A deep attention-assisted and memory-augmented temporal convolutional network based model for rapid lithium-ion battery remaining useful life predictions with limited data

State of health estimation based on modified Gaussian process regression for lithium-ion batteries

(2022) Jiwei Wang et al.   Journal of Energy Storage

Early-stage lifetime prediction for lithium-ion batteries: A deep learning framework jointly considering machine-learned and handcrafted data features

(2022) Zicheng Fei et al.   Journal of Energy Storage

Prognostics of battery cycle life in the early-cycle stage based on hybrid model

(2021) Yu Zhang et al.   ENERGY

Early prediction of battery lifetime via a machine learning based framework

(2021) Zicheng Fei et al.   ENERGY

Forecasting state-of-health of lithium-ion batteries using variational long short-term memory with transfer learning

(2021) Seongyoon Kim et al.   Journal of Energy Storage

Deep neural network battery life and voltage prediction by using data of one cycle only

(2021) Chia-Wei Hsu et al.   APPLIED ENERGY

Lithium-ion batteries remaining useful life prediction based on BLS-RVM

(2021) Zewang Chen et al.   ENERGY

A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system

(2021) Qisong Zhang et al.   ENERGY

End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation

(2021) Te Han et al.   JOURNAL OF POWER SOURCES

A novel method for state of energy estimation of lithium-ion batteries using particle filter and extended Kalman filter

(2021) Xin Lai et al.   Journal of Energy Storage

Remaining useful life prediction of lithium-ion battery using a novel particle filter framework with grey neural network

(2021) Lin Chen et al.   ENERGY

A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM

(2021) Jing Bi et al.   IEEE Transactions on Automation Science and Engineering

State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression

(2020) Xiaoyu Li et al.   ENERGY

A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life

(2020) Lei Ren et al.   IEEE Transactions on Industrial Informatics

Life prediction of lithium-ion batteries based on stacked denoising autoencoders

(2020) Fan Xu et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries

(2019) Fangfang Yang et al.   ENERGY

Data-driven prediction of battery cycle life before capacity degradation

(2019) Kristen A. Severson et al.   Nature Energy

Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network

(2019) Guijun Ma et al.   APPLIED ENERGY

Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks

(2019) Shuzhi Zhang et al.   Journal of Energy Storage

A review of energy storage types, applications and recent developments

(2019) S. Koohi-Fayegh et al.   Journal of Energy Storage

Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles

(2018) Alexander Farmann et al.   APPLIED ENERGY

On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis

(2016) Limei Wang et al.   APPLIED ENERGY

A novel health indicator for on-line lithium-ion batteries remaining useful life prediction

(2016) Yapeng Zhou et al.   JOURNAL OF POWER SOURCES

An ensemble model for predicting the remaining useful performance of lithium-ion batteries

(2013) Yinjiao Xing et al.   MICROELECTRONICS RELIABILITY

Theory of SEI Formation in Rechargeable Batteries: Capacity Fade, Accelerated Aging and Lifetime Prediction

(2012) Matthew B. Pinson et al.   JOURNAL OF THE ELECTROCHEMICAL SOCIETY

Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method

(2011) Wei He et al.   JOURNAL OF POWER SOURCES