ArticleScientific reports2026
Design of a Li-ion battery cooling system incorporating PCM, heat pipes, and liquid circuits using marine predator algorithm-enhanced ANN and multi-verse optimization.
Article in Scientific reports, 2026. 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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Abstract
Managing heat generation in lithium-ion batteries remains a major obstacle, as excessive temperatures and non-uniform thermal distribution can significantly undermine safety, operational efficiency, and service life. To mitigate these challenges, this research introduces a structured hybrid methodology for advancing battery thermal management systems (BTMS). The approach integrates machine learning-driven forecasting, optimization techniques, and multi-criteria evaluation within a four-phase framework: preprocessing of data, predictive modeling through hybrid learning algorithms, optimization of design variables, and systematic decision analysis. Two hybrid predictive schemes were constructed, GA-MLPNN and MPA-MLPNN, to estimate critical thermal and performance indices. Comparative assessment revealed that MPA-MLPNN exhibited the highest precision in forecasting temperature differentials (ΔT, R > 0.9985), whereas GA-MLPNN delivered superior predictive reliability for peak temperature (T
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