Experiments on battery capacity estimation

Modern life heavily relies on the continuous and stable power supply. All kinds of devices and systems are driven by batteries. The behavior of battery has direct impact on the operation and performance of those devices and systems. Thus, the knowledge of state of health and remaining useful life will facilitate the proper use and management of batteries. In this study, battery capacity is estimated with selected machine learning algorithms. Three strategies for using the training data are proposed. Experiments were carried out with the data from Lithium-ion batteries undergoing accelerated aging process through repeated charge and discharge cycles. The preliminary results demonstrate the feasibility of these machine learning approaches.

[1]  B. Saha,et al.  Designing Data-Driven Battery Prognostic Approaches for Variable Loading Profiles : Some Lessons Learned , 2012 .

[2]  Kai Goebel,et al.  Comparison of prognostic algorithms for estimating remaining useful life of batteries , 2009 .

[3]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

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

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

[6]  Seungchul Lee,et al.  Battery prognostics: SOC and SOH prediction , 2012 .

[7]  K. Goebel,et al.  Prognostics in Battery Health Management , 2008, IEEE Instrumentation & Measurement Magazine.

[8]  Jon Atli Benediktsson,et al.  Multiple Classifier Systems , 2015, Lecture Notes in Computer Science.

[9]  Kurt Hornik,et al.  Support Vector Machines in R , 2006 .

[10]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[11]  M. Pal,et al.  Random forests for land cover classification , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[12]  Christophe Croux,et al.  Robust Forecasting with Exponential and Holt-Winters Smoothing , 2007 .

[13]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[14]  Solomon Tesfamariam,et al.  Prediction of lateral spread displacement: data-driven approaches , 2012, Bulletin of Earthquake Engineering.

[15]  David He,et al.  Lithium-ion battery life prognostic health management system using particle filtering framework , 2011 .

[16]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[17]  Roberto Zavala,et al.  Multiple classifier systems in Akatek (Mayan) , 2000 .

[18]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

[19]  C. Holt.  Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .

[20]  José Melo,et al.  Gaussian Processes for regression : a tutorial , 2012 .

[21]  S. Wood.  Generalized Additive Models: An Introduction with R , 2006 .

[22]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[23]  Pawel Lewicki,et al.  Statistics : methods and applications : a comprehensive reference for science, industry, and data mining , 2006 .