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

External short circuit(ESC)of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles.In this study,a novel thermal modelis developed to capture the temperature behavior of batteries under ESC conditions.Experiments were systematically performed under different battery initial state of charge and ambient temperatures.Based 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 ELMs.To 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 cells.It 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.

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

The safety of lithium-ion batteries in electric vehicles(EVs)is attracting more attention.To 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 runaway.This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter(EKF)for on-board applications in EVs.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 voltages.The 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 severity.Soft 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 values.The experimental data are acquired to validate the proposed soft SC fault diagnosis method.The results show that the proposed method is effective and robust in quickly detecting a soft SC fault and accurately estimating soft SC resistance.