Data-Driven Machine Learning Approaches for Advanced Battery Modeling

Abstract

Owing to growing concerns regarding global change, the need for transport electrification has risen over recent years. The efficient design and production of electric vehicles (EVs) is defined by the efficiency, cost, and protection of the batteries. Nearly every unit, mobile phone, and machine in our lives is powered by lithium-ion batteries, which are a cornerstone of green energies and power versatility. Corporations have attempted for many years to anticipate the expiration of battery charging. Better projections will make reliable forecasts of quality and boost long-term planning. This is therefore essential to an integrated battery management unit which can improve and secure the track for the electrification of vehicles. Machine learning algorithms were implemented to predict health status, expense, and remaining useful life. Data models have been emphasized in recent years, and these models tend to be more effective and predictable in combination with machine learning methods without advanced knowledge of the system and are able to achieve high precision at low computational costs. A data-driven machine learning approach seems to be the most common approach, with the support of open-source tools and data-sharing platform for advanced battery modeling, for which scientists accept the integration of machine learning and statistically driven architecture into their research.