scholarly journals Optimizing Wireless Charging Locations for Battery Electric Bus Transit with a Genetic Algorithm

2020 ◽  
Vol 12 (21) ◽  
pp. 8971
Author(s):  
Gang Chen ◽  
Dawei Hu ◽  
Steven Chien ◽  
Lei Guo ◽  
Mingzheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Service Life ◽  
Ghg Emissions ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Bus Transit ◽  
Route Network ◽  
Electric Bus ◽  
Battery Capacity ◽  
Bus Network

Electrifying bus transit has been deemed as an effective way to reduce the emissions of transit vehicles. However, some concerns about on-board battery hinder its further development. Recently, dynamic wireless power transfer (DWPT) technologies have been developed, which enable buses to charge in-motion and overcome the drawback (short service range) with opportunity charging. This paper proposes a mathematic model which optimizes the locations for DWPT devices deployed at stops and size of battery capacity for battery electric buses (BEB) in a multi-route network, which considers the battery’s service life, depth of discharge and weight. A tangible solution algorithm based on a genetic algorithm (GA) is developed to find the optimal solution. A case study based on the bus network from Xi’an China is conducted to investigate the relationship among optimized costs, greenhouse gas (GHG) emissions, battery service life, size of the battery capacity and the number of DWPT devices. The results demonstrated that a bus network powered by DWPT shows better performance in both costs (a 43.3% reduction) and emissions (a 14.4% reduction) compared to that with stationary charging at bus terminals.

Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

2013 ◽  
Vol 365-366 ◽  
pp. 194-198 ◽  
Author(s):  
Mei Ni Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Simulation Software ◽  
Initial Population ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Search Speed ◽  
Vehicle Path

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

Joint Optimization Combining Genetic Algorithm and Numerical Algorithm Based on MATLAB

2014 ◽  
Vol 1065-1069 ◽  
pp. 3442-3445 ◽  
Author(s):  
Yan Hua Guo ◽  
Fei Fei Liu ◽  
Ning Zhang ◽  
Tao Wang
Keyword(s):  
Genetic Algorithm ◽  
Numerical Algorithm ◽  
Parametric Design ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Optimization Method ◽  
Joint Optimization ◽  
Global Optimal ◽  
First Order Method ◽  
Quasi Newton

The mathematic model of a two-bar truss is built in MATLAB and the analysis is carried out by the genetic algorithm toolbox. The parametric model of the planar truss is established by the ANSYS Parametric Design Language. Solutions are obtained using the first-order method native. Genetic algorithms don’t always display better properties than others. Finally, a joint optimization method is proposed, which combines MATLAB genetic algorithm toolbox and the numerical algorithm based on the quasi-Newton method. The method is identified through the numerical example of the two-bar truss. The results indicate the joint optimization method can always converge to the global optimal solution.

scholarly journals Review of the Estimation Methods of Energy Consumption for Battery Electric Buses

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7578
Author(s):  
Ali Saadon Al-Ogaili ◽  
Ali Q. Al-Shetwi ◽  
Hussein M. K. Al-Masri ◽  
Thanikanti Sudhakar Babu ◽  
Yap Hoon ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Ghg Emissions ◽  
Big Data Analytics ◽  
Optimization Methods ◽  
Estimation Methods ◽  
Real World Data ◽  
Battery Capacity ◽  
Bus Network ◽  
The Impact

In the transportation sector, electric battery bus (EBB) deployment is considered to be a potential solution to reduce global warming because no greenhouse gas (GHG) emissions are directly produced by EBBs. In addition to the required charging infrastructure, estimating the energy consumption of buses has become a crucial precondition for the deployment and planning of electric bus fleets. Policy and decision-makers may not have the specific tools needed to estimate the energy consumption of a particular bus network. Therefore, many state-of-the-art studies have proposed models to determine the energy demand of electric buses. However, these studies have not critically reviewed, classified and discussed the challenges of the approaches that are applied to estimate EBBs’ energy demands. Thus, this manuscript provides a detailed review of the forecasting models used to estimate the energy consumption of EBBs. Furthermore, this work fills the gap by classifying the models for estimating EBBs’ energy consumption into small-town depot and big-city depot networks. In brief, this review explains and discusses the models and formulations of networks associated with well-to-wheel (WTW) assessment, which can determine the total energy demand of a bus network. This work also reviews a survey of the most recent optimization methods that could be applied to achieve the optimal pattern parameters of EBB fleet systems, such as the bus battery capacity, charger rated power and the total number of installed chargers in the charging station. This paper highlights the issues and challenges, such as the impact of external factors, replicating real-world data, big data analytics, validity index, and bus routes’ topography, with recommendations on each issue. Also, the paper proposes a generic framework based on optimization algorithms, namely, artificial neural network (ANN) and particle swarm optimization (PSO), which will be significant for future development in implementing new energy consumption estimation approaches. Finally, the main findings of this manuscript further our understanding of the determinants that contribute to managing the energy demand of EBBs networks.

scholarly journals Enabling In-Depot Automated Routing and Recharging Scheduling for Automated Electric Bus Transit Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lei Wang ◽  
Wanjing Ma ◽  
Ling Wang ◽  
Yongli Ren ◽  
Chunhui Yu
Keyword(s):  
Autonomous Vehicles ◽  
Job Shop ◽  
Optimal Solution ◽  
Transit System ◽  
Transit Systems ◽  
Bus Transit ◽  
Electric Bus ◽  
Minimum Number ◽  
First Come First Serve ◽  
High Level

The bus transit system is promising to enable electric and autonomous vehicles for massive urban mobility, which relies on high-level automation and efficient resource management. Besides the on-road automation, the in-depot automated scheduling for battery recharging has not been adequately studied yet. This paper presents an integrated in-depot routing and recharging scheduling (IDRRS) problem, which is modeled as a constraint programming (CP) problem with Boolean satisfiability conditions (SAT). The model is converted to a flexible job-shop problem (FJSP) and is feasible to be solved by a CP-SAT solver for the optimal solution or feasible solutions with acceptable performance. This paper also presents a case study in Shanghai and compares the results from the FJSP model and the first-come first-serve (FCFS) method. The result demonstrates the allocation of routes and chargers under multiple scenarios with different numbers of chargers. The results show that the FJSP model shortens the delay and increases the time conservation for future rounds of operation than FCFS, while FCFS presents the simplicity of programming and better computational efficiency. The multiple random input test suggests that the proposed approach can decide the minimum number of chargers for stochastic charging requests. The proposed method can conserve the investment by increasing the utilization of automated recharging devices, improving vehicles’ in-depot efficiency.

THE ANALYSIS OF PASSENGER TRAFFIC ON THE ROUTES OF THE BUS OF KURSK

2020 ◽  
Vol 71 (4) ◽  
pp. 37-45
Author(s):  
B.A. SEMENIKHIN ◽  
◽  
L.P. KUZNETSOVA ◽  
YU.A. MALNEVA ◽  
A.YU. ALTUKHOV ◽  
...  
Keyword(s):  
Total Power ◽  
Passenger Traffic ◽  
Route Network ◽  
Analysis Of Change ◽  
Bus Network

Results of inspection and the analysis of passenger traffics on routes of the bus of Kursk are presented, the main shortcomings of the existing route network are revealed. The analysis of change of daily volume of transportations of passengers made on the basis of data of it and the previous inspections of passenger traffics and also distribution of total power of a passenger traffic on hours of day is provided. Results of development of rational route bus network of Kursk which is almost completely deprived of the shortcomings inherent in the existing route network are presented.

scholarly journals Inclusion of Hydraulic Controls in Rehabilitation Models of Drainage Networks to Control Floods

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 514
Author(s):  
Leonardo Bayas-Jiménez ◽  
F. Javier Martínez-Solano ◽  
Pedro L. Iglesias-Rey ◽  
Daniel Mora-Melia ◽  
Vicente S. Fuertes-Miquel
Keyword(s):  
Genetic Algorithm ◽  
Drainage System ◽  
Optimal Solution ◽  
Hydraulic Control ◽  
Drainage Systems ◽  
Storm Water Management Model ◽  
Drainage Networks ◽  
Extreme Rain ◽  
Rain Events ◽  
The Cost

A problem for drainage systems managers is the increase in extreme rain events that are increasing in various parts of the world. Their occurrence produces hydraulic overload in the drainage system and consequently floods. Adapting the existing infrastructure to be able to receive extreme rains without generating consequences for cities’ inhabitants has become a necessity. This research shows a new way to improve drainage systems with minimal investment costs, using for this purpose a novel methodology that considers the inclusion of hydraulic control elements in the network, the installation of storm tanks and the replacement of pipes. The presented methodology uses the Storm Water Management Model for the hydraulic analysis of the network and a modified Genetic Algorithm to optimize the network. In this algorithm, called the Pseudo-Genetic Algorithm, the coding of the chromosomes is integral and has been used in previous studies of hydraulic optimization. This work evaluates the cost of the required infrastructure and the damage caused by floods to find the optimal solution. The main conclusion of this study is that the inclusion of hydraulic controls can reduce the cost of network rehabilitation and decrease flood levels.

scholarly journals Business E-NeGotiAtion: A Method Using a Genetic Algorithm for Online Dispute Resolution in B2B Relationships

2021 ◽  
Vol 16 (5) ◽  
pp. 1186-1216
Author(s):  
Nikola Simkova ◽  
Zdenek Smutny
Keyword(s):  
Genetic Algorithm ◽  
Dispute Resolution ◽  
Single Case ◽  
Computer Mediated Communication ◽  
Optimal Solution ◽  
Mediated Communication ◽  
Online Dispute Resolution ◽  
B2b Relationships ◽  
Definition Of ◽  
E Mail

An opportunity to resolve disputes as an out-of-court settlement through computer-mediated communication is usually easier, faster, and cheaper than filing an action in court. Artificial intelligence and law (AI & Law) research has gained importance in this area. The article presents a design of the E-NeGotiAtion method for assisted negotiation in business to business (B2B) relationships, which uses a genetic algorithm for selecting the most appropriate solution(s). The aim of the article is to present how the method is designed and contribute to knowledge on online dispute resolution (ODR) with a focus on B2B relationships. The evaluation of the method consisted of an embedded single-case study, where participants from two countries simulated the realities of negotiation between companies. For comparison, traditional negotiation via e-mail was also conducted. The evaluation confirms that the proposed E-NeGotiAtion method quickly achieves solution(s), approaching the optimal solution on which both sides can decide, and also very importantly, confirms that the method facilitates negotiation with the partner and creates a trusted result. The evaluation demonstrates that the proposed method is economically efficient for parties of the dispute compared to negotiation via e-mail. For a more complicated task with five or more products, the E-NeGotiAtion method is significantly more suitable than negotiation via e-mail for achieving a resolution that favors one side or the other as little as possible. In conclusion, it can be said that the proposed method fulfills the definition of the dual-task of ODR—it resolves disputes and builds confidence.

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