Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Abstract

In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.

Exploring Lithium-Ion Battery Degradation: A Concise Review of Critical Factors, Impacts, Data-Driven Degradation Estimation Techniques, and Sustainable Directions for Energy Storage Systems

“…𝐿𝑖𝑃𝐹 ⟶ 𝐿𝑖𝐹 + 𝑃𝐹 ⎯ 𝑃𝐹 + 2𝐻𝐹 (16) The synthesis of 𝜆 − 𝑀𝑛𝑂 and the presence of HF have been coupled by Mikheenkova et al to explain Mn migration to the negative electrode, as demonstrated by Equation ( 17) [75].…”

“…Recently, battery degradation became an issue of great concern among the researchers around the world [16]. Xiong et al presented a review about the aging mechanism of lithium-ion batteries [17].…”

Optimizing Lithium-Ion Battery Performance: Integrating Machine Learning and Explainable AI for Enhanced Energy Management

“…Advancements in SoH assessment methodologies, including machine learning algorithms and diagnostic technologies, are driving the development of more intelligent battery management systems capable of real-time monitoring, predictive maintenance, and adaptive control strategies. This proactive approach not only prolongs battery lifespan but also promotes sustainable practices by minimizing premature replacements and optimizing resource utilization and recycling efforts [11,12]. Namely, various advanced techniques are available for predicting the performance of lithium-ion batteries, including molecular dynamics simulations and density functional theory (DFT).…”