A new representation model of standard and available active materials for electrochemical batteries

The main goal of this paper is the introduction of a predictive model to calculate the remaining run-time of a battery. It is not meant to increase precision or compete with other methods but to be a reliable model by using a new formulation, more representative of the active material used zones. This paper defines two different states of charge (SOC) based on the concepts of standard and available capacities, named SOCs and SOCav, respectively. Concepts such as uncharged and undischarged capacities and loss charge efficiency are included as well. With these new definitions, an estimation algorithm is proposed. It is implemented as a runtime SOC estimation model based on an ampere-hour counting electrical model. The characterization of SOC model parameters of an 11 Ah Ni-Cd battery is also presented. These tests combine non-standard charge or discharge processes with end-of-charge/discharge detection methods that avoid overcharge or over discharge. Four case studies are carried out: two on a single battery cell and the other two on a 210 battery stack. The results show a good performance, with an approximate improvement of the estimation of 5%. They also show the importance of differentiating standard and available SOC in order to calculate the available capacity.The main goal of this paper is the introduction of a predictive model to calculate the remaining run-time of a battery. It is not meant to increase precision or compete with other methods but to be a reliable model by using a new formulation, more representative of the active material used zones. This paper defines two different states of charge (SOC) based on the concepts of standard and available capacities, named SOCs and SOCav, respectively. Concepts such as uncharged and undischarged capacities and loss charge efficiency are included as well. With these new definitions, an estimation algorithm is proposed. It is implemented as a runtime SOC estimation model based on an ampere-hour counting electrical model. The characterization of SOC model parameters of an 11 Ah Ni-Cd battery is also presented. These tests combine non-standard charge or discharge processes with end-of-charge/discharge detection methods that avoid overcharge or over discharge. Four case studies are carried out: two on a single batter...

[1]  Phl Peter Notten,et al.  REVIEW ARTICLE: State-of-the-art of battery state-of-charge determination , 2005 .

[2]  Manuel Garcia-Plaza,et al.  State of charge estimation model for Ni-Cd batteries considering capacity and efficiency , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[3]  Suleiman Abu-Sharkh,et al.  Rapid test and non-linear model characterisation of solid-state lithium-ion batteries , 2004 .

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

[5]  D. Doerffel,et al.  A critical review of using the peukert equation for determining the remaining capacity of lead-acid and lithium-ion batteries , 2006 .

[6]  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 .

[7]  M. Garcia-Plaza,et al.  A Ni–Cd battery model considering state of charge and hysteresis effects , 2015 .

[8]  D. Sauer,et al.  Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling , 2011 .

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

[10]  Zonghai Chen,et al.  A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries , 2013 .

[11]  John McPhee,et al.  A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation , 2014 .

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

[13]  Hamid Sharif,et al.  Modeling Discharge Behavior of Multicell Battery , 2010, IEEE Transactions on Energy Conversion.

[14]  Shigeta Hara,et al.  Study of charge acceptance for the lead-acid battery through in situ EC-AFM observation — influence of the open-circuit standing time on the negative electrode , 2001 .

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

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

[17]  Marc Thele,et al.  Development of a voltage-behavior model for NiMH batteries using an impedance-based modeling concept , 2008 .

[18]  Rudi Kaiser,et al.  Charging performance of automotive batteries—An underestimated factor influencing lifetime and reliable battery operation , 2007 .

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

[20]  Ramesh R. Rao,et al.  Energy efficient battery management , 2001, IEEE J. Sel. Areas Commun..

[21]  Taesic Kim,et al.  A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects , 2011 .

[22]  Fernando Nuño García,et al.  Intelligent and universal fast charger for Ni-Cd and Ni-MH batteries in portable applications , 2004, IEEE Transactions on Industrial Electronics.

[23]  Eckhard Karden,et al.  Dynamic charge acceptance of lead–acid batteries: Comparison of methods for conditioning and testing , 2012 .

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

[25]  Yanqing Shen,et al.  Adaptive online state-of-charge determination based on neuro-controller and neural network , 2010 .

[26]  Wenhua H. Zhu,et al.  Energy efficiency and capacity retention of Ni–MH batteries for storage applications , 2013 .

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

[28]  M. Verbrugge,et al.  Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena , 2004 .

[29]  Marc Thele,et al.  Modeling of the charge acceptance of lead–acid batteries , 2007 .