Search Results

The traditional vehicle design methods of hybrid electric vehicles are based on the rule-based control strategy, which often adopt the trial and error methods and the model-based numerical optimization methods. But these methods require a large number of repeated tests and a longer-term development cycle. In this paper, approximately the global optimization algorithm was used in control parameters designing through rational design of the penalty weights of objective function. But the optimized parameters apply only to vehicles that operating in the special drive cycle to get better fuel economy. Therefore, a drive cycle recognition algorithm was proposed to identify types of drive cycles in real-time, then an off-line genetic algorithm was adopted to acquire the optimization of control parameters under the various drive cycles, through drive cycle recognition results to choose the best control parameters.

In order to achieve the best shifting performance, the traditional hybrid vehicles shift schedule design based on multi-parameter shift schedule, these shift methods can improve fuel economy and acceleration performance to a certain extent. but it is difficult to obtain the optimal performance because it is a compromise between power and economy shift schedule. This paper provides adaptive shift strategy based on driving style recognition to select the optimal shift schedule, thereby improving the dynamic performance of the vehicle as well as reduced fuel consumption.