High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells
Abstract
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-CoMnO2cathode 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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Li-ion rechargeable batteries are the most promising energy storage solution due to high gravimetric and volumetric energy density, great cycle life, enhanced safety and low self-discharge rate.
“A mong all the energy storage techniques, Li-ion based rechargeable batteries are deemed to be the most promising solution, due to their outperformed perfo rmance such as high gravimetric and volumetric energy density, great cycle life, enhanced safety and low self-discharge rate [1][2][3].”
From A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects (2018) · cites this paper as [1]
Cites
Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell
Doyle, M., Fuller, T. F., & Newman, J. (1993) · Journal of The Electrochemical Society · doi:10.1149/1.2221597 · Cited by 4,328 · see citing works
Accurate electrical battery model capable of predicting runtime and I-V performance
Chen, M., & Rincon-Mora, G. A. (2006) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2006.874229 · Cited by 2,427 · see citing works
Simulation and Optimization of the Dual Lithium Ion Insertion Cell
Fuller, T. F., Doyle, M., & Newman, J. (1994) · Journal of The Electrochemical Society · doi:10.1149/1.2054684 · Cited by 2,057 · see citing works
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.032 · Cited by 1,927 · see citing works
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768 · see citing works
Layered Lithium Insertion Material of LiCo1/3Ni1/3Mn1/3O2 for Lithium-Ion Batteries
Ohzuku, T., & Makimura, Y. (2001) · Chemistry Letters · doi:10.1246/cl.2001.642 · Cited by 1,391 · see citing works
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.031 · Cited by 1,385 · see citing works
Methods for state-of-charge determination and their applications
Piller, S., Perrin, M., & Jossen, A. (2001) · Journal of Power Sources · doi:10.1016/s0378-7753(01)00560-2 · Cited by 1,169 · see citing works
New dynamical models of lead-acid batteries
Ceraolo, M. (2000) · IEEE Transactions on Power Systems · doi:10.1109/59.898088 · Cited by 584 · see citing works
Self-citationDynamical models of lead-acid batteries: implementation issues
Barsali, S., & Ceraolo, M. (2002) · IEEE Transactions on Energy Conversion · doi:10.1109/60.986432 · Cited by 268 · see citing works
Self-citationDynamical Models of Lead-Acid Batteries: Implementation Issues
Barsali, S., & Ceraolo, M. (2002) · IEEE Power Engineering Review · doi:10.1109/mper.2002.4312013 · Cited by 231 · see citing works
Self-citationDetection of Utilizable Capacity Deterioration in Battery Systems
Roscher, M. A., Aßfalg, J., & Bohlen, O. S. (2010) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2010.2090370 · Cited by 169 · see citing works
A Simple, Effective Lead-Acid Battery Modeling Process for Electrical System Component Selection
Jackey, R. A. (2007) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2007-01-0778 · Cited by 166 · see citing works
Self-citationExperimentally-Determined Models for High-Power Lithium Batteries
Ceraolo, M., Lutzemberger, G., & Huria, T. (2011) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2011-01-1365 · Cited by 63 · see citing works
Self-citation(2002) · World Scientific series in robotics and intelligent systems · doi:10.1142/9789812777911_0005 · Cited by 57 · see citing works
Parameterization of a Battery Simulation Model Using Numerical Optimization Methods
Jackey, R. A., Plett, G. L., & Klein, M. J. (2009) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2009-01-1381 · Cited by 43 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Li, K., Wei, F., Tseng, K. J., & Soong, B.-H. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2779411 · Cited by 160 · see citing works
Estimation of state of charge for lithium-ion batteries - A Review
A.M.S.M.H.S.Attanayaka, Department of Electrical Engineering, U. O. M., J.P.Karunadasa, & K.T.M.U.Hemapala (2019) · AIMS Energy · doi:10.3934/energy.2019.2.186 · Cited by 72 · see citing works
Dineva, A. (2024) · Batteries · doi:10.3390/batteries10100356 · Cited by 52 · see citing works
- Authors
- Huria, T., Ceraolo, M., Gazzarri, J., & Jackey, R.
- Affiliations
- University of Pisa (IT)
- MathWorks (United States) (US)
- ORCID
- Venue
- 2012 IEEE International Electric Vehicle Conference
- Publisher
- IEEE
- Published
- 2012-03
- Pages
- 1-8
- OpenAlex
- W2062392099
- Cited by
- 470
- References
- 16
- Citations by year
- 2026: 12 · 2025: 29 · 2024: 38 · 2023: 46 · 2022: 52 · 2021: 48 · 2020: 49 · 2019: 39 · 2018: 23 · 2017: 28 · 2016: 33 · 2015: 34 · 2014: 23 · 2013: 13 · 2012: 3
- FWCI
- 33.36
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Electric Vehicle Conference (publishedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- equivalent circuit model, temperature dependence, parameter estimation, graphite anode, state of charge estimation, discharge dynamics, powertrain electrification, battery modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
