Key points are not available for this paper at this time.
As electric vehicles (EVs) gain momentum in the shift towards sustainable transportation, the efficiency and reliability of energy storage systems become paramount. Lithium-ion batteries stand at the forefront of this transition, necessitating sophisticated battery management systems (BMS) to enhance their performance and lifespan. This research presents an innovative simulation of a 4S3P lithium-ion battery pack using MATLAB R2023b, designed to refine BMS capabilities by employing advanced mathematical modelling and computational intelligence. The simulation meticulously analyses critical operational metrics such as state of charge (SOC), state of health (SOH), temperature variations, and electrical behaviour under diverse load scenarios, offering deep insights into the intricate dynamics of lithium-ion batteries in EV applications. The results corroborate the simulation model’s accuracy in reflecting actual battery pack performance and underscore significant improvements in BMS strategies, especially concerning predictive maintenance and adaptive charging techniques. By seamlessly integrating computational intelligence into BMS, this study lays the groundwork for more durable, efficient, and intelligent energy storage systems in electric vehicles, marking a significant stride in e-mobility technology.
Tapaskar et al. (Tue,) studied this question.