Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery

As the world grapples with the energy crisis and environmental concerns, the focus on renewable energy sources has intensified. Lithium-ion batteries, with their high energy density and low pollution, have emerged as a critical technology in the electric vehicle and energy storage sectors. However, ensuring their efficient and safe operation and maximizing their lifespan is paramount. A groundbreaking review published in the Frontiers in Energy delves into the application of artificial intelligence (AI) and machine learning (ML) in lithium-ion battery health management, offering insights into the state-of-the-art and future directions of this technology.

The demand for accurate and efficient battery health monitoring is on the rise. Lithium-ion batteries, despite their widespread use, face challenges such as corrosion and performance degradation over time. Traditional methods of assessing battery health, while valuable, often lack the precision and adaptability required for the dynamic and complex nature of battery operations. The integration of AI and ML offers a solution by providing advanced data-driven techniques to predict battery life and performance.

The comprehensive review, authored by a team of researchers led by Dr. Lai CHEN from Beijing Institute of Technology, explores the intersection of AI and battery management. It focuses on the use of ML, particularly neural networks (NNs), for simulating and forecasting the state of health (SOH) of lithium-ion batteries. The paper discusses various ML methods, including backpropagation NN, convolutional NN, and long short-term memory NN, highlighting their advantages in SOH estimation.

The study reveals that NNs, a subset of ML, have shown significant potential in modeling the SOH of lithium-ion batteries. They offer high efficiency, low energy consumption, and high robustness. The research also indicates that by utilizing field data and enhancing intelligent screening of battery parameters, NNs can significantly contribute to lithium-ion battery management. The application of NNs in SOH estimation has demonstrated the ability to handle complex nonlinear systems and large amounts of data, making them a promising tool for battery health prediction.

This review is significant as it provides a roadmap for the development of more efficient and reliable battery management systems. By leveraging ML and NNs, the research contributes to the advancement of lithium-ion battery technology, which is crucial for sustainable energy solutions. The findings suggest that the application of AI in battery management can lead to more accurate life predictions, improved safety, and enhanced performance of batteries, thereby supporting the widespread adoption of electric vehicles and renewable energy storage systems.