAE).
- 与传统的独立SOC估计策略相比,性能优越.
- 在动态驾驶条件下 (NEDC,WLTP) 提高预测准确度.
结论:
- 混合LSTM-GA-CC方法为准确的SOC估计提供了强大的解决方案.
- 这种方法有效地解决了个人估计技术的局限性.
- 这些发现支持开发更可靠的BMS用于电动汽车和二次使用电池应用.
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