High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells

The growing need for accurate simulation of advanced lithium cells for powertrain electrification demands fast and accurate modeling schemes. Additionally, battery models must account for thermal effects because of the paramount importance of temperature in kinetic and transport phenomena of electrochemical systems. This paper presents an effective method for developing a multi-temperature lithium cell simulation model with thermal dependence. An equivalent circuit model with one voltage source, one series resistor, and a single RC block was able to account for the discharge dynamics observed in the experiment. A parameter estimation numerical scheme using pulse current discharge tests on high power lithium (LiNi-CoMnO2 cathode and graphite-based anode) cells under different operating conditions revealed dependences of the equivalent circuit elements on state of charge, average current, and temperature. The process is useful for creating a high fidelity model capable of predicting electrical current/voltage performance and estimating run-time state of charge. The model was validated for a lithium cell with an independent drive cycle showing voltage accuracy within 2%. The model was also used to simulate thermal buildup for a constant current discharge scenario.

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

[2]  M. Ceraolo,et al.  New dynamical models of lead-acid batteries , 2000 .

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

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

[5]  T. Ohzuku,et al.  Layered Lithium Insertion Material of LiCo1/3Ni1/3Mn1/3O2 for Lithium-Ion Batteries , 2001 .

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

[7]  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 .

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

[9]  Robyn A. Jackey,et al.  A Simple, Effective Lead-Acid Battery Modeling Process for Electrical System Component Selection , 2007 .

[10]  Gregory L. Plett,et al.  Parameterization of a Battery Simulation Model Using Numerical Optimization Methods , 2009 .

[11]  M. Doyle,et al.  Simulation and Optimization of the Dual Lithium Ion Insertion Cell , 1994 .

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

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

[14]  Uzay Kaymak,et al.  Modeling and Identification , 2002 .

[15]  Michael A. Roscher,et al.  Detection of Utilizable Capacity Deterioration in Battery Systems , 2011, IEEE Transactions on Vehicular Technology.

[16]  Karsten P. Ulland,et al.  Vii. References , 2022 .