State-space modeling for electrochemical performance of Li-ion batteries with physics-informed deep operator networks

Zheng，Qiang1,2,5; Yin，Xiaoguang3; Zhang，Dongxiao1,3,4

2023-12-20

10.1016/j.est.2023.109244

Journal of Energy Storage   影响因子和分区

2352-152X

Online estimation of unobservable internal states is significant for safe operation of Li-ion batteries, and it constitutes one of the main functions of battery management system (BMS). The next-generation BMS expects model-based state estimation, especially with electrochemical models, which are accurate but often costly for solving. Therefore, it is required to build more easily executable state-space representation of electrochemical models for online state estimation. However, the traditional numerical methods for time discretization are relatively complicated, and the discretized system is not very flexible in modifying predictive time intervals. To address such issues, we introduce the concept of physics-informed operator learning for state-space modeling. Specifically, we propose an architecture, termed the physics-informed multiple-input operator network (PI-MIONet), to reformulate the state-space representation of the extended single particle (eSP) model. In this work, the PI-MIONet takes the Li-ion concentration of the whole electrode particle and current densities at the current time as the input functions, and predicts Li-ion concentration at any spatial-temporal location, which means that the forward predictions can be realized with user-defined step size. In addition, due to the capability of taking discretized functions as inputs, the PI-MIONet can be used for estimating states in the form of long vectors, and it can be conducted very efficiently, which makes it highly suitable for online applications in BMS. We verify the predictive performance of PI-MIONet through several synthetic experiments, and successfully apply it to the estimation of Li-ion concentration across the full particle with unscented Kalman filter algorithms.

Li-ion battery Operator learning Physics-informed deep operator networks State-space modeling

SCI ; EI

英语

通讯

WOS:001097372000001

20234314938740

Battery management systems ; Concentration (process) ; Deep learning ; Ions ; Learning systems ; Numerical methods ; State estimation ; State space methods

Ergonomics and Human Factors Engineering:461.4 ; Secondary Batteries:702.1.2 ; Control Systems:731.1 ; Mathematics:921 ; Numerical Methods:921.6

2-s2.0-85174440304

Scopus

1.Eastern Institute for Advanced Study,Eastern Institute of Technology,Ningbo,Zhejiang,315200,China
2.Department of Mechanical Engineering,The Hong Kong Polytechnic University,Kowloon,Hung Hom, Hong Kong SAR,Hong Kong
3.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
4.Department of Mathematics and Theories,Peng Cheng Laboratory,Shenzhen,Guangdong,518000,China
5.Department of Automation,University of Science and Technology of China,Hefei,Anhui,230027,China

Zheng，Qiang,Yin，Xiaoguang,Zhang，Dongxiao. State-space modeling for electrochemical performance of Li-ion batteries with physics-informed deep operator networks[J]. Journal of Energy Storage,2023,73.

Zheng，Qiang,Yin，Xiaoguang,&Zhang，Dongxiao.(2023).State-space modeling for electrochemical performance of Li-ion batteries with physics-informed deep operator networks.Journal of Energy Storage,73.

Zheng，Qiang,et al."State-space modeling for electrochemical performance of Li-ion batteries with physics-informed deep operator networks".Journal of Energy Storage 73(2023).