Multi-mode driving control of a parallel hybrid electric vehicle using driving pattern recognition

作     者：Jeon, SI Jo, ST Park, YI Lee, JM 

作者机构：Seoul Natl Univ Sch Mech & Aerosp Engn Kwanak Ku Seoul 151742 South Korea Seoul Natl Polytech Univ Dept Mech Design Prod Engn Nowon Ku Seoul 139743 South Korea 

出 版 物：《JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME》 (美国机械工程师学会汇刊: 动力系统、测量与控制杂志)

年 卷 期：2002年第124卷第1期

页      面：141-149页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0804[工学-仪器科学与技术] 0811[工学-控制科学与工程] 

主　　题：AUTOMOBILE driving ELECTRIC vehicles ENERGY consumption 

摘      要：Vehicle performance such as fuel consumption and catalyst-out emissions is affected by a driving pattern, which is defined as a driving cycle with grades in this study. To optimize the vehicle performances on a temporary, driving pattern, we developed a multi-mode driving control algorithm using driving pattern recognition and applied it to a parallel hybrid electric vehicle (parallel HEV). The multi-mode driving control is defined as the control strategy which switches a current driving control algorithm to the algorithm optimized in a recognized driving pattern. For this purpose, first, we selected six representative driving patterns, which are composed of three urban driving patterns, one expressway driving pattern, and two suburban driving patterns. A total of 24 parameters such as average cycle velocity, positive acceleration kinetic energy, stop time/total time, average acceleration, and average grade are chosen to characterize the driving patterns. Second, in each representative driving pattern, control parameters of a parallel HEV are optimized by Taguchi method though the fuel-consumption and emissions simulations. And these results are compared with those by parametric study. There are seven control parameters, six of them are weighting factors of performance measures for deciding the ratio of engine power to required power from driving load. And the other is the charging/discharging method of battery. Finally, in driving, a neural network (the Hamming network) decides periodically which representative driving pattern is closest to a current driving pattern by comparing the correlation related to 24 characteristic parameters. And then the current driving control algorithm is switched to the optimal one, assuming the driving pattern does not change in the next period.