Multi-Mode Driving Control of a Parallel Hybrid Electric Vehicle Using Driving Pattern Recognition

Abstract

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.

Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle

“…Hence, the controller based on DP is effective only for the specific driving cycle that is used for the rule extraction. To remedy this problem, the stochastic dynamic programming (SDP) method [9][10][11] and the driving pattern detection within multiple driving cycles [12] had been suggested as the possible solutions. As a general case of the Euler-Lagrange equation, Pontryagin's minimum principle (PMP) was also introduced to obtain optimal control solution for hybrid vehicles [13][14][15][16], where the Hamiltonian is considered as an analytical function.…”

Intelligent HEV Fuzzy Logic Control Strategy Based on Identification and Prediction of Drive Cycle and Driving Trend

“…In foreign countries, Lee J, et al proposed a multi-mode driving controller of a parallel hybrid electric vehicle using driving pattern recognition [4]. Langari, et al proposed an intelligent energy management agent called IEMA for parallel hybrid vehicles which incorporates a driving situation component.…”

Energy Aware Battery Powered Electric Vehicles:A Predictive Model Driven Approach

“…Driving pattern detection based multimode vehicle control is discussed in [7].Road terrain preview based energy management is proposed in [8], where as multi feature statistical approach for distinguishing driving patterns are discussed in [9]. Darnelli proposed a modified SOC tracking considering the driving conditions based on cartographic data [10] In this paper we propose an energy aware battery management system which estimates the SOC of the battery online using an online EKF algorithm.…”