Evidence map›Paper›PMID 40796932›Full record

ArticleScientific reports2025

Design of a liquid cooled battery thermal management system using neural networks, cheetah optimizer and salp swarm algorithm.

Anjan Kumar, Laith Hussein Jasim, Padmanabha Vijaya, Dipak Patel, J Gowrishankar, R Sivaranjani, Ankur Srivastava, Mayank Kundlas, Sarbeswara Hota, Banafshe Hamidi

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Anjan KumarDepartment of electronics and communication engineering, GLA University, Mathura, 281406, India.
Laith Hussein JasimDepartment of computers Techniques engineering, College of technical engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq.
Padmanabha VijayaDepartment of Mathematics and Computer Science, Modern College of Business and Science, Muscat, Oman.
Dipak PatelSchool of Engineering and Technology, Dr. Subhash University, Dr. Subhash Road, Junagadh, 362001, India.
J GowrishankarDepartment of Computer Science Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India.
R SivaranjaniDepartment of CSE Raghu Engineering College, Visakhapatnam, 531162, Andhra Pradesh, India.
Ankur SrivastavaDepartment of CSE, Chandigarh Engineering College, Chandigarh Group of Colleges-Jhanjeri, Mohali, 140307, Punjab, India.
Mayank KundlasCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
Sarbeswara HotaDepartment of Computer Application, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, Odisha, India.
Banafshe HamidiNahal Soft Computing Research Group (NSCRG), Tehran, Iran. banafshe.hamidi.ai@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Addressing a key research gap in the lack of unified AI-based approaches that ensure both high predictive accuracy and informed design trade-offs, this study presents a synergistic methodology that integrates advanced intelligent techniques to optimize the thermal and hydraulic performance of liquid-cooled Li-ion battery thermal management systems (TMS). A TMS with asymmetric U-shaped channels was used as a case study to validate the intelligent proposed framework. Minimizing the maximum temperature (T

Indexed as

Lithium-ion batteryMetaheuristic algorithmsMulti-criteria decision-makingMultilayer perceptron neural networkSalp swarm algorithmThermal management system

Identifiers

PMID40796932
PMCPMC12343889

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.