April 3, 2024

Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions

Key Points

Battery performance optimization occurs through predictive machine learning algorithms, improving efficiency.
Up to 80% of battery effectiveness can be predicted using various machine learning methods, including deep learning.
Analysis employs an operation research model based on supervised and unsupervised learning techniques for data preparation and collection for predictions of battery performance factors like SoC and SoH in real-time monitoring efforts across multiple applications and case studies in the electric vehicle field, demonstrating significant benefits for future development and use within electric mobility initiatives. May enable a deeper understanding of battery dynamics; further testing needed in diverse environments.

Abstract

Abstract 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

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