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Energies
Volume 13
Issue 14
10.3390/en13143532
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Article

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

by
Majid Astaneh
1,
Jelena Andric
1,
Lennart Löfdahl
1,*,
Dario Maggiolo
1,
Peter Stopp
2,
Mazyar Moghaddam
3,
Michel Chapuis
3 and
Henrik Ström
1
1
Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden
2
Gamma Technologies GmbH, Danneckerstrasse 37, D-70182 Stuttgart, Germany
3
Northvolt, Gamla Brogatan 26, 111 20 Stockholm, Sweden
*
Author to whom correspondence should be addressed.
Energies 2020, 13(14), 3532; https://doi.org/10.3390/en13143532
Submission received: 30 April 2020 / Revised: 25 June 2020 / Accepted: 2 July 2020 / Published: 8 July 2020
(This article belongs to the Section E: Electric Vehicles)
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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 lithium-ion battery; battery pack; electrochemical-thermal modeling; calibration optimization; electric vehicle

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MDPI and ACS Style

Astaneh, M.; Andric, J.; Löfdahl, L.; Maggiolo, D.; Stopp, P.; Moghaddam, M.; Chapuis, M.; Ström, H. Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications. Energies 2020, 13, 3532. https://doi.org/10.3390/en13143532

AMA Style

Astaneh M, Andric J, Löfdahl L, Maggiolo D, Stopp P, Moghaddam M, Chapuis M, Ström H. Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications. Energies. 2020; 13(14):3532. https://doi.org/10.3390/en13143532

Chicago/Turabian Style

Astaneh, Majid, Jelena Andric, Lennart Löfdahl, Dario Maggiolo, Peter Stopp, Mazyar Moghaddam, Michel Chapuis, and Henrik Ström. 2020. "Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications" Energies 13, no. 14: 3532. https://doi.org/10.3390/en13143532

APA Style

Astaneh, M., Andric, J., Löfdahl, L., Maggiolo, D., Stopp, P., Moghaddam, M., Chapuis, M., & Ström, H. (2020). Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications. Energies, 13(14), 3532. https://doi.org/10.3390/en13143532

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