Limitations Of Dynamic Programming Research Articles

Optimization-Based Energy Management Strategy for a 48-V Mild Parallel Hybrid Electric Power System

Abstract The application of a 48-V mild hybrid powertrain system is one of the eco-friendly technologies for global CO2 reduction in the present sector of hybrid electric vehicles (HEVs). It is well known that the energy management strategy is crucial for improving the fuel economy of the HEVs. Therefore, the strengths and the limitations of dynamic programming (DP) global optimization strategy and adaptive equivalent consumption minimization strategy (ECMS) for this kind of powertrain system under new European driving cycle (NEDC) and federal test procedure (FTP75) driving cycles are discussed in this paper. The results of the DP global optimization solution have also been adopted as a reference for evaluating the degree of optimality of real-time controllers. The reference start of charge (SOC) was found to be a very important parameter for the real-time ECMS approach. Thus, an adaptive ECMS approach using the SOC obtained from DP global optimization algorithm as a reference SOC in real-time ECMS control was studied. The study results in this paper may provide some theoretical support for future energy management optimization of a 48-V mild hybrid parallel powertrain system.

Investigation of integrated uninterrupted dual input transmission and hybrid energy storage system for electric vehicles

Transportation sector is one of the major sources of pollutants as it contributes more than 80% of CO, while almost all HC and 90% of NOx and PM. Although battery electric vehicles are well-known for reducing environmental pollution, the driving range and battery lifespan put a significant barrier to its large-scale commercialization. In this study, an integrated system, which includes an uninterrupted dual input transmission and hybrid energy storage system, is proposed to improve energy efficiency and extend battery lifespan. Given the limitations of dynamic programming in practice, a real-time optimal control strategy is designed to evaluate the power loss and battery capacity degradation of the proposed integrated system based on detailed mathematical models of individual powertrain components. To achieve a desirable trade-off between battery degradation, energy consumption, and acquisition cost, a mixed-integer multi-objective genetic algorithm is implemented to optimize the parameters of the hybrid energy storage system, while Pareto principal is adopted to find the proper solution according to different purposes. The simulation results reveal that the proposed integrated system shows the potential of saving 15.85%–20.82% of the energy consumption in typical driving cycles and more than 22.61%–31.11% Life-cycle cost compared with the single-ratio transmission-based battery electric vehicles. The selected Pareto front can further enhance Life-cycle cost from 26.53% to 28.13% in the HWFET cycle. It can be concluded that the integrated uninterrupted dual input transmission and hybrid energy storage system not only can improve motor efficiency and reduce energy consumption, it also can extend the battery lifespan to decrease Life-cycle cost compared to conventional single-ratio battery-only EV.

Cortico-Cortical, Cortico-Striatal, and Cortico-Thalamic White Matter Fiber Tracts Generated in the Macaque Brain via Dynamic Programming

Probabilistic methods have the potential to generate multiple and complex white matter fiber tracts in diffusion tensor imaging (DTI). Here, a method based on dynamic programming (DP) is introduced to reconstruct fibers pathways whose complex anatomical structures cannot be resolved beyond the resolution of standard DTI data. DP is based on optimizing a sequentially additive cost function derived from a Gaussian diffusion model whose covariance is defined by the diffusion tensor. DP is used to determine the optimal path between initial and terminal nodes by efficiently searching over all paths, connecting the nodes, and choosing the path in which the total probability is maximized. An ex vivo high-resolution scan of a macaque hemi-brain is used to demonstrate the advantages and limitations of DP. DP can generate fiber bundles between distant cortical areas (superior longitudinal fasciculi, arcuate fasciculus, uncinate fasciculus, and fronto-occipital fasciculus), neighboring cortical areas (dorsal and ventral banks of the principal sulcus), as well as cortical projections to the hippocampal formation (cingulum bundle), neostriatum (motor cortical projections to the putamen), thalamus (subcortical bundle), and hippocampal formation projections to the mammillary bodies via the fornix. Validation is established either by comparison with in vivo intracellular transport of horseradish peroxidase in another macaque monkey or by comparison with atlases. DP is able to generate known pathways, including crossing and kissing tracts. Thus, DP has the potential to enhance neuroimaging studies of cortical connectivity.

Applications and limitations of dynamic programming in behavioral theory

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On the Fitness of Behavior Sequences

Our aim is to demonstrate a method of determining the extent to which behavior maximizes fitness. We believe the temporal organization of behavior to be in part dependent on the animal's genetic makeup and subject to natural selection and that behavioral strategies may be as adaptive as structural characters. The question of the adaptiveness of behavior has been found hard to study in the past (Tinbergen 1963) because of the difficulty of quantitative verification of hypotheses. The method presented here tests specifically whether deployment of behavioral options is optimally related to environmental conditions.

Computational Limitations of Dynamic Programming for Warehouse Location: A Comment

Computational Limitations of Dynamic Programming for Warehouse Location: A Comment

Computational Limitations of Dynamic Programming for Warehouse Location: A Comment

practice. Heuristic methods might be used to obtain good, but not necessarily optimal, solutions. Ballou has presented a new dynamic aspect of the warehouse location problem. However, his approach can not handle realistic problems computationally where a company has a reasonable number of warehouses whose locations are interdependent in terms of profitability during a time period. On the other hand, some static solution procedures such as Shycon and Maffei's [2] or Kuehn and Hamburger's [3] can reflect these interdependencies but do not consider the dynamic factors such as demand changes and moving costs. Combination of both approaches in a computationally feasible manner is still in the realm of further research.

Computational Limitations of Dynamic Programming for Warehouse Location

Computational Limitations of Dynamic Programming for Warehouse Location