State of charge estimation for lithium-ion battery based on Gaussian process regression with deep recurrent kernel. (January 2021)

• Record Type: Journal Article
• Authors: Xiao, Fei; Li, Chaoran; Fan, Yaxiang; Yang, Guorun; Tang, Xin
• Is Part Of: International journal of electrical power & energy systems. Volume 124(2021)
• Publication Date: 2021-01
• Subjects: State of charge estimation -- Lithium-ion battery -- Gaussian process regression -- Deep learning kernel -- Gated recurrent unit -- Neural networks; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2020.106369
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlight: A Gaussian process regression model with deep learning technology is proposed. The deep recurrent kernel captures ordering matters and recurrent structures of data. CP and MWP are applied to evaluate the performance of the probability result. The GRU-GPR achieved accurate and robust estimation result for two datasets. Abstract: Accurate and robust state of charge estimation of lithium-ion battery is a challenging task in battery management system. In this paper, a novel data-driven SOC estimation approach for Lithium-ion (Li-ion) batteries is proposed based on the Gaussian process regression framework. Kernel function selection and hyperparameters optimization are critical for Gaussian process regression due to the reason that kernel function could capture rich structure of data. By integrating the structural properties of deep learning with the flexibility of kernel methods, a new deep learning technology called deep recurrent kernel that fully encapsulates GRU structure is introduced to capture ordering matters and recurrent structures in sequential data. The proposed method could not only learn the mapping relationship from one sequence of measured quantities such as voltage, current, temperature to SOC, but also quantify estimation uncertainty which is essential for making informed decisions for battery management system. The performance of proposed methods is evaluated by two experimental datasets, one under a series of electric vehicle drive cycles and another under high rate pulse discharge test. We demonstrate the proposed method achieves satisfactory performance, as well as performs strong robustness against unknown initial SOC and outliers occurred in voltage, current and temperature.