TY - JOUR
T1 - Review of "grey box" lifetime modeling for lithium-ion battery
T2 - combining physics and data-driven methods
AU - Guo, Wendi
AU - Sun, Zhongchao
AU - Vilsen, Søren Byg
AU - Meng, Jinhao
AU - Stroe, Daniel-Ioan
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Lithium-ion batteries are a popular choice for a wide range of energy storage system applications. The current motivation to improve the robustness of lithium-ion battery applications has stimulated the need for in-depth research into aging effects and the establishment of lifetime prediction models. This paper reviews different combination approaches of physics-based models and data-driven models. The three basic physics-based battery lifetime models are introduced, and requirements and features are compared from an application perspective. Then, state-of-the-art approaches for integrating physics and data-driven methods are systematically reviewed. Flowcharts present each approach to offer the readers a clear understanding. Next, the publication trends are represented by line graphs, and pie charts, including data-driven assisted physical models and physics-guided data-driven, different physical model applications, and data-driven approaches. It is concluded that electrochemical models have great potential to describe complex aging behavior under various conditions. Moreover, machine learning is a promising tool to overcome mechanistic absence and highly nonlinear performance, occupying 78 % of all data-driven methods. Physics-guided data-driven approach started to emerge as an innovative lifetime prediction method after 2020. The application advantages and limitations are compared according to the description of different methods. Furthermore, future perspectives are discussed, with opportunities and challenges. The Prospect of applying physics-guided machine learning looks forward to more inspiration.
AB - Lithium-ion batteries are a popular choice for a wide range of energy storage system applications. The current motivation to improve the robustness of lithium-ion battery applications has stimulated the need for in-depth research into aging effects and the establishment of lifetime prediction models. This paper reviews different combination approaches of physics-based models and data-driven models. The three basic physics-based battery lifetime models are introduced, and requirements and features are compared from an application perspective. Then, state-of-the-art approaches for integrating physics and data-driven methods are systematically reviewed. Flowcharts present each approach to offer the readers a clear understanding. Next, the publication trends are represented by line graphs, and pie charts, including data-driven assisted physical models and physics-guided data-driven, different physical model applications, and data-driven approaches. It is concluded that electrochemical models have great potential to describe complex aging behavior under various conditions. Moreover, machine learning is a promising tool to overcome mechanistic absence and highly nonlinear performance, occupying 78 % of all data-driven methods. Physics-guided data-driven approach started to emerge as an innovative lifetime prediction method after 2020. The application advantages and limitations are compared according to the description of different methods. Furthermore, future perspectives are discussed, with opportunities and challenges. The Prospect of applying physics-guided machine learning looks forward to more inspiration.
KW - data-driven assisted physical models
KW - data-driven methods
KW - lifetime modeling
KW - lithium-ion batteries
KW - physics-based models
KW - physics-guided data-driven
UR - http://www.scopus.com/inward/record.url?scp=85140984050&partnerID=8YFLogxK
U2 - 10.1016/j.est.2022.105992
DO - 10.1016/j.est.2022.105992
M3 - Review article
SN - 2352-152X
VL - 56
JO - Journal of Energy Storage
JF - Journal of Energy Storage
IS - Part A
M1 - 105992
ER -