Renewable and Sustainable Energy Reviews, Volume 113, October 2019,
Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.
Ageing Mechanism; Battery Health Diagnostics And Prognostics; Battery Management Systems; Commercial Technology; Cost Effectiveness; Data-driven Approach; Diagnostics And Prognostics; Electric Vehicle; Electric Vehicles; Estimation And Predictions; Forecasting; Generation Techniques; Ions; Lifetime Prediction; Lithium-ion Batteries; Lithium-ion Battery; Sustainable Energy; Technology Readiness Levels; Global