TSSR algorithm based battery space optimization on thermal management system

ABSTRACT

In recent years, the Li-ion batteries have received substantial consideration for the use of electrical vehicles and mobile electronics because of its excellent life span and high specific energy. It is necessary to connect the battery cells in series or parallel to obtain the essential power. In such cases, certain safety issues namely heat dissipation and rise in temperature must be taken into consideration. The excess temperature in Li-ion batteries affects the working performances to a very large extend. To address such issues, this paper proposes a TSSR based battery thermal management system using ANSYS FLUENT to obtain optimal battery spacing. The tunicate swarm as well as the search and rescue optimization algorithms are combined together to form a tunicate swarm search and rescue (TSSR) algorithm. Here, the battery spacing is optimized by employing TSSR optimization algorithm thereby minimizing the rate of heat transfer in the battery packs independent of specific heat capacity. The CFD model is employed since it plays a significant role in the simulation of battery temperature distribution to examine the battery thermal management system (BTMS). Finally, 10 different test functions are applied to the proposed TSSR algorithm to demonstrate its efficiency and applicability. The performance evaluation and the comparative analysis are carried out and the results reveal that the proposed approach provides optimal battery spacing thus the cooling performances of the battery packs are enhanced.

KEYWORDS:

• Li-ion battery
• battery spacing
• CFD
• TSSR
• temperature
• battery thermal management system

Disclosure statement

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Funding

This work was supported by the Natural Science Foundation of Jiangsu Province (Grants No.BK20170317)