The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus emission.Considering the inherently high regularities of the fixed bus routes,the continuous state Markov decision process(MDP)is adopted to describe a cost function as total gas and electric consumption fee.Then a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the MDP.Next,fitted value iteration algorithm is given to approximate the cost function,and linear regression is used in this fitted value iteration.Simulation results show that this approach is feasible in searching for the control strategy of PHEB.Simultaneously this method has its own advantage comparing with the CDCS mode.Furthermore,a test based on a real PHEB was carried out to verify the applicable of the proposed method.
The optimization of the control strategy of a plug-in hybrid electric bus(PHEB) for the repeatedly driven bus route is a key technique to improve the fuel economy. The widely used rule-based(RB) control strategy is lacking in the global optimization property, while the global optimization algorithms have an unacceptable computation complexity for real-time application. Therefore, a novel hybrid dynamic programming-rule based(DPRB) algorithm is brought forward to solve the global energy optimization problem in a real-time controller of PHEB. Firstly, a control grid is built up for a given typical city bus route, according to the station locations and discrete levels of battery state of charge(SOC). Moreover, the decision variables for the energy optimization at each point of the control grid might be deduced from an off-line dynamic programming(DP) with the historical running information of the driving cycle. Meanwhile, the genetic algorithm(GA) is adopted to replace the quantization process of DP permissible control set to reduce the computation burden. Secondly, with the optimized decision variables as control parameters according to the position and battery SOC of a PHEB, a RB control is used as an implementable controller for the energy management. Simulation results demonstrate that the proposed DPRB might distribute electric energy more reasonably throughout the bus route, compared with the optimized RB. The proposed hybrid algorithm might give a practicable solution, which is a tradeoff between the applicability of RB and the global optimization property of DP.
ZHANG Ya HuiJIAO Xiao HongLI LiangYANG ChaoZHANG Li PengSONG Jian
The single-shaft parallel hybrid powertrain with the automatic mechanical transmission(AMT)is an efficient hybrid driving system in the hybrid electric bus(HEB),while the electromechanical coupling driving control becomes a complicated question to find a transient optimal control method to distribute the power between the engine and the electric machine(EM).This paper proposes an innovative control method to deal with the complicated transient coupling driving process of the electromechanical coupling driving system,considering the accelerating condition and the cruising condition mostly in the city driving cycle of HEB.The EM might be operated at driving mode or generating mode to assist the diesel engine to work in its high-efficiency area.Therefore,the adaptive torque tracking controller has been brought forward to ensure that the EM implements the demand torque as well as compensate the torque fluctuation of diesel engine.The d?q axis mathematical model and back stepping method are employed to deduce the adaptive controller and its adaptive laws.Simulation results demonstrate that the proposed control scheme can make the output torque of two power sources respond rapidly to the demand torque from the powertrain in the given driving condition.The proposed method could be adopted in the real control of HEB to improve the efficiency of the hybrid driving system.
YANG ChaoJIAO XiaoHongLI LiangZHANG YaHuiZHANG LiPengSONG Jian
Existing research on the traction control system(TCS) mainly focuses on control methods, such as the PID control, fuzzy logic control, etc, aiming at achieving an ideal slip rate of the drive wheel over long control periods. The initial output of the TCS (referred to as the torque base in this paper), which has a great impact on the driving performance of the vehicle in early cycles, remains to be investigated. In order to improve the control performance of the TCS in the first several cycles, an algorithm is proposed to determine the torque base. First, torque bases are calculated by two different methods, one based on states judgment and the other based on the vehicle dynamics. The confidence level of the torque base calculated based on the vehicle dynamics is also obtained. The final torque base is then determined based on the two torque bases and the confidence level. Hardware-in-the-loop(HIL) simulation and vehicle tests emulating sudden start on low friction roads have been conducted to verify the proposed algorithm. The control performance of a PID-controlled TCS with and without the proposed torque base algorithm is compared, showing that the proposed algorithm improves the performance of the TCS over the first several cycles and enhances about 5% vehicle speed by contrast. The proposed research provides a more proper initial value for TCS control, and improves the performance of the first several control cycles of the TCS.
LI HongzhiLI LiangSONG JianWU KaihuiQIAO YanjuanLIU XingchunXIA Yongguang
