Investigation on Physics-Based Models of Lithium Ion Batteries in Electric Vehicle Applications: A Review

Aruna, P.; Prabhu, V. Vasan; Vengadakrishnan, Krishnakumar

doi:10.1007/978-981-19-7728-2_3

Cited by 3 publications

(1 citation statement)

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“…Various methodologies are used in the literature for the mathematical modelling of the batteries on different scales (cell, module, and pack). Physics-based models have been demonstrated to require long computational processing times, hence making such models undesirable, especially in relation to BMS programming [55]. However, simplified P2D models are used often because of their quick computational processing time [56].…”

Section: Modelling Techniquesmentioning

confidence: 99%

A Review of the Technical Challenges and Solutions in Maximising the Potential Use of Second Life Batteries from Electric Vehicles

Salek,

Resalati,

Babaie

et al. 2024

Batteries

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The increasing number of electric vehicles (EVs) on the roads has led to a rise in the number of batteries reaching the end of their first life. Such batteries, however, still have a capacity of 75–80% remaining, creating an opportunity for a second life in less power-intensive applications. Utilising these second-life batteries (SLBs) requires specific preparation, including grading the batteries based on their State of Health (SoH); repackaging, considering the end-use requirements; and the development of an accurate battery-management system (BMS) based on validated theoretical models. In this paper, we conduct a technical review of mathematical modelling and experimental analyses of SLBs to address existing challenges in BMS development. Our review reveals that most of the recent research focuses on environmental and economic aspects rather than technical challenges. The review suggests the use of equivalent-circuit models with 2RCs and 3RCs, which exhibit good accuracy for estimating the performance of lithium-ion batteries during their second life. Furthermore, electrochemical impedance spectroscopy (EIS) tests provide valuable information about the SLBs’ degradation history and conditions. For addressing calendar-ageing mechanisms, electrochemical models are suggested over empirical models due to their effectiveness and efficiency. Additionally, generating cycle-ageing test profiles based on real application scenarios using synthetic load data is recommended for reliable predictions. Artificial intelligence algorithms show promise in predicting SLB cycle-ageing fading parameters, offering significant time-saving benefits for lab testing. Our study emphasises the importance of focusing on technical challenges to facilitate the effective utilisation of SLBs in stationary applications, such as building energy-storage systems and EV charging stations.

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Section: Modelling Techniquesmentioning

confidence: 99%