Source: Beijing Institute of Technology Press
Lithium-ion batteries are celebrated for their high specific energy, long service life, and low self-discharge rates. However, ensuring their reliability and safety under various operating conditions is critical for their continued success in industrial applications. Digital twin technology, which creates a virtual replica of a physical entity, offers a promising solution by enabling real-time monitoring and optimization of battery performance. This technology facilitates interactive feedback, data fusion, and iterative optimization, thereby enhancing control, safety monitoring, and data analysis.
Previous efforts in digital twin technology for batteries have focused on frameworks for predicting remaining useful life and managing battery state estimation and balancing. However, these models often overlook certain internal behaviors of the battery, limiting their accuracy and effectiveness. The new research aims to overcome these limitations by developing a comprehensive digital twin model that accurately reflects the internal and external characteristics of lithium-ion batteries under complex working conditions. Researchers has developed a cutting-edge digital twin model that promises to enhance the performance and safety of lithium-ion batteries through a novel mechanism-data fusion approach.
The research team has made significant strides in improving the simulation accuracy of lithium-ion batteries. By considering the impact of electrode particle size on battery characteristics, the researchers developed a detailed electrochemical model. This model was further enhanced by incorporating temperature distribution and particle-level stress, resulting in a comprehensive multi-particle size electrochemical-thermal-mechanical coupling model. The model was extended from individual cells to a 2P2S battery pack, accounting for the varying electrical and thermal effects among individual cells. This extension allows for more accurate simulations of battery pack behavior. The researchers proposed a novel algorithm that combines the least squares method and binary search to update model parameters. This algorithm enables the model to evolve based on real-time data, achieving a close approximation to the physical entity. The digital twin model demonstrated exceptional accuracy in simulating terminal voltage and shell temperature. Experimental results showed that the maximum mean absolute errors (MAEs) were limited to 25 mV for terminal voltage and 0.15 °C for shell temperature under 0.75 C constant current discharging and DST conditions.
The digital twin model's ability to accurately simulate battery performance under complex conditions opens up numerous practical applications. Potential uses include: ①Enhanced Battery Management Systems. The model can be integrated into battery management systems for electric vehicles, ensuring optimal performance and extending battery life. ②Improved Safety Monitoring and Fault Diagnosis: Accurate simulations enable better safety monitoring and fault diagnosis, reducing the risk of battery failures and accidents. ③Optimized Energy Storage Systems: The model can be used to optimize the efficiency and reliability of renewable energy storage systems, contributing to more sustainable energy solutions. Future research will focus on refining the optimization algorithm and reducing the data requirements, further enhancing simulation efficiency and broadening the model's applicability.
Author: Chao Lyu, Shaochun Xu, Junfu Li, Michael Pecht
Title of original paper: Digital twin modeling method for lithium-ion batteries based on data-mechanism fusion driving
Article link: https://doi.org/10.1016/j.geits.2024.100162
Journal: Green Energy and Intelligent Transportation
https://www.sciencedirect.com/science/article/pii/S2773153724000148
Green Energy and Intelligent Transportation
Experimental study
Not applicable
Digital twin modeling method for lithium-ion batteries based on data-mechanism fusion driving
10-Sep-2024
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.