☆ 4.7 Review

A comprehensive overview and comparison of parameter benchmark methods for lithium-ion battery application

JOURNAL OF ENERGY STORAGE (2023)

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Joint estimation of state-of-charge and state-of-health for all cells in the battery pack using leader-followerstrategy

Xiaopeng Tang et al.

Summary: This article proposes a low-computational leader-follower framework for estimating the state-of-charge (SoC) and state-of-health (SoH) of battery packs. The framework uses an enhanced algorithm to handle a selected battery (leader) and updates the states of the other batteries (followers) with lightweight calibrators. Battery-in-the-loop experiments show that the proposed method can achieve accurate estimations with significantly reduced computational time.

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Summary: This paper utilizes feature-based machine learning techniques to predict the capacity fade of large format LiFePO4 batteries in EV applications. Through noise reduction, feature engineering, and stacking ensemble learning, accurate estimations and predictions of battery capacity and remaining useful lifetime are achieved based on field data collection.

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Article Energy & Fuels

Strategically switching metaheuristics for effective parameter estimation of electrochemical lithium-ion battery models

Joonhee Kim et al.

Summary: Electrochemical lithium-ion battery models have attracted significant research attention as an alternative experimental approach for understanding the internal mechanisms and properties of lithium-ion batteries. This study proposes a systematic guide for strategically switching to an appropriate metaheuristic algorithm for accurate parameter estimation. By learning and recommending suitable algorithms using a convolutional neural network, the proposed method significantly reduces the difference between experimental and simulated voltage responses.

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Article Energy & Fuels

Fault data generation of lithium ion batteries based on digital twin: A case for internal short circuit

Zhuchen Yuan et al.

Summary: This paper investigates the validity of using digital twin technique to generate battery failure data for training online early warning algorithms. The authors build a reliable thermal-electrical coupled battery model to form the digital twin and discuss influential factors on the performance of the early warning algorithm. They also discover a temperature difference between the failure point and the measurement location, which hinders timely fault detection and alarm. Additionally, the paper provides guidance on developing fault diagnosis algorithms with the help of digital twin.

JOURNAL OF ENERGY STORAGE (2023)

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Article Engineering, Mechanical

Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Xinghao Du et al.

Summary: This paper proposes a co-estimation framework utilizing the advantages of both recursive least squares (RLS) and recursive total least squares (RTLS) for a higher parameter identification performance of the battery equivalent circuit model (ECM). RLS quickly converges by updating the parameters along the gradient of the cost function, while RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results show that the proposed method has good accuracy, a fast convergence rate, and robustness against noise corruption.

CHINESE JOURNAL OF MECHANICAL ENGINEERING (2023)

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Parametrization of physics-based battery models from input-output data: A review of methodology and current research

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Summary: Physics-based battery models play a crucial role in battery research, development, and control. Accurate parametrization is essential for obtaining useful information from the models. Parametrization of physics-based battery models from input-output data is a growing research area. Successful parametrization requires knowledge of the underlying physical system and understanding of parameter estimation theory. This paper reviews the key aspects of parametrization in this field.

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Article Energy & Fuels

Parameter identification of lithium-ion battery pseudo-2-dimensional models using genetic algorithm and neural network cooperative optimization

Jungsoo Kim et al.

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Article Chemistry, Physical

Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence

Weihan Li et al.

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Electrochemical impedance characteristics at various conditions for commercial solid-liquid electrolyte lithium-ion batteries: Part 1. experiment investigation and regression analysis

Fei Feng et al.

Summary: Solid-liquid electrolyte lithium-ion batteries (SLELBs) have good commercial viability in electric vehicle applications due to their combination of safety and high ionic conductivity. Key battery management algorithms are essential for the safe operation of electric vehicles, and techniques like electrochemical impedance spectroscopy (EIS) can be used to study battery behavior and improve understanding of electrode processes.

ENERGY (2022)

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Article Thermodynamics

Electrochemical impedance characteristics at various conditions for commercial solid-liquid electrolyte lithium-ion batteries: Part. 2. Modeling and prediction

Fei Feng et al.

Summary: Solid-liquid electrolyte lithium-ion batteries (SLELBs) have commercial viability in electric vehicle applications. It is important to accurately understand battery behavior under different factors and build simulation models. Electrochemical impedance spectroscopy (EIS) can be used to study electrode processes and ion transport mechanisms in lithium-ion batteries. In this series of papers, regression models and an orthogonal experiment design are proposed to predict SLELB impedance under temperature and SOC factors. An equivalent circuit model based on the electrode process is established and simplified to obtain the simplified equivalent circuit model (SECM). The experimental results show that the SECM has accurate prediction performance and reduces the number of required experiments.

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An Information Appraisal Procedure: Endows Reliable Online Parameter Identification to Lithium-Ion Battery Model

Xinghao Du et al.

Summary: This article proposes a reliable online parameter identification method for battery ECM, which utilizes a well-designed information appraisal procedure based on the Fisher-information-based Cramer-Rao lower bound (CRLB). The method achieves online parameter updating through a comprehensive appraisal indicator derived recursively from CRLB. The simulation and experimental results confirm the effectiveness of the proposed method.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2022)

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Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling

Huzaifa Rauf et al.

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Summary: Lithium-ion batteries play a crucial role in advancing electromobility, but their sustainability and lifecycle pose challenges that require further research and collaboration. This article presents the pressing challenges of EV LIBs throughout their lifecycle and discusses market instruments and government interventions. It also highlights future research challenges and the need for successful circular economy strategies in battery supply and recycling processes.

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A novel method of parameter identification and state of charge estimation for lithium-ion battery energy storage system

Zuolu Wang et al.

Summary: This study proposes a parameter identification method based on dynamic voltage responses and a hybrid SOC estimation method to improve the accuracy and reliability of SOC estimation. The results show that these methods jointly provide more accurate SOC estimation.

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Aging effect on the variation of Li-ion battery resistance as function of temperature and state of charge

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Enabling high-fidelity electrochemical P2D modeling of lithium-ion batteries via fast and non-destructive parameter identification

Le Xu et al.

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Toward more realistic microgrid optimization: Experiment and high-efficient model of Li-ion battery degradation under dynamic conditions

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Batteries: From China's 13th to 14th Five-Year Plan

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Sensorless Temperature Estimation of Lithium-Ion Battery Based on Broadband Impedance Measurements

Xinghao Du et al.

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IEEE TRANSACTIONS ON POWER ELECTRONICS (2022)

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A vehicle-cloud collaborative method for multi-type fault diagnosis of lithium-ion batteries

Chaojie Xu et al.

Summary: This paper presents a vehicle-cloud collaborative method for multi-type fault diagnosis of lithium-ion batteries. It establishes a simulation model based on experiments and machine learning, extracts feature differences using the cell difference model, and achieves accurate identification and distinction of battery faults.

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Article Energy & Fuels

Lithium battery state-of-health estimation and remaining useful lifetime prediction based on non-parametric aging model and particle filter algorithm

Xiaoyu Li et al.

Summary: In this paper, a data-fusion method is proposed to forecast battery health conditions. The method utilizes Gaussian process regression to establish state and observation equations, and applies a particle filter algorithm for short-term and long-term health estimation and prediction. Experimental results demonstrate the accurate and robust forecasting capability of the proposed method.

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New hybrid scheme with local battery energy storages and electric vehicles for the power frequency service

Qingqing Yang et al.

Summary: This paper presents a new hybrid scheme using EV and local batteries to actively participate in grid services, improving optimal power scheduling and battery aging control in a microgrid environment.

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Article Automation & Control Systems

Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization

Zhongbao Wei et al.

Summary: Accurate estimation of state of charge (SOC) in battery systems is crucial for safe and efficient utilization. This article examines the impact of measurement noises on model parameter identification and SOC estimation, proposing a novel parameterization method to address noise-induced biases and improving immunity to noise corruption.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

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Adaptive model parameter identification for lithium-ion batteries based on improved coupling hybrid adaptive particle swarm optimization- simulated annealing method

Sida Zhou et al.

Summary: This article introduces a coupled hybrid adaptive particle swarm optimization-hybrid simulated annealing algorithm for precise parameter identification, which is validated on three different types of batteries and shows excellent consistency between simulation results and experimental data.

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

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

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CPSO-Based Parameter-Identification Method for the Fractional-Order Modeling of Lithium-Ion Batteries

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IEEE TRANSACTIONS ON POWER ELECTRONICS (2021)

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Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures

Minhwan Seo et al.

Summary: This study proposes an innovative lumped-battery model to improve the accuracy of SOC estimation and battery modeling without pre-experiments. The model parameters are estimated using recursive least squares, and SOC is estimated in real-time using the extended Kalman filter. Experiments show that the proposed method achieves high accuracy in various temperature conditions.

ENERGY (2021)

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Research directions for next-generation battery management solutions in automotive applications

Xiaosong Hu et al.

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Optimal parameter identification of linear and non-linear models for Li-Ion Battery Cells

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ENERGY REPORTS (2021)

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Review Energy & Fuels

Battery energy-storage system: A review of technologies, optimization objectives, constraints, approaches, and outstanding issues

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A comprehensive equivalent circuit model for lithium-ion batteries, incorporating the effects of state of health, state of charge, and temperature on model parameters

Manh-Kien Tran et al.

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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.

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An improved single-particle model with electrolyte dynamics for high current applications of lithium-ion cells

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Global Parameter Sensitivity Analysis of Electrochemical Model for Lithium-Ion Batteries Considering Aging

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