Sensorless battery expansion estimation using electromechanical coupled models and machine learning

Journal of Energy Chemistry, vol. 105, pp. 142–157

Abstract

Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring, improving the accuracy of digital twin simulation and abnormality detection. Therefore, this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning. The proposed method integrates reduced-order impedance models with data-driven mechanical models, coupling the electrochemical and mechanical states through the state of charge (SOC) and mechanical pressure within a state estimation framework. The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure. The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints. Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%. The results demonstrate the model’s estimation capabilities, achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa, outperforming calibrated stiffness models and other machine learning techniques. The model’s robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors. This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.

Authors 7

1. Xue Cai Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   Beijing Jiaotong University · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), Campus-Boulevard 89, 52074 Aachen, Germany

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus Boulevard 89, 52074 Aachen, Germany

   Juelich Aachen Research Alliance, JARA-Energy, 52056 Aachen, Germany

   National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China

2. Caiping Zhang corresponding

   Beijing Jiaotong University

   Affiliation as printed

   National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China

3. Jue Chen

   Beijing Jiaotong University

   Affiliation as printed

   National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China

4. Zeping Chen

   Beijing Jiaotong University

   Affiliation as printed

   National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China

5. Linjing Zhang

   Beijing Jiaotong University

   Affiliation as printed

   National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China

6. Dirk Uwe Sauer Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   Forschungszentrum Jülich · Helmholtz-Institute Münster · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), Campus-Boulevard 89, 52074 Aachen, Germany

   Helmholtz Institute Münster (HI MS), IMD-4, Forschungszentrum Jülich, 52425 Jülich, Germany

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus Boulevard 89, 52074 Aachen, Germany

   Juelich Aachen Research Alliance, JARA-Energy, 52056 Aachen, Germany

7. Weihan Li corresponding Aachen Center for Ageing Institute for Power Electronics and Electrical Drives (ISEA)

   Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), Campus-Boulevard 89, 52074 Aachen, Germany

   Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus Boulevard 89, 52074 Aachen, Germany

   Juelich Aachen Research Alliance, JARA-Energy, 52056 Aachen, Germany