Optimization of Series Hybrid Electric Vehicle Operational Parameters By Simulated Annealing Algorithm

Wang, Zhancheng; Huang, Bufu; Xu, Yangsheng; Liu, Weimin

doi:10.1109/icca.2007.4376618

Cited by 32 publications

(18 citation statements)

References 9 publications

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“…Obviously, the computation time based on convex optimization is obviously less than the DP based method. As shown in Figure 15 and Table 5, the solution based on the convex optimization method can be still acceptable and thus proving its feasibility [39]. Figure 15 compares the SOC trajectories based on different energy management strategies when the battery initial SOC is 0.7.…”

Section: Simulation With Different Initial Socsmentioning

confidence: 81%

Comparisons of Energy Management Methods for a Parallel Plug-In Hybrid Electric Vehicle between the Convex Optimization and Dynamic Programming

et al. 2018

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This paper proposes a comparison study of energy management methods for a parallel plug-in hybrid electric vehicle (PHEV). Based on detailed analysis of the vehicle driveline, quadratic convex functions are presented to describe the nonlinear relationship between engine fuel-rate and battery charging power at different vehicle speed and driveline power demand. The engine-on power threshold is estimated by the simulated annealing (SA) algorithm, and the battery power command is achieved by convex optimization with target of improving fuel economy, compared with the dynamic programming (DP) based method and the charging depleting-charging sustaining (CD/CS) method. In addition, the proposed control methods are discussed at different initial battery state of charge (SOC) values to extend the application. Simulation results validate that the proposed strategy based on convex optimization can save the fuel consumption and reduce the computation burden obviously.

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Section: Simulation With Different Initial Socsmentioning

confidence: 81%

Comparisons of Energy Management Methods for a Parallel Plug-In Hybrid Electric Vehicle between the Convex Optimization and Dynamic Programming

et al. 2018

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“…Plsu and Rizzoni [2005] introduced a modified instantaneous equivalent consumption minimization strategy (ECMS) into a SHEV powertrain control system. A simulated annealing (SA) algorithm was proposed to optimize the operational parameters for SHEV fuel economy and emissions [Wang et al, 2008]. Barsali et al [2004] presented a knowledge-based control strategy for fuel consumption minimization using information of the engine efficiency map, vehicle battery behavior and some overall parameters characterizing the expected trip.…”

Section: Introductionmentioning

confidence: 99%

Slide Mode and Fuzzy Logic Based Powertrain Controller for the Energy Management and Battery Lifetime Extension of Series Hybrid Electric Vehicles

Chen

Zhang

2010

JAEV

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“…Mu proposed a spatial-temporal model to calculate the influence of electric vehicles on electric grids, considering the various strategies of transportation and OD of vehicles [15]. Wang [16] analyzes the impact of electric vehicle interconnection from different aspects and proposes that the impact of the charging load on the power grid can be divided into two categories: local and wide area. Shojaabadi [17] studied the variable factors and concluded that electric vehicles had negative effects on system load and voltage.…”

Section: Related Workmentioning

confidence: 99%

A Deploying Method for Predicting the Size and Optimizing the Location of an Electric Vehicle Charging Stations

Zhang

2018

Information

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Abstract:With the depletion of oil resources and the aggravation of environmental pollution, electric vehicles have a growing future and will be more popular as the main force of new energy consumption. They have attracted greater attention from various countries. The sizing and location problem for charging stations has been a hot point of global research, and these issues are important for government planning for electric vehicles. In this paper, we first built a BASS model to predict the total number of electric vehicles and calculate the size of charging stations in the coming years. Moreover, we also developed a queuing model to optimize the location of charging stations and solved this issue by using the exhaustion method, which regards minimum cost as the objective function. After that, the model was tested using data from a city in China. The results show that the model in this paper is good at predicting the number of electric vehicles in the coming years and calculating the size of charging stations. At the same time, it can also optimize the distribution of charging stations and make them more balanceable. Thus, this model is beneficial for the government in planning the development of electric vehicles in the future.

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Optimization of Series Hybrid Electric Vehicle Operational Parameters By Simulated Annealing Algorithm

Cited by 32 publications

References 9 publications

Comparisons of Energy Management Methods for a Parallel Plug-In Hybrid Electric Vehicle between the Convex Optimization and Dynamic Programming

Comparisons of Energy Management Methods for a Parallel Plug-In Hybrid Electric Vehicle between the Convex Optimization and Dynamic Programming

Slide Mode and Fuzzy Logic Based Powertrain Controller for the Energy Management and Battery Lifetime Extension of Series Hybrid Electric Vehicles

A Deploying Method for Predicting the Size and Optimizing the Location of an Electric Vehicle Charging Stations

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