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Alternative TitleResearch on RUL Prediction of Lithium - Ion Batteries Based on a New Capacity Degradation Model
李亚滨1,2; 林硕1; 袁学庆2; 刘竞远2; 李亚滨3,4; 林硕3; 袁学庆4; 刘竞远4
Source Publication计算机仿真
Contribution Rank1
Keyword新容量退化模型 粒子滤波算法 锂离子电池 剩余使用寿命

为准确预测锂离子电池剩余使用寿命(remaining useful life,RUL),建立能有效描述锂离子电池非线性退化特征的模型非常必要。采用新颖的回归方程构建容量退化模型,与双指数退化模型的对比表明:该模型具有更强的描述能力。依赖于此模型,提出了基于新容量退化模型和粒子滤波(particle filtering,PF)算法的锂离子电池剩余寿命预测方法,并与非线性退化自回归模型(nonlinear degradation auto regression,ND-AR)和正则化粒子滤波算法的混合方法(regularized particle filter,RPF)的预测结果做比较。结果表明:该方法对不同锂离子电池具有较好的适应性,能给出比ND-AR和RPF的混合方法更高精度的预测结果,且收敛性较好。

Other Abstract

In order to accurately predict remaining useful life of lithium - ion batteries, building an efficient model that can represents the nonlinear degradation feature of lithium - ion batteries is very essential. In this work, a novel regression equation was adopted to build the capacity degradation model, comparison with double exponential degradation model shows that the model used in this paper has a better description capability. Relying on this model, we proposed a method for Remaining Useful Life (RUL) prediction of lithium - ion batteries based on a new capacity degradation model and Particle Filtering(PF) algorithm, and it was compared with the fusion prognostic method of the nonlinear degradation auto regression model(ND – AR) and the regularized particle filter algorithm(RPF). The results show that the method proposed in this paper has a good adaptability to different lithium - ion batteries, can achieve a more accurate prediction than the fusion method of ND - AR and RPF, and has a good convergence.

Document Type期刊论文
Corresponding Author袁学庆; 袁学庆
Recommended Citation
GB/T 7714
李亚滨,林硕,袁学庆,等. 基于新容量退化模型的锂电池RUL预测研究[J]. 计算机仿真,2020,37(2):120-124.
APA 李亚滨.,林硕.,袁学庆.,刘竞远.,李亚滨.,...&刘竞远.(2020).基于新容量退化模型的锂电池RUL预测研究.计算机仿真,37(2),120-124.
MLA 李亚滨,et al."基于新容量退化模型的锂电池RUL预测研究".计算机仿真 37.2(2020):120-124.
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