Optimizing thermoelectric energy harvesting using deep reinforcement learning for dynamic energy management and system efficiency.

ABSTRACT: By addressing the drawbacks of static optimization techniques, this research seeks to improve the dynamic energy management of thermoelectric generators (TEGs). Finding the best deep reinforcement learning (DRL) algorithm to maximize energy distribution, prolong battery life, and boost system efficiency in the face of the variable conditions present in waste heat recovery systems is the goal. The TEG system was modeled using the Markov decision process and implemented in a computer-simulated environment. Three advanced DRL algorithms were used: soft actor-critic (SAC), proximal policy optimization (PPO), and deep Q-networks (DQN); which were trained to act as intelligent controllers. The performance of each algorithm was systematically evaluated and compared using key metrics, including av

SUBMITTER: Chaudhari CN 

PROVIDER: S-EPMC12706069 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature