4.7 Article

Accurate state of health estimation for lithium-ion batteries under random charging scenarios

Related references

Note: Only part of the references are listed.
Review Energy & Fuels

A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions

Friedrich von Buelow et al.

Summary: The ageing of Lithium-ion batteries depends on their operation during charging, discharging, and rest phases, and can be forecasted to determine the state of health (SOH) of the battery. This SOH forecasting is valuable for fleet managers of battery electric vehicle (BEV) fleets to plan vehicle replacement and optimize operational strategies. However, there are limitations in the applicability and comparability of existing models due to different data sets, metrics, output values, and forecast horizons.

JOURNAL OF ENERGY STORAGE (2023)

Article Thermodynamics

Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training

Hongqian Zhao et al.

Summary: Accurate and early detection of voltage faults is crucial for protecting property and passengers. This study develops a precise voltage prediction and fault diagnosis method using a gated recurrent unit neural network and incremental training. The method can predict battery voltage in advance and detect faults with high accuracy.

ENERGY (2023)

Article Chemistry, Physical

High-efficient prediction of state of health for lithium-ion battery based on AC impedance feature tuned with Gaussian process regression

Jia Wang et al.

Summary: This study focuses on extracting and tuning health indicators (HIs) for efficient state of health (SOH) prediction in lithium-ion battery (LIB) systems. By using machine learning, valuable HIs can be extracted from electrochemical impedance spectroscopy (EIS). The study utilizes the Gaussian process regression (GPR) model to extract 6-dimensional features (x(DRT)) from 120-dimensional impedance data (x(EIS)) and tunes them into x(ARD). The results show that the tuned x(ARD) has stronger robustness and requires less training time compared to x(EIS) for the ARD-GPR model. This method offers a promising solution for battery SOH prediction with enhanced robustness and reduced training time.

JOURNAL OF POWER SOURCES (2023)

Article Engineering, Mechanical

A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability

Gyumin Lee et al.

Summary: This study proposes a convolutional neural network model to estimate the future SOH value of Li-ion batteries using recurrence plots and Gramian angular fields. Five types of convolutional neural network models are developed and the contribution of each temporal feature is obtained. The experimental results confirm the proposed approach's effectiveness in reducing qualification test time and achieving accurate SOH estimation.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2023)

Article Energy & Fuels

The co-estimation of states for lithium-ion batteries based on segment data

Donghui Li et al.

Summary: In order to accurately estimate the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of lithium-ion batteries, this paper proposes a SOC-SOH-RUL co-estimation method using segment data of constant current charge. The method extracts fused health features (FHF) from the constant current charging segment data, and uses Gaussian process regression (GPR) to establish a capacity degradation model for SOH estimation. The equivalent circuit model (ECM) parameters and current SOH are used for SOC estimation, and the FHF prediction model is established for RUL estimation. Experimental results show high accuracy, stability, and applicability of the proposed method.

JOURNAL OF ENERGY STORAGE (2023)

Article Thermodynamics

Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation

Lin Chen et al.

Summary: This study introduces a novel metabolic extreme learning machine (MELM) framework for accurate estimation of battery state-of-health (SOH) across different types of batteries with unknown usage levels. The framework utilizes a degradation state model based on extreme learning machine (ELM) to map the relationship between degradation features and dynamics. By incorporating a metabolic mechanism and grey model, the MELM framework effectively reduces errors and achieves reliable SOH estimation.

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 Chemistry, Physical

The capacity prediction of Li-ion batteries based on a new feature extraction technique and an improved extreme learning machine algorithm

Ting Tang et al.

Summary: This study proposed a new feature extraction technique and an improved algorithm for accurate prediction of the remaining useful life of lithium-ion batteries. Experimental results demonstrate that the proposed method can enhance prediction accuracy.

JOURNAL OF POWER SOURCES (2021)

Article Thermodynamics

A method to estimate battery SOH indicators based on vehicle operating data only

L. Vichard et al.

Summary: Batteries are complex systems that are affected by variable ambient operating conditions, and understanding their dynamic behavior and degradation laws under actual conditions is essential for durability improvement. This study proposes a method to model batteries based on experimental data from postal vehicles, which shows promising results in estimating state of health indicators linked to internal resistance and available capacity. The proposed model aims to provide accurate state of charge estimation onboard and contribute to a better understanding of battery degradation laws.

ENERGY (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 Thermodynamics

Research on state of health prediction model for lithium batteries based on actual diverse data

Di Zhou et al.

Summary: This study introduces a SOH prediction model that evaluates prediction uncertainty using data from different batches of batteries, improving model accuracy and avoiding overfitting through data filtration.

ENERGY (2021)

Article Automation & Control Systems

A Relative State of Health Estimation Method Based on Wavelet Analysis for Lithium-Ion Battery Cells

Jun Xu et al.

Summary: This article proposes a novel relative SOH estimation method based on wavelet analysis, which can obtain the SOH differences for series-connected lithium-ion battery cells in the battery pack without calculating the SOH of each battery cell. Experimental results show that the estimation error of the rSOH is limited within 5%, demonstrating the effectiveness of the proposed method.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

Article Green & Sustainable Science & Technology

Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications

Sijia Yang et al.

Summary: This paper reviews existing characteristic parameters in defining battery SOH, proposes suggestions, and discusses the impact of external factors on battery degradation. SOH monitoring goals and applications are summarized based on parameters such as capacity, impedance, and aging mechanisms.

JOURNAL OF CLEANER PRODUCTION (2021)

Article Engineering, Electrical & Electronic

An Ensemble Learning-Based Data-Driven Method for Online State-of-Health Estimation of Lithium-Ion Batteries

Bin Gou et al.

Summary: This article proposes a novel ensemble learning method to accurately estimate the state-of-health of lithium-ion batteries. By combining extreme learning machine and Pearson correlation analysis, the method improves the accuracy and reliability of the estimation results.

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION (2021)

Review Green & Sustainable Science & Technology

A review of the state of health for lithium -ion batteries: Research status and suggestions

Huixin Tian et al.

JOURNAL OF CLEANER PRODUCTION (2020)

Article Energy & Fuels

Data-Driven Online Health Estimation of Li-Ion Batteries Using A Novel Energy-Based Health Indicator

Wei Liu et al.

IEEE TRANSACTIONS ON ENERGY CONVERSION (2020)

Article Automation & Control Systems

Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries

Yandan Tan et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

Article Engineering, Electrical & Electronic

A Hierarchical and Flexible Data-Driven Method for Online State-of-Health Estimation of Li-Ion Battery

Wei Liu et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

Article Engineering, Electrical & Electronic

State-of-Health Estimation and Remaining-Useful-Life Prediction for Lithium-Ion Battery Using a Hybrid Data-Driven Method

Bin Gou et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

Review Green & Sustainable Science & Technology

State estimation for advanced battery management: Key challenges and future trends

Xiaosong Hu et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)

Article Automation & Control Systems

Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression

Jingwen Wei et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Article Engineering, Electrical & Electronic

The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles

Ping Shen et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2018)

Review Green & Sustainable Science & Technology

A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations

M. S. Hossain Lipu et al.

JOURNAL OF CLEANER PRODUCTION (2018)

Article Computer Science, Interdisciplinary Applications

The Whale Optimization Algorithm

Seyedali Mirjalili et al.

ADVANCES IN ENGINEERING SOFTWARE (2016)