Multi-objective design optimization of battery thermal management system for electric vehicles
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{SU:2021:ATE,
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author = "Shaosen Su and Wei Li and Yongsheng Li and
Akhil Garg and Liang Gao and Quan Zhou",
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title = "Multi-objective design optimization of battery thermal
management system for electric vehicles",
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journal = "Applied Thermal Engineering",
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year = "2021",
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volume = "196",
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pages = "117235",
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keywords = "genetic algorithms, genetic programming, Serpentine
channel, U-shaped channel, Genetic programming model,
Liquid cooling",
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ISSN = "1359-4311",
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URL = "https://www.sciencedirect.com/science/article/pii/S1359431121006736",
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DOI = "doi:10.1016/j.applthermaleng.2021.117235",
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abstract = "Lithium batteries are commonly used as the primary
power storage unit for electric vehicles, and their
performance is sensitive to temperature. Thus, the
battery thermal management system is crucially needed
to allow the EVs to work safely and efficiently. This
paper mainly focuses on the performance analysis and
design optimization of the battery thermal management
system with a U-shaped cooling channel. A Computational
fluid dynamics model of a battery thermal management
system is built to study the battery temperature
distribution and pressure distribution. Through the
establishment of the genetic programming model,
sensitivity analysis and parameter interaction analysis
are carried out to analyze the influence of cooling
plate thickness, cooling plate wall thickness, inlet
coolant temperature and flow velocity on the
comprehensive performance of the battery thermal
management system. A new surrogate-assisted
multi-objective optimization scheme is proposed by
introducing an integrated AI system that includes a
surrogate battery model built with genetic programming
(GP) and a design optimizer driven by the
second-generation non-dominated sorting genetic
algorithm (NSGA-II). Results show that the inlet
coolant temperature has the most significant influence
on the rise of battery temperature (59.87percent) but
has no influence on the pressure drop. The structural
parameters of the cooling plate and the velocity of the
inlet coolant have apparent effects on the uniformity
of the battery temperature distribution and the
pressure drop. The battery thermal management system
achieves an ideal comprehensive performance when the
thickness of the cooling plate is 4.50 mm, the
thickness of the cooling plate wall is 1.49 mm, the
inlet coolant temperature is 298.15 K, and the inlet
coolant velocity is 0.29 m/s. Under such optimized
parameter settings, the max temperature rise of the
battery reduces from 7.72 K to 7.69 K, the standard
deviation of the temperature distribution 2.54 K (a
drop of 0.02 K), and the pressure drop decrease from
1022.1 Pa to 436.43 Pa (decrease by 57.3percent). Such
results have guiding significance for the design of the
battery thermal management system with a U-shaped
channel and the application of genetic programming in
system performance analysis and optimization",
- }
Genetic Programming entries for
Shaosen Su
Wei Li
Yongsheng Li
Akhil Garg
Liang Gao
Quan Zhou
Citations