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Fuel Economy optimization for Different Drive Cycles in Hybrid Electric Vehicle

Fuel Economy Optimization for Different Drive Cycles in Hybr...

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International conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Kanchan Yadav Nishesh Nigam Sanjeev Kumar Shah Ahmed Akhayyat Shashank Pareek Jasgurpreet Singh Chohan Department of Electrical Engineering GLA University Mathura Dept. of Computer Science and Engg. IES College of Technology Bhopal IES University Bhopal Electronics & Communication Engineering Department Uttaranchal Institute of Technology Uttaranchal University Dehradun India College of Technical Engineering The Islamic University Najaf Iraq Dept. of Computer Science and Engg. Chandigarh University Mohal India University Centre for Research and Development Chandigarh University Mohali India

The important control parameters for the hybrid electric vehicle are the lower and upper limit of state of charge (SOC). This SOC of the vehicle directly affects the economy and the emission rates. In this work, the parallel HEV is modelled by using ADVISOR (Advanced Vehicle Simulator). Different SOC limits are taken for testing the emission performance and fuel economy for the same designed driving cycles. With the simulation results we will be able to specify best upper and lower limits of SOC such that vehicle will achieve optimum fuel economy and emission performance. For control and energy management, the most important factor which needs to be taken into concern is the 'State of Charge (SOC)‘ value for the battery. Practically 20 % to 80% is the range which is usually set for the state of charge constraint The simulation is performed by taking repetitive velocity profiles (drive cycles) of two different curves i.e., UDDS (Urban Dynamometer Driving Schedule) and ECE. The SOC and emission curves are observed for these different drive cycles and the results having emission rates for HC, CO and NOx (in g/miles) are tabulated. This study's results shed light on why and how SOC caps affect HEVs' gas mileage and pollution ratings. The vehicle's total efficiency and environmental effect can be improved by identifying the ideal SOC range for various driving cycles. These findings can be used to inform the evolution of control and energy management systems for HEVs, leading to a more environmentally friendly and efficient transportation network in the future.

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optimization of Energy Storage Capacity to Smooth Wind Power Fluctuation 3

Optimization of Energy Storage Capacity to Smooth Wind Power...

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3rd International conference on Environmental Prevention and Pollution control Technologies, EPPCT 2021

作者： Cao, Zenggong Wang, Chunyi Peng, Bo Wang, Yasong Du, Peng Guo, Yinglei Wei, Zhen Dong, Xiaoming State Grid Shandong Electric Power Company Shandong Province Jinan250001 China Qingdao Electric Power Supply Company of State Grid Shandong Electric Power Company Shandong Province Qingdao266002 China Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Shandong Province Jinan250061 China

The uncertainty and randomness of wind power generation bring hidden trouble to the safe operation of power distribution network. Combining energy storage system with wind power generation can effectively improve the output characteristics of wind power generation. In this paper, considering the investment cost of energy storage and the effect of suppressing the fluctuation of wind power output, the optimization of energy storage capacity under the scenario of wind power grid connection is studied. Firstly, the multi-objective capacity optimization model of the energy storage system is established to minimize the cost of the energy storage system and the variance of wind power system output equipped with energy storage. Then, the multi-objective optimization problem is transformed into a single objective optimization problem by introducing the weight coefficient of the objective function and the particle swarm optimization algorithm (PSO) is used to solve the problem. Finally, through the test of IEEE-33 bus distribution network system, the relationship curve between energy storage capacity and wind power fluctuation suppression effect is obtained. The results of numerical examples show the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

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Noise optimization and Linearization of RF Mixer using Inductive Peaking and Source Degeneration for Radio Local Area network and Low Power Wireless Access Systems Applications

Noise Optimization and Linearization of RF Mixer using Induc...

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International conference on VLSI Systems, Architecture, Technology and Applications (VLSI-SATA)

作者： Avvaru Subramanyam R.V.S. Satyanarayana Department of E.C.E Sri Venkateswara University College of Engineering Tirupati India

ISBN: (纸本)9781665455473

Low Power Wireless Access Systems (LP-WAS) are omnipresent, accomplishing innumerable technological needs. Edict to meet the cutting-edge requirements in terms of cost, power as well as size, they impose very rigid specifications w.r.t. the performance of various subsystems. Inductive-peaking in conjunction with inductive source degeneration technique (IPISD) is proposed for enhanced performance of mixer, to gratify the need of LP-WAS including Radio Local Area network (R-LAN) applications. It landscapes a double balanced down conversion mixer topology to aim at concurrent optimization of noise figure as well as linearity. Post Layout Simulation (PLS) results reason for a Single Side Band Noise Figure (SSB-nf) of 7.85 dB, Conversion Gain (CG) of 13.71 dB, 2.66 dBm third order input intercept point (IIP 3 ), 71.26 dB Spurious Free Dynamic Range (SFDR), and a 1-dB Gain Compression (1-dB GC) of −9.36 dBm at 3.49 mW of d.c. power consumption (P dc ) for 5.5 GHz Radio Frequency (RF) input to yield 100 MHz Intermediate frequency (IF) in 180 nm CMOS technology/process node with 1.8 V supply voltage (V DD ).

关键词： Wireless communication Radio frequency Noise figure Linearity Voltage Very large scale integration Mixers

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Data-Driven System Level Synthesis 3

Data-Driven System Level Synthesis

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Xue, Anton Matni, Nikolai Univ Penn Philadelphia PA 19104 USA

We establish data-driven versions of the System Level Synthesis (SLS) parameterization of linear systems. In particular, optimization over achievable closed-loop finite-horizon system responses of linear-time-invariant dynamics can be posed using only data from past trajectories without explicitly identifying a system model. We first show an exact equivalence between the traditional and data-driven SLS parameterizations under the idealized assumption of noise-free trajectories. This is then extended to the case with process noise, where techniques from robust SLS are used to characterize and bound the effects of noise on closed-loop performance. Furthermore, we use tools from matrix concentration to show that simple trajectory averaging suffices to mitigate the impact of noise. We end with numerical experiments demonstrating the soundness of our methods.

关键词： Data-driven control model-free control system level synthesis

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Deep Reinforcement Learning for Cost-Efficient Power control in Multi-Cell networks

Deep Reinforcement Learning for Cost-Efficient Power Control...

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On-Line conference (ONCON), Industrial Electronics Society Annual

作者： Chang Sun Xiaokai Nie Xin Zhao Wenwu Yu School of Automation Southeast University Nanjing China Shenzhen Research Institute Southeast University Shenzhen China School of Mathematics Southeast University Nanjing China

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

Energy consumption is a critical concern in multi-cell networks. To reduce the energy cost, renewable energy has been accommodated to offer cellular networks with auxiliary energy supply. Most existing studies of cellular networks powered by hybrid energy sources are based on ideal battery models. However, the energy storage efficiency of batteries in reality is influenced by environmental factors and the natural degradation of batteries over time. In this paper, we investigate the cost-efficient transmit power control in multi-cell networks with hybrid energy sources and time-varying energy storage efficiency. Considering such dynamic networks characterized by the stochastic property of energy harvesting, channel conditions, and time-varying energy storage efficiency, deep reinforcement learning (DRL) is adopted to learn the optimal power control strategy without requiring prior knowledge of the environment. To enhance the robustness and accommodate varying levels of exploration and exploitation during different training phases of DRL, an enhanced proximal policy optimization algorithm is proposed. Specifically, a dynamic clipping threshold is designed based on an adaptive adjustment mechanism, which allows modifying the clipping threshold in accordance with the agent's performance. Simulation results show that our proposed DRL algorithm can significantly improve the cost efficiency of the multi-cell network compared with other DRL algorithms.

关键词： Training Cellular networks Costs Heuristic algorithms Simulation Power control Deep reinforcement learning Hybrid power systems Batteries Energy harvesting

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Input Convex Neural networks for Building MPC 3

Input Convex Neural Networks for Building MPC

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Bunning, Felix Schalbetter, Adrian Aboudonia, Ahmed de Badyn, Mathias Hudoba Heer, Philipp Lygeros, John Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland Empa Dubendorf Urban Energy Syst Lab D uben Switzerland Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland

Model Predictive control in buildings can significantly reduce their energy consumption. The cost and effort necessary for creating and maintaining first principle models for buildings make data-driven modelling an attractive alternative in this domain. In MPC, the models form the basis for an optimization problem whose solution provides the control signals to be applied to the system. The fact that this optimization problem has to be solved repeatedly in real-time implies restrictions on the learning architectures that can be used. Here, we adapt Input Convex Neural networks that are generally only convex for one-step predictions, for use in building MPC. We introduce additional constraints to their structure and weights to achieve a convex input-output relationship for multistep ahead predictions. We assess the consequences of the additional constraints for the model accuracy and test the models in a real-life MPC experiment in an apartment in Switzerland. In two five-day cooling experiments, MPC with Input Convex Neural networks is able to keep room temperatures within comfort constraints while minimizing cooling energy consumption.

关键词： Input Convex Neural networks Model Predictive control Building Energy Management

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Invariant Policy optimization: Towards Stronger Generalization in Reinforcement Learning 3

Invariant Policy Optimization: Towards Stronger Generalizati...

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3rd Annual conference on Learning for Dynamics and control (L4DC)

作者： Sonar, Anoopkumar Pacelli, Vincent Majumdar, Anirudha Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Princeton Univ Dept Mech & Aerosp Engn Princeton NJ 08544 USA

A fundamental challenge in reinforcement learning is to learn policies that generalize beyond the operating domains experienced during training. In this paper, we approach this challenge through the following invariance principle: an agent must find a representation such that there exists an action-predictor built on top of this representation that is simultaneously optimal across all training domains. Intuitively, the resulting invariant policy enhances generalization by finding causes of successful actions. We propose a novel learning algorithm, Invariant Policy optimization (IPO), that implements this principle and learns an invariant policy during training. We compare our approach with standard policy gradient methods and demonstrate significant improvements in generalization performance on unseen domains for linear quadratic regulator and grid-world problems, and an example where a robot must learn to open doors with varying physical properties.

关键词： Reinforcement Learning Generalization Invariance Causality

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Improving Reactive Power Management and Voltage Profiles in Hybrid Renewable Energy Grids using Heterogeneous Context-Aware Graph Convolutional network

Improving Reactive Power Management and Voltage Profiles in ...

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Automation, Computing and Renewable Systems (ICACRS), International conference on

作者： M. Siva Ramkumar Gokul Gopan Arun M Jayant Giri Khaled Al-Qawasmi Dept of ECE SNS College of Technology India Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai TN India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

With the growing integration of renewable energy sources (RES) into power grids (PG), managing reactive power and maintaining voltage stability have become critical challenges. This paper proposes a novel approach using a Heterogeneous Context-Aware Graph Convolutional network (HCAGCN) to improve reactive power management and voltage profiles in hybrid renewable energy grids (REG). The objective of the proposed method is to improve the voltage profile, reduces the power loss. The proposed HCAGCN approach effectively models complex relationships among various components in hybrid REG, enabling dynamic adjustment of reactive power and enhancing overall voltage stability. By then, the proposed method is implemented in MATLAB platform and evaluated their performance with various existing methods such as Artificial Neural network (ANN), Firefly Algorithm (FA), Kronecker Neural network (KNN), Genetic Algorithm (GA), and Ant Lion optimization (ALO). The HCAGCN technique achieves the lowest current THD at 0.81% and voltage THD at 3.49%, significantly outperforming the numerous existing methods.

关键词： Reactive power Renewable energy sources Graph convolutional networks Artificial neural networks Power system stability Hybrid power systems Stability analysis Voltage control MATLAB Genetic algorithms

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optimization and Transmit Power control Based on Deep Learning with Inaccurate Channel Information in Underlay Cognitive Radio network 3

Optimization and Transmit Power Control Based on Deep Learni...

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2020 3rd International conference on Modeling, Simulation and optimization Technologies and Applications, MSOTA 2020

作者： Zong, Xuekai Zhu, Xiaomei Zhu, Aichun Byrne, John Oliver College of Computer Science and Technology Nanjing Tech University Nanjing211816 China

In cognitive radio network(CRN), power control often faces the complicated iterations and large calculations, resulting in poor real time performance of the system. In this paper, a deep learning-based power control is proposed for CRNs, where the secondary users (SUs) can share the same channel of primary users (PUs) without causing excessive interference to the communication of PU. In the novel scheme, the DNN model is used to treat the input and output of the power control algorithm as unknown non-linear mappings and fit them, which determines the proportion of transmit power allocated to each SU, considering the interference caused to the PU. With this scheme, the maximization of the SUs sum-rate can be achieved. Furthermore, due to some errors in the practical samples of channel information, an auto-encoder is used to compress the channel coefficient through an encoder and reconstruct them through a decoder before DNN training. The simulations results show that the power control method using a combination of auto-encoding and DNN can improve the real-Time performance of the system. And the sum-rate of SU is improved while the interference caused to the PU can be regulated even with the inaccurate channel information. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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A Line Load and Fault Detection Device Based on FPGA+DSP Architecture

A Line Load and Fault Detection Device Based on FPGA+DSP Arc...

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3rd International conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2024

作者： Ou, Guangyun Li, Wenyue Qi, Wang Qiu, Jiejin Hu, Huixia Bai, Junjian Deng, Lixue Mo, Yanbin Guangxi Beitou IT Innovation Technology Investment Group Co. Ltd Nanning China Guangxi Center for Applied Mathematics Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin China

ISBN: (纸本)9789819627660

With the rapid development of electronic information technology and industrial automation, the application of measurement technology in product automatic detection is becoming increasingly widespread. In response to the limitations of traditional frequency response methods, this design proposes a line load and fault detection device based on FPGA + DSP architecture. Through amplitude frequency characteristic analysis, the measurement of a single RCL component and the identification of the load structure network are achieved. The system adopts TI’s DSP chip and Altera’s FPGA as the core controller, and uses SCI protocol and SPI protocol to control the digital to analog converter and data display. Actual testing has shown that the device can accurately identify load open circuit and short circuit faults, and can identify 8 types of RLC component structural networks. The measurement error is small, the measurement time is short, and the performance meets the expected indicators. The success of this design provides new ideas for product detection devices and has broad application prospects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： DSP FPGA Frequency response method Load structure network RCL component measurement

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