☆ 4.6 Review

A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction

IEEE ACCESS (2022)

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Article Engineering, Electrical & Electronic

Battery State-of-Health Estimation Based on Incremental Capacity Analysis Method: Synthesizing From Cell-Level Test to Real-World Application

Chengqi She et al.

Summary: This article proposes an enabling SOH estimation scheme based on the ICA method for real-world EVs. The effectiveness of the proposed method is verified using the datasets collected from both well-controlled laboratory tests and daily operating EVs. The results show that the proposed method can realize an accurate pack-level SOH estimation both for laboratory battery packs and real-world EVs.

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS (2023)

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Article Engineering, Electrical & Electronic

Remaining Useful Life Prediction of Lithium-Ion Batteries: A Hybrid Approach of Grey-Markov Chain Model and Improved Gaussian Process

Mingye Zhu et al.

Summary: This article proposes a hybrid approach of the Grey-Markov chain model (GMCM) and the improved Gaussian process for accurate prediction of the lithium-ion battery's future capacity and remaining useful life (RUL). The historical capacity data is decomposed into degradation trend and regeneration phenomena using ensemble empirical mode decomposition. The degradation and regeneration prediction are performed using the GMCM method and the multioutput mixture of Gaussian processes, respectively. An online calibration strategy is also proposed for further prediction accuracy improvement. Extensive experiments validate the superiority of the proposed GPGM approach over other methods in terms of one-step and multistep prediction.

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS (2023)

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Article Thermodynamics

A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery

Lisen Yan et al.

Summary: This paper proposes a hybrid framework combining a model-based method and a data-driven method for accurately predicting the remaining useful life of lithium-ion batteries. The method improves prediction accuracy by dynamically updating parameters with particle filters and optimizing the performance of the support vector regression model using an artificial bee colony algorithm. Experimental results demonstrate the effectiveness of the proposed method, particularly in the early stage.

ENERGY (2022)

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Article Automation & Control Systems

An Enhanced Equivalent Circuit Model With Real-Time Parameter Identification for Battery State-of-Charge Estimation

Farshid Naseri et al.

Summary: This article introduces an efficient modeling approach based on the Wiener structure to enhance the capacity of classical equivalent circuit models in capturing the nonlinearities of Li-ion batteries. The proposed method combines a linear ECM with a static output nonlinearity block to achieve superior nonlinear mapping property while maintaining real-time efficiency. The observability of the battery model is analytically proven, and an efficient parameter estimator is introduced for real-time estimation of the Wiener model's parameters.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2022)

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Article Automation & Control Systems

Co-Estimation of State-of-Charge and State-of- Health for Lithium-Ion Batteries Using an Enhanced Electrochemical Model

Yizhao Gao et al.

Summary: This article presents a scheme using a simplified reduced-order electrochemical model and dual nonlinear filters for the reliable co-estimations of cell state-of-charge (SOC) and state-of-health (SOH). By accessing unmeasurable physical variables such as surface and bulk solid-phase concentration, the feasibility and performance of SOC estimator are revealed. Aging factors including loss of lithium ions, loss of active materials, and resistance increment are identified to improve the precision of SOC estimation for aged cells.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2022)

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Article Engineering, Multidisciplinary

An enhanced particle filter technology for battery system state estimation and RUL prediction

Mohamed Ahwiadi et al.

Summary: The paper proposes an enhanced PF technology to improve the accuracy of battery state estimation and RUL prediction. The limitations of PF in sample degeneracy, impoverishment, and lack of new measurement data are effectively addressed by introducing enhanced particle method and evolving fuzzy predictor.

MEASUREMENT (2022)

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Article Engineering, Electrical & Electronic

A Data-Driven Method for Battery Charging Capacity Abnormality Diagnosis in Electric Vehicle Applications

Zhenpo Wang et al.

Summary: In this article, a data-driven method based on massive real-world EV operating data is proposed for diagnosing battery charging capacity abnormalities. By utilizing multiple input parameters and a tree-based prediction model for training, along with a statistical method for abnormality diagnosis, the proposed method demonstrates the highest prediction accuracy.

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION (2022)

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Article Automation & Control Systems

Adaptive Ensemble-Based Electrochemical-Thermal Degradation State Estimation of Lithium-Ion Batteries

Yang Li et al.

Summary: This article proposes a computationally efficient state estimation method for lithium-ion batteries based on a degradation-conscious high-fidelity electrochemical-thermal model. The algorithm uses an ensemble-based state estimator with the singular evolutive interpolated Kalman filter (SEIKF) to ease the computational burden caused by the nonlinear nature of the battery model. Unlike existing schemes, the proposed algorithm ensures mass conservation without additional constraints, simplifying the tuning process and improving convergence speed. The proposed scheme addresses model uncertainty and measurement errors through adaptive adjustment of the SEIKF's error covariance matrices. Comparisons with well-established nonlinear estimation techniques show that the adaptive ensemble-based Li-ion battery state estimator provides excellent performance in terms of accuracy, computational speed, and robustness.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2022)

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Article Computer Science, Information Systems

Available Capacity Computation Model Based on Long Short-Term Memory Recurrent Neural Network for Gelled-Electrolyte Batteries in Golf Carts

Ching-Ming Lai et al.

Summary: This paper presents a deep neural network model for investigating the behavior of gel batteries. The proposed model accurately estimates the state of charge and the remaining service life of the batteries, providing guidance for vehicle service management systems.

IEEE ACCESS (2022)

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Review Materials Science, Multidisciplinary

A Review of Factors Affecting the Lifespan of Lithium-ion Battery and its Health Estimation Methods

Xiaoqiang Zhang et al.

Summary: This paper introduces the definition of SOH of lithium battery and factors affecting its aging, summarizes methods for estimating the SOH of lithium batteries, and proposes future research directions and emphasis areas based on current problems and challenges in estimating the SOH of lithium batteries.

TRANSACTIONS ON ELECTRICAL AND ELECTRONIC MATERIALS (2021)

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Article Automation & Control Systems

Constrained Ensemble Kalman Filter for Distributed Electrochemical State Estimation of Lithium-Ion Batteries

Yang Li et al.

Summary: This article proposes a novel model-based estimator for the distributed electrochemical states of lithium-ion batteries. A reduced-order battery model is obtained through systematic simplifications of a high-order electrochemical-thermal coupled model, capturing local state dynamics inside the battery. The constrained ensemble Kalman filter (EnKF) based on a physics-based equivalent circuit model is designed to detect internal variables and address slow convergence issues.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Article Thermodynamics

A capacity prediction framework for lithium-ion batteries using fusion prediction of empirical model and data-driven method

Yuejiu Zheng et al.

Summary: This paper proposes a framework for battery capacity prediction based on the feedforward empirical model and the feedback data-driven method. By modifying the parameters of the empirical model based on the difference of estimated capacities, the fusion capacity is predicted accurately.

ENERGY (2021)

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Review Engineering, Multidisciplinary

A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries

Ming-Feng Ge et al.

Summary: This review discusses the importance of estimating the SOH and predicting the RUL of lithium-ion batteries, summarizes the current research status, methods classification, advantages and limitations, as well as future development trends and challenges.

MEASUREMENT (2021)

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Article Engineering, Electrical & Electronic

A Novel State-of-Health Estimation for Lithium-Ion Battery via Unscented Kalman Filter and Improved Unscented Particle Filter

Feng Zhu et al.

Summary: This paper introduces a novel strategy that combines the UKF and IUPF algorithms to improve the accuracy and speed of SOH assessment for lithium-ion batteries. The experimental results show that the UKFIUPF algorithm achieves more accuracy and faster estimation of SOH compared to the IUPF approach.

IEEE SENSORS JOURNAL (2021)

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Article Engineering, Industrial

A dual-LSTM framework combining change point detection and remaining useful life prediction

Zunya Shi et al.

Summary: The study introduces a Dual-LSTM framework for degradation analysis and RUL prediction using LSTM. The framework achieves high-precision RUL prediction by connecting change point detection and RUL prediction, introducing a novel health index function, and leveraging historical information.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)

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Article Automation & Control Systems

Dynamic Bayesian Network-Based Lithium-Ion Battery Health Prognosis for Electric Vehicles

Guangzhong Dong et al.

Summary: Battery prognostics and health management are crucial for lithium-ion batteries in electric vehicles. This article presents a probabilistic method for battery degradation modeling and health prognosis based on dynamic Bayesian network (DBN). Experimental tests show that the proposed methods can provide accurate and reliable battery state-of-health estimation and remaining useful life prediction.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

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Article Automation & Control Systems

A Robust Online Parameter Identification Method for Lithium-Ion Battery Model Under Asynchronous Sampling and Noise Interference

Zhongrui Cui et al.

Summary: This article discusses the online identification of a battery model and its accuracy under various conditions. It analyzes the mechanisms of sampling asynchronization and measurement noise, and proposes an improved RLS algorithm to reduce identification errors.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

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Article Automation & Control Systems

Feature Analyses and Modeling of Lithium-Ion Battery Manufacturing Based on Random Forest Classification

Kailong Liu et al.

Summary: This article proposes a random forest-based classification framework to analyze feature variables and achieve reliable classification in lithium-ion battery manufacturing, quantifying feature importance and correlations. By studying data from different stages, the framework investigates the effects on electrode properties and provides a promising solution for reducing model dimension and conducting efficient sensitivity analysis of battery manufacturing.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2021)

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Article Multidisciplinary Sciences

Battery lifetime prediction and performance assessment of different modeling approaches

Md Sazzad Hosen et al.

Summary: This study utilized a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cells to develop and train different capacity fade models, with the nonlinear autoregressive network (NARXnet) being able to most accurately predict capacity degradation while minimizing computational effort. The research highlights the importance of selecting the lifetime method and model performance in understanding the complex and nonlinear aging behavior of Li-ion batteries.

ISCIENCE (2021)

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Article Thermodynamics

Development of hybrid thermal management techniques for battery packs

Seham Shahid et al.

Summary: This study investigates the thermal management issue of electric vehicle and hybrid electric vehicle battery pack using passive and hybrid techniques. Results show a substantial improvement in temperature uniformity, with a reduction in temperature difference and variance. The hybrid battery pack achieves high levels of temperature uniformity without the need for moving parts or pumping power.

APPLIED THERMAL ENGINEERING (2021)

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Article Automation & Control Systems

Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction

Meng Ma et al.

Summary: Accurate prediction of remaining useful life (RUL) is critical in the field of prognostics and health management (PHM). A novel deep neural network named CLSTM is proposed in this article, which combines convolutional operation with LSTM to improve prediction accuracy and computation efficiency for RUL of rotating machineries. Results show that the proposed CLSTM network outperforms current deep learning algorithms in URL prediction and system prognosis.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Review Thermodynamics

Lithium Plating Mechanism, Detection, and Mitigation in Lithium-Ion Batteries

Xianke Lin et al.

Summary: The success of electric vehicles depends largely on energy storage systems, particularly lithium-ion batteries, which face challenges such as metallic lithium deposition. Significant research efforts have been made to understand the lithium plating mechanisms, but challenges remain in the development of fast charging technologies.

PROGRESS IN ENERGY AND COMBUSTION SCIENCE (2021)

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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)

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Article Chemistry, Physical

Predicting battery end of life from solar off-grid system field data using machine learning

Antti Aitio et al.

Summary: Using big data techniques, researchers accurately diagnose the health of solar-connected batteries, predicting end of life in advance to address the issue of hundreds of millions lacking access to electricity.

JOULE (2021)

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Article Chemistry, Physical

Deep neural network battery impedance spectra prediction by only using constant-current curve

Yanzhou Duan et al.

Summary: In this paper, a deep learning-based method using a convolutional neural network is proposed to predict impedance spectra in lithium ion batteries over their lifespan. The results show accurate predictions of impedance spectra, validating the effectiveness of the method.

ENERGY STORAGE MATERIALS (2021)

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Article Chemistry, Physical

Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries

Jinpeng Tian et al.

Summary: The proposed method in this paper utilizes offline OCV test results to estimate aging diagnosis of lithium ion batteries at an electrode level, achieving fast diagnosis. The estimated aging parameters are close to the results obtained by offline tests, enabling reconstruction of OCV-Q curves for battery capacity estimation with high accuracy. The influence of voltage ranges on estimation results is also discussed in the study.

ENERGY STORAGE MATERIALS (2021)

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Article Engineering, Electrical & Electronic

A Remaining Useful Life Prediction Method in the Early Stage of Stochastic Degradation Process

Yuhan Zhang et al.

Summary: A novel method for predicting remaining useful life (RUL) in the early stage of degradation is proposed, which uses Wiener process and LSTM network to model degradation process, predict RUL, and quantify uncertainty. The high accuracy of the proposed methods is demonstrated in a practical case study of lithium-ion batteries.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS (2021)

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Article Automation & Control Systems

Online Capacity Estimation for Lithium-Ion Battery Cells via an Electrochemical Model-Based Adaptive Interconnected Observer

Anirudh Allam et al.

Summary: This article introduces a method for monitoring the state and capacity of aging batteries using electrochemical models and temperature-dependent models. By means of an adaptive observer, the estimation of states and aging parameters is achieved, and validation results demonstrate its accuracy and robustness.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2021)

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Article Automation & Control Systems

Novel Fitting Algorithm for Parametrization of Equivalent Circuit Model of Li-Ion Battery From Broadband Impedance Measurements

Jussi Sihvo et al.

Summary: The impedance of lithium-ion batteries offers crucial information for improving performance and safety of the batteries. Conventional fitting algorithms like CNLS may have high computational burden, but a novel fitting algorithm based on battery impedance's geometric shape in the complex-plane shows fast and accurate results with low computational effort. ECM parameters have been found to be dependent on the battery SOC at different temperatures.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

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Article Engineering, Electrical & Electronic

Empirical Capacity Measurements of Electric Vehicles Subject to Battery Degradation From V2G Services

Andreas Thingvad et al.

Summary: This paper investigates the long-term effects on batteries performing an energy demanding V2G service, with the usable battery capacity of a fleet of commercially owned EVs decreasing from 23 kWh initially to 18.9 kWh after five years of operation.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

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Article Electrochemistry

Practical identifiability of electrochemical P2D models for lithium-ion batteries

Vincent Laue et al.

Summary: Electrochemical models are crucial for the development of lithium-ion batteries, but parameterization poses challenges of identifiability and overfitting. A combination of static and dynamic tests is needed for accurate simulation results.

JOURNAL OF APPLIED ELECTROCHEMISTRY (2021)

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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)

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Article Engineering, Electrical & Electronic

Optimal Discretization Approach to the Enhanced Single-Particle Model for Li-Ion Batteries

Isaiah Oyewole et al.

Summary: This paper proposes an optimal discretization approach for model reduction of enhanced single-particle models (eSPMs), aiming to accurately predict battery dynamics and reduce computational costs. Through optimized uneven discretization, the model can predict battery dynamics accurately with improved computational cost efficiency.

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION (2021)

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Article Chemistry, Physical

Deep neural network battery charging curve prediction using 30 points collected in 10 min

Jinpeng Tian et al.

Summary: The study introduces a method to accurately estimate the entire constant-current charging curves using a deep neural network, which can capture the curves accurately in a short amount of time and demonstrate effectiveness through validation, as well as the advantage of transfer learning.

JOULE (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

A Comparative Analysis of Lithium Ion Battery Input Profiles for Remaining Useful Life Prediction by Cascade Forward Neural Network

Shaheer Ansari et al.

Summary: The proposed model in this paper utilizes a Multi-Battery Input Profile (MBIP) based Cascade Forward Neural Network (CFNN) to predict the Remaining Useful Life (RUL) of Lithium-ion batteries. The model was trained using NASA battery datasets and showed varying prediction accuracy for different batteries due to capacity regeneration phenomena. Performance metrics such as RMSE, MSE, and MAE were observed to evaluate the model's performance.

2021 IEEE WORLD AI IOT CONGRESS (AIIOT) (2021)

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Proceedings Paper Green & Sustainable Science & Technology

Lithium-ion Battery State of Energy Estimation Using Deep Neural Network and Support Vector Regression

Prashant Shrivastava et al.

Summary: This study compared two different SOE estimation methods for electric vehicle applications, using DNN and SVR models trained on drive cycle datasets, with results showing that DNN outperformed SVR in terms of accuracy.

2021 IEEE 12TH ENERGY CONVERSION CONGRESS AND EXPOSITION - ASIA (ECCE ASIA) (2021)

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Article Engineering, Electrical & Electronic

Remaining Useful Life Estimation for Prognostics of Lithium-Ion Batteries Based on Recurrent Neural Network

Marcantonio Catelani et al.

Summary: A hybrid approach combining condition monitoring and physics model is proposed to enhance the accuracy and precision of remaining useful life estimation for lithium-ion batteries.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Article Computer Science, Information Systems

Interacting Multiple Model Strategy for Electric Vehicle Batteries State of Charge/Health/ Power Estimation

Sara Rahimifard et al.

Summary: This paper proposes an online adaptive strategy for high accuracy estimation of battery SoC, SoH, and SoP, utilizing IMM strategy and SVSF filter to determine the battery's internal resistance and SoC bias. The efficacy of the proposed strategy is tested and validated using experimental data obtained from accelerated aging tests conducted on Lithium Polymer automotive battery cells.

IEEE ACCESS (2021)

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Review Chemistry, Multidisciplinary

Questions and Answers Relating to Lithium-Ion Battery Safety Issues

Wensheng Huang et al.

Summary: This article comprehensively discusses issues such as electric vehicle accidents, lithium-ion battery safety, existing safety technology, and solid-state batteries, clarifying the failure mechanisms of lithium-ion batteries and providing advice on creating safer battery systems. The aim is to promote a safer future for battery applications and a wider acceptance of electric vehicles.

CELL REPORTS PHYSICAL SCIENCE (2021)

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Article Engineering, Electrical & Electronic

Remaining Useful Life Assessment for Lithium-Ion Batteries Using CNN-LSTM-DNN Hybrid Method

Brahim Zraibi et al.

Summary: This paper suggests a hybrid method utilizing machine learning for predicting the remaining useful life of lithium-ion batteries, demonstrating the potential of new data-driven estimation approaches in battery life prediction.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2021)

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Article Computer Science, Information Systems

State-of-Charge and State-of-Health Estimation for Lithium-Ion Batteries Based on Dual Fractional-Order Extended Kalman Filter and Online Parameter Identification

Liuyi Ling et al.

Summary: The study proposes a method for estimating SOC and SOH of lithium-ion batteries based on a dual extended Kalman filter and fractional-order model, which significantly improves estimation accuracy and verifies the accuracy of SOH estimation.

IEEE ACCESS (2021)

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Review Engineering, Manufacturing

Review on State of Health estimation methodologies for lithium-ion batteries in the context of circular economy

Akash Basia et al.

Summary: This paper reviews different State of Health (SoH) estimation techniques for repurposing End of Life (EoL) Lithium-ion batteries in electric vehicles (EVs), presents a novel feature-based classification, and investigates the challenges in estimating SoH for Li-ion battery, along with possible solutions. Furthermore, the study suggests proposals for developing SoH estimation methods for EV batteries in the context of the circular economy.

CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY (2021)

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Article Computer Science, Information Systems

Batteries State of Health Estimation via Efficient Neural Networks With Multiple Channel Charging Profiles

Noman Khan et al.

Summary: PHM plays a main role in handling the risk of failure, accurate capacity estimation of batteries in ESS is mandatory for their safe operations, a MCCPs based BMS is proposed for estimating batteries health/capacity through deep learning.

IEEE ACCESS (2021)

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Peiyi Sun et al.

FIRE TECHNOLOGY (2020)

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A Sine-Wave Heating Circuit for Automotive Battery Self-Heating at Subzero Temperatures

Yunlong Shang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Engineering, Electrical & Electronic

Combined State and Parameter Estimation of Lithium-Ion Battery With Active Current Injection

Ziyou Song et al.

IEEE TRANSACTIONS ON POWER ELECTRONICS (2020)

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Article Engineering, Electrical & Electronic

On-Board State Estimation in Electrical Vehicles: Achieving Accuracy and Computational Efficiency Through an Electrochemical Model

M. K. S. Verma et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

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Article Chemistry, Physical

A comprehensive single-particle-degradation model for battery state-of-health prediction

J. Li et al.

JOURNAL OF POWER SOURCES (2020)

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Article Automation & Control Systems

Battery Adaptive Observer for a Single-Particle Model With Intercalation-Induced Stress

Dong Zhang et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2020)

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Article Engineering, Electrical & Electronic

Research on Remaining Useful Life Prediction of Rolling Element Bearings Based on Time-Varying Kalman Filter

Lingli Cui et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)

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Article Engineering, Electrical & Electronic

Optimal Multistage Charging of NCA/Graphite Lithium-Ion Batteries Based on Electrothermal-Aging Dynamics

Xiaosong Hu et al.

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION (2020)

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Review Engineering, Electrical & Electronic

Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures

Xiaosong Hu et al.

IEEE INDUSTRIAL ELECTRONICS MAGAZINE (2020)

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State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

Dickshon N. T. How et al.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2020)

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Article Engineering, Electrical & Electronic

State-of-Health Estimation Based on Differential Temperature for Lithium Ion Batteries

Jinpeng Tian et al.

IEEE TRANSACTIONS ON POWER ELECTRONICS (2020)

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Article Multidisciplinary Sciences

Mutual modulation between surface chemistry and bulk microstructure within secondary particles of nickel-rich layered oxides

Shaofeng Li et al.

NATURE COMMUNICATIONS (2020)

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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)

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Article Automation & Control Systems

Health Prognosis for Electric Vehicle Battery Packs: A Data-Driven Approach

Xiaosong Hu et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2020)

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Article Energy & Fuels

Battery heating for lithium-ion batteries based on multi-stage alternative currents

Lei Zhang et al.

JOURNAL OF ENERGY STORAGE (2020)

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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)

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Real-Time Parameter Estimation of an Electrochemical Lithium-Ion Battery Model Using a Long Short-Term Memory Network

Huiyong Chun et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

A Sequential Bayesian Updated Wiener Process Model for Remaining Useful Life Prediction

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IEEE ACCESS (2020)

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State of Health Monitoring and Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Temporal Convolutional Network

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IEEE ACCESS (2020)

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State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

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IEEE ACCESS (2020)

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Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

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A Battery Management System With a Lebesgue-Sampling-Based Extended Kalman Filter

Wuzhao Yan et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

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Article Automation & Control Systems

Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

Robert R. Richardson et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

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Electrochemical Impedance Spectroscopy-Based Electric Circuit Modeling of Lithium-Sulfur Batteries During a Discharging State

Daniel-Ioan Stroe et al.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2019)

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An Enhanced Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction

Mohamed Ahwiadi et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2019)

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Article Engineering, Electrical & Electronic

Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles

Rui Xiong et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2019)

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Article Energy & Fuels

Data-driven prediction of battery cycle life before capacity degradation

Kristen A. Severson et al.

NATURE ENERGY (2019)

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Article Engineering, Electrical & Electronic

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State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review

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A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation

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

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Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine

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A Framework for Simplification of PDE-Based Lithium-Ion Battery Models

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Hybrid Energy Storage Systems for Electric Vehicles: An Experimental Analysis of Performance Improvements at Subzero Temperatures

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Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method

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Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

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Development of first principles capacity fade model for Li-ion cells

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Effect of porosity on the capacity fade of a lithium-ion battery - Theory

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