Battery Performance Under Real-World Conditions: Degradation, Diagnostics, and Lifetime Prediction

Battery research and deployment have reached a critical stage where accurate understanding of performance under real-world conditions is essential. While laboratory tests have enabled vast progress in optimizing design and chemistry, real operational environments—marked by fluctuating loads, temperatures, charging profiles, and user behaviors—introduce complex degradation phenomena that remain difficult to predict. The gap between laboratory characterization and field applications limits confidence in lifetime estimation and hinders adoption across electric mobility, stationary storage, and renewable integration. Recent advances in electrochemical impedance spectroscopy, differential voltage analysis, data-driven models, and digital twins have begun to address these challenges, yet a unified framework for understanding and translating multi-scale degradation remains lacking.This Research Topic aims to explore the mechanisms, monitoring strategies, and predictive tools that define battery performance in realistic operating contexts. It seeks to bridge the divide between controlled laboratory studies and real-world applications by integrating empirical data, physics-based modelling, and machine learning approaches. Key questions include: How do temperature gradients, mechanical stresses, and non‑uniform utilization accelerate aging? How can diagnostics predict end-of-life before critical failure? What indicators offer the most reliable basis for real-time lifetime estimation and health management strategies? By addressing these, the collection aims to advance methods for safer, more durable, and cost-effective battery systems with measurable predictive accuracy.To gather further insights into performance under operational conditions, we welcome articles addressing, but not limited to, the following themes:-- Field testing and in‑situ measurement of electrochemical degradation mechanisms-- Integration of data-driven diagnostics with electrochemical and mechanical models-- Influence of operating conditions and environmental factors on lifecycle performance-- Development and validation of lifetime prediction algorithms and digital twin frameworks-- Multiscale characterization bridging material degradation to system‑level reliability-- Case studies from electric vehicles, grid storage, and portable electronics-- Standardization efforts for benchmarking real‑world battery health monitoringWe invite Original Research, Review, Mini Review, Perspective articles, FAIR2 Data that contribute to advancing understanding of battery aging and diagnostics under practical conditions. Topic editors: Mohit Mehta, Claudio Rabissi, Debalina Deb, Aparna Akula Source: https://www.frontiersin.org/research-topics/82349/battery-performance-under-real-world-conditions-degradation-diagnostics-and-lifetime-prediction