Parametrization of physics-based battery models from input-output data: A review of methodology and current research

被引:76
作者
Andersson, Malin [1 ,3 ]
Streb, Moritz [2 ]
Ko, Jing Ying [2 ]
Klass, Verena Lofqvist [3 ]
Klett, Matilda [2 ,3 ]
Ekstrom, Henrik [2 ,4 ]
Johansson, Mikael [1 ]
Lindbergh, Goran [2 ]
机构
[1] KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, Dept Intelligent Syst, Div Decis & Control Syst, Malvinas Vag 10, S-11428 Stockholm, Sweden
[2] KTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Dept Chem Engn, Appl Electrochem, Tekn Ringen 42, S-11428 Stockholm, Sweden
[3] Scania CV AB, Granparksvagen 10, S-15148 Sodertalje, Sweden
[4] COMSOL AB, Tegnergatan 23, S-11140 Stockholm, Sweden
关键词
Battery model; Parameter estimation; Optimization; Sensitivity; Identifiability; Experiment design; LITHIUM-ION BATTERY; SINGLE-PARTICLE MODEL; PARAMETER SENSITIVITY-ANALYSIS; FULL CELL PARAMETERIZATION; CAPACITY FADE ANALYSIS; PHYSICOCHEMICAL MODEL; ELECTROCHEMICAL MODEL; MULTIOBJECTIVE OPTIMIZATION; IDENTIFIABILITY ANALYSIS; DEGRADATION PHYSICS;
D O I
10.1016/j.jpowsour.2021.230859
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Physics-based battery models are important tools in battery research, development, and control. To obtain useful information from the models, accurate parametrization is essential. A complex model structure and many unknown and hard-to-measure parameters make parametrization challenging. Furthermore, numerous applications require non-invasive parametrization relying on parameter estimation from measurements of current and voltage. Parametrization of physics-based battery models from input-output data is a growing research area with many recent publications. This paper aims to bridge the gap between researchers from different fields that work with battery model parametrization, since successful parametrization requires both knowledge of the underlying physical system as well as understanding of theory and concepts behind parameter estimation. The review encompasses sensitivity analyses, methods for parameter optimization, structural and practical identifiability analyses, design of experiments and methods for validation as well as the use of machine learning in parametrization. We highlight that not all model parameters can accurately be identified nor are all relevant for model performance. Nonetheless, no consensus on parameter importance could be shown. Local methods are commonly chosen because of their computational advantages. However, we find that the implications of local methods for analysis of non-linear models are often not sufficiently considered in reviewed literature.
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页数:24
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