☆ 4.6 Review

Physics-based battery SOC estimation methods: Recent advances and future perspectives

JOURNAL OF ENERGY CHEMISTRY (2024)

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Article Chemistry, Applied

Deep learning-based battery state of charge estimation: Enhancing estimation performance with unlabelled training samples

Liang Ma et al.

Summary: This paper proposes an improved deep neural network method for the estimation of state of charge (SOC) by effectively utilizing both unlabelled and labelled samples. The method uses an input reconstruction network to refor-mulate the time dependency features of the voltage and current, extracting useful information from the unlabelled samples. Experimental results demonstrate that the SOC estimation accuracy of the deep neural network trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.

JOURNAL OF ENERGY CHEMISTRY (2023)

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Article Energy & Fuels

A novel convolutional informer network for deterministic and probabilistic state-of-charge estimation of lithium-ion batteries

Runmin Zou et al.

Summary: Accurate and real-time estimation of state-of-charge (SOC) for lithium-ion batteries (LiB) is essential for battery management system. A novel method called the Laplace distribution-based convolutional Informer network is proposed to address the nonlinearity and complex dynamic properties of LiB, achieving deterministic and probabilistic SOC estimation. The proposed model utilizes convolutional neural network (CNN) and Informer network with attention mechanism, enabling accurate SOC point estimates and reliable interval estimates for different batteries under various operating conditions.

JOURNAL OF ENERGY STORAGE (2023)

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Article Chemistry, Applied

Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer

Wentao Ma et al.

Summary: This article introduces a novel LSTM model for state of charge (SOC) estimation of lithium-ion battery in the battery management system (BMS) of electric vehicles. The model utilizes MKMPE loss as the learning criterion to achieve robust SOC estimation under non-Gaussian noise contamination and employs a heap-based optimizer to optimize the hyperparameters. The proposed hybrid model, HBO-MKMPE-LSTM, demonstrates good robustness and accuracy in various cases.

JOURNAL OF ENERGY CHEMISTRY (2023)

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Article Electrochemistry

Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation

Soumya Singh et al.

Summary: In order to accurately forecast the lifetime and degradation mechanisms of lithium-ion batteries, this paper integrates the physics-based battery model and the machine learning model using the physics-informed neural networks (PINN) framework. The results show that PINN can predict the state of charge and state of health of lithium-ion cells with high accuracy, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, providing adequate predictions for unseen scenarios.

BATTERIES-BASEL (2023)

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Review Chemistry, Physical

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium-Ion Batteries: A Review

Shicong Ding et al.

Summary: Precisely predicting the aging of lithium-ion cells under different operating conditions is crucial for ensuring the performance of applications like electric vehicles and energy storage systems. Accurate battery-aging prediction models provide insights for material and battery research and overcome barriers in AI/ML for faster prediction. This study reviews deterioration mechanisms at the material and cell level, explores relationships between aging mechanisms, degradation modes, and external factors, and highlights advances in electrochemical models and machine learning for reliable remaining useful life prediction.

ADVANCED ENERGY MATERIALS (2023)

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Article Chemistry, Applied

Flexible path planning-based reconfiguration strategy for maximum capacity utilization of battery pack

Xinghua Liu et al.

Summary: Maximizing the utilization of lithium-ion battery capacity is crucial for alleviating range anxiety in electric vehicles. A capacity utilization scheme based on a path planning algorithm is proposed to address the issue of battery pack inconsistency and reduce safety hazards. By using alternating cell discharge and finding the optimal energy path, the proposed scheme improves battery pack consistency and decreases relay loss.

JOURNAL OF ENERGY CHEMISTRY (2023)

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Article Chemistry, Applied

Multi-time scale identification of key kinetic processes for lithium-ion batteries considering variable characteristic frequency

Haotian Shi et al.

Summary: An efficient adaptive multi-time scale identification strategy is proposed in this paper to achieve high-fidelity modeling of complex kinetic processes inside the battery. A second-order equivalent circuit model network is constructed, and two coupled sub-filters are developed to decouple the kinetic processes based on the time-scale information. The proposed method can reduce the dispersion of parameter identification results and pave the way for adaptive state estimators and efficient embedded applications.

JOURNAL OF ENERGY CHEMISTRY (2023)

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Review Energy & Fuels

Review on state of charge estimation techniques of lithium-ion batteries: A control-oriented approach

Nourallah Ghaeminezhad et al.

Summary: Energy storage is a critical issue in modern technology, and lithium-ion batteries have proven to be effective for energy storage. Monitoring the battery's state of charge is essential for optimizing its performance and lifespan. However, accurately estimating the state of charge is challenging due to the nonlinear behavior of batteries over their lifetime.

JOURNAL OF ENERGY STORAGE (2023)

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Article Energy & Fuels

Evaluating the heat generation characteristics of cylindrical lithium-ion battery considering the discharge rates and N/P ratio

Longxing Wu et al.

Summary: This paper establishes an electrochemical-thermal model (ETM) to evaluate the heat generation characteristics of cylindrical LIBs considering the discharge rates and N/P ratio. The model is validated based on experimental data under different ambient temperatures. The results show that the heat generation in the negative electrode is dominant and the reversible term has a significant influence on the total heat generation of LIBs.

JOURNAL OF ENERGY STORAGE (2023)

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Article Energy & Fuels

A self-correction single particle model of lithium-ion battery based on multi-population genetic algorithm

Guorong Zhu et al.

Summary: Currently, the Single Particle (SP) model and the Extended Single Particle (ESP) model are being heavily researched due to their potential usage in battery management systems. However, the models heavily rely on accurate model parameters, which are challenging to measure as they change with battery condition during use. This study proposes a Self-Correction Single Particle Model (SC-SPM) using the Multi-Population Genetic Algorithm (MPGA) to self-correct measured values based on terminal voltage changes, providing more accurate results for the solid phase diffusion process. Simulations verify that the SC-SPM is more accurate in observing solid phase diffusion when the battery condition changes beyond a certain range, and aids in better estimating other battery states for BMS. Additionally, the single step calculation time is less than 0.5 seconds, meeting the requirements for online application.

JOURNAL OF ENERGY STORAGE (2023)

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Article Engineering, Electrical & Electronic

Application of Nanogenerators in the Field of Acoustics

Xiaofei Yu et al.

Summary: The nanogenerator is a device that can convert extra mechanical energy from the environment or the human body into electrical energy effectively. With high flexibility, low power consumption, and the ability to achieve self-powered characteristics, nanogenerators have great potential in the fields of energy harvesting and acoustics. They can significantly improve the flexibility, portability, and efficiency of acoustic devices, and also function as microphones and speakers.

ACS APPLIED ELECTRONIC MATERIALS (2023)

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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 Energy & Fuels

Low-complexity state of charge and anode potential prediction for lithium-ion batteries using a simplified electrochemical model-based observer under variable load condition

Longxing Wu et al.

Summary: This paper proposes a low-complexity SOC and anode potential prediction method for LIBs using a simplified electrochemical model (SEM)-based observer under variable load condition. The effectiveness of the proposed method is assessed by combining data collected by experiment and COMOSL simulation, showing a good balance between accuracy and computation cost.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2022)

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Article Engineering, Mechanical

Physics-informed machine learning model for battery state of health prognostics using partial charging segments

Sara Kohtz et al.

Summary: In this study, an advanced physics-informed ML framework is proposed for the accurate estimation of battery state-of-health (SoH) in lithium-ion batteries (LIBs). The framework includes three steps: utilizing a physics-based finite element model to calculate the effects of aging on LIB capacity loss, fusing the model results with experimental data, and training a Gaussian Process Regression model to map voltage curves to specific parameters. The framework demonstrates a favorable performance in SoH estimation.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

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Review Automation & Control Systems

Towards Long Lifetime Battery: AI-Based Manufacturing and Management

Kailong Liu et al.

Summary: This paper critically reviews the state-of-the-art AI-based manufacturing and management strategies for achieving long battery lifetime. It showcases the advantages of AI-based battery manufacturing and smart batteries in improving battery health. The most commonly adopted AI solutions for battery life diagnostic, including state-of-health estimation and ageing prediction, are reviewed and their advantages and drawbacks are discussed. Efforts to design suitable AI solutions for enhancing battery longevity are also presented. The challenges and potential strategies in this field are suggested.

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2022)

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Review Electrochemistry

Electrochemical models: methods and applications for safer lithium-ion battery operation

Sankhadeep Sarkar et al.

Summary: This paper provides a detailed analysis of battery models from a modeling and application perspective, addressing the identification of internal cell degradation and abnormal behavior, the impact of model simplifications, the application of simplified models for safe and efficient battery operation, and the prospects and obstacles of using model-based algorithms.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2022)

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Review Chemistry, Applied

A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

Kai Luo et al.

Summary: This paper emphasizes the significance of predicting the state of charge and the state of health of lithium-ion batteries in the field of energy storage. The review highlights the utilization of machine learning and deep learning approaches to establish data-driven models for accurate and efficient battery performance prediction. The details, advantages, and limitations of these approaches are presented, compared, and summarized. Additionally, future challenges and opportunities are discussed.

JOURNAL OF ENERGY CHEMISTRY (2022)

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Article Chemistry, Physical

Battery state-of-charge estimation amid dynamic usage with physics-informed deep learning

Jinpeng Tian et al.

Summary: Accurate estimation of state of charge (SOC) is vital for the reliable operations of lithium-ion batteries. This study proposes a solution that incorporates domain knowledge into deep learning-based SOC estimation, resulting in improved accuracy. By decoupling voltage and current sequences, and fusing SOC estimation results from deep neural networks (DNNs) with short-term Ampere-hour predictions, the proposed method achieves significant reduction in estimation errors.

ENERGY STORAGE MATERIALS (2022)

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Article Engineering, Chemical

Physics-informed neural network approach for heat generation rate estimation of lithium-ion battery under various driving conditions

Hui Pang et al.

Journal of Energy Chemistry (2022)

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Article Engineering, Electrical & Electronic

Initial Rotor Position Estimation Method for Aircraft Three-Stage Starter/Generator Based on Self-Injected Harmonic Signal

Jiadan Wei et al.

IEEE Transactions on Transportation Electrification (2022)

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Article Engineering, Electrical & Electronic

Physics-Informed Recurrent Neural Network With Fractional-Order Gradients for State-of-Charge Estimation of Lithium-Ion Battery

Yanan Wang et al.

Summary: This paper introduces a neural network algorithm for battery management systems that uses fractional-order gradients to improve the estimation of the state of charge of lithium-ion batteries. The proposed physics-informed RNN algorithm achieves satisfactory SOC estimation accuracy under operational conditions and can be applied to other state estimation and fault prognosis for batteries.

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION (2022)

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Article Automation & Control Systems

State Estimation for Lithium-Ion Batteries With Phase Transition Materials Via Boundary Observers

Shumon Koga et al.

Summary: Lithium iron phosphate is a common active material in lithium-ion batteries, undergoing phase transitions during normal charge and discharge. A novel boundary observer using the backstepping method for PDEs is derived for estimating the concentration of lithium ions and moving boundary radius in phase transition materials. The gap between this observer and a complete SoC estimation algorithm for lithium-ion batteries with phase transition materials is discussed.

JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME (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 Chemistry, Physical

An optimal charging algorithm to minimise solid electrolyte interface layer in lithium-ion battery

M. S. S. Malik et al.

Summary: This article introduces a novel control algorithm for online optimal charging of lithium-ion batteries by explicitly incorporating degradation mechanisms, focusing on minimizing the growth of the solid electrolyte interface (SEI) layer. The algorithm uses a first-order layer growth rate model in a nonlinear model predictive control approach, with results compared to traditional constant current constant voltage (CCCV) methods. The proposed strategy also ensures that overpotential caused by lithium plating remains in a healthy regime.

JOURNAL OF POWER SOURCES (2021)

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Article Chemistry, Physical

Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement

Zhongbao Wei et al.

Summary: With advancements in technology, smart batteries and management systems have emerged as the new focus. Research is shifting towards smart batteries and smart cell management to address the limitations of traditional BMS. Emerging sensing techniques and system integration innovations present new opportunities and challenges for battery management.

JOURNAL OF POWER SOURCES (2021)

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Article Electrochemistry

Perspective-Combining Physics and Machine Learning to Predict Battery Lifetime

Muratahan Aykol et al.

Summary: This article discusses several architectures for integrating physics-based and machine learning models to improve the ability to forecast battery lifetime. The ease of implementation, advantages, limitations, and viability of each architecture are analyzed based on the latest technology in the battery and machine learning fields.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2021)

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Review Green & Sustainable Science & Technology

A review on various optical fibre sensing methods for batteries

Gaoce Han et al.

Summary: A high performance and easy-to-implement battery sensing system that can monitor the battery status in real time is critically needed. Optical fibre sensors have shown significant advantages in battery sensing due to their advanced capabilities such as fast measurement of multiple parameters with high sensitivity and robustness. Challenges and future research directions for battery sensing are also discussed.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)

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Article Green & Sustainable Science & Technology

Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends

Haifeng Dai et al.

Summary: This study reviews the development of battery management systems and introduces a multilayer design architecture for advanced battery management. Future trends in research and development focus on achieving better safety, performance, and interconnectivity in next-generation battery management.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)

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Article Engineering, Multidisciplinary

Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method

Rui Xiong et al.

Summary: This study proposed a multistage model fusion algorithm to co-estimate state of charge (SOC) and capacity of lithium-ion batteries in electric transports, achieving high accuracy, fast convergence speed, and anti-noise performance.

ENGINEERING (2021)

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Article Automation & Control Systems

Electrochemical-distributed thermal coupled model-based state of charge estimation for cylindrical lithium-ion batteries

Guangwei Chen et al.

Summary: This paper introduces a modified electrochemical-distributed thermal coupled model (MEDTM) for improving the accuracy of State of Charge (SoC) estimation. The model designs a robust H-infinity temperature observer and a SoC observer, which are validated through experiments and simulations, showing good performance in accurately estimating temperature and SoC.

CONTROL ENGINEERING PRACTICE (2021)

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Article Thermodynamics

Global Sensitivity Analysis on Temperature-Dependent Parameters of A Reduced-Order Electrochemical Model And Robust State-of-Charge Estimation at Different Temperatures

Guodong Fan et al.

Summary: This paper presents a robust estimator based on a reduced-order electrochemical model for accurate state-of-charge estimation of lithium ion batteries at different ambient temperatures. A global sensitivity analysis investigates the impact of temperatures on model performance, and a robust adaptive extended Kalman filter is designed to actively compensate potential model uncertainties. Results show the proposed algorithm's robustness against initial estimation errors, driving cycle inputs, and ambient temperatures, with extensive comparative analysis with a conventional extended Kalman filter.

ENERGY (2021)

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Article Automation & Control Systems

Model-Based Estimation of Lithium Concentrations and Temperature in Batteries Using Soft-Constrained Dual Unscented Kalman Filtering

Stefano Marelli et al.

Summary: This paper proposes an electrochemical model-based estimator for the concentration and temperature of a Li-ion cell, utilizing a dual unscented Kalman filter for accurate estimation of spatial distribution. Simulation analysis shows high accuracy in estimating state of charge, lithium concentration, and temperature.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2021)

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Article Energy & Fuels

A novel adaptive dual extended Kalman filtering algorithm for the Li-ion battery state of charge and state of health co-estimation

Wenhua Xu et al.

Summary: The capacity attenuation characteristics of Li-ion batteries were analyzed by aging experiment, and a novel adaptive dual extended Kalman filter algorithm was proposed to collaboratively estimate SOC and SOH, which was verified in experiments for feasibility and accuracy.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2021)

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Article Chemistry, Physical

A numerically efficient method of solving the full-order pseudo-2-dimensional (P2D) Li-ion cell model

Sangwoo Han et al.

Summary: An efficient method for solving the full-order pseudo-2-dimensional (P2D) Li-ion cell model is presented in this study, involving solving a subset of differential equations sequentially to minimize computational cost. Implemented using the finite volume method (FVM) in MATLAB, the proposed method accurately matches internal dynamics of the full-order P2D model implemented in COMSOL Multiphysics, potentially critical for incorporating degradation mechanisms and developing fast charging protocols.

JOURNAL OF POWER SOURCES (2021)

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Article Chemistry, Applied

Overcharge-to-thermal-runaway behavior and safety assessment of commercial lithium-ion cells with different cathode materials: A comparison study

Zhenpo Wang et al.

Summary: This paper investigated the overcharge behaviors and thermal runaway features of large format lithium-ion cells with different cathode materials. It was found that NCM cells have better performance and overcharge tolerance, while LFP cells exhibit less severe response to overcharging. A safety assessment score system was proposed based on these findings to evaluate the safety of the cells.

JOURNAL OF ENERGY CHEMISTRY (2021)

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Article Engineering, Multidisciplinary

Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

Ruixin Yang et al.

Summary: A novel thermal model was developed to capture the temperature behavior of lithium-ion batteries under external short circuit conditions, which showed higher computational efficiency, better fitting, and prediction accuracy compared to a traditional thermal model.

ENGINEERING (2021)

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Article Energy & Fuels

Investigation of the performance of direct forecasting strategy using machine learning in State-of-Charge prediction of Li-ion batteries exposed to dynamic loads

Adrienn Dineva et al.

Summary: This paper focuses on the prediction of terminal voltage and State-of-Charge (SoC) of Li-ion batteries under different dynamic loads, using a direct multi-step forecasting strategy combined with Machine Learning. A feature bank composed of discharge profiles obtained at different C-rates is used for model building, and special care is devoted to the design of appropriate training data for simulating intensive dynamic load scenarios. The results show that the application of direct multi-step forecasting strategy using XGBoost is an efficient alternative to predict the terminal voltage and SoC of Li-ion batteries under dynamic loads.

JOURNAL OF ENERGY STORAGE (2021)

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Article Electrochemistry

Improved parameter identification and state-of-charge estimation for lithium-ion battery with fixed memory recursive least squares and sigma-point Kalman filter

Changcheng Sun et al.

Summary: A novel online joint SOC estimation method combining the FMRLS method and SPKF algorithm is proposed in this paper to dynamically identify model parameters and estimate battery SOC, which has been verified to have a high SOC estimation precision, and the addition of hysteresis effect significantly improves SOC estimation accuracy.

ELECTROCHIMICA ACTA (2021)

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Review Energy & Fuels

Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles

Ingvild B. Espedal et al.

Summary: Energy storage systems (ESSs) are crucial for the future of electric vehicles, but their safety and management need improvement. State-of-charge (SoC) has become a significant research area for lithium-ion batteries (LIBs) in recent years, yet faces challenges in accurate modeling and assessment of internal environments.

ENERGIES (2021)

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Article Electrochemistry

A Comprehensive Physics-Based Equivalent-Circuit Model and State of Charge Estimation for Lithium-Ion Batteries

Hui Pang et al.

Summary: A physics-based equivalent-circuit model and state-of-charge estimation method were proposed for accurately assessing and predicting the performance of lithium-ion batteries, particularly excelling under high current working conditions.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2021)

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Article Energy & Fuels

An algorithm for state of charge estimation based on a single-particle model

Lichao Ren et al.

Summary: This study introduces an extended single-particle model (ESP) for more accurate estimation of the state of charge (SOC) of Li-ion batteries. A new closed-loop SOC estimation algorithm is proposed based on the combination of the ESP model and ampere-hour integration, which significantly reduces initial SOC errors without increasing computational complexity.

JOURNAL OF ENERGY STORAGE (2021)

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Article Computer Science, Artificial Intelligence

Physical-based training data collection approach for data-driven lithium-ion battery state-of-charge prediction

Jie Li et al.

Summary: This study focuses on optimizing the training dataset by using simple measurable data sets to improve prediction accuracy, reduce training time, and apply to online estimation. It was found that randomly generated data sets can be successfully applied to various dynamic battery operating conditions. The XGBoost method shows excellent performance for SOC prediction, with fast learning time, short running time, and accurate results.

ENERGY AND AI (2021)

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Review Chemistry, Multidisciplinary

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Xiang Chen et al.

Summary: This paper summarizes the application of machine learning in rechargeable batteries, including exploring new functional theory calculations and molecular dynamics simulations, as well as mining valuable information from experimental and theoretical datasets. This has led to the establishment of a structure-function correlation for predicting ionic conductivity and battery lifespan, along with advantages in strategy optimization.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2021)

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Article Computer Science, Artificial Intelligence

Recovering large-scale battery aging dataset with machine

Xiaopeng Tang et al.

Summary: By combining industrial data with accelerated aging tests, high-quality battery aging datasets can be recovered to improve the assessment of battery aging with up to 90% experimental time saved.

PATTERNS (2021)

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Article Energy & Fuels

Modeling strategy for progressive failure prediction in lithium-ion batteries under mechanical abuse

Hanfeng Yin et al.

Summary: This paper investigates the progressive failure behavior of battery components under mechanical abuse through experiments and simulations, utilizing detailed, intermediate, and fully homogenized models. The results provide insights into the mechanism of short circuit induced by mechanical abuse, as well as comparing the feasibility of the three models in predicting critical failure events.

ETRANSPORTATION (2021)

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Review Physics, Applied

Physics-informed machine learning

George Em Karniadakis et al.

Summary: Physics-informed learning seamlessly integrates data and mathematical models through neural networks or kernel-based regression networks for accurate inference of realistic and high-dimensional multiphysics problems. Challenges remain in incorporating noisy data seamlessly, complex mesh generation, and addressing high-dimensional problems.

NATURE REVIEWS PHYSICS (2021)

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Review Computer Science, Information Systems

Lithium-Ion Battery Pack Robust State of Charge Estimation, Cell Inconsistency, and Balancing: Review

Mina Naguib et al.

Summary: Lithium-ion battery packs are essential for electric vehicles, requiring an accurate and adaptable battery management system to monitor and control the large number of interconnected cells. The BMS functions include battery state estimation, cell balancing, thermal management, and fault diagnosis, with robust state of charge estimation crucial for providing accurate remaining range indications. This paper discusses various SOC estimation methods and the impact of cell inconsistency on pack performance and SOC estimation.

IEEE ACCESS (2021)

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Article Automation & Control Systems

Electrode-Level State Estimation in Lithium-Ion Batteries via Kalman Decomposition

Dong Zhang et al.

Summary: The paper presents a strategy to analyze the local observability of a single particle model (SPM) with both electrodes, and propose a state estimator based on the nonlinear SPM dynamics. The approach enables provably convergent estimates and proves estimation error system stability, exploiting the conservation of lithium property.

IEEE CONTROL SYSTEMS LETTERS (2021)

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Article Energy & Fuels

An adaptive sigma-point Kalman filter with state equality constraints for online state-of-charge estimation of a Li(NiMnCo)O2/Carbon battery using a reduced-order electrochemical model

Yalan Bi et al.

APPLIED ENERGY (2020)

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Article Energy & Fuels

Active equalization control strategy of Li-ion battery based on state of charge estimation of an electrochemical-thermal coupling model

Yuzhen Lin et al.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2020)

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Article Energy & Fuels

Lithium-ion battery SOC/SOH adaptive estimation via simplified single particle model

Zhaohui Cen et al.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2020)

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Article Chemistry, Physical

Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical-thermal-neural-network model

Fei Feng et al.

JOURNAL OF POWER SOURCES (2020)

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Article Electrochemistry

Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model

Boyang Liu et al.

ELECTROCHIMICA ACTA (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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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 Chemistry, Physical

Systematic parameter identification of a control-oriented electrochemical battery model and its application for state of charge estimation at various operating conditions

Guodong Fan

JOURNAL OF POWER SOURCES (2020)

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Article Chemistry, Physical

Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis

Matthieu Dubarry et al.

JOURNAL OF POWER SOURCES (2020)

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Article Computer Science, Information Systems

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

Lihua Liu et al.

IEEE ACCESS (2020)

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Article Chemistry, Physical

Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter

Weihan Li et al.

JOURNAL OF POWER SOURCES (2020)

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Article Computer Science, Information Systems

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

Carlos Vidal et al.

IEEE ACCESS (2020)

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Article Automation & Control Systems

State-of-Charge Estimation Using an EKF-Based Adaptive Observer

Sepideh Afshar et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2019)

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Article Automation & Control Systems

Identifiability and Parameter Estimation of the Single Particle Lithium-Ion Battery Model

Adrien M. Bizeray et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2019)

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Article Energy & Fuels

The improved open-circuit voltage characterization test using active polarization voltage reduction method

Jufeng Yang et al.

APPLIED ENERGY (2019)

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Article Electrochemistry

A physics-based distributed-parameter equivalent circuit model for lithium-ion batteries

Yang Li et al.

ELECTROCHIMICA ACTA (2019)

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New fast charging method of lithium-ion batteries based on a reduced order electrochemical model considering side reaction

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Parameter identification and systematic validation of an enhanced single-particle model with aging degradation physics for Li-ion batteries

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Internal strain and temperature discrimination with optical fiber hybrid sensors in Li-ion batteries

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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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An Interconnected Observer for Concurrent Estimation of Bulk and Surface Concentration in the Cathode and Anode of a Lithium-ion Battery

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A mechanism identification model based state-of-health diagnosis of lithium-ion batteries for energy storage applications

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Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles

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An adaptive physics-based reduced-order model of an aged lithium-ion cell, selected using an interacting multiple-model Kalman filter

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Estimation of State-of-Charge and State-of-Health for Lithium-Ion Degraded Battery Considering Side Reactions

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State of charge estimation based on a simplified electrochemical model for a single LiCoO2 battery and battery pack

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Reduced-Order Electrochemical Model-Based SOC Observer With Output Model Uncertainty Estimation

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Battery State Estimation for a Single Particle Model With Electrolyte Dynamics

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Observability Analysis and State Estimation of Lithium-Ion Batteries in the Presence of Sensor Biases

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Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical-Thermal Coupling

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On state-of-charge determination for lithium-ion batteries

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A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part I: Model development and observability analysis

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A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

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Evaluation of electrochemical models based battery state-of-charge estimation approaches for electric vehicles

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Battery state of health monitoring by estimation of the number of cyclable Li-ions

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State-of-Charge estimation from a thermal-electrochemical model of lithium-ion batteries

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A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter

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A method for SOC estimation based on simplified mechanistic model for LiFePO4 battery

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State-of-charge estimation in lithium-ion batteries: A particle filter approach

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A comparative study and review of different Kalman filters by applying an enhanced validation method

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State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model

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Electrochemical Model-Based State of Charge Estimation for Li-Ion Cells

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2015)

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Nonlinear Robust Observers for State-of-Charge Estimation of Lithium-Ion Cells Based on a Reduced Electrochemical Model

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Adaptive Estimation of the State of Charge for Lithium-Ion Batteries: Nonlinear Geometric Observer Approach

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2015)

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Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part II: Pseudo-two-dimensional model simplification and state of charge estimation

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Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

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Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter

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A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

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State of charge estimation for lithium-ion batteries: An adaptive approach

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CONTROL ENGINEERING PRACTICE (2014)

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The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2014)

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Adaptive Partial Differential Equation Observer for Battery State-of-Charge/State-of-Health Estimation Via an Electrochemical Model

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Enhancing the estimation accuracy in low state-of-charge area: A novel onboard battery model through surface state of charge determination

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Article Engineering, Electrical & Electronic

Reduced-Order Electrochemical Model Parameters Identification and State of Charge Estimation for Healthy and Aged Li-Ion Batteries-Part II: Aged Battery Model and State of Charge Estimation

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Reduced order model (ROM) of a pouch type lithium polymer battery based on electrochemical thermal principles for real time applications

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ELECTROCHIMICA ACTA (2013)

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Article Automation & Control Systems

Electrochemical Model Based Observer Design for a Lithium-Ion Battery

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2013)

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Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles

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A comparative study of equivalent circuit models for Li-ion batteries

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JOURNAL OF POWER SOURCES (2012)

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State of Charge and Loss of Active Material Estimation of a Lithium Ion Cell under Low Earth Orbit Condition Using Kalman Filtering Approaches

Saeed Khaleghi Rahimian et al.

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Comparison of single particle and equivalent circuit analog models for a lithium-ion cell

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Article Electrochemistry

Multi-Domain Modeling of Lithium-Ion Batteries Encompassing Multi-Physics in Varied Length Scales

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2011)

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Article Energy & Fuels

Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer

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Algorithms for Advanced Battery-Management Systems MODELING, ESTIMATION, AND CONTROL CHALLENGES FOR LITHIUM-ION BATTERIES

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IEEE CONTROL SYSTEMS MAGAZINE (2010)

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State of charge estimation using an unscented filter for high power lithium ion cells

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2010)

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Lithium-Ion Battery State of Charge and Critical Surface Charge Estimation Using an Electrochemical Model-Based Extended Kalman Filter

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Experiment-driven electrochemical modeling and systematic parameterization for a lithium-ion battery cell

Alexander P. schmidt et al.

JOURNAL OF POWER SOURCES (2010)

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Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries

Alexander P. Schmidt et al.

JOURNAL OF POWER SOURCES (2010)

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Article Energy & Fuels

Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries

Kong Soon Ng et al.

APPLIED ENERGY (2009)

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Article Chemistry, Physical

Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs - Part 2: Simultaneous state and parameter estimation

Gregory L. Plett

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Online estimation of the state of charge of a lithium ion cell

Shriram Santhanagopalan et al.

JOURNAL OF POWER SOURCES (2006)

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Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs - Part 3. State and parameter estimation

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Methods for state-of-charge determination and their applications

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Comparison between computer simulations and experimental data for high-rate discharges of plastic lithium-ion batteries

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