Abstract
The battery system is one of the core technologies of the new energy electric vehicle, so the frequent occurrence of safety accidents seriously limits the large-scale promotion and application. An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to estimate the current system status, which improves the accuracy and timeliness of fault identification. It is feasible in the application of electric vehicles. To ensure the effectiveness of the signal, the proposed method is adopted using the simple mean filter to clean the data with eliminate wrong points. After the variance analysis, covariance, a horizontal variance of the filtered data, a modified feature parameters matrix is presented. The dimension is reduced by principal component analysis to improve the engineering application ability. Furthermore, a comprehensive GA-ELM-based identification method is proposed to reduce the resulting identification error of extreme learning machines due to the initial value change. More importantly, the sensitivity and accuracy of different solutions are compared and verified, which shows the technique has great potential in battery fault diagnosis based on the voltage signal.
Highlights
With the improvement of human living environment requirements, countries worldwide have paid great attention to the deterioration of the global environment and climate warming
The zero-emission electric vehicle industry has become an essential field of competition and development among countries, and battery-driven electric vehicles and hybrid electric vehicles have entered a period of rapid evolution [1,2,3,4]
The particle filter (PF) is used to identify parameters, and the terminal voltage is measured in real-time to provide fault diagnosis for a lithium battery
Summary
With the improvement of human living environment requirements, countries worldwide have paid great attention to the deterioration of the global environment and climate warming. Liu et al [37] proposed an integrated learning method for battery fault diagnosis based on Ruboost and reformulated three indicators to characterize essential electrode qualities Their test was carried out using capacity information from lithium titanate and lithium iron phosphate batteries. Yu et al [38] proposed using the ammeter method to correct SOC and compared and studied the main features of various open-circuit voltage models in an application, which provided a reference for the direction of fault diagnosis of lithium batteries based on voltage method. Sbarufatti et al [41] proposed an adaptive fault diagnosis model based on radial basis function neural network (RBFNN) In this method, the particle filter (PF) is used to identify parameters, and the terminal voltage is measured in real-time to provide fault diagnosis for a lithium battery. The sweep frequency range is 10 Hz~55 Hz, the ambient temperature is 298 K, and the acceleration is 10 g
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
