scholarly journals Essential Dynamics for Developing Models for Control of Connected and Automated Electrified Vehicles: Part A - Powertrain

2021 ◽  
Author(s):  
Sadra Hemmati ◽  
Rajeshwar yadav ◽  
Kaushik Surresh ◽  
Darrell Robinette ◽  
Mahdi Shahbakhti
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Dynamic Models ◽  
Mode Switching ◽  
Test Vehicle ◽  
Operating Modes ◽  
Prediction Horizon ◽  
Vehicle Operation ◽  
Energy Penalty ◽  
Power Needs

Abstract Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Even though the CAV technology is applicable to both conventional and electrified powertrains, the energy saving opportunities are more apparent when the CAVs are Hybrid Electric Vehicles (HEVs). This is because of the flexibility in the vehicle powertrain and possibility of choosing optimum powertrain modes based on the predicted traction power needs. In this paper, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption.

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

2022 ◽  
pp. 095440702110707
Author(s):  
Sadra Hemmati ◽  
Rajeshwar Yadav ◽  
Kaushik Surresh ◽  
Darrell Robinette ◽  
Mahdi Shahbakhti
Keyword(s):  
Energy Consumption ◽  
Hybrid Electric Vehicle ◽  
Mode Switching ◽  
Short Time Scale ◽  
Test Vehicle ◽  
Operating Modes ◽  
Vehicle Operation ◽  
Energy Penalty ◽  
Hybrid Electric ◽  
Power Needs

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

A Novel Coal Based Cogeneration System for Substitute Natural Gas and Power With CO2 Capture and Moderate Recycle of the Chemical Unconverted Gas

2013 ◽  
Author(s):  
Sheng Li ◽  
Hongguang Jin ◽  
Lin Gao
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Energy Saving ◽  
Co2 Capture ◽  
Co2 Concentration ◽  
Low Energy ◽  
The Novel ◽  
Good Efficiency ◽  
Cogeneration System ◽  
Energy Penalty

Cogeneration of substitute natural gas (SNG) and power from coal efficiently and CO2 capture with low energy penalty during coal utilization are very important technical paths to clean coal technologies for China which is rich in coal but lack of natural gas resources. This paper integrates a novel coal based cogeneration system with CO2 capture for SNG and power, and presents the energetic and exergy analysis based on the thermodynamic formulas and the use of ASPEN PLUS 11.0. In the novel system, instead of separation from the gas before synthesis traditionally, CO2 will be removed from the unconverted gas after synthesis, whose concentration can reach as high as 55% before separation and is much higher than 30% in traditional SNG production system. And by moderate recycle instead of full recycle of chemical unconverted gas back into SNG synthesis, the sharp increase in energy consumption for SNG synthesis with conversion ratios will be avoided, and by using part of the chemical unconverted gas, power is cogenerated efficiently. Thermodynamic analysis shows that the benefit from both systematic integration and high CO2 concentration makes the system have good efficiency and low energy penalty for CO2 capture. The overall efficiency of the system ranges from 53%–62% at different recycle ratios. Compared to traditional single production systems (IGCC with CO2 capture for power, traditional SNG system for SNG production), the energy saving ratio (ESR) of the novel system is 16%–21%. And compared to IGCC and traditional SNG system, the energy saving benefit from cogeneration can even offset the energy consumption for CO2 separation and realize zero energy penalties for CO2 capture systematically. Sensitivity analysis hints that an optimized recycle ratio of unconverted gas and chemicals to power output ratio (CPOR) can maximize system performance and minimize the energy penalty for CO2 capture.

Coal Based Cogeneration System for Synthetic/Substitute Natural Gas and Power With CO2 Capture After Methanation: Coupling Between Chemical and Power Production

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
Sheng Li ◽  
Hongguang Jin ◽  
Lin Gao
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Chemical Synthesis ◽  
Energy Saving ◽  
Co2 Capture ◽  
Low Energy ◽  
The Novel ◽  
Clean Coal ◽  
Cogeneration System ◽  
Energy Penalty

Cogeneration of synthetic natural gas (SNG) and power from coal efficiently and CO2 capture with low energy penalty during coal utilization are very important technical paths to implement clean coal technologies in China. This paper integrates a novel coal based cogeneration system with CO2 capture after chemical synthesis to produce SNG and power, and presents the energetic and exergy analysis based on the thermodynamic formulas and the use of ASPEN PLUS 11.0. In the novel system, instead of separation from the gas before chemical synthesis traditionally, CO2 will be removed from the unconverted gas after synthesis, whose concentration can reach as high as 55% before separation and is much higher than 30% in traditional SNG production system. And by moderate recycle instead of full recycle of chemical unconverted gas back into SNG synthesis, the sharp increase in energy consumption for SNG synthesis with conversion ratios will be avoided, and by using part of the chemical unconverted gas, power is cogenerated efficiently. Thermodynamic analysis shows that the benefit from both systematic integration and high CO2 concentration makes the system have good efficiency and low energy penalty for CO2 capture. The overall efficiency of the system ranges from 53%–62% at different recycle ratios. Compared to traditional single product systems (IGCC with CO2 capture for power, traditional SNG system for SNG production), the energy saving ratio (ESR) of the novel system is 16%–21%. And compared to IGCC and traditional SNG system, the energy saving benefit from cogeneration can even offset the energy consumption for CO2 separation, and thus zero energy/efficiency penalties for CO2 capture can be realized through system integration when the chemicals to power output ratio (CPOR) varies in the range of 1.0–4.6. Sensitivity analysis hints that an optimized recycle ratio of the unconverted gas and CPOR can maximize system performance (The optimized Ru for ESR maximum is around 9, 4.2, and 4.0, and the corresponding CPOR is around 4.25, 3.89, and 3.84, at τ = 4.94, 5.28 and 5.61), and minimize the efficiency penalty for CO2 capture (The optimized Ru for minimization of CO2 capture energy penalty is around 6.37 and the corresponding CPOR is around 3.97 at τ = 4.94, ε = 16.5). The polygeneration plant with CO2 capture after chemical synthesis has a good thermodynamic and environmental performance and may be an option for clean coal technologies and CO2 emission abatement.

scholarly journals Algorithm of Energy-Saving Control Over Hammers of Active Excavator Buckets

2021 ◽  
Vol 346 ◽  
pp. 03033
Author(s):  
Yako Liberman ◽  
Konstantin Letnev ◽  
Lyubov Gorbunova
Keyword(s):  
Control System ◽  
Energy Consumption ◽  
Energy Saving ◽  
Frozen Soils ◽  
Operating Modes ◽  
Hammer Head ◽  
All Speeds ◽  
Energy Saving Control ◽  
Command Signal

The article considers the operation of excavators designed to mine hard or frozen soils. To reduce significant energy consumption, which characterizes the process, active buckets equipped with special hammers are used. Minimizing the energy consumption of such buckets can be achieved by optimally controlling their operating modes. Expressions for the energy consumed by a hammer, characteristics of its head, operating modes, soil are derived, with the minimum of the energy estimated. On the basis of those expressions, an algorithm of energy-saving control over hammers of active excavator buckets is formed and described. It determines the corresponding speed of impact of the hammer head on the soil which can be supplied to the hammer control system in the form of a command signal. The combination of all speeds when digging a track will provide the required performance of the excavator, with each speed set to the minimum, from the viewpoint of optimizing the energy consumption, necessary for the work of the hammer.

scholarly journals An Online Energy-Saving Driving Strategy for Metro Train Operation Based on the Model Predictive Control of Switched-Mode Dynamical Systems

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4933 ◽  
Author(s):  
Fei Shang ◽  
Jingyuan Zhan ◽  
Yangzhou Chen
Keyword(s):  
Energy Consumption ◽  
Model Predictive Control ◽  
Energy Saving ◽  
Predictive Control ◽  
Control Algorithm ◽  
Optimization Problem ◽  
Online Optimization ◽  
Mode Switching ◽  
Train Control ◽  
Train Operation

With the rapid development of urban rail transit systems and the consequent sharp increase of energy consumption, the energy-saving train operation problem has been attracting much attention. Extensive studies have been devoted to optimal control of a single metro train in an inter-station run to minimize the energy consumption. However, most of the existing work focuses on offline optimization of the energy-saving driving strategy, which still needs to be tracked in real train operation. In order to attain better performance in the presence of disturbances, this paper studies the online optimization problem of the energy-saving driving strategy for a single metro train, by employing the model predictive control (MPC) approach. Firstly, a switched-mode dynamical system model is introduced to describe the dynamics of a metro train. Based on this model, an MPC-based online optimization problem is formulated for obtaining the optimal mode switching times with minimal energy consumption for a single train in an inter-station run. Then we propose an algorithm to solve the constrained optimization problem at each time step by utilizing the exterior point penalty function method. The proposed online optimal train control algorithm which determines the mode switching times can not only improve the computational efficiency but also enhances the robustness to disturbances in real scenarios. Finally, the effectiveness and advantages of this online optimal train control algorithm are illustrated through case studies of a single train in an inter-station run.

The Research of Measuring Approach and Energy Efficiency for Hadoop Periodic Jobs

2015 ◽  
Vol 8 (1) ◽  
pp. 206-210 ◽  
Author(s):  
Yu Junyang ◽  
Hu Zhigang ◽  
Han Yuanyuan
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Energy Estimate ◽  
Cloud Platform ◽  
Periodic Tasks ◽  
Current Consumption ◽  
Reduce Energy Consumption

Current consumption of cloud computing has attracted more and more attention of scholars. The research on Hadoop as a cloud platform and its energy consumption has also received considerable attention from scholars. This paper presents a method to measure the energy consumption of jobs that run on Hadoop, and this method is used to measure the effectiveness of the implementation of periodic tasks on the platform of Hadoop. Combining with the current mainstream of energy estimate formula to conduct further analysis, this paper has reached a conclusion as how to reduce energy consumption of Hadoop by adjusting the split size or using appropriate size of workers (servers). Finally, experiments show the effectiveness of these methods as being energy-saving strategies and verify the feasibility of the methods for the measurement of periodic tasks at the same time.

Design and Implementation of Low Energy Wireless Network Nodes based on Hardware Compression Acceleration

2019 ◽  
Vol 12 ◽  
Author(s):  
Hui Yang ◽  
Anand Nayyar
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Energy Saving ◽  
Optimization Design ◽  
Hardware Acceleration ◽  
Transmission Efficiency ◽  
General Purpose ◽  
Storage Space ◽  
General Purpose Processor ◽  
Compression Time

: In the fast development of information, the information data is increasing in geometric multiples, and the speed of information transmission and storage space are required to be higher. In order to reduce the use of storage space and further improve the transmission efficiency of data, data need to be compressed. processing. In the process of data compression, it is very important to ensure the lossless nature of data, and lossless data compression algorithms appear. The gradual optimization design of the algorithm can often achieve the energy-saving optimization of data compression. Similarly, The effect of energy saving can also be obtained by improving the hardware structure of node. In this paper, a new structure is designed for sensor node, which adopts hardware acceleration, and the data compression module is separated from the node microprocessor.On the basis of the ASIC design of the algorithm, by introducing hardware acceleration, the energy consumption of the compressed data was successfully reduced, and the proportion of energy consumption and compression time saved by the general-purpose processor was as high as 98.4 % and 95.8 %, respectively. It greatly reduces the compression time and energy consumption.

scholarly journals Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 81
Author(s):  
Rongjiang Ma ◽  
Shen Yang ◽  
Xianlin Wang ◽  
Xi-Cheng Wang ◽  
Ming Shan ◽  
...  
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Energy Saving ◽  
Air Conditioning ◽  
Storage System ◽  
Operation Mode ◽  
Systematic Method ◽  
Potential Analysis ◽  
Energy Saving Potential ◽  
Air Conditioning Systems

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems.

scholarly journals Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 920
Author(s):  
Liesle Caballero ◽  
Álvaro Perafan ◽  
Martha Rinaldy ◽  
Winston Percybrooks
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Mobile Robot ◽  
Energy Budget ◽  
Dynamic Models ◽  
Pearson Correlation ◽  
Experimental Conditions ◽  
Grid Map ◽  
Proposed Model ◽  
Exploration Task

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

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