一种基于改进的遗传算法BP和自适应扩展卡尔曼波器的离子电池充电状态估计的新融合方法
Liling Cao1, Changfu Shao1, Zheng Zhang1
1College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
准确估计电动汽车离子电池的充电状态 (SOC) 对安全至关重要. 一种新的IGA-BP-AEKF方法使用自适应扩展的卡尔曼波器和优化的神经网络来提高SOC估计的准确性.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 准确的电荷状态 (SOC) 估计对于电动汽车 (EV) 的安全性和性能至关重要.
- 离子电池是电动汽车的主要动力来源,需要精确的监控.
- 现有的同等电路模型需要改进的参数识别,以提高准确性.
研究的目的:
- 为三元离子电池开发一种高度准确和稳定的SOC估计方法.
- 改进二级RC等效电路模型的在线参数识别.
- 通过一种新的聚变算法来提高SOC评估的精度.
主要方法:
- 为三元离子电池建立了二级RC等效电路模型.
- 使用忘记因子递归最小方程 (FFRLS) 估计器进行在线参数识别.
- 提出了IGA-BP-AEKF融合方法,将一个自适应扩展卡尔曼波器 (AEKF) 与一个改进的基因算法优化的反向传播神经网络 (IGA-BPNN) 集成在一起.
主要成果:
- 在FUDS条件下,IGA-BP-AEKF方法在SOC估计中表现出卓越的准确性和稳定性.
- 达到0.0119的最高误差,0.0083的平均绝对误差 (MAE) 和0.0088.88的根平均平方误差 (RMSE).
- 验证了拟议的IGA-BP-EKF算法在现有方法上的优越性.
结论:
- 开发的IGA-BP-AEKF方法显著提高了电动汽车中离子电池的SOC估计精度.
- 使用FFRLS在线参数识别可以提高电池等效电路模型的准确性.
- 融合方法为关键电池管理系统提供了强大而可靠的解决方案.
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