Minimum charging-cost tracking based optimization algorithm with dynamic programming technique for plug-in hybrid electric vehicles

2011 ◽  
Author(s):  
Zhihao Li ◽  
Alireza Khaligh ◽  
Navid Sabbaghi
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicles ◽  
Optimization Algorithm ◽  
Hybrid Electric Vehicles ◽  
Programming Technique ◽  
Hybrid Electric

scholarly journals Applying forward dynamic programming to combined energy and thermal management optimization of hybrid electric vehicles

2018 ◽  
Vol 51 (31) ◽  
pp. 383-389 ◽  
Author(s):  
Lukas Engbroks ◽  
Daniel Görke ◽  
Stefan Schmiedler ◽  
Jochen Strenkert ◽  
Bernhard Geringer
Keyword(s):  
Dynamic Programming ◽  
Thermal Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Hybrid Electric ◽  
Management Optimization ◽  
Forward Dynamic Programming

A Dynamic Programming Control Strategy for HEV

2012 ◽  
Vol 263-266 ◽  
pp. 541-544 ◽  
Author(s):  
Babici Leandru Corneliu Cezar ◽  
Onea Alexandru
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
The State ◽  
State Of Charge ◽  
Battery Pack ◽  
Matlab Simulink ◽  
Hybrid Electric

Dynamic programming is a very powerful algorithmic paradigm which solves a problem by identifying subproblems and tackling them one by one. First the smallest are solved, and then using their answers, it can be figured out larger ones, until the whole lot of them is solved. This paper presents a control strategy for hybrid electric vehicles, based on the dynamic programming, applied in MATLAB, Simulink environment, using ADVISOR. It was tried this method due to the calculation speed of the suitable torque and speed required from the engine, considering the driver power request (torque and speed), and the state of charge (SOC) of the batteries. Using the fuel converter (FC) fuel map, and the remaining SOC of the battery pack, it was designed an algorithm that will chose at each time the required torque and speed from the first and second source of power.

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.

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