Abstract
Since driving cycle greatly affects load power demand, driving cycle identification (DCI) is proposed to predict power demand that can be expected to prepare for the power distribution between battery and supercapacitor. The DCI is developed based on a learning vector quantization (LVQ) neural network method, which is assessed in both training and validation based on the statistical data obtained from six standard driving cycles. In order to ensure network accuracy, characteristic parameter and slide time window, which are two important factors ensuring the network accuracy for onboard hybrid energy storage system (HESS) applications in electric vehicles, are discussed and designed. Based on the identification results, Multi-level Haar wavelet transform (Haar-WT) is proposed for allocating the high frequency components of power demand into the supercapacitor which could damage battery lifetime and the corresponding low frequency components into the battery system. The proposed energy management system can better increase system efficiency and battery lifetime compared with the conventional sole frequency control. The advantages are demonstrated based on a randomly generated driving cycle from the standard driving cycle library via simulation.
Highlights
Electric vehicles are considered as one of the most promising transportation tools for addressing issues faced by automotive industry worldwide on energy and environment [1,2,3,4]
Technologies employed for all kinds of electric vehicles are various, but their performances are largely dependent on the characteristics of adopted energy storage system (ESS) [5,6]
The learning vector quantization (LVQ) neural network method has been widely used in the control field for prediction and identification because of its effectiveness and high accuracy [40,41,42], and it is proposed to identify the types of driving cycles in this work
Summary
Electric vehicles are considered as one of the most promising transportation tools for addressing issues faced by automotive industry worldwide on energy and environment [1,2,3,4]. Technologies employed for all kinds of electric vehicles are various, but their performances are largely dependent on the characteristics of adopted energy storage system (ESS) [5,6]. Of all the ESSs, batteries are one of the most widely used energy sources for electric vehicles, which has been an emerging area for ensuring a successful application of electric vehicles. Batteries alone as a power source have faced some challenges for practical engineering applications, such as higher energy efficiency, smaller voltage drops, larger vehicle acceleration or deceleration rates, better uphill climbing performance. A supercapacitor can be added onboard to form a hybrid energy storage system (HESS) such that the battery and the supercapacitor each play a complementary role based on their individual dynamic characteristics [9,10]. A combination of these two types of ESSs will yield an equivalent
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.
