DIICAN: Dual Time-scale State-Coupled Co-estimation of SOC, SOH and RUL for Lithium-Ion Batteries

by   Ningbo Cai, et al.

Accurate co-estimations of battery states, such as state-of-charge (SOC), state-of-health (SOH,) and remaining useful life (RUL), are crucial to the battery management systems to assure safe and reliable management. Although the external properties of the battery charge with the aging degree, batteries' degradation mechanism shares similar evolving patterns. Since batteries are complicated chemical systems, these states are highly coupled with intricate electrochemical processes. A state-coupled co-estimation method named Deep Inter and Intra-Cycle Attention Network (DIICAN) is proposed in this paper to estimate SOC, SOH, and RUL, which organizes battery measurement data into the intra-cycle and inter-cycle time scales. And to extract degradation-related features automatically and adapt to practical working conditions, the convolutional neural network is applied. The state degradation attention unit is utilized to extract the battery state evolution pattern and evaluate the battery degradation degree. To account for the influence of battery aging on the SOC estimation, the battery degradation-related state is incorporated in the SOC estimation for capacity calibration. The DIICAN method is validated on the Oxford battery dataset. The experimental results show that the proposed method can achieve SOH and RUL co-estimation with high accuracy and effectively improve SOC estimation accuracy for the whole lifespan.


Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting

Battery discharge capacity forecasting is critically essential for the a...

Two-stage Early Prediction Framework of Remaining Useful Life for Lithium-ion Batteries

Early prediction of remaining useful life (RUL) is crucial for effective...

Invariant learning based multi-stage identification for Lithium-ion battery performance degradation

By informing accurate performance (e.g., capacity), health state managem...

CyFormer: Accurate State-of-Health Prediction of Lithium-Ion Batteries via Cyclic Attention

Predicting the State-of-Health (SoH) of lithium-ion batteries is a funda...

Microgrid Day-Ahead Scheduling Considering Neural Network based Battery Degradation Model

Battery energy storage system (BESS) can effectively mitigate the uncert...

Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning

A state of charge estimator is an essential component of battery managem...

State-of-Charge Estimation of a Li-Ion Battery using Deep Forward Neural Networks

This article presents two Deep Forward Networks with two and four hidden...

Please sign up or login with your details

Forgot password? Click here to reset