Efficient battery thermal management is critical for the performance, safety, and lifespan of modern electric and hybrid vehicles under dynamic operating conditions. This study presents an integrated experimental and data-driven analysis of a hybrid cooling system combining thermoelectric Peltier modules with Al 2 O 3 -based nanofluid-assisted liquid cooling. The system is benchmarked against a conventional radiator-based cooling configuration under varying flow rates (1–4 LPM) and nanoparticle concentrations (0.5–2.0 vol.%). The results demonstrate that demand-controlled thermoelectric activation enhances cooling performance by approximately 8%, while reducing auxiliary energy consumption by nearly 30%. A maximum heat-transfer enhancement of 53.4% is achieved at 4 LPM and 2.0 vol.% nanofluid concentration, whereas the highest temperature reduction of 31.6% is observed at low flow conditions (1 LPM). To enable predictive thermal analysis, multiple machine learning models are developed and compared with a Physics-Informed Neural Network (PINN). The PINN improves prediction accuracy by approximately 50% while ensuring physical consistency by embedding governing heat-transfer constraints. The proposed hybrid nanofluid-thermoelectric cooling framework offers a compact, energy-efficient, and scalable solution for next-generation electric vehicle battery thermal management, with enhanced thermal performance, adaptive control capability, and predictive reliability.
Sharma et al. (2026) studied this question.