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Xianbin Yang

Jilin University

H指数:10

被引数:435

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Early-stage degradation trajectory prediction for lithium-ion batteries: A generalized method across diverse operational conditions

锂离子电池的早期退化轨迹预测: 跨不同操作条件的通用方法

JOURNAL OF POWER SOURCES

IF7.9

2024-08-013

PRE

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

深度学习在金属离子电池电极材料信息学辅助设计中的应用

energy environment

IF14.6

2024-05-0112

OAAI

SOH estimation method for lithium-ion batteries under low temperature conditions with nonlinear correction

具有非线性校正的低温条件下锂离子电池SOH估计方法

JOURNAL OF ENERGY STORAGE

IF9.8

2024-01-014

PRE

Electric vehicle lifecycle carbon emission reduction: A review

CARBON NEUTRALIZATION

IF12

2023-08-162

OAAI

Lithium-Ion Battery State of Health Estimation with Multi-Feature Collaborative Analysis and Deep Learning Method

基于多特征协同分析和深度学习的锂离子电池健康状态估计

BATTERIES-BASEL

IF4.8

2023-02-0813

OA

State of health and remaining useful life prediction for lithium-ion batteries based on differential thermal voltammetry and a long and short memory neural network

基于差热伏安法和长短记忆神经网络的锂离子电池健康状态和剩余寿命预测

IF11

2022-12-0513

PRE

Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep learning model

IF4.1

2022-12-019

OAAI

The Dilemma of C-Rate and Cycle Life for Lithium-Ion Batteries under Low Temperature Fast Charging

低温快速充电下锂离子电池的C率和循环寿命的困境

BATTERIES-BASEL

IF4.8

2022-11-1120

OA

Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model

JOURNAL OF POWER SOURCES

IF7.9

2022-11-0146

PRE

Early Prediction of the Health Conditions for Battery Cathodes Assisted by the Fusion of Feature Signal Analysis and Deep-Learning Techniques

BATTERIES-BASEL

IF4.8

2022-10-0111

OA