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.
Highlights
The automotive industry has faced a paradigm shift towards electromobility in recent years to reduce greenhouse gas emissions, air pollution, and dependence on fossil fuels [1].Lithium-ion (Li-ion) battery storage systems have undergone a substantial growth in popularity, as they play an indispensable role in emerging electric vehicles (EVs) [2,3]
The calibration optimization results based on the proposed methodology are elaborated for the battery cells and the battery pack under consideration
The following dimensionless parameters are defined first; the dimensional values are normalized by their respective maximum: open circuit voltage (OCV) ∗ =
Summary
Lithium-ion (Li-ion) battery storage systems have undergone a substantial growth in popularity, as they play an indispensable role in emerging electric vehicles (EVs) [2,3]. Driving off-road electric vehicles has a positive impact to the environment, as they generate zero emissions during the operation. Electric mining vehicles provide a great improvement in underground air quality, as the heavily generated exhaust gasses from diesel machines. Model considers the major principles of the transport phenomena in a Li-ion battery cell and it is used for a wide variety of applications [14]. The electrochemical-thermal model is composed of a diverse set of parameters, including cell engineering design specifications and material properties which influence the model predictive capability. Model calibration and parameter identification play a pivotal role in the model predictability and accuracy
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