Lithium-ion batteries make a significant contribution to green transportation, including electric vehicles, hybrid vehicles, and plug-in hybrid vehicles, because of their outstanding performance [1–3]. The high price of lithium-ion batteries is one of limitations for their widely application. In order to reduce the economic costs of lithium-ion batteries, it is critical to improve the battery performance and cycle life, which are mostly affected by many factors such as operation temperature, current and battery configuration. The state of health (SOH) measures capacity loss, which is important in determining the useful lifespan of a battery.

SOH estimation is based on the battery capacity and/or the internal resistance, depending on specific applications [4–5]. In recent years, many researchers focused on the accuracy of the SOH estimation [6-8]. However, a single SOH value estimated through the ratio of final to initial capacity and/or the final to initial internal resistance ratio is unreliable in predicting the SOH of the battery. Accurate SOH estimation requires the analysis of the aging mechanism of lithium-ion batteries.

In this work, capacity deviation from that of the new battery is considered to be one aspect of SOH evaluation system. To make SOH expression provide more details about aging origins, the capacity  Q needs to be expressed in terms of the each ICA peak area and position. Hence, this paper adopts Lorentz functions to fit the ICA curve, and then the analytical capacity equation Q=f(V)  is obtained by integrating the Lorentz functions. The proposed analytical capacity equation based on ICA curve, contains the parameters like peak area and position. Therefore, the variation of the parameters in the proposed analytical capacity equation could reflect aging origins arise from lithium inventory, loss of active material and increase in resistance, which are supplements to the capacity loss for SOH estimation. Combining the variation of the parameters in the proposed analytical capacity equation and capacity loss, the paper establishes a SOH evaluation system.

References

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[2]J. Kim and B. H. Cho, "State-of-charge estimation and state-of-health prediction of a Li-ion degraded battery based on an EKF combined with a per unit system", IEEE Trans. Veh. Technol., vol. 60, no. 9, pp. 4249 -4260, November 2011.

[3]J. Jiang, Q. Liu, C. Zhang and W. Zhang, "Evaluation of acceptable charging current of power Li-ion batteries based on polarization characteristics", IEEE Trans. Ind. Electron., vol. 61, no. 12, pp.6844 -6851, December 2014.

[4]M. Gholizadeh and F. R. Salmasi, "Estimation of state of charge, unknown nonlinearities, state of health of a lithium-ion battery based on a comprehensive unobservable model",  IEEE Trans. Ind. Electron., vol. 61, no. 3, pp.1335 -1344, March 2014.

[5]I.-S. Kim, "A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer", IEEE Trans. Power Electron., vol. 25, no. 4, pp.1013 -1022, April 2010

[6]Z. Guo, X. Qiu, G. Hou, B. Y. Liaw, and C. Zhang, "State of health estimation for lithium ion batteries based on charging curves", J. Power Sources, vol. 249, pp. 457-462, March 2014.

[7]H.-T. Lin, T.-J. Liang and S.-M. Chen, "Estimation of battery state of health using probabilistic neural network", IEEE Trans. Ind. Inf.,  vol. 9,  no. 2,  pp.679 -685, May 2013.

[8]C. R. Gould, C. M. Bingham, D. A. Stone and P. Bentley, "New battery model and state-of-health determination through subspace parameter estimation and state-observer techniques", IEEE Trans. Veh. Technol., vol. 58, no. 8, pp. 3905 -3916, October 2009.