Multi-time Scale State-of-Health Assessment Research of Lithium-ion Batteries

Project Description

Identify through modelling the state of health of lithium ion batteries

With the continuous development of new energy vehicles, large-scale energy storage, special robots, and aerospace equipment, the demand for battery systems continues to increase. Lithium-ion batteries have the advantages of high energy density, low self-discharge rate, and long cycle life in the process of energy storage. They are the main development direction of the new energy industry and an important part of smart grids, energy storage systems, and the energy internet. There are differences between cells in the battery pack system, resulting in significant error accumulation and unreasonable attenuation of cycle life, which has become the main bottleneck restricting the development of lithium-ion battery industrialization. The direct cause of this phenomenon is that some cells are over-charged or over-discharged. The fundamental reason is that the cognition of battery pack characteristics and operation mechanism is imperfect and reliable state estimation and dynamic optimization mechanism has not been formed. The basic theoretical research of strong robust prediction is the key to solving the developmental challenges of lithium-ion batteries, and it is also the current research hotspot and cutting-edge technology. Through the deep integration of battery system modelling and artificial intelligence technology, this project conducts systematic research on state prediction and its optimization strategy, and introduces a new theoretical basis for improving overall energy efficiency. This project promotes the reform and innovation of the energy industry system and breaks through the key technical bottleneck that restricts the strong robustness cycle life estimation of battery systems. It has important theoretical significance and practical value.