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Battery management systems (BMS) play a critical role in ensuring the safety and efficiency of electric vehicle (EV) batteries. Recent advancements in artificial intelligence (AI) technology have led to the development of new AI-based algorithms for BMS. This paper presents a review of the literature on AI-based algorithms integration techniques fo...
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Electric Vehicles (EVs) are cost-effective and widely recognized for their significant role in reducing greenhouse gas (GHG) emissions. However, concerns surrounding range anxiety and charge anxiety have hindered their widespread adoption. To address these concerns, traffic engineers have been working on developing control strategies to reduce ener...
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... The EV battery is a crucial field of research and development focusing on utilizing ML techniques to improve battery performance. Machine Learning techniques adapt optimum strategy in real-time with data collected from traffic patterns, terrain, and energy-efficient goals to maximize battery performance and range (Abdulkadirmohendis, 2023). They continuously monitor and analyze battery behavior, enabling early detection of faulty operation and prognosis in EV batteries . ...
... • Recurrent neural network (RNN): These networks use context units to consider historical information, making them suitable for tasks with sequential data like time series analysis and natural language processing.RNN is used in Abdulkadirmohendis (2023), Shahriar et al. (2020), and Venugopal et al. (2021). • Convolution neural network (CNN): CNNs are widely used for image and video analysis. ...
... ML techniques can adjust the charging and discharging parameters to optimize the battery's performance and efficiency. AI algorithm ensures that the battery operates within safe operating limits and thermal runaway (Abdulkadirmohendis, 2023) ...
This research paper explores the importance of optimizing the performance of electric vehicle (EV) batteries to align with the rapid growth in EV usage. It uses predictive machine learning (ML) techniques to achieve this optimization. The paper covers various ML methods like supervised, unsupervised, and deep learning (DL) and ways to measure their effectiveness. Significant battery performance factors, such as state of charge (SoC), state of health (SoH), state of function (SoF), and remaining useful life (RUL), are discussed, along with methods to collect and prepare data for accurate predictions. The paper introduces an operation research model for optimizing the performance of EV Batteries. It also looks at challenges unique to battery systems and ways to overcome them. The study showcases ML models' ability to predict battery behavior for real‐time monitoring, efficient energy use, and proactive maintenance. The paper categorizes different applications and case studies, providing valuable insights and forward‐looking perspectives for researchers, practitioners, and policymakers involved in improving EV battery performance through predictive ML.
This article is categorized under: Technologies > Classification
Fundamental Concepts of Data and Knowledge > Explainable AI
Technologies > Machine Learning
