Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle

Zou, Yuan; Li, Teng; Sun, Fuchun; Peng, Huei

doi:10.3390/en6042305

Cited by 209 publications

(86 citation statements)

References 24 publications

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“…Otherwise, for the optimization, some artificial shift costs can be assigned in order reduce the number of gearshifts. These artificial shift costs are then considered in the cost function of the optimization without including them in the calculation of the fuel consumption [10,26,40]. For the method presented in this paper, the model for the gearshift costs is not restricted to the form presented in Equation (5).…”

Section: Gearbox Modelmentioning

confidence: 99%

“…The control of the power flows among these devices is commonly referred to as the energy management or supervisory control [1]. There are many approaches [2] to design an energy management strategy, covering heuristic approaches [3][4][5][6] as well as optimization-based approaches, such as deterministic dynamic programming (DP) [7][8][9][10][11][12][13], Pontryagin's minimum principle (PMP) [14][15][16][17][18][19][20][21] and stochastic dynamic programming (SDP) [22][23][24][25][26]. Recently, convex optimization [27] has attracted attention in the research field of energy management for HEVs.…”

Section: Introductionmentioning

confidence: 99%

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Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

et al. 2014

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This paper presents a novel method to solve the energy management problem for hybrid electric vehicles (HEVs) with engine start and gearshift costs. The method is based on a combination of deterministic dynamic programming (DP) and convex optimization. As demonstrated in a case study, the method yields globally optimal results while returning the solution in much less time than the conventional DP method. In addition, the proposed method handles state constraints, which allows for the application to scenarios where the battery state of charge (SOC) reaches its boundaries.

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Section: Gearbox Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

et al. 2014

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“…Bellman's principle of optimality approach is used, such that, the entire journey is divided into an equal number of subdivisions [23]. At each subdivision, the fuel consumption by the ICE has to be minimized.…”

Section: Dynamic Programming Optimizationmentioning

confidence: 99%

Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles

Alzorgan¹,

Carroll²,

Al-Masalmeh³

et al. 2016

SAE Technical Paper Series

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The environmental impact of the fossil fuels has increased tremendously in the last decade. This impact is one of the most contributing factors of global warming. This research aims to reduce the amount of fuel consumed by vehicles through optimizing the control scheme for the future route information. Taking advantage of more degrees of freedom available within PHEV, HEV, and FCHEV "energy management" allows more margin to maximize efficiency in the propulsion systems. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevations are obtained by use of Geographic Information System (GIS) maps to optimize the controller. The optimization is then reflected on the powertrain of the vehicle.The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade to prepare the vehicle for minimizing energy consumption during an uphill and potential energy harvesting during a downhill.The control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the PHEV, and HEV. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This approach allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications.ii The Thesis presents multiple control strategies designed, implemented, and tested using real-world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes. Future work will take advantage of vehicle connectivity and ADAS systems to utilize Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), traffic information, and sensor fusion to further optimize the PHEV and HEV toward more energy efficient operation.iii ACKNOWLEDGMENTS I would first wish to thank my advisor Dr. Abdel Ra'ouf Mayyas of the Ira A. Fulton Schools of Engineering at Arizona State University, for his support and guidance throughout the work on this research. He hired me to be a Graduate Research Assistance on the ASU EcoCAR3 team. His support was treasured, especially when facing difficulties working on the project. I want to thank the committee members for the experience they provided me through meetings and classes, which was essential in conducting this research.

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“…Dynamic Programming is a popular off-line method to determine the optimal control law given all future trip information (Sciarretta et al, 2004;Irani, 2009;Yuan et al, 2013). The Dynamic Program is based on Bellman's Principle of Optimality (Naidu, 2003), which states that a problem can be broken into sub-problems, and the solution to each sub-problem is dependent on the sub-problem before it.…”

Section: Dynamic Programmingmentioning

confidence: 99%

Automated topology optimisation of hybrid electric vehicle powertrains

Ing

McPhee

2015

IJEHV

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Abstract:Hybrid Electric powertrains are comprised of gasoline and electric powertrains components. These components can be connected in numerous configurations; as the number of components increase, the number of configurations increase exponentially. In this research, an algorithm was developed to automatically assemble and compare all possible hybrid electric powertrain configurations. Combinatorics was used to discover all possible combinations of: an internal combustion engine, high-torque low-speed electric motor, low-torque high-speed electric motor, planetary gearset, five-speed discrete gearbox, and battery. The Graph Theoretic Method was used to generate the powertrain models. The steadystate system models were solved symbolically, and Dynamic Programming was used to determine the optimal control law that minimized fuel consumption. To save computation time, topologies were evaluated using a multi-stage screening process. The component sizes of the top 24 topologies were optimized and ranked by fuel consumption. It was found that the Parallel and Powersplit-like topologies with discrete gearboxes were most efficient. The best performing topology is a Powersplit Hybrid type, with a discrete gearbox connected to the final drive and the output gear of the planetary carrier and electric motor in parallel.Keywords: hybrid electric vehicles; hybrid electric powertrains; topology optimization; linear graph theory; Graph Theoretic Method; design automation Biographical notes: IntroductionConventional transportation vehicles use combustion engines for propulsion because fossil fuels are energy dense and relatively inexpensive. Rising fuel prices, depletion of fossil fuels, and public environmental awareness have made electric vehicles an attractive alternative. Electric vehicles use electricity instead of gas for propulsion. They are more efficient, quieter, and do not create tailpipe emissions. Unfortunately, conventional electric vehicles are heavily reliant on batteries for energy storage. Batteries are expensive, heavy, and require charging equipment. Fuel cells have been proposed as an alternative to batteries, but hydrogen fuelling infrastructure is not widely available, and high pressure hydrogen storage technology is not mature. As an intermittent step from gasoline to electric vehicles, Hybrid Electric Vehicles (HEVs) have emerged in the marketplace.Hybrid Electric powertrains combine elements from traditional gasoline powertrains and electric powertrains. This is advantageous because they operate 10-20% more efficiently, and can refuel at any gasoline station (Chen et al., 2009;del Re et al., 2010;Chan, 2007). Including additional powertrain components create challenges. Complex controllers are required to ensure the system is operating efficiently. Cost and weight also increase with the number of components.The number of potential powertrain configurations increase exponentially as more components are included. Novel powertrain architectures can be identified through human

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Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle

Cited by 209 publications

References 24 publications

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles

Automated topology optimisation of hybrid electric vehicle powertrains

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