TY - JOUR
T1 - An Application of Genetic programming for Lithium-ion Battery Pack Enclosure Design
T2 - International Conference on Sustainable Energy and Green Technology 2018, SEGT 2018
AU - Shahin, M. E.
AU - Yun, Liu
AU - Chin, C. M.M.
AU - Gao, Liang
AU - Wang, Chin Tsan
AU - Niu, Xiaodong
AU - Goyal, Ankit
AU - Garg, Akhil
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2019/7/2
Y1 - 2019/7/2
N2 - For 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.
AB - For 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.
UR - http://www.scopus.com/inward/record.url?scp=85068727531&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/268/1/012065
DO - 10.1088/1755-1315/268/1/012065
M3 - Conference article
AN - SCOPUS:85068727531
SN - 1755-1307
VL - 268
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012065
Y2 - 11 December 2018 through 14 December 2018
ER -
