Automotive Innovation ›› 2022, Vol. 5 ›› Issue (2): 134-145.doi: 10.1007/s42154-022-00175-3
An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge
Huanyang Huang1 · Jinhao Meng1 · Yuhong Wang1 · Lei Cai2 · Jichang Peng3 · Ji Wu4 · Qian Xiao5 · Tianqi Liu1 ·
Remus Teodorescu6
- 1. College of Electrical Engineering, Sichuan University, Chengdu, China 2. Faculty of Computer Science and Engineering, Shanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an, China 3. Smart Grid Research Institute, Nanjing Institute of Technology, Nanjing, China 4. Department of Automation, University of Science and Technology of China, Hefei, China 5. Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China 6. Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge
Huanyang Huang1 · Jinhao Meng1 · Yuhong Wang1 · Lei Cai2 · Jichang Peng3 · Ji Wu4 · Qian Xiao5 · Tianqi Liu1 ·#br# Remus Teodorescu6
- 1. College of Electrical Engineering, Sichuan University, Chengdu, China 2. Faculty of Computer Science and Engineering, Shanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology, Xi’an, China 3. Smart Grid Research Institute, Nanjing Institute of Technology, Nanjing, China 4. Department of Automation, University of Science and Technology of China, Hefei, China 5. Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China 6. Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
摘要： In the long-term prediction of battery degradation, the data-driven method has great potential with historical data recorded by the battery management system. This paper proposes an enhanced data-driven model for Lithium-ion (Li-ion) battery state of health (SOH) estimation with a superior modeling procedure and optimized features. The Gaussian process regression (GPR) method is adopted to establish the data-driven estimator, which enables Li-ion battery SOH estimation with the uncertainty level. A novel kernel function, with the prior knowledge of Li-ion battery degradation, is then introduced to improve the modeling capability of the GPR. As for the features, a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high efficiency. In the first stage, an optimal partial charging voltage is selected by the grid search; while in the second stage, the principal component analysis is conducted to increase both estimation accuracy and computing efficiency. Advantages of the proposed method are validated on two datasets from different Li-ion batteries: Compared with other methods, the proposed method can achieve the same accuracy level in the Oxford dataset; while in Maryland dataset, the mean absolute error, the root-mean-squared error, and the maximum error are at least improved by 16.36%, 32.43%, and 45.46%, respectively.