Energy Consumption Modeling Algorithm for Structural Level Oriented Embedded Software

2013 ◽  
Vol 7 (2) ◽  
pp. 638-646
Author(s):  
Liang Qiao
Keyword(s):  
Energy Consumption ◽  
Embedded Software ◽  
Structural Level ◽  
Energy Consumption Modeling ◽  
Modeling Algorithm

scholarly journals Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1800
Author(s):  
Linfei Hou ◽  
Fengyu Zhou ◽  
Kiwan Kim ◽  
Liang Zhang
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Mobile Robot ◽  
Load Capacity ◽  
Working Environment ◽  
Energy Consumption Analysis ◽  
Mecanum Wheel ◽  
Save Energy ◽  
Energy Consumption Modeling ◽  
Robot Platform

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

Abstract and Concrete Data Type Optimizations at the UML and C/C++ Level for Dynamic Embedded Software

2010 ◽  
pp. 55-83
Author(s):  
Christos Baloukas ◽  
Marijn Temmerman ◽  
Anne Keller ◽  
Stylianos Mamagkakis ◽  
Francky Catthoor ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Embedded System ◽  
High Performance ◽  
Real Life ◽  
Holistic Approach ◽  
Embedded Software ◽  
Data Types ◽  
Design Engineers ◽  
Implementation Level ◽  
Abstraction Levels

An embedded system is a special-purpose system that performs predefined tasks, usually with very specific requirements. Since the system is dedicated to a specific task, design engineers can optimize it by exploiting very specialized knowledge, deriving an optimally customized system. Low energy consumption and high performance are both valid optimization targets to increase the value and mobility of the final system. Traditionally, conceptual embedded software models are built irrespectively of the underlying hardware platform, whereas embedded-system specialists typically start their optimization crusade from the executable code. This practice results in suboptimal implementations on the embedded platform because at the source-code level not all the inefficiencies introduced at the modelling level can be removed. In this book chapter, we describe both novel UML transformations at the modelling level and C/C++ transformations at the software implementation level. The transformations at both design abstraction levels target the data types of dynamic embedded software applications and provide optimizations guided by the relevant cost factors. Using a real life case study, we show how our transformations result in significant improvement in memory footprint, performance and energy consumption with respect to the initial implementation. Moreover, thanks to our holistic approach, we are able to identify new and non-trivial solutions that could hardly be found with the traditional design methods.

scholarly journals Development of Hybrid Vehicle Energy Consumption Model for Transportation Applications—Part II: Traction Force-Speed Based Energy Consumption Modeling

2019 ◽  
Vol 10 (2) ◽  
pp. 22 ◽  
Author(s):  
Siriorn Pitanuwat ◽  
Hirofumi Aoki ◽  
Satoru IIzuka ◽  
Takayuki Morikawa
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Traction Force ◽  
Hybrid Vehicles ◽  
Specific Power ◽  
Mode Transition ◽  
Hybrid Powertrain ◽  
Modeling Methods ◽  
Consumption Model ◽  
Energy Consumption Modeling

In the transportation sector, the fuel consumption model is a fundamental tool for vehicles’ energy consumption and emission analysis. Over the past decades, vehicle-specific power (VSP) has been enormously adopted in a number of studies to estimate vehicles’ instantaneous driving power. Then, the relationship between the driving power and fuel consumption is established as a fuel consumption model based on statistical approaches. This study proposes a new methodology to improve the conventional energy consumption modeling methods for hybrid vehicles. The content is organized into a two-paper series. Part I captures the driving power equation development and the coefficient calibration for a specific vehicle model or fleet. Part II focuses on hybrid vehicles’ energy consumption modeling, and utilizes the equation obtained in Part I to estimate the driving power. Also, this paper has discovered that driving power is not the only primary factor that influences hybrid vehicles’ energy consumption. This study introduces a new approach by applying the fundamental of hybrid powertrain operation to reduce the errors and drawbacks of the conventional modeling methods. This study employs a new driving power estimation equation calibrated for the third generation Toyota Prius from Part I. Then, the Traction Force-Speed Based Fuel Consumption Model (TFS model) is proposed. The combination of these two processes provides a significant improvement in fuel consumption prediction error compared to the conventional VSP prediction method. The absolute maximum error was reduced from 57% to 23%, and more than 90% of the predictions fell inside the 95% confidential interval. These validation results were conducted based on real-world driving data. Furthermore, the results show that the proposed model captures the efficiency variation of the hybrid powertrain well due to the multi-operation mode transition throughout the variation of the driving conditions. This study also provides a supporting analysis indicating that the driving mode transition in hybrid vehicles significantly affects the energy consumption. Thus, it is necessary to consider these unique characteristics to the modeling process.

scholarly journals Energy Consumption Modeling and Analyses in Automotive Manufacturing Plant

2016 ◽  
Vol 138 (10) ◽  
Author(s):  
Lujia Feng ◽  
Laine Mears
Keyword(s):  
Energy Consumption ◽  
Energy Modeling ◽  
Manufacturing Plant ◽  
Automotive Manufacturing ◽  
Manufacturing Plants ◽  
Modeling Approach ◽  
Paint Spray ◽  
World Energy ◽  
Spray Booth ◽  
Energy Consumption Modeling

Manufacturing plants energy consumption accounts for a large share in world energy usage. Energy consumption modeling and analyses are widely studied to understand how and where the energy is used inside of the plants. However, a systematic energy modeling approach is seldom studied to describe the holistic energy in the plants. Especially using layers of models to share information and guide the next step modeling is rarely studied. In this paper, a manufacturing system temporal and organizational framework was used to guide the systematic energy modeling approach. Various levels of models were established and tested in an automotive manufacturing plant to illustrate how the approach can be implemented. A detail paint spray booth air unit was described to demonstrate how to investigate the most sensitive variables in affecting energy consumption. While considering the current plant metering status, the proposed approach is advanced in information sharing and improvement suggestion determination.

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