arXiv:2605.00898v2 Announce Type: replace-cross Abstract: Accurate forecasting of battery health indicators, including remaining capacity and lifetime, is of paramount importance for ensuring the reliability, safety, and operational efficiency of applications such as electric vehicles and large scale energy storage infrastructures. The result of the forecasting can be adopted to build an advanced monitoring mechanism for continuous checking batteries' health status to assist in the efficient real-time management of numerous applications. This research investigates the development and implement
The increasing demand for electric vehicles and large-scale energy storage necessitates more sophisticated and accurate battery management systems, especially as AI capabilities advance.
Accurate battery health prediction is critical for advancing the reliability, safety, and operational efficiency of key energy technologies, impacting both economic and environmental objectives.
The ability to precisely forecast battery health using AI could lead to more efficient energy management, longer battery lifespans, and reduced risks in critical applications.
- · Electric Vehicle Manufacturers
- · Renewable Energy Storage Developers
- · AI Software Developers
- · Smart Grid Operators
- · Traditional Battery Management System Providers
- · Fossil Fuel Dependent Industries
Improved battery management will extend the lifespan and performance of electric vehicles and grid-scale storage.
Enhanced battery efficiency will accelerate the adoption of renewable energy and electric transportation, reducing reliance on traditional energy sources.
A more stable and efficient energy infrastructure, underpinned by advanced battery management, could reduce energy costs and enhance national energy security.
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Read at arXiv cs.LG