State of charge estimation for lithium-ion battery based on Gaussian process regression with deep recurrent kernel

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.

Automated summary

A novel data-driven SOC estimation approach for Lithium-ion batteries is proposed in this paper, utilizing a technology that integrates deep learning with kernel methods to capture structural properties and estimate uncertainty. The proposed method demonstrates satisfactory performance and strong robustness against unknown initial SOC and outliers, making informed decisions for battery management system.