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.

scholarly journals Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xiaolong Xu ◽  
Yuan Xue ◽  
Mengmeng Cui ◽  
Yuan Yuan ◽  
Lianyong Qi
Keyword(s):  
Energy Consumption ◽  
Complex Systems ◽  
Optimization Problem ◽  
Renewable Energy Sources ◽  
Resource Scheduling ◽  
Multi Objective Optimization ◽  
Vm Migration ◽  
Multi Objective ◽  
Migration Cost ◽  
Scheduling Method

By means of the complex systems, multiple renewable energy sources are integrated to provide energy supply for users. Considering that there are massive services needed to process in complex systems, the mobile services are offloaded from mobile devices to edge servers for efficient implementation. In spite of the benefits of complex systems and edge servers, massive resource requirements for implementing the increasing resource requests decrease the execution efficiency and affect the whole resource usage of edge servers. Therefore, it remains an issue to achieve dynamic scheduling of the computing resources across edge servers. With the consideration of this issue, a Balanced Resource Scheduling Method, named BRSM, for trade-offs between virtual machine (VM) migration cost and energy consumption of VM migrations for edge server management, named BRSM, is designed in this paper. Technically, we analyze the load conditions of edge servers and formulate the energy consumption of VM migrations and VM migration cost as a multi-objective optimization problem. Then, we propose a dynamic resource scheduling method for WMAN to deal with the multi-objective optimization problem. In addition, nondominated sorting genetic algorithm III (NSGA-III) is adopted to generate optimal resource scheduling strategies. Finally, we conduct experiment simulations to testify the efficiency of the proposed method BRSM.

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 Optimal Peak Shifting of a Microgrid Load Based on Deep Q-Learning Network

2020 ◽  
Author(s):  
Mostafa Algabalawy ◽  
Nesreen Hosny
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Energy Requirement ◽  
Future Research ◽  
Multi Objective Optimization ◽  
Additional Energy ◽  
Multi Objective

Abstract Background: Peak periods are a result of consumers generally using electricity at similar times and periods as each other, for example, turning lights on when returning home from work, or the widespread use of air conditioners during the summer. Without peak shifting, the grid’s system operators are forced to use peaked plants to provide the additional energy, the operation of which is incredibly expensive and dangerous to the environment due to their high levels of carbon emissions.Methods: Battery storage system (BSS) has been used to allow for the purchase the energy during off-peak periods for later use, with the primary objective of achieving peak shifting, is explored. In addition, reduction in energy consumption and the lowering of consumer’s utility bills are also sought, making this a multi-objective optimization problem. Reinforcement learning methods are implemented to provide a solution to this problem by finding an optimal control policy which defines when is best to purchase and store energy with the objectives in mind.Results: This achieves over a 20% reduction in energy consumption and consumers’ energy bills, as well as achieving perfect peak shifting, thereby removing peaking plants from the equation entirely. This result was obtained using a simulator that the author has developed specifically for this task, which handles the model training, testing, and evaluation process. Secondly, the development of a novel technique, automatic penalty shaping, was also found to be crucial to the success of the learned model. This technique enabled the automatic shaping of the reward signal, forcing the agent to pay equal attention to multiple individual signals, a necessity when applying reinforcement learning to multi-objective optimization problems. The policy does, however, attempt to overcharge the battery about 7% of the time, and promising methods to address this has been proposed as a direction for future research.Conclusion: The aim of this task was to verify that reinforcement learning is a suitable solution method to the peak demand problem. That is, can reinforcement learning be used in conjunction with a BSS to purchase energy at off-peak periods, in order to flatten the energy requirement profile of consumers. Such an achievement would prevent the grid’s system operators from needing to use peaked plants to provide additional energy during peak periods, lowering carbon emissions and energy prices for the consumer. This peak-shifting would allow the grid’s system operators to be able to more easily predict electricity demand, thereby reducing their need to generate more energy than necessary, again lowering the tariffs for energy for the consumer. Secondary aims of directly reducing the energy consumption and utility bills were also sought, making this a multi-objective optimization problem. The used data, in conjunction with the created simulator which performs the full training and testing phases of the models, to find an optimal policy using the deep Q-network (DQN) and Proximal Policy Optimization (PPO) reinforcement learning algorithms. Finally, the proposed algorithm is able to achieve perfect peak shifting, a reduction in the monthly utility bill by 21% and also a reduction in energy consumption by 23%, achieving all of the aims of the task.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

Sign in / Sign up

Export Citation Format

Share Document