Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications

Majid Astaneh, Jelena Andric, Lennart Löfdahl, Dario Maggiolo, Peter Stopp, Mazyar Moghaddam, Michel Chapuis and Henrik Ström
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Majid Astaneh: Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden
Jelena Andric: Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden
Lennart Löfdahl: Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden
Dario Maggiolo: Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden
Peter Stopp: Gamma Technologies GmbH, Danneckerstrasse 37, D-70182 Stuttgart, Germany
Mazyar Moghaddam: Northvolt, Gamla Brogatan 26, 111 20 Stockholm, Sweden
Michel Chapuis: Northvolt, Gamla Brogatan 26, 111 20 Stockholm, Sweden
Henrik Ström: Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden

Energies, 2020, vol. 13, issue 14, 1-27

Abstract: Large-scale introduction of electric vehicles (EVs) to the market sets outstanding requirements for battery performance to extend vehicle driving range, prolong battery service life, and reduce battery costs. There is a growing need to accurately and robustly model the performance of both individual cells and their aggregated behavior when integrated into battery packs. This paper presents a novel methodology for Lithium-ion (Li-ion) battery pack simulations under actual operating conditions of an electric mining vehicle. The validated electrochemical-thermal models of Li-ion battery cells are scaled up into battery modules to emulate cell-to-cell variations within the battery pack while considering the random variability of battery cells, as well as electrical topology and thermal management of the pack. The performance of the battery pack model is evaluated using transient experimental data for the pack operating conditions within the mining environment. The simulation results show that the relative root mean square error for the voltage prediction is 0.7–1.7% and for the battery pack temperature 2–12%. The proposed methodology is general and it can be applied to other battery chemistries and electric vehicle types to perform multi-objective optimization to predict the performance of large battery packs.

Keywords: lithium-ion battery; battery pack; electrochemical-thermal modeling; calibration optimization; electric vehicle (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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