Optimization Techniques in Electrochemical Devices Using AI

The development of electrochemical devices has long been identified as a promising area for applications such as energy storage and conversion due to their environment-friendly, high-efficiency, and portable nature. There has been much focus on various electrochemical devices for over a century, including batteries, fuel cells, sensors, electrolyzers, and supercapacitors. A systematic approach to designing materials for electrochemical devices is necessary, which typically involves the design of electrodes, electrolytes, catalysts, and other components. In the modern era, the introduction of AI (Artificial Intelligence) has marked the beginning of a new phase in the progress of electrochemical devices. Various approaches, including neural networks, genetic algorithms, and machine learning models, are employed to optimize electrochemical devices. For example, the efficacy of AI in active chlorine production, battery management systems, and solid oxide fuel cells is remarkable. AI helps predict material properties and behaviors with greater accuracy, optimizes electrochemical machining parameters, and significantly reduces reliance on costly and time-consuming experiments and computational methods. This chapter will examine the impact of AI on the advancement of electrochemical devices, facilitating material discovery, enhancing sensor technology, and optimizing manufacturing processes. The recent technological advancements significantly align with several United Nations Sustainable Development Goals (SDGs), notably SDG 7, which focuses on affordable and clean energy, SDG 9, emphasizing industry, innovation, and infrastructure, and SDG 13, dedicated to Climate Action. By facilitating the development of cleaner energy technologies and enhancing production efficiency, AI-driven electrochemical devices are essential in promoting sustainable industrial growth and addressing the challenges posed by climate change.