Navigating the Spectrum of Battery Health Management in Electric Vehicles: A Comprehensive Review

Strategic Insights -

Research investigating the health management of lithium-ion batteries in electric vehicles has highlighted the challenges posed by degradation due to internal and external factors. Investigating the state of health, remaining useful life, and battery ageing has led to the development of new methodologies, including machine learning and cloud-integrated frameworks. A comprehensive review of these methods has emphasized the need for model interpretability, real-time validation, and long-term performance assessment.

Key Takeaways:

  • Research has identified the need for accurate and interpretable models that ensure sustainable electric vehicle battery operation.
  • The existing literature lacks a consolidated comparative analysis of traditional, adaptive, and emerging data-driven approaches to battery health management.
  • Machine learning and cloud-integrated frameworks have emerged as key strategies for state of health, remaining useful life, and ageing prediction.
  • The review emphasizes the need for model interpretability, real-time validation, and long-term performance assessment.
  • Saranathan L and colleagues have contributed to the academic and industrial understanding of predictive battery management.
  • Indragandhi Vairavasundaram and Bragadeshwaran Ashok are listed as co-authors on this research project.
  • The study highlights the importance of battery cycling databases in predictive analytics.
  • The review paper aims to guide future research toward the development of accurate, interpretable, and scalable models.

Statistics:

  • The review paper addresses the critical problem of accurately estimating the state of health (SoH), remaining useful life (RUL), and battery ageing in dynamic operating environments.
  • The study found that lithium-ion batteries are a cornerstone of modern electric vehicles due to their high energy density, long lifespan, and excellent performance.
  • The research identified key challenges and research gaps in battery health management, emphasizing the need for model interpretability and real-time validation.
  • The study reported that predictive analytics have improved with the advent of high-performance computing and open-source data.
  • The findings have significant implications for the development of sustainable electric vehicles.

Sources:

  • Navigating the spectrum of battery health management in electric vehicles: A comprehensive review. Results in Engineering, 2025,27():106038. (Results in Engineering - https://www.journals.elsevier.com/results-in-engineering).
  • NewsRx. Recent Findings from School of Electrical Engineering Highlight Research in Engineering (Navigating the spectrum of battery health management in electric vehicles: A comprehensive review). Journal of Engineering. September 8, 2025; p 1407.