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.

SUMMARYThe design procedure for an adaptive power management control strategy, based on a driving pattern recognition algorithm is proposed. The design goal of the control strategy is to minimize fuel consumption and engine-out NOx and PM emissions on a set of diversified driving schedules. Six representative driving patterns (RDP) are designed to represent different driving scenarios. For each RDP, the Dynamic Programming (DP) technique is used to find the global optimal control actions. Implementable, sub-optimal control algorithms are then extracted by analyzing the behavior of the DP control actions. A driving pattern recognition (DPR) algorithm is subsequently developed and used to classify the current driving pattern into one of the RDPs; thus, the most appropriate control algorithm is selected adaptively. This "multi-mode" control scheme was tested on several driving cycles and was found to work satisfactorily.

The adaptive multi-mode control strategy (AMMCS) is defined as the control strategy that switches control parameters for the purpose of adjusting vehicles to diverse traffic conditions and driver’s habits. This strategy is composed of off-line and on-line procedures. In the off-line procedure, several sets of control parameters are optimized under representative driving patterns (RDP). In the on-line procedure, the control parameter switching or interpolation is periodically activated based on the driving pattern recognition (DPR) algorithm, assuming that the driving pattern during the future control horizon doesn’t change significantly compared to the past pattern. The AMMCS is conceptually similar to one of predictive control theories, namely the receding horizon control which is also known as model predictive control. The AMMCS is expected to be applied well to hybrid electric vehicle (HEV) system which is very sensitive to driving patterns. Furthermore, the AMMCS can be combined with the two conventional control strategies using global and local optimization techniques to improve performances further. The design goal of the AMMCS is to minimize fuel consumption and NOx for a pre-transmission single shaft parallel HEV.

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