scholarly journals A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

2021 ◽  
Vol 13 (20) ◽  
pp. 11331
Author(s):  
Kwangho Ko ◽  
Tongwon Lee ◽  
Seunghyun Jeong
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Fuel Consumption ◽  
Fuel Economy ◽  
Gps Data ◽  
Data Logger ◽  
Monitoring Method ◽  
Acceleration Rate ◽  
Consumption Data ◽  
Wide Range

A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.

Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications

2018 ◽  
Vol 2672 (24) ◽  
pp. 41-48 ◽  
Author(s):  
Jacob Holden ◽  
Harrison Van Til ◽  
Eric Wood ◽  
Lei Zhu ◽  
Jeffrey Gonder ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Real World ◽  
Energy Use ◽  
Average Energy ◽  
Estimation Model ◽  
Energy Estimation ◽  
Conventional Vehicle ◽  
Look Up Table ◽  
Wide Range

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin–destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

Combining Diesel Generators With Ultracapacitors to Enhance Stability and Reliability

2014 ◽  
Author(s):  
M. Averbukh ◽  
A. Kuperman ◽  
G. Geula ◽  
S. Gadelovitch ◽  
V. Yuhimenko
Keyword(s):  
Control System ◽  
Fuel Consumption ◽  
Fuel Economy ◽  
Fuel Efficiency ◽  
Diesel Generator ◽  
Load Power ◽  
Light Load ◽  
Wide Range ◽  
Auxiliary Power Units ◽  
Diesel Generators

Diesel generator based auxiliary power units (DG-APU) are widely used in different civil and military applications. Fuel economy and service life are probably the most important issues concerning their operation. Controlling engine throttle position in accordance with the load power allows regulating fuel supply to the engine to optimize fuel consumption. Despite the advantage of the method, control stability is sacrificed in case of light load operation as follows. When the DG-APU is running with a light load, engine throttle position should be nearly closed in order to minimize fuel consumption. If a load step is applied in such situation, engine velocity may drop sharply until complete stop because of insufficient control system bandwidth. This is why velocity and throttle position of a DG-APU should not be decreased below some level even if load power is low to maintain reliability at the expense of increased specific fuel consumption. Moreover, for small diesel-generators the throttle position is usually fixed. Thereby, relatively wide range load power variations (typical for many of diesel-generator applications) cause excessive fuel consumption. The situation may be sufficiently improved by connecting ultracapacitors (UC) on the DG-APU output terminals, introducing additional inertia allowing smoothing engine velocity decrease during a sudden load increase thus providing more time to the control system to regulate throttle position. As a result, DG-APU would be operated much more efficiently at light loads without sacrificing stability. Moreover, the UC may be used at as starter motor power source, removing starting stress from electrochemical batteries. Present work investigates the improvements in UC-supported DG-APU fuel efficiency and stability compared to conventional technical solutions. The research is based on mathematical modeling of the entire system, verified by experiments. The results support the presented ideas and quantitatively demonstrate the improved fuel economy and reliability of small DG-APUs.

scholarly journals General-Purpose and Scalable Internal-Combustion Engine Model for Energy-Efficiency Studies

Machines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 26
Author(s):  
Mika Laurén ◽  
Giota Goswami ◽  
Anna Tupitsina ◽  
Suraj Jaiswal ◽  
Tuomo Lindh ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Diesel Engine ◽  
Fuel Consumption ◽  
Internal Combustion Engines ◽  
Internal Combustion ◽  
System Level ◽  
Model Parameters ◽  
Consumption Data ◽  
Wide Range ◽  
Hybrid Powertrains

Hybrid powertrains that combine electric machines and internal-combustion engines offer substantial opportunities to increase the energy efficiency and minimize the exhaust emissions of vehicles and nonroad working machines. Due to the wide range of applications of such powertrains, simulation tools are used to evaluate and compare the energy efficiency of hybrid powertrains for application-specific working cycles in virtual environments. Therefore, the accurate modeling of the powertrain components of a hybrid system is important. This paper presents an agile calculation tool that can generate realistic fuel consumption data of a scalable diesel engine. This method utilizes a simple efficiency model of the combustion and crank train friction model to generate the fuel consumption map in the operating area of a typical diesel engine. The model parameters are calibrated to produce accurate fuel consumption data in the initial phase of system-level simulations. The proposed method is also validated by using three real engine datasets, and the comparison of results is presented.

scholarly journals MATHEMATICAL MODEL OF DIESEL ENGINE CHARACTERISTICS FOR DETERMINING THE PERFORMANCE OF TRACTION DYNAMICS OF WHEEL-TYPE TRACTOR

2020 ◽  
Vol 4 ◽  
pp. 90-100
Author(s):  
Oleksii Rebrov ◽  
Andrii Kozhushko ◽  
Boris Kalchenko ◽  
Anatoliy Mamontov ◽  
Alexander Zakovorotniy ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Fuel Consumption ◽  
Fuel Economy ◽  
Objective Assessment ◽  
Engine Performance ◽  
Field Tests ◽  
Early Spring ◽  
Variable Load ◽  
Wide Range ◽  
Agricultural Operations

Wheel-type tractors carry out a range of processing operations, with the exception of early spring work, when caterpillar tractors are used to reduce the compaction effect on the soil. Therefore, to plan the costs and reserves associated with fuel consumption, it is necessary to have an estimate of the fuel economy of the tractor in basic agricultural operations. An objective assessment of fuel consumption requires a mathematical model that describes the fuel characteristics of the engine, taking into account the speed and load torque in a wide range of variation. Verification of the model is possible only with experimental data. Since the efficiency and fuel economy of a tractor depends not only on engine performance, but also on the perfection of the transmission, the running system and the rational choice of speed, it is necessary to take into account the time-varying nature of the tractor’s traction load. The complex of agricultural operations can be divided into characteristic cycles of load change over time. This principle is the basis of PowerMix test cycles, which are conducted on a concrete track to ensure repeatability of the experiment. The use of the variable load on the tractor in the PowerMix tests is positive, but in actual field tests the results may differ due to the instability of the soil properties. On the other hand, PowerMix field cycles can be taken as standard test loads in the simulation of tractor traction tests on the ground

Improving Energy Efficiency in Internet of Things using Artificial Bee Colony Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
M. Sivaram ◽  
V. Porkodi ◽  
Amin Salih Mohammed ◽  
S. Anbu Karuppusamy
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Artificial Bee Colony ◽  
Trust Model ◽  
Sensor Nodes ◽  
Remote Areas ◽  
Bee Colony ◽  
Important Concern ◽  
Wide Range ◽  
Iot Devices

Background: With the advent of IoT, the deployment of batteries with a limited lifetime in remote areas is a major concern. In certain conditions, the network lifetime gets restricted due to limited battery constraints. Subsequently, the collaborative approaches for key facilities help to reduce the constraint demands of the current security protocols. Aim: This work covers and combines a wide range of concepts linked by IoT based on security and energy efficiency. Specifically, this study examines the WSN energy efficiency problem in IoT and security for the management of threats in IoT through collaborative approaches and finally outlines the future. The concept of energy-efficient key protocols which clearly cover heterogeneous IoT communications among peers with different resources has been developed. Because of the low capacity of sensor nodes, energy efficiency in WSNs has been an important concern. Methods: Hence, in this paper, we present an algorithm for Artificial Bee Colony (ABC) which reviews security and energy consumption to discuss their constraints in the IoT scenarios. Results: The results of a detailed experimental assessment are analyzed in terms of communication cost, energy consumption and security, which prove the relevance of a proposed ABC approach and a key establishment. Conclusion: The validation of DTLS-ABC consists of designing an inter-node cooperation trust model for the creation of a trusted community of elements that are mutually supportive. Initial attempts to design the key methods for management are appropriate individual IoT devices. This gives the system designers, an option that considers the question of scalability.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

Sign in / Sign up

Export Citation Format

Share Document