Abstract
In the trend of urgent demand of energy saving for public transportation, the series-parallel plug-in hybrid electric bus (SPPHEB) with energy saving potential is proposed. The fuel economy of hybrid power train depends to a significant degree on its control strategy, and then an energy management strategy based on nonlinear model predictive control (NMPC) is obtained for better fuel economy performance. Firstly, the quasi-static model of the plant is described and the reference curve of the state of charge (SOC) and the predictive torque are formulated and illustrated. Then the NMPC framework for SPPHEB, which explored the torque prediction method in the prediction domain and the prediction method of the reference SOC trajectory on the whole working condition, is introduced and completed by adopting dynamic programming (DP) algorithm to solve the nonlinear optimization problem. Finally, the NMPC strategy is simulated in Simulink, and its optimization performance is compared with other strategies such as DP, equivalent consumption minimization strategy (ECMS) and charge-depleting and charge-sustaining (CDCS). The simulation result is that compared with the CDCS strategy, NMPC strategy shows an economic improvement by 18.86%, and 10.36% improvement compared with the ECMS strategy. The good performance of the NMPC strategy is due in part to the consideration of the reference SOC trajectory mechanism and the prediction of the expected torque. The NMPC-based EMS considered both optimization performance and computation burden, which may provide a prospect for further practical application of real vehicles.
Highlights
Hybrid vehicles usually contain two or more power sources
To verify the optimization performance of nonlinear model predictive control (NMPC)-based energy management strategy (EMS), several strategies of the series-parallel plug-in hybrid electric bus (SPPHEB) based on NMPC, dynamic programming (DP), equivalent consumption minimization strategy (ECMS) and charge-depleting and chargesustaining (CDCS) are illustrated and tested
The economic optimization performance of the EMS is sorted in descending order which is DP, NMPC, ECMS and CDCS
Summary
Hybrid vehicles usually contain two or more power sources. For different configurations, there are different advantages [1], [2]. The series-parallel hybrid power train has more operating mode and works further efficiently. In [6], a hierarchical energy management strategy (EMS) is proposed to reduce stress on battery and fuel cell, to lift power performance and fuel economy of HEV. That MPC based EMS uses simplified or linearized model of HEV instead of accurate nonlinear model considering the fact that nonlinear mode predictive control (NMPC) would form a relatively complicated mathematical programming problem. The NMPC-based EMS with complex models requires a additional, suitable numerical solution, i.e., an evolutionary algorithm Considering both optimization performance and computation burden of that proposed control strategy for series-parallel HEV, dynamic programming (DP) is applied to solve the NMPC problem. Where m is the vehicle mass, g is the acceleration of gravity, fr is the rolling resistance, θ is the angle of the road, CD is the road grade angle, ρ is air density, δ is correction coefficient of rotating mass, A is frontal areas of the bus, Vveh and r are the vehicle speed and the radius of wheel respectively
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