Lithium-ion Battery State of Health Estimation with Recurrent Convolution Neural Networks
Journal article, 2022

State of health (SOH) estimation of lithium-ion batteries is one of the major functions conducted by battery management systems. Machine learning and neural networks become popular for SOH estimation in recent years, especially when huge amounts of battery data from laboratories and end-users are available. This paper proposed a new SOH estimation structure, which combines recurrent neural network and convolution neural network together. Measurements not only from the studied cycle but also from the previous two cycles is used together to estimate the studied cycle SOH. By this way, hidden information of aging trends among cycles is utilized. Furthermore, two-dimensional filters in convolution layers are applied to study features of voltage, current, and temperature during the charging process. Validation results show that the proposed SOH estimation structure can reduce the validation set loss by 31.5% and 18.8% respectively, compared to the long-short-term model and another proposed reference structure that combines recurrent neural network and feedforward neural network together.

RECURRENT NEURAL NETWORK

LITHIUM-ION BATTERIES

MACHINE LEARNING

CONVOLUTION NEURAL NETWORK

STATE OF HEALTH ESTIMATION

Author

Bowen Jiang

Chalmers, Electrical Engineering, Electric Power Engineering

Yujing Liu

Chalmers, Electrical Engineering, Electric Power Engineering

Junfei Tang

Chalmers, Electrical Engineering, Electric Power Engineering

IET Conference Proceedings

27324494 (eISSN)

Vol. 2022 4 479-484

Subject Categories

Probability Theory and Statistics

Control Engineering

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1049/icp.2022.1097

More information

Latest update

11/3/2023