过度拟合
锂离子电池
电池(电)
均方误差
遗传算法
荷电状态
算法
人工神经网络
多收费
稳健性(进化)
控制理论(社会学)
计算机科学
数学
人工智能
机器学习
统计
化学
功率(物理)
物理
生物化学
控制(管理)
量子力学
基因
作者
Hong Xu,Shunli Wang,Yongcun Fan,Jialu Qiao,Wenhua Xu
摘要
Accurate state of charge (SOC) for the lithium-ion battery is not only related to user experience but also the top target to avoid overcharge and overdischarge and to use it safely. The back propagation (BP) neural network is widely used in SOC estimation, but there exist some issues, such as easily falling local extreme value, converging slowly, or even unable to converge and even overfitting. The Drosophila algorithm has a simple algorithm and strong global optimization ability, but there is also a problem of direct inheritance to reduce the optimization ability. To solve these problems, an individual migration dynamic step Drosophila (Improved Drosophila) algorithm combined with the BP neural network is proposed to estimate the SOC of lithium-ion batteries and improve estimation accuracy. In addition, the performance of the proposed method is compared with that of its traditional algorithms and other commonly used functions. The experiments are carried out to verify the ternary lithium-ion battery under DST and BBDST conditions., the mean absolute error is less than 0.8%, and the root mean square error is less than 1.4%. The SOC estimation is carried out when the current data under the DST condition are missing, which also has good estimation performance, which shows the robustness of the algorithm. Compared to other algorithms, there is good estimation accuracy.
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