Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions,WIREs Data Mining and Knowledge Discovery

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Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2024-04-04 , DOI: 10.1002/widm.1539
Vankamamidi S. Naresh ₁ , Guduru V. N. S. R. Ratnakara Rao ₂ , D. V. N. Prabhakar ₂

Affiliation

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

中文翻译：

优化电动汽车电池性能的预测机器学习：技术、挑战和解决方案

本研究论文探讨了优化电动汽车 (EV) 电池性能以适应电动汽车使用量快速增长的重要性。它使用预测机器学习 (ML) 技术来实现这种优化。本文涵盖了各种机器学习方法，例如监督学习、无监督学习和深度学习 (DL) 以及衡量其有效性的方法。讨论了重要的电池性能因素，例如充电状态 (SoC)、健康状态 (SoH)、功能状态 (SoF) 和剩余使用寿命 (RUL)，以及收集和准备数据以进行准确预测的方法。本文介绍了一种优化电动汽车电池性能的运筹学模型。它还探讨了电池系统特有的挑战以及克服这些挑战的方法。该研究展示了机器学习模型预测电池行为的能力，以实现实时监控、高效能源利用和主动维护。该论文对不同的应用和案例研究进行了分类，为参与通过预测机器学习提高电动汽车电池性能的研究人员、从业者和政策制定者提供了宝贵的见解和前瞻性观点。本文分类如下：

技术 > 分类

数据和知识的基本概念 > 可解释的人工智能

技术 > 机器学习

更新日期：2024-04-04

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