TY - GEN
T1 - DATA-DRIVEN CONTROL CO-DESIGN FOR INDIRECT LIQUID COOLING PLATE WITH MICROCHANNELS FOR BATTERY THERMAL MANAGEMENT
AU - Liu, Zheng
AU - Xu, Yanwen
AU - Wu, Hao
AU - Wang, Pingfeng
AU - Li, Yumeng
N1 - Publisher Copyright:
© 2023 American Society of Mechanical Engineers (ASME). All rights reserved.
PY - 2023
Y1 - 2023
N2 - The demand for high-performance electric vehicles keeps increasing with the booming electric vehicles market. Thus, battery cooling is significant in enabling the battery to work under harsh discharge process. Thanks to its high efficiency and low cost, indirect liquid cooling is a widely used cooling method for batteries. Researchers are trying to optimize the plant or control design separately for a better cooling effect. However, they can only produce suboptimal results with low efficiency. Motivated by the imperfections of existing battery cooling systems, we aim to lower the cost of indirect liquid cooling for batteries considering the plant design and control design using data-driven co-design optimization. First, a finite element model of the battery was built to predict the temperature and validate our findings against experimental data. Then, a Gaussian process-based surrogate model combined with Monte Carlo simulation extended the prediction to many scenarios under harsh discharge process. Finally, the surrogate model obtained the optimal plant and control designs. The finite element model validated the optimal design, which lowered the cost by 10%.
AB - The demand for high-performance electric vehicles keeps increasing with the booming electric vehicles market. Thus, battery cooling is significant in enabling the battery to work under harsh discharge process. Thanks to its high efficiency and low cost, indirect liquid cooling is a widely used cooling method for batteries. Researchers are trying to optimize the plant or control design separately for a better cooling effect. However, they can only produce suboptimal results with low efficiency. Motivated by the imperfections of existing battery cooling systems, we aim to lower the cost of indirect liquid cooling for batteries considering the plant design and control design using data-driven co-design optimization. First, a finite element model of the battery was built to predict the temperature and validate our findings against experimental data. Then, a Gaussian process-based surrogate model combined with Monte Carlo simulation extended the prediction to many scenarios under harsh discharge process. Finally, the surrogate model obtained the optimal plant and control designs. The finite element model validated the optimal design, which lowered the cost by 10%.
KW - Battery cooling
KW - Battery thermal management
KW - Control co-design
KW - Data-driven method
KW - Lithium-ion batteries
UR - http://www.scopus.com/inward/record.url?scp=85178576747&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85178576747&partnerID=8YFLogxK
U2 - 10.1115/DETC2023-116921
DO - 10.1115/DETC2023-116921
M3 - Conference contribution
AN - SCOPUS:85178576747
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 49th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Y2 - 20 August 2023 through 23 August 2023
ER -