Estimating the benefits of energy-efficient train driving strategies: a model calibration with real data

The aim of this paper is to shed light on the question regarding whether the integration of an electric battery as a part of a domestic installation may increase its energy efficiency in comparison with a conventional case. When a battery is included in such an installation, two types of electrical conversion must be considered, i.e., AC/DC and DC/AC, and hence the corresponding losses due to these converters must not be forgotten when performing the analysis. The efficiency of the whole system can be increased if one of the mentioned converters is avoided or simply when its dimensioning is reduced. Possible ways to achieve this goal can be: to use electric vehicles as DC suppliers, the use of as many DC home devices as possible, and LED lighting or charging devices based on renewables. With all this in mind, several scenarios are proposed here in order to have a look at all possibilities concerning AC and DC powering. With the aim of checking these scenarios using real data, a case study is analyzed by operating with electricity consumption mean values.

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Feedforward Tactical Optimization for Energy-Efficient Operation of Freight Trains: The Swiss Case

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118776508 ◽

2018 ◽

Vol 2672 (10) ◽

pp. 278-288 ◽

Cited By ~ 2

Author(s):

Valerio De Martinis ◽

Ambra Toletti ◽

Francesco Corman ◽

Ulrich A. Weidmann ◽

Andrew Nash

Keyword(s):

Energy Efficiency ◽

Energy Efficient ◽

Real Data ◽

Efficient Solutions ◽

Future Market ◽

Speed Profile ◽

Efficient Operation ◽

The North ◽

Simulation Based ◽

Profile Optimization

The optimization of rail operation for improving energy efficiency plays an important role for the current and future market of rail freight services and helps rail compete with other transport modes. This paper presents a feedforward simulation-based model that performs speed profile optimization together with minor rescheduling actions. The model’s purpose is to provide railway operators and infrastructure managers with energy-efficient solutions that are tailored especially for freight trains. This work starts from the assumption that freight train characteristics are completely defined only a few hours before actual departure; therefore, small specific feedforward adjustments that do not affect the surrounding operation can still be considered. The model was tested in a numerical example. The example clearly shows how the optimized solutions can be evaluated with reference to energy saved and robustness within the rail traffic. The evaluation is based on real data from the North–South corridor crossing Switzerland from Germany to Italy.

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Energy Efficient Interest Forwarding in NDN-Based Wireless Sensor Networks

Mobile Information Systems ◽

10.1155/2016/3127029 ◽

2016 ◽

Vol 2016 ◽

pp. 1-15 ◽

Cited By ~ 12

Author(s):

Shuai Gao ◽

Hongke Zhang ◽

Beichuan Zhang

Keyword(s):

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Energy Efficient ◽

Real Data ◽

Wireless Sensor ◽

Efficient Design ◽

Total Energy Consumption ◽

Broadcast Storm ◽

Flexible Mode

Recently there has been a new emerging trend in integrating Named Data Networking (NDN) and wireless sensor networks (WSNs) together to implement real data-centric Internet of Things (IoT). However, the main solutions in current literature lack energy efficient design to meet the severely limited energy resources in WSNs. In this paper, we propose a dual mode Interest forwarding scheme (called DMIF in short) for NDN-based WSNs. The DMIF consists of two combined forwarding modes, in which several energy efficient mechanisms including flexible mode shift, flooding scope control, broadcast storm avoidance, packet suppression, and energy weight factors are designed to save and balance the energy consumption. We extend the ndnSIM to support wireless multihop communication to validate the proposed scheme. Simulation experiments show that the DMIF outperforms the baseline schemes in terms of total energy consumption, energy equilibrium rate, and network lifetime.

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Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration

Sensors ◽

10.3390/s20195539 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5539 ◽

Cited By ~ 1

Author(s):

Mădălin-Dorin Pop ◽

Octavian Proștean ◽

Tudor-Mihai David ◽

Gabriela Proștean

Keyword(s):

Fuzzy Inference System ◽

Model Calibration ◽

Fuzzy Inference ◽

Real Data ◽

Traffic Model ◽

Model Parameters ◽

Inference System ◽

Car Following ◽

Car Following Model ◽

Takagi Sugeno

Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.

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