基于SSA-SVR模型的锂离子电池剩余容量预测OA
Prediction of Remaining Capacity of Lithium-Ion Batteries Based on SSA-SVR Model
基于SSA-SVR算法进行锂离子电池剩余容量预测研究,阐述支持向量回归机(SVR)的基本原理.利用麻雀搜索算法(SSA)对SVR关键参数进行全局寻优,提高SVR预测电池剩余寿命的精度.建立SSA-SVR模型,利用NASA PCoE研究中心电池数据进行预测试验,与标准SVR、基于遗传算法的SVR算法预测结果进行对比.结果表明:SSA-SVR算法具有更好的预测精度和更强的泛化性.
This study predicts the remaining capacity of lithium-ion batteries by the SSA-SVR algorithm,and elabrates the basic principles of Support Vector Regression(SVR).Sparrow Search Algorithm(SSA)is utilized for global optimization of key parameters in S VR to enhance the precision of battery remaining life prediction.A SSA-SVR model is established,pre-testing is conducted using battery data from NASA PCoE demonstrate that SSA-SVR algorithm has better predictive accuracy and greater generalization capability.research center,and a comparison with standard SVR and genetic algorithm-based SVR prediction results is performed.
宋健;李明林
福州大学机械工程及自动化学院,福建 福州 350116福州大学机械工程及自动化学院,福建 福州 350116
信息技术与安全科学
锂离子电池剩余容量支持向量机麻雀搜索算法
lithium-ion batteryremaining capacitysupport vector machinesparrow search algorithm
《机械制造与自动化》 2026 (2)
101-107,7
国家自然科学基金资助项目(11372074)
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