This paper presents a direct mathematical approach for determining the state of charge (SOC)-dependent equivalent cost factor in hybrid-electric vehicle (HEV) supervisory control problems using globally optimal dynami...
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This paper presents a direct mathematical approach for determining the state of charge (SOC)-dependent equivalent cost factor in hybrid-electric vehicle (HEV) supervisory control problems using globally optimal dynamic programming (DP). It therefore provides a rational basis for designing equivalent cost minimization strategies (ECMS) which achieve near optimal fuel economy (FE). The suggested approach makes use of the Pareto optimality criterion that exists in both ECMS and DP, and as such predicts the optimal equivalence factor for a drive cycle using DP marginal cost. The equivalence factor is then further modified with corrections based on battery SOC, with the aim of making the equivalence factor robust to drive cycle variations. Adaptive logic is also implemented to ensure battery charge sustaining operation at the desired SOC. Simulations performed on parallel and power-split HEV architectures demonstrate the cross-platform applicability of the DP-informed ECMS approach. Fuel economy data resulting from the simulations demonstrate that the robust controller consistently achieves FE within 1% of the global optimum prescribed by DP. Additionally, even when the equivalence factor deviates substantially from the optimal value for a drive cycle, the robust controller can still produce FE within 1-2% of the global optimum. This compares favorably with a traditional ECMS controller based on a constant equivalence factor, which can produce FE 20-30% less than the global optimum under the same conditions. As such, the controller approach detailed should result in ECMS supervisory controllers that can achieve near optimal FE performance, even if component parameters vary from assumed values (e. g., due to manufacturing variation, environmental effects or aging), or actual driving conditions deviate largely from standard drive cycles.
This research is about the development of a dynamic programming model for solving fuzzy linear programming problems. Initially, fuzzy dynamic linear programming model FDLP is developed. This research revises the estab...
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This research is about the development of a dynamic programming model for solving fuzzy linear programming problems. Initially, fuzzy dynamic linear programming model FDLP is developed. This research revises the established dynamic programming model for solving linear programming problems in a crisp environment. The mentioned approach is upgraded to address the problem in an uncertain environment. dynamic programming model can either be passing forward or backward. In the proposed approach backward dynamic programming approach is adopted to address the problem. It is then followed by implementing the proposed method on the education system of Pakistan. The education system of Pakistan comprises of the Primary, Middle, Secondary, and Tertiary education stages. The problem is to maximize the efficiency of the education system while achieving the targets with minimum usage of the constrained resources. Likewise the model tries to maximize the enrollment in the Primary, Middle, Secondary and Tertiary educational categories, subject to the total available resources in a fuzzy uncertain environment. The solution proposes that the enrollment can be increased by an amount 9997130, by increasing the enrollment in the Middle and Tertiary educational categories. Thus the proposed method contributes to increase the objective function value by 30%. Moreover, the proposed solutions violate none of the constraints. In other words, the problem of resources allocation in education system is efficiently managed to increase efficiency while remaining in the available constrained resources. The motivation behind using the dynamic programming methodology is that it always possesses a numerical solution, unlike the other approaches having no solution at certain times. The proposed fuzzy model takes into account uncertainty in the linear programming modeling process and is more robust, flexible and practicable.
The paper presents a dynamic, discrete optimization model with returns in ordered structures. It generalizes multiobjective methods used in vector optimization in two ways: from real vector spaces to ordered structure...
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The paper presents a dynamic, discrete optimization model with returns in ordered structures. It generalizes multiobjective methods used in vector optimization in two ways: from real vector spaces to ordered structures and from the static model to the dynamic model. The proposed methods are based on isotone homomorphisms. These methods can be applied in dynamic programming with returns in ordered structures. The provided numerical example shows an application of fuzzy numbers and random variables with stochastic dominance in dynamic programming. The paper also proposes applications in the following problems: a problem of allocations in the market model, a location problem, a railway routing problem, and a single-machine scheduling problem. (C) 2010 Elsevier B.V. All rights reserved.
Finding the optimal balance between electricity demand and production constrained to economic and comfort variables requires intelligent decision and control. This article addresses the formulation of three models tha...
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Finding the optimal balance between electricity demand and production constrained to economic and comfort variables requires intelligent decision and control. This article addresses the formulation of three models that optimize control of a heating, ventilation and air conditioning (HVAC) system in an experimental room, which are coupled with two thermal models of the indoor temperature. Electricity is supplied by the grid and a photovoltaic system with batteries. The primary objective is to maximize users comfort while minimizing cost constrained to: thermal comfort;variable electricity price;and available electricity in batteries that are charged by a PV system. Three models are developed: (i) dynamic programming with simplified thermal model (STM), (ii) genetic algorithm with STM, and (iii) genetic algorithm with EnergyPlus. The genetic algorithm model that uses EnergyPlus to simulate indoor temperature generally achieves higher convergence to the optimal value, which also is the one that uses more electricity from the PV system to operate the HVAC. The dynamic programming performs better than the genetic algorithm (both coupled with STM). However, it is limited by the fact that uses STM, which is a less accurate model to simulate indoor temperature especially because it is not considering thermal inertia.
The objective of this paper is to study by means of dynamic programming the optimal control of nonlinear continuous systems. We apply to these systems a development of block pulse for the state and a nonuniform discre...
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The objective of this paper is to study by means of dynamic programming the optimal control of nonlinear continuous systems. We apply to these systems a development of block pulse for the state and a nonuniform discretisation of the state space. As a particular case of a nonlinear system, we analysed a continuous dual control problem, and we carried out an implementation of a stochastic control policy on a real process, a DC motor.
A new dynamic programming approach, which was introduced to least squares problems, was discussed. The formulation introduces two cost functions, which is new to dynamic programming literature. The first cost function...
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A new dynamic programming approach, which was introduced to least squares problems, was discussed. The formulation introduces two cost functions, which is new to dynamic programming literature. The first cost function is the square of the length of the current discrepancy vector and the second is the square of the length of the current solution vector. The two cost functions are to be minimized simultaneously by optimally selecting the mimimum length vector solution.
The concept of a super value node is developed to extend the theory of influence diagrams to allow dynamic programming to be performed within this graphical modeling framework. The operations necessary to exploit the ...
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The concept of a super value node is developed to extend the theory of influence diagrams to allow dynamic programming to be performed within this graphical modeling framework. The operations necessary to exploit the presence of these nodes and efficiently analyze the models are developed. The key result is that by representing value function separability in the structure of the graph of the influence diagram, formulation is simplified and operations on the model can take advantage of the separability. From the decision analysis perspective, this allows simple exploitation of separability in the value function of a decision problem. This allows algorithms to be designed to solve influence diagrams that automatically recognize the opportunity for applying dynamic programming. From the decision processes perspective, influence diagrams with super value nodes allow efficient formulation and solution of nonstandard decision process structures. They also allow the exploitation of conditional independence between state variables.< >
This paper proposes a DP(dynamic programming)-based optimisation method of charging an EV (electric vehicle) fleet modelled as a single, so-called aggregate battery. The main advantage of the approach is that it provi...
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This paper proposes a DP(dynamic programming)-based optimisation method of charging an EV (electric vehicle) fleet modelled as a single, so-called aggregate battery. The main advantage of the approach is that it provides a globally optimal solution, with a relatively non-excessive computational load owing to a low order of the aggregate battery model. The method is illustrated through a case study of an isolated, hypothetically electrified delivery truck transport system charged from both grid and RES (renewable energy sources). Two scenarios of energy production from RES (with and without excess in RES production), along with several electricity price models are studied. The DP optimisation results are compared with the results obtained by an existing heuristic charging algorithm used in EnergyPLAN software to illustrate the DP algorithm advantages in minimising the charging energy cost and satisfying the aggregate battery charge sustaining conditions. The proposed DP optimisation method can be used in various energy planning studies, as well as a core of the supervisory/aggregator level of hierarchical EV fleet charging strategies. (C) 2015 Elsevier Ltd. All rights reserved.
In this paper, dynamic programming (DP) algorithm is applied to automatically segment multivariate time series. The definition and recursive formulation of segment errors of univariate time series are extended to mult...
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In this paper, dynamic programming (DP) algorithm is applied to automatically segment multivariate time series. The definition and recursive formulation of segment errors of univariate time series are extended to multivariate time series, so that DP algorithm is computationally viable for multivariate time series. The order of autoregression and segmentation are simultaneously determined by Schwarz's Bayesian information criterion. The segmentation procedure is evaluated with artificially synthesized and hydrometeorological multivariate time series. Synthetic multivariate time series are generated by threshold autoregressive model, and in real-world multivariate time series experiment we propose that besides the regression by constant, autoregression should be taken into account. The experimental studies show that the proposed algorithm performs well.
An optimal control algorithm is derived using dynamic programming. This algorithm is then derived for the optimal control of the PUMA 560 manipulator. The results show that, there is always one and only one leading li...
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An optimal control algorithm is derived using dynamic programming. This algorithm is then derived for the optimal control of the PUMA 560 manipulator. The results show that, there is always one and only one leading link at an instant of the motion for the time optimal control case. For the mixed time-energy optimal problem, the results show that each link may use the inertial, gravitational and such effects to save energy. It is also observed that, some of the links needs zero torque to achieve their maximum velocities throughout the motion.
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