A review of next-generation thermoelectric generators: Advanced geometric and nanomaterial optimization and intelligent performance enhancement

Abstract

The demand for sustainable energy solutions has accelerated the development of thermoelectric generators (TEGs) as an effective technology for harvesting waste heat. TEGs employ the Seebeck effect to convert thermal energy into electricity. They offer advantages such as solid-state operation, scalability, and minimal maintenance. However, their widespread implementation is hindered by low energy conversion efficiency. This review comprehensively analyzes the state-of-the-art in TEG technology, focusing on geometric optimization, material enhancements, and AI-driven performance improvements. Innovations in TEG designs, including segmented, variable-geometry, asymmetrical, and multistage architectures, are examined in relation to their impact on power output and efficiency. In addition, the integration of artificial intelligence (AI), computational fluid dynamics (CFD), and nanomaterials in predictive modeling and real-time optimization is discussed. AI-driven strategies such as evolutionary computation and machine learning have demonstrated significant potential in optimizing TEG configurations for maximum efficiency. Despite recent breakthroughs, challenges persist in large-scale implementation, fabrication complexity, and cost-effectiveness. Future research should prioritize the development of high-performance thermoelectric materials, advanced manufacturing techniques, and multi-physics simulation models to facilitate the next generation of TEG applications in waste heat recovery, automotive applications, and renewable energy generation. This review highlights emerging trends and outlines strategic research directions to accelerate the adoption of TEGs in sustainable energy generation.