Thermal Energy Storage with Nano-PCM for Optimizing Performance of Solar-Assisted Hybrid Electric Vehicles

Abstract

The transition toward sustainable mobility necessitates intelligent thermal energy management strategies, especially in solar-assisted hybrid electric vehicles (HEVs) where fluctuating solar input and dynamic operational loads challenge system efficiency. This chapter presents a comprehensive framework integrating algorithmic intelligence and social pedagogy to optimize thermal energy storage (TES) using nano-enhanced phase change materials (Nano-PCMs). The application of advanced computational techniques—such as genetic algorithms, reinforcement learning, and hybrid optimization models—enables precise control and real-time adaptation of TES performance under variable environmental conditions. The synergistic incorporation of Nano-PCMs significantly enhances the thermal conductivity and energy density of storage systems, supporting efficient heat absorption and release during vehicular operation. The the integration of pedagogical perspectives ensures user-centric design and societal alignment, enhancing both functional reliability and public acceptance. The chapter also explores adaptive thermal regulation strategies based on solar irradiance variability, predictive modeling for battery temperature control, and multi-objective optimization for system efficiency. By bridging material science, artificial intelligence, and behavioral insights, this interdisciplinary approach advances the development of intelligent, energy-resilient transport systems. The proposed framework not only contributes to reducing fossil fuel dependency and greenhouse gas emissions but also establishes a foundation for future research in sustainable vehicular thermal management.

Keywords

Identifiers

DOI

10.71443/9789349552654-08

Journal

Solar Thermal Technologies and Nano-Enhanced Phase Change Materials for High-Efficiency Electric and Solar Mobility

Year

2025