4.7 Article

Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network

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

A data-fusion framework for lithium battery health condition Estimation Based on differential thermal voltammetry

Xiaoyu Li et al.

Summary: Battery state forecasting and health management are crucial for ensuring the safety and stability of battery systems. Accurate state estimation is essential for energy management and extending battery lifespan. This study proposes a closed-loop battery capacity estimation framework using Gaussian process regression and multi-output Gaussian process regression to improve the accuracy and robustness of battery state of health estimation.

ENERGY (2022)

Article Thermodynamics

Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation

Hongyan Zuo et al.

Summary: The article establishes a coupled electrochemical-thermal model to describe the surface temperature of a power lithium manganate battery during the discharging process, and estimates the SOC values under different discharging conditions using an improved functional link neural network model. The results indicate that the improved neural network model has higher SOC estimation accuracy.

ENERGY (2022)

Article Thermodynamics

Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model

Zhicheng Xu et al.

Summary: An accurate estimation of the state of charge (SOC) and state of health (SOH) of lithium-ion batteries is crucial for their management system, as well as for the safety and performance of electric vehicles and energy storage systems. A model-based method utilizing an equivalent circuit model and a minimalist electrochemical model has been proposed to simultaneously assess SOC and SOH, showing promising results with a mean error of around 2% for predicting battery capacity. This co-estimation method proves to be effective in real-time tracking of battery health and state of charge.

ENERGY (2022)

Article Thermodynamics

A method for capacity prediction of lithium-ion batteries under small sample conditions

Meng Zhang et al.

Summary: This study proposes a new method under small sample conditions, the deep adaptive continuous time-varying cascade network based on extreme learning machines (CTC-ELM), which can effectively expand the sample set of lithium-ion batteries and achieve high accuracy in capacity estimation.

ENERGY (2022)

Review Thermodynamics

A review on second-life of Li-ion batteries: prospects, challenges, and issues

Mohammad Shahjalal et al.

Summary: This paper provides a comprehensive overview of the present state of second-life Li-ion batteries by exploring relevant literature, including surveys on the fundamentals of Li-ion battery degradation and experimental approaches, as well as discussions on the obstacles and methods of reusing and recycling Li-ion batteries, related applications, cost issues, and business models.

ENERGY (2022)

Article Green & Sustainable Science & Technology

An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries

Penghua Li et al.

Summary: This study proposes an end-to-end prognostic framework for state-of-health (SOH) estimation and remaining useful life (RUL) prediction. A hybrid neural network is used to capture hierarchical features and temporal dependencies, with a Bayesian optimization algorithm for automatic configuration selection. The experiments show superior performance in accuracy compared to existing methods.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2022)

Article Thermodynamics

A CNN-ABC model for estimation and optimization of heat generation rate and voltage distributions of lithium-ion batteries for electric vehicles

Sercan Yalcin et al.

Summary: Accurately estimating battery voltage and heat generation rate is crucial for safe and efficient operations of electric vehicles. This study proposes a novel scheme combining convolutional neural network and artificial bee colony algorithm for estimating HGR and voltage. The proposed model dynamically reveals hidden features in the data and uses new objective and fitness functions to optimize the estimates.

INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER (2022)

Article Energy & Fuels

A novel heat dissipation structure based on flat heat pipe for battery thermal management system

Yueqi Wang et al.

Summary: The study focuses on the thermal performance and heat dissipation of batteries in flying cars. Flat heat pipe technology is proposed to solve the battery thermal issues, and its effectiveness is verified through experiments. The results show that the battery's maximum temperature occurs at the end of the takeoff stage, while the maximum temperature difference appears during the forward flight segment. With suitable heat dissipation structures and control methods for different discharge conditions and environmental factors, the battery's thermal performance can be improved.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2022)

Article Computer Science, Artificial Intelligence

A High-Efficient Hybrid Physics-Informed Neural Networks Based on Convolutional Neural Network

Zhiwei Fang

Summary: The article introduces a hybrid physics-informed neural network (hybrid PINN) for solving partial differential equations (PDEs) by using an approximation of the differential operator. This method has a convergent rate, avoiding the issue of bad predictions by neural networks. It is the first work to have a machine learning PDE solver with a convergent rate like numerical methods.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

Article Electrochemistry

Thermal Modelling Utilizing Multiple Experimentally Measurable Parameters

Anosh Mevawalla et al.

Summary: This paper presents three equivalent thermal circuit models for estimating the internal impedance of a LiFePO4 battery. Experimental measurements and modeling results show good agreement. The models include two 0D models and one 2D model, which analyze the effects of heat sources and thermal conductivity on the battery's thermal behavior.

BATTERIES-BASEL (2022)

Article Thermodynamics

Continuous modelling of cyclic ageing for lithium-ion batteries

Domen Seruga et al.

Summary: This paper presents a novel method for continuously tracking battery performance degradation under cyclic loading by introducing a damage parameter and using a hysteresis damage operator model. The approach requires only a small number of durability tests and accurately predicts the remaining battery life.

ENERGY (2021)

Article Thermodynamics

An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine

Lei Yao et al.

Summary: This study proposes an intelligent fault diagnosis method based on support vector machine for Lithium-ion batteries. It includes denoising, modification of covariance matrix to reduce current fluctuation influence, and optimization of SVM parameters through grid search method to achieve high accuracy and timeliness.

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 Thermodynamics

Remaining useful life prediction of lithium battery based on capacity regeneration point detection

Qiuhui Ma et al.

Summary: This study combines particle filter and Mann-Whitney U test to detect the capacity regeneration point of lithium batteries, using autoregressive model and PF algorithm for RUL prediction. The method is validated through experiments, showing improved prediction accuracy and reduced error rate.

ENERGY (2021)

Article Chemistry, Physical

Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health

Kaveh Khodadadi Sadabadi et al.

Summary: This paper developed a remaining useful life (RUL) prediction algorithm based on estimation of parameters of an enhanced single particle model (eSPM) that could be implemented using vehicle charging data. The proposed method estimates parameters associated with battery aging, uses them to design a RUL predictor, and validates the algorithm using experimental data collected on LMO-NMC battery cells, demonstrating the feasibility of inferring battery state of health and RUL from readily available charging data in plug-in battery-electric or hybrid vehicles.

JOURNAL OF POWER SOURCES (2021)

Article Thermodynamics

Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries

Jin-zhen Kong et al.

Summary: This paper proposes a voltage-temperature health feature extraction method to improve PHM of lithium-ion batteries. This method can accurately estimate and predict the health conditions and remaining useful life of lithium-ion batteries. Results show that this method provides higher accuracies than existing methods.

ENERGY (2021)

Article Engineering, Industrial

A sample entropy based prognostics method for lithium-ion batteries using relevance vector machine

Shun Jia et al.

Summary: This paper proposes a new method combining sample entropies and relevance vector machine (RVM) to estimate the remaining useful life (RUL) of lithium-ion batteries. Experimental results show that the method with multiple entropy inputs can more accurately describe the battery degradation process, achieving higher prediction accuracy, and has potential for estimating the remaining useful life of industrial machinery.

JOURNAL OF MANUFACTURING SYSTEMS (2021)

Article Energy & Fuels

A machine-learning prediction method of lithium-ion battery life based on charge process for different applications

Yixin Yang

Summary: This paper focuses on accurate prediction of battery cycle life and remaining useful life for lithium-ion batteries, utilizing various data curves and a hybrid convolutional neural network for modeling prediction, while also introducing feature attention algorithm and multiscale cycle attention algorithm to enhance prediction performance.

APPLIED ENERGY (2021)

Article Green & Sustainable Science & Technology

Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries

Pan Ding et al.

Summary: A new forecasting approach based on wavelet packet decomposition, two-dimensional convolutional neural network, and adaptive multiple error corrections is proposed for lithium-ion battery RUL prediction. The model considers a bivariate Dirichlet mixture model to address the heteroscedasticity of unpredictable residuals. Numerical analysis using experimental data demonstrates the accuracy and superiority of the proposed model over existing techniques, showing its forecasting stability.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)

Article Energy & Fuels

State-of-charge estimation of lithium ion batteries based on adaptive iterative extended Kalman filter

Zhigang He et al.

Summary: This paper proposes an adaptive iterative extended Kalman filter method for estimating the SOC of electric vehicle batteries, which combines Thevenin equivalent circuit model and improved Sage-Husa estimator, considers the influence of temperature on battery performance, and demonstrates through experiments its good performance in terms of accuracy and computational complexity.

JOURNAL OF ENERGY STORAGE (2021)

Article Energy & Fuels

Remaining useful life prediction of lithium-ion battery based on extended Kalman particle filter

Bin Duan et al.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2020)

Article Multidisciplinary Sciences

Closed-loop optimization of fast-charging protocols for batteries with machine learning

Peter M. Attia et al.

NATURE (2020)

Article Chemistry, Physical

Directly integrated all-solid-state flexible lithium batteries on polymer substrate

Haena Yim et al.

JOURNAL OF POWER SOURCES (2020)

Article Energy & Fuels

Data-driven prediction of battery cycle life before capacity degradation

Kristen A. Severson et al.

NATURE ENERGY (2019)

Article Automation & Control Systems

Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture

Boyuan Yang et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

Article Chemistry, Physical

Progress and prospect on failure mechanisms of solid-state lithium batteries

Jun Ma et al.

JOURNAL OF POWER SOURCES (2018)