Study And Its Application On Power Management Of Plug-in Hybrid Electric Bus

Vehicle controller is one of core components of hybrid electric vehicle. The performance of its control strategy will directly affect the power management between engine and motor, and have important influence on the vehicle fuel economy and driving performance. This paper mainly focuses on the energy optimization for single-shaft parallel and single-shaft series-parallel powertrains of Plug-in Hybrid Electric Bus(PHEB) with AMT.Firstly, for the quick charge PHEB, CKZ6116 PHEV, with single-shaft parallel hybrid powertrain and PHEB, KLQ6108 GC, with single-shaft series-parallel hybrid powertrain, the control-oriented models are established according to the characteristics of different configurations, respectively.Secondly, considering the characteristic of city buses, which run along the fixed driving cycle day after day and have highly statistical regularity, a stochastic dynamic programming(SDP) algorithm, which based on a large amount of historical data, is adopted to optimize the energy management of PHEB. And in the optimization process, a large number of driving cycle can be generated randomly due to the statistical transition probability of driver demand. Therefore the result is optimization ―in average sense‖. However, the driving condition is very complicated, in different time and different road segment, the traffic and road condition may change a lot, while the statistical regularity of historical driving cycle will not reflect the real-time changes of driving cycle, therefore, it’s essential to introduce an adaptive factor, which can change according to the variation of driver demand, based on the optimization of SDP to adjust the AMT shift points dynamically, and realize the coordination between the driving performance and fuel economy of PHEB.Finally, a two-step method is put forward to optimize the energy management of single-shaft hybrid powertrain with AMT. In first step, the two motor are regarded as an equivalent one, and the power split between engine and equivalent motor are pre-optimized under different operating modes; In second step, SDP is adopted to optimize operating model and the power split between the engine and the equivalent motor. Combining with the pre-optimized results of first step, the terminal optimization will be realized. Finally, the real-time performance of the proposed method is verified by using automatic code generation technology on a hardware in loop(HIL) system, which based on x PC target.