Chun Chang

Due to the frequent occurrence of electric vehicles safety accidents caused by battery system failures, in order to ensure the normal operation of the vehicle, it is crucial to do a fault diagnosis of the electric vehicle lithium battery. This paper presents a fault diagnosis method for lithium batteries based on optimal variational modal decomposition and dimensionless feature parameters for identifying faulty batteries. The method first preprocesses the voltage signal of a lithium battery by optimal variable mode decomposition to obtain the high- and low-frequency components of the signal and reconstructs the high- and low-frequency components. Then, the dimensionless feature parameters are extracted according to the reconstructed signal, and feature reduction of the dimensionless feature parameters is carried out by a locally linear embedding algorithm. Finally, a local outlier factor algorithm is used to detect faulty batteries. After verified by the operation data before the real electric vehicle's thermal runaway failure, this method can detect the faulty battery timely and accurately.

In order to ensure the driving safety of electric vehicles and avoid potential failures, it is important to properly estimate the state of health (SOH) of lithium-ion batteries. In this paper, a method of lithium-ion battery SOH estimation based on electrochemical impedance spectroscopy (EIS) and an algorithm fused by Elman neural network and cuckoo search (CS-Elman) is proposed. First, by extracting 19 features of EIS and using principal component analysis to reduce dimension, we obtain four principal components as model inputs. Second, CS algorithm optimizes the weights and thresholds of Elman algorithm. Next, we use the CS-Elman model to estimate the battery SOH and verify the model with the remaining battery data. In addition, we propose a variable temperature estimation model and verify the feasibility of the model between 25 °C and 45 °C. Finally, the experimental results show that the mean absolute error of the method is less than 1.36%.

Accurate estimation of the state of health (SOH) is an important guarantee for safe and reliable battery operation. In this paper, an online method based on indirect health features (IHFs) and sparrow search algorithm fused with deep extreme learning machine (SSA-DELM) of lithium-ion batteries is proposed to estimate SOH. First, the temperature and voltage curves in the battery discharge data are acquired, and the optimal intervals are obtained by ergodic method. Discharge temperature difference at equal time intervals (DTD-ETI) and discharge time interval with equal voltage difference (DTI-EVD) are extracted as IHF. Then, the input weights and hidden layer thresholds of the DELM algorithm are optimized using SSA, and the SSA-DELM model is applied to the estimation of battery's SOH. Finally, the established model is experimentally validated using the battery data, and the results show that the method has high prediction accuracy, strong algorithmic stability, and good adaptability.