Premium
Surrogate model‐based heat dissipation optimization of air‐cooling battery packs involving herringbone fins
Author(s) -
Li Congbo,
Li Yongsheng,
Gao Liang,
Garg Akhil,
Li Wei
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6387
Subject(s) - surrogate model , battery (electricity) , kriging , thermal management of electronic devices and systems , heat generation , automotive engineering , water cooling , simulation , air cooling , engineering , computer science , mechanical engineering , power (physics) , thermodynamics , machine learning , physics
Summary For lithium‐ion batteries (LIBs) used in electric vehicles (EVs), their performance is greatly affected by temperature. To ensure the safety and reliability of EVs, a reliable and efficient battery thermal management system (BTMS) is essential. This paper mainly analyzes air cooling BTMS. First, we propose an air‐cooling heat dissipation method for battery modules with herringbone fins and long sleeves, and prove the effectiveness of the program by comparing it with the finless and sleeveless schemes. Second, parametric modeling of the scheme is carried out, the response surface methodology (RSM), radial basis function (RBF), and Kriging surrogate model of the scheme are constructed through design experiments. Then, the Kriging model is determined by the model evaluation index to be well adapted to the problem. We build a multi‐objective optimization model (MOOM) and optimize it by multi‐objective genetic algorithm (MOGA). Finally, it is found that the maximum temperature ( T max ) and the maximum temperature difference (Δ T max ) decrease by 1.5 K and 36.79% without considering the cooling cost. When considering the cooling cost, T max and Δ T max dropped by 0.69 K and 17.92%, respectively. The optimization result improves the heat dissipation effect of the battery module and controls the cooling cost within the required range. Besides, optimization analysis can be carried out according to different actual cost requirements, which provide corresponding guidance for large‐scale air‐cooling heat dissipation analysis of LIBs.