Robust State of Health estimation of lithium-ion batteries using convolutional neural network and random forest
出版年份 2022 全文链接
标题
Robust State of Health estimation of lithium-ion batteries using convolutional neural network and random forest
作者
关键词
Lithium-ion battery, SOH estimation, Partial discharge, Convolutional neural network, Random forest
出版物
Journal of Energy Storage
Volume 48, Issue -, Pages 103857
出版商
Elsevier BV
发表日期
2022-01-17
DOI
10.1016/j.est.2021.103857
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network
- (2020) Penghua Li et al. JOURNAL OF POWER SOURCES
- State-of-Health Estimation Based on Differential Temperature for Lithium Ion Batteries
- (2020) Jinpeng Tian et al. IEEE TRANSACTIONS ON POWER ELECTRONICS
- A novel deep learning framework for state of health estimation of lithium-ion battery
- (2020) Yaxiang Fan et al. Journal of Energy Storage
- Data-driven prediction of battery cycle life before capacity degradation
- (2019) Kristen A. Severson et al. Nature Energy
- Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
- (2019) Yi Li et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- A deep learning method for online capacity estimation of lithium-ion batteries
- (2019) Sheng Shen et al. Journal of Energy Storage
- A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation
- (2018) J. Li et al. APPLIED ENERGY
- A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter
- (2018) Yi Li et al. JOURNAL OF POWER SOURCES
- An easy-to-parameterise physics-informed battery model and its application towards lithium-ion battery cell design, diagnosis, and degradation
- (2018) Yu Merla et al. JOURNAL OF POWER SOURCES
- Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
- (2018) Zheng Chen et al. Applied Sciences-Basel
- Parameter Identification and Maximum Power Estimation of Battery/Supercapacitor Hybrid Energy Storage System based on Cramer-Rao Bound Analysis
- (2018) Ziyou Song et al. IEEE TRANSACTIONS ON POWER ELECTRONICS
- Random forest regression for online capacity estimation of lithium-ion batteries
- (2018) Yi Li et al. APPLIED ENERGY
- State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis
- (2018) Xiaoyu Li et al. JOURNAL OF POWER SOURCES
- Towards a smarter battery management system: A critical review on battery state of health monitoring methods
- (2018) Rui Xiong et al. JOURNAL OF POWER SOURCES
- Current Profile Optimization for Combined State of Charge and State of Health Estimation of Lithium Ion Battery Based on Cramer–Rao Bound Analysis
- (2018) Ziyou Song et al. IEEE TRANSACTIONS ON POWER ELECTRONICS
- Diagnosis of Electric Vehicle Batteries Using Recurrent Neural Networks
- (2017) Gae-Won You et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
- (2017) Hicham Chaoui et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking
- (2016) Caihao Weng et al. APPLIED ENERGY
- Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach
- (2016) Gae-won You et al. APPLIED ENERGY
- State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter
- (2016) Jun Bi 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
- Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries
- (2016) Yu Merla et al. JOURNAL OF POWER SOURCES
- Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery
- (2016) Zhongbao Wei et al. JOURNAL OF POWER SOURCES
- Toward Vehicle-Assisted Cloud Computing for Smartphones
- (2015) Hongli Zhang et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies
- (2015) Juan Antonio Guerrero-ibanez et al. IEEE WIRELESS COMMUNICATIONS
- Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles
- (2015) Yuan Zou et al. JOURNAL OF POWER SOURCES
- On-line optimization of battery open circuit voltage for improved state-of-charge and state-of-health estimation
- (2015) Shijie Tong et al. JOURNAL OF POWER SOURCES
- Rapidly falling costs of battery packs for electric vehicles
- (2015) Björn Nykvist et al. Nature Climate Change
- On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression
- (2013) Caihao Weng et al. JOURNAL OF POWER SOURCES
- Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electricvehicles
- (2012) D. Andre et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects
- (2011) Taesic Kim et al. IEEE TRANSACTIONS ON ENERGY CONVERSION
- Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries
- (2009) Kong Soon Ng et al. APPLIED ENERGY
- Identify capacity fading mechanism in a commercial LiFePO4 cell
- (2009) Matthieu Dubarry et al. JOURNAL OF POWER SOURCES
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started