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As the automotive industry progresses, electric vehicles (EV) grow with increasing demand throughout the globe. Nickel-metal hydride (NiMH) battery and lithium-ion (Li-ion) are widely utilized in EV due to their advantages such as impressive energy density, good power density, and low self-discharge. However, the batteries must be operated within their optimum range for safety and good thermal management to enable an extended lifespan, lower costs, and for improving safety. The need for a liquid cold plate (LCP) to be utilized in EV batteries is now highly reliable on the distribution of the specified temperature instead of only standard cooling systems. The main objective of this paper is to create a design of fin that can increase the rate of heat transfer in the electric vehicle battery. This project is about the analysis of Electric vehicle battery cooling using fins which includes the design of a fin and analysis of it using analysis software, Ansys. It was done to find its heat transfer rate and Nusselt number.
This has many applications in the field of electric vehicle manufacturing where these types of fins can help in maximum heat transfer thereby increasing the life of the vehicle and decreasing the possibilities of failures. In this paper, analysis has been carried out at different air velocities with different fin modeling as real fins. The geometry of the fins has a high impact on the heat transfer coefficient. The main goal is to find a suitable design of fin that can efficiently help in the cooling of the battery by increasing the rate of heat transfer.
Keywords:
Fins, Honeycomb fin, Perforated Honeycomb fin.
Cite Article:
"Analysis of electric vehicle battery cooling using fins", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 7, page no.570 - 574, July-2022, Available :http://www.ijrti.org/papers/IJRTI2207081.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator