Synthetic methods for the evaluation of the State of Health (SOH) of nickel-metal hydride (NiMH) batteries

Abstract The State of Health (SOH) of a battery is important to know the maximum energy that a battery can release while is operative and to plan the correct maintenance. In this work, we have implemented a diagnostic method to evaluate the SOH of single nickel metal hydride (NiMH) cells, that have an important role in power tools applications. Single NiMH cells of 1.2 V and 1.3 Ah have been characterized with Electrochemical Impedance Spectroscopy (EIS) technique to evaluate the SOH with synthetic methods. Through the study of an equivalent circuit model, we determine that three free parameters synthesize the information contained in an impedance spectrum. A new simplification allows us to construct a diagnostic diagram with only two degree of freedom. A mathematical approach based on the Dempster–Schafer Theory of Evidence has been implemented to interpret the diagnostic diagram. Combining the points obtained by the impedance spectra (IS) at different State of Charge (SOC) and SOH, the Theory of Evidence can improve the estimation of the SOH iteratively, a great advantage compared to the classic Theory of Probability.

[1]  Benedikt Battke,et al.  A review and probabilistic model of lifecycle costs of stationary batteries in multiple applications , 2013 .

[2]  Mauro Gamberi,et al.  Technical and economic design of photovoltaic and battery energy storage system , 2014 .

[3]  U. Troeltzsch,et al.  Characterizing aging effects of lithium ion batteries by impedance spectroscopy , 2006 .

[4]  D. Depernet,et al.  Online impedance spectroscopy of lead acid batteries for storage management of a standalone power plant , 2012 .

[5]  D. Cuscueta,et al.  An EIS based study of a Ni–MH battery prototype. Modeling and identification analysis , 2010 .

[6]  Antonio Sciarretta,et al.  Advances in Electrochemical Models for Predicting the Cycling Performance of Traction Batteries: Experimental Study on Ni-MH and Simulation , 2010 .

[7]  D. Berndt,et al.  Valve-regulated lead-acid batteries , 2001 .

[8]  C. Fennie,et al.  Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries , 2004 .

[9]  P. Bernard,et al.  Life duration of Ni–MH cells for high power applications , 2002 .

[10]  Pritpal Singh,et al.  Equivalent circuit parameters of nickel/metal hydride batteries from sparse impedance measurements , 2004 .

[11]  Alvin J. Salkind,et al.  Impedance modeling of intermediate size lead–acid batteries , 2003 .

[12]  Bruce J. Tatarchuk,et al.  A simplified equivalent circuit model for simulation of Pb–acid batteries at load for energy storage application , 2011 .

[13]  Hongwen He,et al.  Comparison study on the battery models used for the energy management of batteries in electric vehicles , 2012 .

[14]  A. Salkind,et al.  Dynamic characterization of small lead-acid cells , 2001 .

[15]  Hazlie Mokhlis,et al.  Emergence of energy storage technologies as the solution for reliable operation of smart power systems: A review , 2013 .

[16]  Hua Wang,et al.  Modelling and control of hybrid UPS system with backup PEM fuel cell/battery , 2012 .

[17]  José L. Bernal-Agustín,et al.  Optimum load management strategy for wind/diesel/battery hybrid power systems , 2012 .

[18]  L. Serrao,et al.  An aging model of Ni-MH batteries for hybrid electric vehicles , 2005, 2005 IEEE Vehicle Power and Propulsion Conference.

[19]  David A. Stone,et al.  Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles , 2005, IEEE Transactions on Vehicular Technology.

[20]  E. Barsoukov,et al.  Impedance spectroscopy : theory, experiment, and applications , 2005 .

[21]  Giorgio Rizzoni,et al.  Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles , 2011, IEEE Transactions on Vehicular Technology.

[22]  A. Miraoui,et al.  Review of adaptive systems for lithium batteries State-of-Charge and State-of-Health estimation , 2012, 2012 IEEE Transportation Electrification Conference and Expo (ITEC).

[23]  Maher Chaabene,et al.  Dynamic model to follow the state of charge of a lead-acid battery connected to photovoltaic panel , 2012 .

[24]  F. Huet.  A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries , 1998 .

[25]  Jay Lee,et al.  A review on prognostics and health monitoring of Li-ion battery , 2011 .

[26]  S. Rodrigues,et al.  A review of state-of-charge indication of batteries by means of a.c. impedance measurements , 2000 .

[27]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .

[28]  Haisheng Chen,et al.  Progress in electrical energy storage system: A critical review , 2009 .

[29]  Andreas Jossen,et al.  Methods for state-of-charge determination and their applications , 2001 .

[30]  Weifang Fang.  Fundamental modeling the performance and degradation of HEV Lithium-ion battery , 2010 .

[31]  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 .

[32]  M. Wohlfahrt‐Mehrens,et al.  Ageing mechanisms in lithium-ion batteries , 2005 .

[33]  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.

[34]  K. M. Tsang,et al.  State of health detection for Lithium ion batteries in photovoltaic system , 2013 .