Yadpf: A Reusable Deterministic Dynamic Programming Implementation in Matlab

2021 ◽  
Author(s):  
Auralius Manurung ◽  
Lisa Kristiana ◽  
Nur Uddin
Keyword(s):  
Dynamic Programming ◽  
Deterministic Dynamic ◽  
Implementation In Matlab

Role of Trip Information Preview in Fuel Economy of Plug-In Hybrid Vehicles

2009 ◽  
Author(s):  
Chen Zhang ◽  
Ardalan Vahidi ◽  
Xiaopeng Li ◽  
Dean Essenmacher
Keyword(s):  
Dynamic Programming ◽  
Fuel Economy ◽  
Substantial Reduction ◽  
Hybrid Vehicles ◽  
Hilly Terrain ◽  
Additional Increase ◽  
Charging Station ◽  
Complete Knowledge ◽  
Deterministic Dynamic

This paper investigates the role of partial or complete knowledge of future driving conditions in fuel economy of Plug-in Hybrid Vehicles (PHEVs). We show that with the knowledge of distance to the next charging station only, substantial reduction in fuel use, up to 18%, is possible by planning a blended utilization of electric motor and the engine throughout the entire trip. To achieve this we formulate a modified Equivalent Consumption Minimization Strategy (ECMS) which takes into account the traveling distance. We show further fuel economy gain, in the order of 1–5%, is possible if the future terrain and velocity are known; we quantify this additional increase in fuel economy for a number of velocity cycles and a hilly terrain profile via deterministic dynamic programming.

scholarly journals Parameter-Free Sampled Fictitious Play for Solving Deterministic Dynamic Programming Problems

2015 ◽  
Vol 169 (2) ◽  
pp. 631-655 ◽  
Author(s):  
Irina S. Dolinskaya ◽  
Marina A. Epelman ◽  
Esra Şişikoğlu Sir ◽  
Robert L. Smith
Keyword(s):  
Dynamic Programming ◽  
Fictitious Play ◽  
Deterministic Dynamic

Comparison of Optimal Supervisory Control Strategies for a Series Plug-In Hybrid Electric Vehicle Powertrain

2011 ◽  
Author(s):  
Rakesh Patil ◽  
Zoran Filipi ◽  
Hosam Fathy
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicle ◽  
Supervisory Control ◽  
Hybrid Electric Vehicle ◽  
Control Strategies ◽  
Deterministic Dynamic ◽  
Significant Difference ◽  
Power Split ◽  
Battery State Of Charge ◽  
Hybrid Electric

This paper uses dynamic programming to compare the optimal fuel and electricity costs associated with two supervisory control strategies from the plug-in hybrid electric vehicle (PHEV) literature. One strategy blends fuel and electricity for propulsion throughout the useful range of battery state of charge (SOC), while the second strategy switches from all-electric to blended operation at a predefined SOC threshold. Both strategies are optimized for a series PHEV powertrain using deterministic dynamic programming (DDP) to ensure a fair comparison. The DDP algorithm is implemented in a novel manner using a backward-looking powertrain model instead of forward-looking models used in previous research. The paper’s primary conclusion is that there is no significant difference in the performance of the two control strategies for the series PHEV considered. This result contrasts sharply with previous results for parallel and power-split PHEVs, and is examined for different relative fuel and electricity prices and trip lengths.

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