Optimal component sizing for a parallel hybrid bus based on dynamic programming

Gao, Wei; Zou, Yuan; Sun, Fuchun

doi:10.1109/itec-ap.2014.6941261

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Dynamic programming is an effective means to achieve this type of optimal control sequence [28,29]. It discretizes continuous control on the timeline, converts it into a multi-stage decision-making process and finally decomposes it into a series of simple single-step decision-making problems.…”

Section: Dynamic Programming Strategymentioning

confidence: 99%

Optimal Control Strategy for Series Hybrid Electric Vehicles in the Warm-Up Process

et al. 2018

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To address the problems of low efficiency and high fuel consumption during the cold start and warm-up processes of internal combustion engines, a series hybrid electric vehicle was selected as the research object and two optimal control strategies were designed. A bench test was performed to determine the following: (a) the influence of engine coolant temperature on effective thermal efficiency; and (b) the relationship between engine operating conditions and coolant temperature increase rate. On the basis of the test results, two sets of warm-up process optimization control strategies were designed using a dynamic programming method and a fuzzy control method based on equivalent consumption minimization strategy (ECMS). The test results show that the fuzzy control method for the coolant temperature can effectively shorten the time required to warm up the engine, and the energy consumption of warm-up process can be reduced by nearly 10% through the dynamic programming method.

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Section: Dynamic Programming Strategymentioning

confidence: 99%

Optimal Control Strategy for Series Hybrid Electric Vehicles in the Warm-Up Process

et al. 2018

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“…Some applications are structures with active control [1][2][3], mechatronics [4][5][6][7], mechanisms [8][9][10][11][12][13], microelectrical mechanical systems (MEMS) [14][15][16], and chemical processing applications [17,18]. These applications include important systems such as robotics [19], aeronautical structures, machine tools, sensors and automotive applications [20,21]. Such smart products consist of both a physical system, called a plant or artifact, and a controller.…”

Section: Introductionmentioning

confidence: 99%

Relationship between coupling and the controllability Grammian in co-design problems

2015

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a b s t r a c tDesign of smart products requires optimization of both the physical device, or artifact, and its controller. While some components of coupling can be computed a priori, the existence and strength of coupling between these problems over the entire Pareto frontier currently cannot be computed until they are solved. If coupling is expected to be present, then the problem is often solved as a simultaneous, or all-in-one, optimization. This solution process is more difficult, computationally intensive, and operationally inconvenient than a sequential solution method. Consequently, knowing in advance whether coupling is weak or nonexistent is useful.In this paper, a general formulation for the control problem is given, which includes components for the speed of response, accuracy of the response, and control effort. While this general formulation does not permit a priori determination of coupling, the existence and strength of coupling can be determined for several special cases, which represent common and important control problems. Controllability is a property of the uncontrolled system and does not depend on the controller design. These relationships between coupling and controllability can be utilized in problem formulation, choice of solution method, and development of Control Proxy Functions (CPFs) for each of these problem types. The concepts developed are demonstrated via design of a positioning gantry and a MEMS actuator.

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Optimal component sizing for a parallel hybrid bus based on dynamic programming

Cited by 3 publications

References 6 publications

Optimal Control Strategy for Series Hybrid Electric Vehicles in the Warm-Up Process

Optimal Control Strategy for Series Hybrid Electric Vehicles in the Warm-Up Process

Relationship between coupling and the controllability Grammian in co-design problems

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