A novel Neural-ODE model for the state of health estimation of lithium-ion battery using charging curve

TL;DR

This paper presents ACLA, a hybrid neural-ODE model integrating attention, CNN, and LSTM, to improve the accuracy and generalizability of lithium-ion battery health estimation across multiple datasets.

Contribution

The paper introduces ACLA, a novel hybrid neural-ODE model that enhances SOH estimation accuracy and generalization for lithium-ion batteries.

Findings

ACLA outperforms NODE and ANODE with lower RMSE in SOH estimation.

ACLA demonstrates high accuracy across multiple datasets.

The model effectively utilizes charging curve data for health prediction.

Abstract

The state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safe and reliable operation of electric vehicles. Nevertheless, the prevailing SOH estimation methods often have limited generalizability. This paper introduces a data-driven approach for estimating the SOH of LIBs, which is designed to improve generalization. We construct a hybrid model named ACLA, which integrates the attention mechanism, convolutional neural network (CNN), and long short-term memory network (LSTM) into the augmented neural ordinary differential equation (ANODE) framework. This model employs normalized charging time corresponding to specific voltages in the constant current charging phase as input and outputs the SOH as well as remaining useful of life. The model is trained on NASA and Oxford datasets and validated on the TJU and HUST datasets. Compared to the benchmark models NODE and ANODE, ACLA exhibits higher accuracy with root mean square errors (RMSE) for SOH estimation as low as 1.01% and 2.24% on the TJU and HUST datasets, respectively.