scholarly journals A Rule-Based Energy Management Strategy Based on Dynamic Programming for Hydraulic Hybrid Vehicles

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Haicheng Zhou ◽  
Zhaoping Xu ◽  
Liang Liu ◽  
Dong Liu ◽  
Lingling Zhang
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Management Strategy ◽  
Optimal Solution ◽  
Hybrid Vehicles ◽  
Engine Control ◽  
Energy Management Strategy ◽  
Rule Based ◽  
Hydraulic Hybrid ◽  
Global Optimal

Energy management strategy is very important for hydraulic hybrid vehicles to improve fuel economy. The rule-based energy management strategies are widely used in engineering practice due to their simplicity and practicality. However, their performances differ a lot from different parameters and control actions. A rule-based energy management strategy is designed in this paper to realize real-time control of a novel hydraulic hybrid vehicle, and a control parameter selection method based on dynamic programming is proposed to optimize its performance. Firstly, the simulation model of the hydraulic hybrid vehicle is built and validated by the data tested from prototype experimental platform. Based on the simulation model, the optimization method of dynamic programming is used to find the global optimal solution of the engine control for the UDDS drive cycle. Then, the engine control parameters of the rule-based energy management strategy are selected according to the engine control trajectory of the global optimal solution. The simulation results show that the 100 km fuel consumption of the proposed rule-based energy management strategy is 12.7L, which is very close to the global optimal value of 12.4L and is suboptimal.

Receding Horizon Real-Time Energy Management Strategy for Hybrid Vehicles

2013 ◽  
Author(s):  
Bram de Jager ◽  
Thijs van Keulen
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Vehicles ◽  
State Control ◽  
Energy Management Strategy ◽  
Receding Horizon ◽  
Fuel Use ◽  
Commodity Hardware ◽  
The Future

Indirect optimal control and dynamic programming are combined in a receding horizon controller to obtain an energy management strategy for hybrid vehicles. This combination permits the use of inaccurate predictions of the future, instead of requiring exact knowledge, and allows the use of mixed state-control constraints, like voltage constraints for batteries. The controller can run in real-time on commodity hardware and, using a prediction of the future based on geographic information only, obtains a fuel use within 0.2% of the optimal fuel use computed with the exact speed and power trajectory of the vehicle known in advance. All this for a planned distance of more than 500 [km].

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