Prediction of lithium-ion battery remaining useful life based on hybrid data-driven method with optimized parameter

Lithium-ion battery remaining useful life (RUL) is a key parameter on battery management system. An accurate battery RUL can provide reference for replacement and remind safety risk when battery life is nearly end. There are lots of researches on battery RUL prediction. However, until now, the accuracy problem of battery RUL prediction algorithm based on little sample is still not solved. Since a single method is hard to solve the complex problem of battery RUL prediction, this paper introduces an optimized hybrid data-driven method combined with discrete grey model (DGM) and relevance vector machine (RVM). The method takes advantages of trend forecast from DGM and non-linear regression ability of RVM. The kernel parameter of RVM in the algorithm is optimized by an artificial fish swarm algorithm (AFSA). The algorithm also provides confidence interval of the prediction results, which describe the probability distribution of prediction results. Experimental data analysis with NASA battery data set shows the impact of kernel parameter on result error and the improvement on accuracy of battery RUL prediction by the parameter optimization.

[1]  Jun Xu,et al.  Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications , 2013 .

[2]  Kwok L. Tsui,et al.  A naive Bayes model for robust remaining useful life prediction of lithium-ion battery , 2014 .

[3]  Wenjian Wang,et al.  Error estimation based on variance analysis of k-fold cross-validation , 2017, Pattern Recognit..

[4]  Tom Gorka,et al.  Method for estimating capacity and predicting remaining useful life of lithium-ion battery , 2014, 2014 International Conference on Prognostics and Health Management.

[5]  Michael Buchholz,et al.  On-board state-of-health monitoring of lithium-ion batteries using linear parameter-varying models , 2013 .

[6]  Li Xiao,et al.  An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm , 2002 .

[7]  Xiaoning Jin,et al.  Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter , 2014 .

[8]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[9]  Jianbao Zhou,et al.  Remaining Useful Life Estimation with Dynamic Grey Relevance Vector Machine for Lithium-ion Battery , 2013 .

[10]  Zonghai Chen,et al.  An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks , 2016 .

[11]  Asok Ray,et al.  Identification of the battery state-of-health parameter from input–output pairs of time series data , 2015 .

[12]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .

[13]  Kai Goebel,et al.  Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework , 2009 .

[14]  Zhen Liu,et al.  An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries , 2013, Microelectron. Reliab..

[15]  Sifeng Liu,et al.  Discrete grey forecasting model and its optimization , 2009 .