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Chinese Journal of Mechanical Engineering 2024年第2期37卷 330-339页

作者： Rui Xiong Zian Zhao Cheng Chen Xinggang Li weixiang shen School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVIC 3122Australia

Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’***,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery *** study proposes an electrothermal model-based method to accurately predict battery ***,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery ***,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal ***,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

关键词： Electric vehicles Lithium-ion batteries Remaining charging time Electrothermal model

Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

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Journal of Energy Chemistry 2024年第3期90卷 120-132,I0005页

作者： Ziyou Zhou Yonggang Liu Chengming Zhang weixiang shen Rui Xiong State Key Laboratory of Mechanical Transmission for Advanced Equipment&College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400000China Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of Electrical Engineering Harbin Institute of TechnologyHarbin 150000HeilongjiangChina School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion *** main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation *** addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging *** incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV *** analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional *** validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 *** this level of precision for OCV estimation requires only around 50 s collection of partial charging *** validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed *** cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV *** method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

关键词： Lithium-ion battery Open-circuit voltage Health diagnosis Deep learning

Managing the surge:A comprehensive review of the entire disposal framework for retired lithium-ion batteries from electric vehicles

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Journal of Energy Chemistry 2024年第5期92卷 648-680页

作者： Ruohan Guo Feng Wang M.Akbar Rhamdhani Yiming Xu weixiang shen Department of Engineering Technologies School of ScienceComputing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia Department of Chemistry and Biotechnology School of ScienceComputing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia Department Mechanical and Product Design Engineering School of EngineeringSwinburne University of TechnologyMelbourne VIC 3122Australia

Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has *** paper seeks to offer a comprehensive overview of the entire disposal framework for R-LIBs,encompassing a broad spectrum of activities,including screening,repurposing and ***,we delve deeply into a thorough examination of current screening technologies,shifting the focus from a mere enumeration of screening methods to the exploration of the strategies for enhancing screening ***,we outline battery repurposing with associated key factors,summarizing stationary applications and sizing methods for R-LIBs in their second life.A particular light is shed on available reconditioning solutions,demonstrating their great potential in facilitating battery safety and lifetime in repurposing scenarios and identifying their techno-economic *** the realm of battery recycling,we present an extensive survey of pre-treatment options and subsequent material recovery ***,we introduce several global leading recyclers to illustrate their industrial processes and technical ***,relevant challenges and evolving trends are investigated in pursuit of a sustainable end-of-life management and disposal *** hope that this study can serve as a valuable resource for researchers,industry professionals and policymakers in this field,ultimately facilitating the adoption of proper disposal practices.

关键词： Lithium-ion battery Battery reproposing and recycling Miaieiials recovery technologies Techno-economic issues End-of-life management Disposal framework

On-board Diagnosis of Soft Short Circuit Fault in Lithium-ion Battery Packs for Electric Vehicles Using an Extended Kalman Filter

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CSEE Journal of Power and Energy Systems 2022年第1期8卷 258-270页

作者： Ruixin Yang Rui Xiong weixiang shen National Engineering Laboratory for Electric Vehicles Department of Vehicle EngineeringSchool of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Faculty of Science Engineering and TechnologySwinburne University of TechnologyMelbourneVic.3122Australia

The safety of lithium-ion batteries in electric vehicles(EVs)is attracting more *** ensure battery safety,early detection is necessary of a soft short circuit(SC)which may evolve into severe SC faults,leading to fire or thermal *** paper proposes a soft SC fault diagnosis method based on the extended Kalman filter(EKF)for on-board applications in *** the proposed method,the EKF is used to estimate the state of charge(SOC)of the faulty cell by adjusting a gain matrix based on real-time measured *** SOC difference between the estimated SOC and the calculated SOC through coulomb counting for the faulty cell is employed to detect soft SC faults,and the soft SC resistance values are further identified to indicate the degree of fault *** SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance *** experimental data are acquired to validate the proposed soft SC fault diagnosis *** results show that the proposed method is effective and robust in quickly detecting a soft SC fault and accurately estimating soft SC resistance.

关键词： Battery safety electric vehicles external short circuit fault diagnosis internal short circuit soft short circuit

A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries

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Chinese Journal of Mechanical Engineering 2020年第4期33卷 98-112页

作者： Jinpeng Tian Rui Xiong weixiang shen Ju Wang Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Faculty of Science Engineering and TechnologySwinburne University of TechnologyHawthornVictoria 3122Australia

State of charge(SOC)estimation for lithium ion batteries plays a critical role in battery management systems for electric *** fractional order models(FOMs)which come from frequency-domain modelling have provided a distinct insight into SOC *** this article,we compare five state-of-the-art FOMs in terms of SOC *** this end,firstly,characterisation tests on lithium ion batteries are conducted,and the experimental results are used to identify FOM *** identification results show that increasing the complexity of FOMs cannot always improve *** model R(RQ)W shows superior identification accuracy than the other four ***,the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles,memory lengths,ambient temperatures,cells and voltage/current *** evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of *** more complex models can have better robustness against temperature variation,R(RQ),the simplest FOM,can overall provide satisfactory *** results on different cells demonstrate the generalisation ability of FOMs,and R(RQ)outperforms other ***,R(RQ)shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.

关键词： Electric vehicle Lithium ion battery Fractional order model State of charge

Model Predictive Control Based Real-time Energy Management for Hybrid Energy Storage System

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CSEE Journal of Power and Energy Systems 2021年第4期7卷 862-874页

作者： Huan Chen Rui Xiong Cheng Lin weixiang shen National Engineering Laboratory for Electric Vehicles Department of Vehicle EngineeringSchool of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Faculty of Science Engineering and TechnologySwinburne University of TechnologyMelbourneVic3122Australia

An accurate driving cycle prediction is a vital function of an onboard energy management strategy(EMS)for a battery/ultracapacitor hybrid energy storage system(HESS)in electric *** this paper,we address the requirements to achieve better EMS performances for a ***,a long short-term niemory・based method is proposed to predict driving cycles under the framework of a model predictive control(MPC)***,the performances of three EMSs based on fuzzy logic,MPC,and dynamic programming are systematically evaluated and *** online implementation,the MPC-based EMS can alleviate the stress on the battery in the HESS and significantly reduce energy dissipation by up to 15.3% in comparison with the fuzzy logic-based ***,the impact of battery aging on EMS performances is explored;it indicates that battery aging consciousness can slightly extend battery ***,a hardware-in-the-loop test platform is established to verify the effectiveness of the MPC-based EMS for the power allocation of a HESS in electric vehicles.

关键词： Energy management hybrid energy storage system long short-term memory model predictive control battery aging consciousness

Fractional order battery modelling methodologies for electric vehicle applications:Recent advances and perspectives

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Science China(Technological Sciences) 2020年第11期63卷 2211-2230页

作者： TIAN JinPeng XIONG Rui shen weixiang SUN FengChun Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Faculty of Science Engineering and TechnologySwinburne University of TechnologyHawthornVictoria 3122Australia

Accurate modelling of lithium ion batteries is crucial for battery management in electric *** studies have revealed the fractional order nature of lithium ion batteries,leading to fractional order modelling *** this paper,a comprehensive review of the fractional order battery models and their applications in battery management of electric vehicles is provided from the perspectives of frequency and time *** the frequency domain,the fractional order models to fit electrochemical impedance spectroscopy data are investigated,followed by their applications in health diagnosis,battery heating and charging *** the time domain,the fractional order models adopted for voltage simulation are discussed,followed by their applications in battery state estimation and fault ***,from the perspectives of time domain and frequency domain applications,critical challenges and research trends for future work in terms of fractional order modelling are highlighted to advance the development of next-generation battery management.

关键词： lithium ion battery fractional calculus fractional order model battery management electric vehicle electrochemical impedance spectroscopy

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

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Engineering 2021年第3期7卷 395-405页

作者： Ruixin Yang Rui Xiong weixiang shen Xinfan Lin National Enginering Laboratory for Electric Vehiles school of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Faculty of Science Engineerng and TexchnologySwinburne Lniversity of TechnologyHawthornVIC 3122Australia Department of Mecharical and Arospace Engineering University of CaliforniaDavisCA 95616LSA

External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric *** this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC *** were systematically performed under different battery initial state of charge and ambient *** on the experimental results,we employed an extreme learming machine(ELM)-based thermal(ELMT)model to depict battery temperature behavior under ESC,where a lumped-state thermal model was used to replace the activation function of conventional *** demonstrate the effectiveness of the proposed model,wecompared the ELMT model with a multi-lumped-state thermal(MLT)model parameterized by thegenetic algorithm using the experimental data from various sets of battery *** is shown that the ELMT model can achieve higher computa-tional efficiency than the MLT model and better fitting and prediction accuracy,where the average root mean squared error(RMSE)of the fitting is 0.65℃ for the ELMT model and 3.95℃ for the MLT model,and the RMES of the prediction under new data set is 3.97℃ for the ELMT model and 6.11℃ for the MLT model.

关键词： Electric vehicles Battery safety External short circuit Temperature prediction Extreme learning machine

Semi-supervised estimation of capacity degradation for lithium ion batteries with electrochemical impedance spectroscopy

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Journal of Energy Chemistry 2023年第1期76卷 404-413,I0010页

作者： Rui Xiong Jinpeng Tian weixiang shen Jiahuan Lu Fengchun Sun Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management ***,they are generally developed in a supervised manner which requires a considerable number of input features and corresponding capacities,leading to prohibitive costs and efforts for data *** response to this issue,this study proposes a convolutional neural network(CNN)based method to perform end-to-end capacity estimation by taking only raw impedance spectra as *** importantly,an input reconstruction module is devised to effectively exploit impedance spectra without corresponding capacities in the training process,thereby significantly alleviating the cost of collecting training *** large battery degradation datasets encompassing over 4700 impedance spectra are developed to validate the proposed *** results show that accurate capacity estimation can be achieved when substantial training samples with measured capacities are ***,the estimation performance of supervised machine learning algorithms sharply deteriorates when fewer samples with measured capacities are *** this case,the proposed method outperforms supervised benchmarks and can reduce the root mean square error by up to 50.66%.A further validation under different current rates and states of charge confirms the effectiveness of the proposed *** method provides a flexible approach to take advantage of unlabelled samples for developing data-driven models and is promising to be generalised to other battery management tasks.

关键词： Lithium-ion battery Capacity degradation Electrochemical impedance spectroscopy Deep learning

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

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Engineering 2021年第10期7卷 1469-1482页

作者： Rui Xiong Ju Wang weixiang shen Jinpeng Tian Hao Mu Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Faculty of Science Engineering and TechnologySwinburne University of TechnologyHawthornVIC 3122Australia

Lithium-ion batteries(LIBs)have emerged as the preferred energy storage systems for various types of electric transports,including electric vehicles,electric boats,electric trains,and electric *** energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge(SOC)and capacity in *** study proposes a multistage model fusion algorithm to co-estimate SOC and ***,based on the assumption of a normal distribution,the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable ***,a differential error gain with forward-looking ability is introduced into a proportional–integral observer(PIO)to accelerate convergence ***,a fusion algorithm is developed by combining a multistage model and proportional–integral–differential observer(PIDO)to co-estimate SOC and capacity under a complex application ***,the convergence and anti-noise performance of the fusion algorithm are ***,the hardware-in-the-loop platform is set up to verify the performance of the fusion *** validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2%and 3.3%,respectively.

关键词： State of charge Capacity estimation Model fusion Proportional-integral-differential observer Hardware-in-the-loop