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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

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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

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