scholarly journals Multi-Objective Optimization of the Gate Driver Parameters in a SiC-Based DC-DC Converter for Electric Vehicles

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3720
Author(s):  
Milad Moradpour ◽  
Paolo Pirino ◽  
Michele Losito ◽  
Wulf-Toke Franke ◽  
Amit Kumar ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Electromagnetic Interference ◽  
Optimization Problem ◽  
Experimental Tests ◽  
Wide Band ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gate Driver ◽  
Efficient Power

DC-DC converters are being used for power management and battery charging in electric vehicles (EVs). To further the role of EVs in the market, more efficient power electronic converters are needed. Wide band gap (WBG) devices such as silicon carbide (SiC) provide higher frequency and lower power loss, however, their high di/dt and dv/dt transients result in higher electromagnetic interference (EMI). On the other hand, some gate driver parameters such as gate resistor ( R G ) have a contradictory effect on efficiency ( η ) and EMI. The idea of this paper is to investigate the values of these parameters using a multi-objective optimization method to optimize η and EMI at the same time. To this aim, first, the effect of high and low side R G on η and EMI in the half-bridge configuration is studied. Then, the objective functions of the optimization problem are obtained using a numerical regression method on the basis of the experimental tests. Then, the values of the gate resistors are obtained by solving the multi-objective optimization problem. Finally, η and EMI of the converter in the optimum gate resistor design are compared to those in the conventional design to validate the effectiveness of the proposed design approach.

scholarly journals Comparison of the Economic and Environmental Performance of V2H and Residential Stationary Battery: Development of a Multi-Objective Optimization Method for Homes of EV Owners

2019 ◽  
Vol 10 (4) ◽  
pp. 78
Author(s):  
Ryosuke Kataoka ◽  
Akira Shichi ◽  
Hiroyuki Yamada ◽  
Yumiko Iwafune ◽  
Kazuhiko Ogimoto
Keyword(s):  
Electric Vehicles ◽  
Environmental Performance ◽  
Optimization Method ◽  
Energy Costs ◽  
Multi Objective Optimization ◽  
Environmental Aspects ◽  
Multi Objective ◽  
Investment Cost ◽  
Detached House

The use of batteries of electric vehicles (EVs) for home electricity applications using a bidirectional charger, a process called vehicle-to-home (V2H), is attracting the attention of EV owners as a valuable additional benefit of EVs. To motivate owners to invest in V2H, a quantitative evaluation to compare the performance of EV batteries with that of residential stationary batteries (SBs) is required. In this study, we developed a multi-objective optimization method for the household of EV owners using energy costs including investment and CO2 emissions as indices and compared the performances of V2H and SB. As a case study, a typical detached house in Japan was assumed, and we evaluated the economic and environmental aspects of solar power self-consumption using V2H or SB. The results showed that non-commuting EV owners should invest in V2H if the investment cost of a bidirectional charger is one third of the current cost as compared with inexpensive SB, in 2030. In contrast, our results showed that there were no advantages for commuting EV owners. The results of this study contribute to the rational setting of investment costs to increase the use of V2H by EV owners.

scholarly journals Multi-objective Optimization of Compliant Parallel Platform for Desired Stiffness and Workspace

2021 ◽  
Author(s):  
Hongwei Xu ◽  
Haibo Zhou ◽  
Zhiqiang Li ◽  
Xia Ju
Keyword(s):  
Numerical Model ◽  
Optimization Problem ◽  
Structural Parameters ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Element Analysis ◽  
Multi Objective ◽  
Performance Indexes ◽  
Parallel Platform ◽  
The Relationship

Abstract Stiffness and workspace are crucial performance indexes of a precision mechanism. In this paper, an optimization method is presented, for a compliant parallel platform to achieve desired stiffness and workspace. First, a numerical model is proposed to reveal the relationship between structural parameters, desired stiffness and workspace of the compliant parallel platform. Then, the influence of the various parameters on stiffness and workspace of the platform is analyzed. Based on Gaussian distribution, the multi-objective optimization problem is transformed into a single-objective one, in order to guarantee convergence precision. Furthermore, particle swarm optimization is used to optimize the structural parameters of the platform, which significantly improve its stiffness and workspace. Last, the effectiveness of the proposed numerical model is verified by finite element analysis and experiment.

scholarly journals Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime

2020 ◽  
Vol 13 (8) ◽  
pp. 1705-1726
Author(s):  
Theresia Perger ◽  
Hans Auer
Keyword(s):  
Energy Consumption ◽  
San Francisco ◽  
Electric Vehicles ◽  
Optimization Problem ◽  
Route Planning ◽  
Planning System ◽  
Decision Variable ◽  
Multi Objective Optimization ◽  
Battery Lifetime ◽  
Multi Objective

Abstract In contrast to conventional routing systems, which determine the shortest distance or the fastest path to a destination, this work designs a route planning specifically for electric vehicles by finding an energy-optimal solution while simultaneously considering stress on the battery. After finding a physical model of the energy consumption of the electric vehicle including heating, air conditioning, and other additional loads, the street network is modeled as a network with nodes and weighted edges in order to apply a shortest path algorithm that finds the route with the smallest edge costs. A variation of the Bellman-Ford algorithm, the Yen algorithm, is modified such that battery constraints can be included. Thus, the modified Yen algorithm helps solving a multi-objective optimization problem with three optimization variables representing the energy consumption with (vehicle reaching the destination with the highest state of charge possible), the journey time, and the cyclic lifetime of the battery (minimizing the number of charging/discharging cycles by minimizing the amount of energy consumed or regenerated). For the optimization problem, weights are assigned to each variable in order to put emphasis on one or the other. The route planning system is tested for a suburban area in Austria and for the city of San Francisco, CA. Topography has a strong influence on energy consumption and battery operation and therefore the choice of route. The algorithm finds different results considering different preferences, putting weights on the decision variable of the multi-objective optimization. Also, the tests are conducted for different outside temperatures and weather conditions, as well as for different vehicle types.

Increasing Output in Transfer Lines through Adaptive Buffer Operation

2016 ◽  
Author(s):  
Jeanette Hamiga
Keyword(s):  
Production System ◽  
Optimization Problem ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Labor Costs ◽  
Serial Transfer ◽  
Transfer Lines ◽  
Multi Objective ◽  
Simulation Based ◽  
Decoupling Effect

This thesis is intended for engineers and scientists in the field of production. It deals with the goal of increasing output in (serial) transfer lines and simultaneously decreasing labor costs without need of change to the structure of the production system. For this the method adaptive buffer operation is developed, implemented and validated. Adaptive buffer operation proposes a different way of operating buffers, improving the decoupling effect of buffers. The buffers are filled to certain target fill levels at fixed moments (times of the day). Apart from the target fill levels further parameters, e.g. moments of intervention or the intervention frequency, are identified. To find out how to operate the buffers and which parameter combinations work best, a simulation-based optimization method is proposed. This method is split into the evaluative ­methodology, here simulation, and the generative technique of evolution strategies, solving the multi-objective optimization problem. Proo...

scholarly journals Parameter Matching Optimization of a Powertrain System of Hybrid Electric Vehicles Based on Multi-Objective Optimization

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 875 ◽  
Author(s):  
Xiaoling Fu ◽  
Qi Zhang ◽  
Jiyun Tang ◽  
Chao Wang
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Powertrain System ◽  
Parameter Matching ◽  
Hybrid Electric ◽  
Fuel Consumption And Emissions

Aiming at problems of large computational complexity and poor reliability, a parameter matching optimization method of a powertrain system of hybrid electric vehicles based on multi-objective optimization is proposed in this paper. First, according to the vehicle basic parameters and performance indicators, the parameter ranges of different components were analyzed and calculated; then, with the weight coefficient method, the multi-objective optimization (MOO) problem of fuel consumption and emissions was transformed into a single-objective optimization problem; finally, the co-simulation of AVL Cruise and Matlab/Simulink was achieved to evaluate the effects of parameter matching through the objective function. The research results show that the proposed parameter matching optimization method for hybrid electric vehicles based on multi-objective optimization can significantly reduce fuel consumption and emissions of a vehicle simultaneously and thus provides an optimized vehicle configuration for energy management strategy research. The method proposed in this paper has a high application value in the optimization design of electric vehicles.

scholarly journals Identification of Essential Proteins using a Novel Multi-objective Optimization Method

2020 ◽  
Author(s):  
Chong Wu ◽  
Houwang Zhang ◽  
Le Zhang ◽  
Hanying Zheng
Keyword(s):  
Protein Interactions ◽  
Optimization Problem ◽  
State Of The Art ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Protein Protein Interactions ◽  
Generalization Ability ◽  
Essential Proteins ◽  
Multi Objective ◽  
Voting Strategy

<p>Using graph theory to identify essential proteins is a hot topic at present. These methods are called network-based methods. However, the generalization ability of most network-based methods is not satisfactory. Hence, in this paper, we consider the identification of essential proteins as a multi-objective optimization problem and use a novel multi-objective optimization method to solve it. The optimization result is a set of Pareto solutions. Every solution in this set is a vector which has a certain number of essential protein candidates and is considered as an independent predictor or voter. We use a voting strategy to assemble the results of these predictors. To validate our method, we apply it on the protein-protein interactions (PPI) datasets of two species (Yeast and Escherichia coli). The experiment results show that our method outperforms state-of-the-art methods in terms of sensitive, specificity, F-measure, accuracy, and generalization ability.</p>

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