Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system’s proper operation. A direct way to estimate the SOH is through the measurement of the battery’s capacity; however, this measurement during the battery’s operation is not that easy in practice. Moreover, the battery is always running under randomized loading conditions, which makes the SOH estimation even more difficult. Therefore, this paper proposes an indirect SOH estimation method that relies on indirect health indicators (HIs) that can be measured easily during the battery’s operation. These indicators are extracted from the battery’s voltage and current and the number of cycles the battery has been through, which are far easier to measure than the battery’s capacity. An empirical model based on an elastic net is developed to build the quantitative relationship between the SOH and these indirect HIs, considering the possible multi-collinearity between these HIs. To further improve the accuracy of SOH estimation, we introduce a particle filter to automatically update the model when capacity data are obtained occasionally. We use a real dataset to demonstrate our proposed method, showing quite a good performance of the SOH estimation. The results of the SOH estimation in the experiment are quite satisfactory, which indicates that the method is effective and accurate enough to be used in real practice.

[1]  Fan Li,et al.  A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter , 2015, Microelectron. Reliab..

[2]  정재식,et al.  A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation , 2011 .

[3]  Qiang Miao,et al.  Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model , 2013 .

[4]  Chao Hu,et al.  Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning , 2015, DAC 2015.

[5]  Yu Peng,et al.  Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction , 2013 .

[6]  Chenbin Zhang,et al.  An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles , 2016 .

[7]  Suleiman M. Sharkh,et al.  A Novel Data-Driven Fast Capacity Estimation of Spent Electric Vehicle Lithium-ion Batteries , 2014 .

[8]  Yongqi Li,et al.  Investigation of the internal resistance in LiFePO4 cells for battery energy storage system , 2014, 2014 9th IEEE Conference on Industrial Electronics and Applications.

[9]  C. García,et al.  Collinearity diagnostic applied in ridge estimation through the variance inflation factor , 2016 .

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

[11]  Jae Wan Park,et al.  On-line optimization of battery open circuit voltage for improved state-of-charge and state-of-health estimation , 2015 .

[12]  Alan Millner,et al.  Modeling Lithium Ion battery degradation in electric vehicles , 2010, 2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply.

[13]  Chao Lyu,et al.  Remaining capacity estimation of Li-ion batteries based on temperature sample entropy and particle filter , 2014 .

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

[15]  Lixin Wang,et al.  Model-based method for estimating LiCoO2 battery state of health and behaviors , 2016, 2016 IEEE International Conference on Prognostics and Health Management (ICPHM).

[16]  Matthieu Dubarry,et al.  Synthesize battery degradation modes via a diagnostic and prognostic model , 2012 .

[17]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[18]  Albert K. Kurtz,et al.  Pearson Product Moment Coefficient of Correlation , 1979 .

[19]  Xiaohong Su,et al.  A lithium-ion battery RUL prognosis method using temperature changing rate , 2016, 2016 IEEE International Conference on Prognostics and Health Management (ICPHM).

[20]  Mohammad Rezvani,et al.  A Comparative Analysis of Techniques for Electric Vehicle Battery Prognostics and Health Management (PHM) , 2011 .

[21]  Matthew Daigle,et al.  Adaptation of an Electrochemistry-based Li-Ion Battery Model to Account for Deterioration Observed Under Randomized Use , 2014 .

[22]  James M. Lucas,et al.  Exponentially weighted moving average control schemes: Properties and enhancements , 1990 .

[23]  Y. Nishi.  Lithium ion secondary batteries; past 10 years and the future , 2001 .

[24]  Zonghai Chen,et al.  A novel state of health estimation method of Li-ion battery using group method of data handling , 2016 .

[25]  K. Goebel,et al.  An integrated approach to battery health monitoring using bayesian regression and state estimation , 2007, 2007 IEEE Autotestcon.

[26]  C. Niznik.  Min-max vs. max-min flow control algorithms for optimal computer network capacity assignment , 1984 .

[27]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[28]  Chen Lu,et al.  A review of stochastic battery models and health management , 2017 .

[29]  Chen Lu,et al.  Li-ion battery capacity estimation: A geometrical approach , 2014 .

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

[31]  Hicham Chaoui,et al.  State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks , 2017, IEEE Transactions on Vehicular Technology.

[32]  Yi-Jun He,et al.  State of health estimation of lithium‐ion batteries: A multiscale Gaussian process regression modeling approach , 2015 .

[33]  Abdul Basit Khan,et al.  State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation , 2017 .

[34]  Mehrdad Saif,et al.  An electrochemical model-based particle filter approach for Lithium-ion battery estimation , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).