What question did this study set out to answer?

The aim is to enhance thermal management in lithium-ion batteries using a hybrid methodology integrating machine learning and optimization techniques.

March 5, 2026Scientific ReportsOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

NANaim Ben Ali BLBorhen Louhichi WHWaqed H. Hassan

Discussion

Member takes

Overview

This research develops a hybrid system to manage temperature in lithium-ion batteries, suggesting effective design strategies for various applications.

Key Points

The aim is to enhance thermal management in lithium-ion batteries using a hybrid methodology integrating machine learning and optimization techniques.
Introduced a four-phase framework for battery thermal management systems (BTMS)
Utilized GA-MLPNN and MPA-MLPNN for predictive modeling of thermal indices
Performed multi-objective optimization using MOMVO algorithm
Conducted geometric spacing analyses to ascertain effects on thermal regulation
MPA-MLPNN provided the highest precision in forecasting temperature differentials (ΔT, R > 0.9985)
GA-MLPNN excelled in predicting peak temperature (Tmax, R > 0.9986) and energy density (ED, R > 0.999)
Optimal designs reduced hotspots by adopting thicker designs (~3.8-3.9 mm; Tmax ~ 38-39 °C) while lower energy densities (~139-144 Wh/kg)
Thinner designs increased energy density (~157 Wh/kg) but degraded thermal stability
Intermediate configurations achieved balanced outcomes, confirming geometry's critical role in thermal management

Cite This Study

Ali et al. (2026) studied this question.

synapsesocial.com/papers/69a91cbed6127c7a504bfaf0 https://doi.org/10.1038/s41598-026-41155-5

View Full Paper

Ask AI

Bookmark

March 5, 2026Scientific ReportsOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

NANaim Ben Ali BLBorhen Louhichi WHWaqed H. Hassan

Discussion

Member takes

Overview

This research develops a hybrid system to manage temperature in lithium-ion batteries, suggesting effective design strategies for various applications.

Key Points

The aim is to enhance thermal management in lithium-ion batteries using a hybrid methodology integrating machine learning and optimization techniques.
Introduced a four-phase framework for battery thermal management systems (BTMS)
Utilized GA-MLPNN and MPA-MLPNN for predictive modeling of thermal indices
Performed multi-objective optimization using MOMVO algorithm
Conducted geometric spacing analyses to ascertain effects on thermal regulation
MPA-MLPNN provided the highest precision in forecasting temperature differentials (ΔT, R > 0.9985)
GA-MLPNN excelled in predicting peak temperature (Tmax, R > 0.9986) and energy density (ED, R > 0.999)
Optimal designs reduced hotspots by adopting thicker designs (~3.8-3.9 mm; Tmax ~ 38-39 °C) while lower energy densities (~139-144 Wh/kg)
Thinner designs increased energy density (~157 Wh/kg) but degraded thermal stability
Intermediate configurations achieved balanced outcomes, confirming geometry's critical role in thermal management

Cite This Study

Ali et al. (2026) studied this question.

synapsesocial.com/papers/69a91cbed6127c7a504bfaf0 https://doi.org/10.1038/s41598-026-41155-5

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark