Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles
Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles
复制标题
用于电动汽车锂离子电池状态估计的先进机器学习方法
DOI：
10.1109/tte.2015.2512237
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发表时间：
2016-06-01
影响因子：
7
通讯作者：
Yang, Yalian
中科院分区：
文献类型：
--
作者：
Hu, Xiaosong;Li, Shengbo Eben;Yang, Yalian
为了实现电动汽车（EV）中可靠的电池管理，通过机器学习方法开发了一种先进的荷电状态（SOC）估计器。提出了一种基于遗传算法的模糊C-均值聚类方法，对锂离子电池基于行驶循环的试验数据进行聚类分析。聚类结果用于学习模型的拓扑结构和前因参数。然后采用递归最小二乘算法提取其后件参数。为了保证良好的准确性和弹性，最后采用反向传播学习算法同时优化前件和后件。实验结果表明，所提出的估计具有足够的精度和优于传统的模糊建模方法建立。
To fulfill reliable battery management in electric vehicles (EVs), an advanced State-of-Charge (SOC) estimator is developed via machine learning methodology. A novel genetic algorithm-based fuzzy C-means (FCM) clustering technique is first used to partition the training data sampled in the driving cycle-based test of a lithium-ion battery. The clustering result is applied to learn the topology and antecedent parameters of the model. Recursive least-squares algorithm is then employed to extract its consequent parameters. To ensure good accuracy and resilience, the backpropagation learning algorithm is finally adopted to simultaneously optimize both the antecedent and consequent parts. Experimental results verify that the proposed estimator exhibits sufficient accuracy and outperforms those built by conventional fuzzy modeling methods.