Electric vehicles (EVs) are being considered as the promising solution for carbon‐free transportation and the future of mobility. Battery electric vehicles (BEV) are the true EVs that completely run from electricity supplied by their batteries (Li‐ion). For dynamic simulation of BEV and use of the battery effectively, it is essential to model the battery and estimate its parameters accurately. Generally, estimation of the battery parameters requires complex, time‐consuming, and expensive methods. This chapter focuses on a simple equivalent circuit methods (ECM) is used for battery modeling. Estimation of battery parameters is done by comparing the proposed model output to the known catalogue output with the help of a newly formed heuristic optimizer‐slime mould algorithm (SMA). Slime mould algorithm is based on the morphological transformations, oscillation, and foraging of slime mould found in nature, and have shown remarkable exploration and exploitation capabilities. The evaluation of 31 parameters for first‐order ECM is done by taking the fitness function as the sum of squared‐error of model voltage and catalogue voltage, for charging as well as discharging scenarios. Additionally, four well‐known algorithms, ant lion optimizer (ALO), dragonfly algorithm (DA), grey wolf optimizer (GWO), and sine‐cosine algorithm (SCA), are examined for comparison of performance and efficiency in terms of the parameters, accuracy (minimized error), and convergence. A statistical study of the mean, standard deviation, and best and worst values for each method is also performed to ensure that the results are stable and reliable. Finally, a Wilcoxon rank‐sum test with Holm‐Bonferroni adjustments and a Kruskal‐Wallis test are used to evaluate and verify the significance of the data. For first‐order ECM, performance is slightly better than other algorithms.
Parameter Estimation of First‐Order RC Model of Lithium‐Ion Batteries in Electric Vehicles Using Slime Mould Algorithm
Linear and Nonlinear System Modeling ; 173-195
08.10.2024
23 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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