4.7 Article

A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction

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。
Article Thermodynamics

State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression

Yajun Zhang et al.

Summary: This study proposes a novel method combining VC model and SVR for battery SOH estimation. The experimental results show that the SVR models achieve high accuracy in SOH estimation and are robust against different initial aging statuses and cycle conditions.

ENERGY (2022)

Article Thermodynamics

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

Yu Zhang et al.

Summary: This paper proposes a hybrid prediction model RF-ABC-GRNN, integrating RF, ABC, and GRNN, to accurately predict the remaining useful life of lithium-ion batteries in the early-cycle stage. By screening high-importance feature combinations and optimizing parameters, the model can make accurate predictions earlier.

ENERGY (2021)

Article Thermodynamics

Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network

Gong Cheng et al.

Summary: This study combined the EMD method and B-LSTM neural network to develop models for estimating SOH and predicting RUL, which showed higher accuracy and robustness compared to other models.

ENERGY (2021)

Article Chemistry, Physical

Online capacity estimation of lithium-ion batteries with deep long short-term memory networks

Weihan Li et al.

Summary: There is a growing demand for modern diagnostic systems for batteries in real-world operation, especially for estimating their health status such as remaining capacity. A data-driven capacity estimation model using recurrent neural networks with long short-term memory capability has been developed for cells under real-world working conditions. This model is robust, can handle input noise, adapt to variations in input time series length, and generate viable estimation even with incomplete input data.

JOURNAL OF POWER SOURCES (2021)

Article Energy & Fuels

Lithium-ion battery capacity estimation-A pruned convolutional neural network approach assisted with transfer learning

Yihuan Li et al.

Summary: This paper proposes a framework that combines the concepts of transfer learning and network pruning to build compact Convolutional Neural Network models on a relatively small dataset for improved online battery capacity estimation performance. It transfers the pre-trained model on a large battery dataset to a small dataset of the targeted battery through transfer learning technique to enhance estimation accuracy, and then prunes the transferred model using a contribution-based neuron selection method to reduce the model size and computational complexity while maintaining performance.

APPLIED ENERGY (2021)

Article Energy & Fuels

A health indicator extraction based on surface temperature for lithium-ion batteries remaining useful life prediction

Hailin Feng et al.

Summary: In this paper, a new health indicator (HI) is proposed to predict the remaining useful life (RUL) of lithium-ion batteries from the discharge surface temperature, which is convenient for real-time measurement and online estimation. The results show that the new HI is effective for degradation modeling, with a RUL prediction error of less than 5 cycles for 5#, 6# and 7# batteries.

JOURNAL OF ENERGY STORAGE (2021)

Article Automation & Control Systems

A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery

Kailong Liu et al.

Summary: This article applies advanced machine-learning techniques to predict future capacities and RUL for lithium-ion batteries with uncertainty quantification. The combined LSTM+GPR model outperforms other counterparts and provides accurate results for both 1-step and multistep ahead capacity predictions, demonstrating good adaptability and reliable uncertainty quantification for battery health diagnosis even at the early battery cycle stage.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

Article Engineering, Industrial

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

Fan Xu et al.

Summary: This study proposes a deep learning-based stacked denoising autoencoder (SDAE) method to directly predict battery life by extracting various battery features. By using the clustering by fast search (CFS) method to filter and select essential features, the accuracy and efficiency of lithium-ion battery life prediction are improved.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)

Article Energy & Fuels

Practical state estimation using Kalman filter methods for large-scale battery systems

Zhuo Wang et al.

Summary: This study demonstrates how cell-level state estimation techniques can be used to achieve accurate SOC estimation on large-scale BESSs, and how parameters of DSPKF can be optimized using a genetic algorithm. The results show that using DSPKF for SOC estimation provides more accurate results compared to commercial BESS battery management systems, and when combined with TLS method, capacity estimation error can be reduced to less than 1%.

APPLIED ENERGY (2021)

Article Materials Science, Multidisciplinary

Fe3O4-embedded rGO composites as anode for rechargeable FeOx-air batteries

Wai Kian Tan et al.

MATERIALS TODAY COMMUNICATIONS (2020)

Review Green & Sustainable Science & Technology

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

Yujie Wang et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)

Article Energy & Fuels

A novel deep learning framework for state of health estimation of lithium-ion battery

Yaxiang Fan et al.

JOURNAL OF ENERGY STORAGE (2020)

Review Chemistry, Physical

Battery Lifetime Prognostics

Xiaosong Hu et al.

Article Automation & Control Systems

Remaining Useful Life Prediction of Lithium-Ion Battery Based on Gauss-Hermite Particle Filter

Yan Ma et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2019)

Article Energy & Fuels

Data-driven prediction of battery cycle life before capacity degradation

Kristen A. Severson et al.

NATURE ENERGY (2019)

Article Engineering, Multidisciplinary

Lithium-Ion Battery Degradation Indicators Via Incremental Capacity Analysis

David Ansean et al.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2019)

Article Automation & Control Systems

A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain

Houde Dai et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

Review Green & Sustainable Science & Technology

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Yi Li et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)

Article Computer Science, Artificial Intelligence

Efficient Online Learning Algorithms Based on LSTM Neural Networks

Tolga Ergen et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

Article Automation & Control Systems

Battery Health Prognosis Using Brownian Motion Modeling and Particle Filtering

Guangzhong Dong et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Article Computer Science, Hardware & Architecture

Data-Driven Battery Lifetime Prediction and Confidence Estimation for Heavy-Duty Trucks

Sergii Voronov et al.

IEEE TRANSACTIONS ON RELIABILITY (2018)

Article Automation & Control Systems

A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics

Datong Liu et al.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2015)

Article Computer Science, Interdisciplinary Applications

Grey Wolf Optimizer

Seyedali Mirjalili et al.

ADVANCES IN ENGINEERING SOFTWARE (2014)

Article Engineering, Electrical & Electronic

Behavior and state-of-health monitoring of Li-ion batteries using impedence spectroscopy and recurrent neural networks

Akram Eddahech et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2012)

Article Computer Science, Artificial Intelligence

Intelligent prognostics for battery health monitoring based on sample entropy

Achmad Widodo et al.

EXPERT SYSTEMS WITH APPLICATIONS (2011)