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

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), temperature difference (ΔT), and pressure drop (ΔP) were considered key design objectives. In the first phase, predictive modeling was performed using multilayer perceptron neural networks (MLPNN) optimized by three metaheuristic algorithms: cheetah optimizer (CO), grey wolf optimizer (GWO), and marine predators algorithm (MPA). The results showed outstanding accuracy, with the CO-MLPNN achieving R > 0.9999 for predicting T, while the GWO-MLPNN models performed best for ΔT (R > 0.9969) and ΔP (R > 0.9999). In the second phase, the multi-objective salp swarm algorithm (MOSSA) was benchmarked against MOPSO, with the latter producing more diverse and convergent Pareto fronts. The optimal solutions spanned T = 32.3-38 °C, ΔT = 3.7-5.1 °C, and ΔP = 15-50 Pa. Design trends indicated a preference for higher mass flow rates and longer channels, enhancing thermal regulation. The third phase employed the VIKOR method to generate 21 decision-making scenarios reflecting various stakeholder priorities, facilitating robust, application-specific design strategies. This novel framework not only improves the accuracy and comprehensiveness of battery TMS design but also promotes sustainability by supporting efficient, adaptive, and intelligent engineering decisions.