【摘要】 针对锂离子电池健康状态(state-of-health,SOH)估计与剩余有效工作时间(remaining useful life,RUL)预测进行探讨.提出了一种利用SOH参数反应电池状况,并且建模预测电池RUL的方法.改进了现有研究成果在RUL预测中不能更新其概率密度的缺陷.同时应用支持向量回归机(SVR-PF)改进标准粒子滤波算法具有粒子贫化效应的缺点.仿真结果表明提出的参数准确地反应了电池的状况,同时也准确地预测了电池的RUL;SVR-PF具有比粒子滤波更强的平滑与预测能力.更多还原

【Abstract】 Lithium-ion batteries are important power sources of the electronic devices.A method based on the SOH(state-of-health)parameters was proposed to estimate the lithium-ion battery SOH and RUL(remaining useful life).The improvement was made towards the defect that the current research works do not update the probability density during the RUL prediction process.Moreover,the SVR-PF(support vector regression-particle filter)algorithm was applied to improve the degeneracy phenomenon of the standard particle filter.The simulation results show that the proposed parameters estimate the battery SOH well,and accurately predict the RUL;the SVR-PF has good smoothing and prediction capability.更多还原