Bin Guo

Optimization of Fuzzy Controller Based on Genetic Algorithm

The power required to drive the Hybrid electric generated by combination of internal combustion engine and electric motor. To make the power train of the hybrid electric vehicle as efficient as possible, proper management of the different energy elements is essential. This task is completed by the hybrid electric vehicle control strategy. A genetic-fuzzy control strategy is proposed for Hybrid electric vehicle in this paper. The genetic-fuzzy controller is a fuzzy logic controller that is tuned by a genetic algorithm. The objective of optimization is to decrease fuel consumption and emissions in two different test cycles NEDC and UDDS, the results demonstrate that compared with fuzzy logic control strategy, genetic-fuzzy control strategy can get better control effects. The effectiveness of this approach can reduce fuel consumption and emissions without sacrificing vehicle performance.

Design and Simulation of Fuzzy Control Strategy for Parallel Hybrid Electric Vehicle

A torque distribution controller based on the fuzzy logic for a double-clutch parallel hybrid electric vehicle was designed to determine the distribution of torque between engine and motor. Fuzzy logic controller is designed bases on speed, driver commands and battery state of charge. The motor auxiliary drive, ensuring the state of charge normal and providing a part of power to meet the needs of vehicle make the vehicle work in the best fuel economy state. The research results show that the fuzzy logic controller can well be assigned the torque between engine and motor. Compared to the tradition deterministic rule-based control strategy, the fuzzy logic control strategy can adjust the engine work point obviously, thereby enhancing the engine efficiency, improving fuel economy and emissions performance.