An Application of Genetic programming for Lithium-ion Battery Pack Enclosure Design: Modelling of Mass, Minimum Natural Frequency and Maximum Deformation Case원문보기
AbstractFor ensuring the safety of battery pack and its enclosure, the mechanical design is crucial for generating lower deformation, lower stresses and vibrations during its actual operation. In addition, the minimum mass of battery pack is needed for lower energy consumption of pack. Therefore, th...
AbstractFor ensuring the safety of battery pack and its enclosure, the mechanical design is crucial for generating lower deformation, lower stresses and vibrations during its actual operation. In addition, the minimum mass of battery pack is needed for lower energy consumption of pack. Therefore, the problem to be solved can be formulated as multi-objective optimization and much desire one for electric vehicle industry application. In this paper, the application of evolutionary approach of Genetic programming (GP) is illustrated for battery pack casing design considering the design requirements having higher mechanical performance. Data generated from finite element simulation was used as input in GP. The analysis concluded that the GP perform satisfactorily. GP models for three design outputs predicted the values in compare to actual values with errors RMSE and MAPE of .00154 and .00715, .000033 and 1.16, .52 and .48, respectively. These results can be used to design the battery pack enclosures. The similar models can be applied to different independent parameters to find out the possible relation in between them to correlate the results, find out the criticality of the individual parameter and then optimize the design accordingly.
AbstractFor ensuring the safety of battery pack and its enclosure, the mechanical design is crucial for generating lower deformation, lower stresses and vibrations during its actual operation. In addition, the minimum mass of battery pack is needed for lower energy consumption of pack. Therefore, the problem to be solved can be formulated as multi-objective optimization and much desire one for electric vehicle industry application. In this paper, the application of evolutionary approach of Genetic programming (GP) is illustrated for battery pack casing design considering the design requirements having higher mechanical performance. Data generated from finite element simulation was used as input in GP. The analysis concluded that the GP perform satisfactorily. GP models for three design outputs predicted the values in compare to actual values with errors RMSE and MAPE of .00154 and .00715, .000033 and 1.16, .52 and .48, respectively. These results can be used to design the battery pack enclosures. The similar models can be applied to different independent parameters to find out the possible relation in between them to correlate the results, find out the criticality of the individual parameter and then optimize the design accordingly.
참고문헌 (19)
Journal of Power Sources Wang 258 321 2014 10.1016/j.jpowsour.2014.02.064
AI-Helper
※ AI-Helper는 부적절한 답변을 할 수 있습니다.