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A Neural Network-Based Regression Study for a Hybrid Battery Thermal Management System under Fast Charging
- Siqi Chen - Tongji University, Colleage of Automative studies, China Tongji University, Clean Energy Automotive Engineering Center, China ,
- Guangxu Zhang - Tongji University, Colleage of Automative studies, China Tongji University, Clean Energy Automotive Engineering Center, China ,
- Dongdong Qiao - Tongji University, Colleage of Automative studies, China Tongji University, Clean Energy Automotive Engineering Center, China ,
- Xueyuan Wang - Tongji University, Colleage of Automative studies, China Tongji University, Clean Energy Automotive Engineering Center, China ,
- Bo Jiang - Tongji University, Colleage of Automative studies, China Tongji University, Clean Energy Automotive Engineering Center, China ,
- Haifeng Dai - Tongji University, Colleage of Automative studies, China Tongji University, Clean Energy Automotive Engineering Center, China ,
- Jiangong Zhu - Tongji University, Colleage of Automative studies, China Tongji University, Clean Energy Automotive Engineering Center, China ,
- Xuezhe Wei - Tongji University, Colleage of Automative studies, China Tongji University, Clean Energy Automotive Engineering Center, China
Journal Article
14-11-02-0015
ISSN: 2691-3747, e-ISSN: 2691-3755
Sector:
Topic:
Citation:
Chen, S., Zhang, G., Qiao, D., Wang, X. et al., "A Neural Network-Based Regression Study for a Hybrid Battery Thermal Management System under Fast Charging," SAE Int. J. Elec. Veh. 11(2):189-202, 2022, https://doi.org/10.4271/14-11-02-0015.
Language:
English
Abstract:
Fast charging is significant for the driving convenience of an electric vehicle
(EV). However, this technology causes lithium (Li)-ion batteries’ massive heat
generation under such severe current rates. To ensure the thermal performance
and lifespan of a Li-ion battery module under fast charging, an artificial
neural network (ANN) regression method is proposed for a hybrid phase change
material (PCM)—liquid coolant-based battery thermal management system (BTMS)
design. Two ANN regression models are trained based on experimental data
considering two targets: maximum temperature (Tmax
) and temperature standard deviation (TSD) of the
hybrid cooling-based battery module. The regression accuracy reaches 99.942% and
99.507%, respectively. Four sets of experimental data are employed to validate
the reliability of this method, and the cooling effect (Tmax
and TSD) of the hybrid BTMS are predicted using the
trained ANN regression models. Comparison results indicate that the deviations
between the predicted value and the experimental value are acceptable, which
prove the accuracy of the ANN regression models. This proposed method combines
regression modelling with experimental tests to achieve the desired design
efficiency and control, which can be utilized for efficient BTMS design,
especially with more complex factors such as the future fast-charging
requirements.