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<p>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, <i>R</i> &gt; 0.9985), whereas GA-MLPNN delivered superior predictive reliability for peak temperature (T<sub>max</sub>, <i>R</i> &gt; 0.9986) and energy density (ED, <i>R</i> &gt; 0.999). Multi-objective optimization using the MOMVO algorithm illuminated inherent trade-offs among heat distribution, maximum temperature reduction, and energy storage capacity. For instance, adopting thicker MHPA layers (~ 3.8–3.9&#xa0;mm) and taller configurations (~ 18–25&#xa0;mm) effectively reduced hotspots, producing ΔT values of ~ 1.8–2.5&#xa0;°C and T<sub>max</sub> of ~ 38–39&#xa0;°C, though at the expense of lower ED (~ 139–144 Wh/kg). Conversely, thinner designs (~ 2.6–2.8&#xa0;mm) elevated energy density (~ 157 Wh/kg) but impaired thermal stability. Intermediate geometries offered more balanced outcomes, with spacing analyses confirming geometry as a decisive factor in thermal regulation. Finally, multi-criteria evaluation through the CoCoSo method translated Pareto-optimal fronts into practical design recommendations, demonstrating adaptability across diverse operational priorities, including safety-focused, high-capacity, and compromise-oriented battery applications.</p>

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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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月2日

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  • Naim Ben Ali,
  • Borhen Louhichi,
  • Waqed H. Hassan,
  • As’ad Alizadeh,
  • Abdullah Abed Hussein,
  • Walid Aich,
  • Khalil Hajlaoui,
  • Sam Aminian

摘要

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 (Tmax, R > 0.9986) and energy density (ED, R > 0.999). Multi-objective optimization using the MOMVO algorithm illuminated inherent trade-offs among heat distribution, maximum temperature reduction, and energy storage capacity. For instance, adopting thicker MHPA layers (~ 3.8–3.9 mm) and taller configurations (~ 18–25 mm) effectively reduced hotspots, producing ΔT values of ~ 1.8–2.5 °C and Tmax of ~ 38–39 °C, though at the expense of lower ED (~ 139–144 Wh/kg). Conversely, thinner designs (~ 2.6–2.8 mm) elevated energy density (~ 157 Wh/kg) but impaired thermal stability. Intermediate geometries offered more balanced outcomes, with spacing analyses confirming geometry as a decisive factor in thermal regulation. Finally, multi-criteria evaluation through the CoCoSo method translated Pareto-optimal fronts into practical design recommendations, demonstrating adaptability across diverse operational priorities, including safety-focused, high-capacity, and compromise-oriented battery applications.