Model identification of lithium-ion batteries in the portable power system

Portable Power System (PPS) supplies energy for electronic devices outdoors. The lithium-ion batteries are adopted as a kind of ideal energy storage unit for the PPS. In order to monitor batteries, parameter identification should be performed on batteries. To achieve the tradeoff between the accuracy and simplicity, the first order RC model is used as the fundamental model for lithium-ion batteries. Under the working condition, an online parameter identification method, which is combined with the recursive least square (RLS), is proposed in a batch-type working manner. Although the RLS-based identification method can only be applicable to time-invariant systems, the combination of the RLS method and the proposed batch-type working manner can work well to identify the model parameters during the operation of PPS.

[1]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[2]  Xiaosong Hu,et al.  A comparative study of equivalent circuit models for Li-ion batteries , 2012 .

[3]  Jay Lee,et al.  Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility , 2014 .

[4]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background , 2004 .

[5]  C. M. Shepherd.  Design of Primary and Secondary Cells II . An Equation Describing Battery Discharge , 1965 .

[6]  Xiaosong Hu,et al.  Online model identification of lithium-ion battery for electric vehicles , 2011 .

[7]  Roger A. Dougal,et al.  Dynamic lithium-ion battery model for system simulation , 2002 .

[8]  van der Arjan Schaft,et al.  Proceedings of the 48th IEEE Conference on Decision and Control, and the 28th Chinese Control Conference , 2009 .

[9]  D. Wheeler,et al.  Modeling of lithium-ion batteries , 2003 .

[10]  Saraju P. Mohanty,et al.  IntellBatt: Toward a Smarter Battery , 2010, Computer.

[11]  Christian Fleischer,et al.  Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .

[12]  Michael D. Ross.  A Simple but Comprehensive Lead-Acid Battery Model for Hybrid System Simulation , 2002 .

[13]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[14]  U. Sauer,et al.  Adaptive algorithms for monitoring of lithium ion batteries in electric vehicles , 2014 .

[15]  Hongwen He,et al.  Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles , 2012 .

[16]  Zhengqiang Pan,et al.  Study of rate dependence of impedance of lithium ion batteries , 2015, 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG).

[17]  Min Chen,et al.  Accurate electrical battery model capable of predicting runtime and I-V performance , 2006, IEEE Transactions on Energy Conversion.

[18]  Christian Fleischer,et al.  On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation , 2014 .

[19]  M. Doyle,et al.  Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell , 1993 .