Computationally efficient optimal power management for plug-in hybrid electric vehicles based on spatial-domain two-scale dynamic programming

2008 ◽  
Author(s):  
Qiuming Gong ◽  
Yaoyu Li ◽  
Zhong-Ren Peng
Keyword(s):  
Dynamic Programming ◽  
Power Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Spatial Domain ◽  
Computationally Efficient ◽  
Optimal Power ◽  
Hybrid Electric

Computationally Efficient Optimal Power Management for Plug-In Hybrid Electric Vehicles With Spatial Domain Dynamic Programming

2008 ◽  
Author(s):  
Qiuming Gong ◽  
Yaoyu Li ◽  
Zhong-Ren Peng
Keyword(s):  
Dynamic Programming ◽  
Global Optimization ◽  
Real Time ◽  
Traffic Flow ◽  
Power Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Computationally Efficient ◽  
Hybrid Electric ◽  
Charge Depletion

The plug-in hybrid electric vehicles (PHEV), utilizing more battery power, has become a next-generation HEV with great promise of higher fuel economy. Global optimization charge-depletion power management would be desirable. This has so far been hampered due to the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). Combined with the Intelligent Transportation Systems (ITS), our previous work developed a two-scale dynamic programming approach as a nearly globally optimized charge-depletion strategy for PHEV power management. Trip model is obtained via GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling. The main drawback was the dependency of external server for obtaining the macroscale SOC profile, which makes it difficult to handle the impromptu change of driving decision. In this paper, a computationally efficient strategy is proposed based on road segmentation and lookup table methods. Simulation results have shown its great potential for real-time implementation.

Sign in / Sign up

Export Citation Format

Share Document