A Novel SOH Prediction Framework for the Lithium-ion Battery Using Echo State Network

Li-ion batteries provide lightweight, high energy density power sources for a variety of devices. Therefore, monitoring battery health in an effective way could increase the reliability and stability of the prediction system. So in this paper, we present a novel prediction framework based on Echo State Network to realize the prediction for battery state of health by training and testing battery impedance values and capacity values. To evaluate the proposed prediction approach, we have executed experiments with lithium-ion battery. Experimental results prove its effectiveness and confirm the estimation system can be effectively applied to the battery health state prediction. Moreover, the prediction system can run multiple data sets at a time to make the estimation process more efficient. Therefore, we can choose a battery which meets the requirement through the comparison between different batteries’ prediction results.

[1]  R. Gouriveau,et al.  Fuel Cells prognostics using echo state network , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[2]  Dirk Uwe Sauer,et al.  Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data , 2012 .

[3]  Sun Zechang,et al.  A new SOH prediction concept for the power lithium-ion battery used on HEVs , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.

[4]  IL-Song Kim,et al.  A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer , 2010, IEEE Transactions on Power Electronics.

[5]  Yu Peng,et al.  Data-driven prognostics for lithium-ion battery based on Gaussian Process Regression , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).

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

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

[8]  Jorge F. Silva,et al.  Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena , 2013, IEEE Transactions on Instrumentation and Measurement.