تولید گازهای گلخانه ای و تجارت کردن اقتصاد سوخت برای خودروهای هیبریدی با استفاده از منطق فازی

In this paper, a generalized fuzzy logic controller (FLC) is used to optimize the fuel economy and reduce the emissions of hybrid vehicles with parallel configuration. Using the driver input, the state of charge (SOC) of the energy storage, the motor/generator speed, the current gear ratio and vehicle speed, a set of 44 rules have been developed, in a fuzzy controller, to effectively determine the power split between the electric machine and the internal combustion engine (ICE). The underlying theme of the fuzzy controller is to optimize the fuel flow and reduce NOx emission. The parameters in the fuzzy rules can be adjusted to trade-off the fuel economy and the NOx emission of the vehicle. Simulation results are used to assess the performance of the controller. A forward-looking hybrid vehicle simulation model is used to implement the control strategies. By using fuzzy logic, trade-off between fuel economy and emission improvement has been shown.

A hybrid system, using a combination of an internal combustion engine (ICE) and electric motor(s), is an important concept to improve fuel economy and reduce emission of vehicles. In the last two decades, the automotive industry has been actively working on several hybrid configurations. This activity has resulted in actual production or plans for near-term production of hybrid vehicles by the major automotive companies [1]. To improve the fuel economy and reduce the emissions of hybrid vehicles, it is important to optimize not only the architecture and components of the hybrid vehicles, but also the energy management strategy. The energy management strategy controls the energy flow among all components as well as the power generation and conversion in the individual components. There are several approaches for the development of energy management strategies [2], [3], [4], [5], [6] and [7]. One approach optimizes the engine operation, thus not using the full potential of hybrid technology. A second approach optimizes the instantaneous operation of the hybrid system. This can be done by the minimization of the current equivalent fuel flow and/or the instantaneous emission. In the third approach, global optimization is used where the total fuel consumption and/or emission of the vehicle in a specific driving cycle are minimized. For the implementation of the energy management strategy, fuzzy logic controller (FLC) is used in this paper. The fuzzy logic controller will be used to implement the energy management strategy that optimizes the operation of the overall hybrid system, based on instantaneous vehicle information. The fuzzy logic controller will be used for both fuel economy optimization and emission reduction by having different parameters tuning. Previous research work [2] and [3] has indicated that fuzzy logic control is suitable for hybrid vehicle control. It is a suitable method for non-linear systems, with time-varying components. It is flexible, since any energy management strategy approach can be implemented using fuzzy logic. It is also tolerant to imprecise measurements and to component variability. The results of the fuzzy logic are essentially multi-dimensional look-up table-based control. In Section 2 of this paper, the parallel hybrid vehicle under consideration will be described. Section 3 discusses the energy management strategy concept used by the fuzzy logic. Section 4, an enhanced fuzzy control relative to the one given in [2] and [3], is presented and the controller is tuned for either fuel economy optimization or emission reduction. Simulation results for the fuzzy logic controller tuned for optimized fuel economy and the one tuned for NOx emission are shown in Section 5.

A generalized fuzzy logic based power controller for parallel hybrid vehicles can be used for both fuel economy optimization and emission reduction. The controller has the potential of improving the composite urban/highway fuel economy by more than 10% (when tuned for fuel economy) over traditional strategies that optimize the engine operation only. It also has the potential of reducing the NOx emission by more than 25% over the composite urban/highway cycle (when tuned for NOx emission reduction) over traditional strategies that optimize engine operation. The controller insures that the driver inputs (from brake and accelerator pedals) are satisfied consistently, the battery is sufficiently charged all the time, fuel economy is optimized and/or emission is reduced. The efficiency and emission maps are used to design the controller. Small changes in the controllers result in emphasis on either optimizing fuel economy or reducing emissions. The fuzzy controller consists of 44 rules. Future work: • The main advantage of fuzzy logic control is its robustness to component and system variations as well as to input and output measurement noise. The robustness of the generalized fuzzy logic controller should be investigated in more detail. • The control system, designed in this work, is based on certain given component sizes. To improve the overall vehicle performance, optimization of component sizes and control parameters should be conducted simultaneously. • Adapting and learning elements should be added to the controller, to enable on-line tuning of the fuzzy rules.