Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Data-driven health estimation and lifetime prediction of lithium-ion batteries
T2 - A review
AU - Li, Y.
AU - Liu, K.
AU - Foley, A.M.
AU - Aragon Zülke, Alana
AU - Berecibar, M.
AU - Nanini-Maury, E.
AU - Van Mierlo, J.
AU - Hoster, H.E.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
KW - Ageing mechanism
KW - Battery health diagnostics and prognostics
KW - Data-driven approach
KW - Electric vehicle
KW - Lithium-ion battery
KW - Sustainable energy
U2 - 10.1016/j.rser.2019.109254
DO - 10.1016/j.rser.2019.109254
M3 - Journal article
VL - 113
JO - Renewable and Sustainable Energy Reviews
JF - Renewable and Sustainable Energy Reviews
SN - 1364-0321
M1 - 109254
ER -