A review of lithium-ion battery state of health estimation and prediction methods
L Yao, S Xu, A Tang, F Zhou, J Hou, Y **ao… - World Electric Vehicle …, 2021 - mdpi.com
Lithium-ion power batteries have been widely used in transportation due to their advantages
of long life, high specific power, and energy. However, the safety problems caused by the …
of long life, high specific power, and energy. However, the safety problems caused by the …
A critical review of online battery remaining useful lifetime prediction methods
Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining
service life of lithium-ion batteries has become an important issue. This article reviews the …
service life of lithium-ion batteries has become an important issue. This article reviews the …
Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network
S Zhao, C Zhang, Y Wang - Journal of Energy Storage, 2022 - Elsevier
In order for lithium-ion batteries to function reliably and safely, accurate capacity and
remaining useful life (RUL) predictions are essential, but challenging. Some current deep …
remaining useful life (RUL) predictions are essential, but challenging. Some current deep …
An enhanced equivalent circuit model with real-time parameter identification for battery state-of-charge estimation
This article introduces an efficient modeling approach based on the Wiener structure to
reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the …
reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the …
Adaptive self-attention LSTM for RUL prediction of lithium-ion batteries
Z Wang, N Liu, C Chen, Y Guo - Information Sciences, 2023 - Elsevier
To achieve an accurate remaining useful life (RUL) prediction for lithium-ion batteries (LIBs),
this study proposes an adaptive self-attention long short-term memory (SA-LSTM) prediction …
this study proposes an adaptive self-attention long short-term memory (SA-LSTM) prediction …
A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability
Previous machine learning models for state-of-health (SOH) estimation of Li-ion batteries
have relied on prescribed statistical features. However, there is little theoretical …
have relied on prescribed statistical features. However, there is little theoretical …
Remaining useful life prediction of lithium-ion batteries using a hybrid model
F Yao, W He, Y Wu, F Ding, D Meng - Energy, 2022 - Elsevier
Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to the
stable operation and timely maintenance of a battery system. However, the capacity of an …
stable operation and timely maintenance of a battery system. However, the capacity of an …
State of health prediction for lithium-ion battery using a gradient boosting-based data-driven method
P Qin, L Zhao, Z Liu - Journal of Energy Storage, 2022 - Elsevier
Abstract Accurate SOH (State of Health) prediction of lithium-ion battery is of great
significance for battery maintenance and safe driving of electric vehicles. To obtain the …
significance for battery maintenance and safe driving of electric vehicles. To obtain the …
Detecting abnormality of battery lifetime from first‐cycle data using few‐shot learning
The service life of large battery packs can be significantly influenced by only one or two
abnormal cells with faster aging rates. However, the early‐stage identification of lifetime …
abnormal cells with faster aging rates. However, the early‐stage identification of lifetime …
Collaborative online RUL prediction of multiple assets with analytically recursive Bayesian inference
By using in situ health information, many existing studies for online remaining useful life
(RUL) prediction adopt a stochastic process-based degradation model and a computation …
(RUL) prediction adopt a stochastic process-based degradation model and a computation …