Battery Management

Battery management systems aim to optimize battery usage, extending lifespan and improving efficiency across diverse applications. Current research heavily utilizes reinforcement learning algorithms, such as Q-learning and Proximal Policy Optimization (PPO), to dynamically control charging and discharging based on factors like energy prices and renewable energy availability, particularly in sectors like agriculture and transportation. These advancements are crucial for integrating renewable energy sources, reducing energy costs, and improving the sustainability of various systems, from smart homes to heavy-duty vehicles. Furthermore, accurate remaining useful life prediction models are being developed to enhance reliability and maintenance planning.