Influence of connecting plate resistance upon LiFePO4 battery performance

The primary challenge to the commercialization of any electric vehicle is the performance management of the battery pack. The performance of the battery module is influenced by the resistance of the inter-cell connecting plates (ICCP) and the position of the battery module posts (BMP). A new battery cell model based on the Matlab–Simscape platform is developed and validated using a constant current discharge test and a pulse discharge test. Taken the ICCP as resistors, a parallel-connected battery module model (PCBMM) is established based on the battery cell model. The effect of inter-cell connecting plate resistance (ICCPR) on the battery module performance is simulated. Simulation results indicate that the ICCPR causes unevenly current flow among the battery cells. The battery cell directly connected to the BMP is the first one reaching its end-of-discharge (EOD) voltage. Also, it presents the lowest terminal voltage and state of charge (SOC) during the discharge process. The battery cell directly connected to the BMP goes into deep discharge state more easily. Therefore, it performs higher aging rate. The aging of the battery cell causes over-discharge of the adjacent battery cells. The reasonable ratio of the ICCPR to the battery ohmic internal resistance (OIR) is discussed for different average currents and different numbers of battery cells, to guarantee the maximum SOC evaluation error within a target value of 0.05.

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

[2]  Gui Chang-qing.  Difference between Li-ion single battery and battery group , 2011 .

[3]  Stephen Yurkovich,et al.  A technique for dynamic battery model identification in automotive applications using linear parameter varying structures , 2009 .

[4]  S. Barsali,et al.  Dynamical Models of Lead-Acid Batteries: Implementation Issues , 2002, IEEE Power Engineering Review.

[5]  J. R. Garayoa Guajardo,et al.  Accurate state-of-charge determination for lithium-iron phosphate battery packs , 2011 .

[6]  Qian Xu,et al.  Numerical investigations of flow field designs for vanadium redox flow batteries , 2013 .

[7]  Massimo Ceraolo,et al.  Experimentally-Determined Models for High-Power Lithium Batteries , 2011 .

[8]  Valerie H. Johnson,et al.  Battery performance models in ADVISOR , 2002 .

[9]  Ahmad Pesaran,et al.  Fail-safe design for large capacity lithium-ion battery systems , 2012 .

[10]  Jianqiu Li,et al.  Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles , 2013 .

[11]  J. Thevenin,et al.  Passivating films on lithium electrodes. An approach by means of electrode impedance spectroscopy , 1985 .

[12]  Shen Dan.  Internal resistance identification of Li-ion battery and its application in battery life estimation , 2009 .

[13]  Rui Liu,et al.  Numerical investigation of thermal behaviors in lithium-ion battery stack discharge , 2014 .

[14]  Mao-Sung Wu,et al.  Numerical simulation for the discharge behaviors of batteries in series and/or parallel-connected battery pack , 2006 .

[15]  Wei Shyy,et al.  Prediction of the theoretical capacity of non-aqueous lithium-air batteries , 2013 .

[16]  Tianshou Zhao,et al.  Effects of SOC-dependent electrolyte viscosity on performance of vanadium redox flow batteries , 2014 .

[17]  Lin Chen.  An Analysis on the Influence of Inconsistencies Upon the Service Life of Power Battery Packs , 2006 .

[18]  Jianqiu Li,et al.  LiFePO4 battery pack capacity estimation for electric vehicles based on charging cell voltage curve transformation , 2013 .

[19]  M. A. Roscher,et al.  Reliable State Estimation of Multicell Lithium-Ion Battery Systems , 2011, IEEE Transactions on Energy Conversion.

[20]  Hongwen He,et al.  Model-based state of charge and peak power capability joint estimation of lithium-ion battery in plug-in hybrid electric vehicles , 2013 .

[21]  Jianqiu Li,et al.  A review on the key issues for lithium-ion battery management in electric vehicles , 2013 .

[22]  Hongwen He,et al.  Adaptive state of charge estimator for lithium-ion cells series battery pack in electric vehicles , 2013 .

[23]  Ken Darcovich,et al.  Modelling the impact of variations in electrode manufacturing on lithium-ion battery modules , 2012 .

[24]  José L. Bernal-Agustín,et al.  Comparison of different lead–acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems , 2014 .

[25]  Xie Jing-ying.  The reasons of rapid decline in cycle life of Li-ion battery , 2009 .

[26]  M. Ceraolo,et al.  High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells , 2012, 2012 IEEE International Electric Vehicle Conference.

[27]  Nigel P. Brandon,et al.  Module design and fault diagnosis in electric vehicle batteries , 2012 .

[28]  Stephen Yurkovich,et al.  Battery cell state-of-charge estimation using linear parameter varying system techniques , 2012 .