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A fuzzy logic Approach on the Evaluation of Driving Styles and Investigation of Drivability Calibration Effects

GAZI UNIVERSITY JOURNAL OF SCIENCE 2022年第2期35卷 668-680页

作者： Aksit, Samet Yavuz, Akif Sen, Osman Taha AVL Turkey Res & Engn TR-34885 Istanbul Turkey Istanbul Tech Univ Fac Mech Engn TR-34437 Istanbul Turkey

Increased customer expectations lead the automobile manufacturers to develop innovative solutions, such as mode selection functions that provide different performance and comfort settings for the drivers. Almost all brands have different types of driving modes installed on their vehicles, such as sport mode, economy mode, off-road mode, etc. In the current technology, the mode selection is manually done by the driver. Thus, no effort is taken to match the driver style with available driving modes. However, driving mode selection should be done through an intelligent system such as vehicle control unit, in order to optimize customer expectations related to vehicle performance, driving comfort, and fuel consumption. This can be achieved by the analysis of all drivability maneuvers during any driving cycle. Based on the results of these analyses, drivability calibration settings of the vehicle can be adjusted depending on driver behaviors. In addition, fuel consumption can be improved using suitable calibration for each driver type. In this study, an experimental investigation is carried out in which vehicle data is collected for eleven different drivers at three different drivability calibrations. Furthermore, fuzzy logic algorithms are utilized in order to distinguish the driver characteristics. First, data from nine drivers are used in order to train the fuzzy logic approach. Then, the trained fuzzy logic scheme is used to assess the characteristics of two other drivers, who were left out in the training data set. Hence, it is aimed to obtain an intelligent prediction procedure that can estimate the characteristics of a driver based on their driving styles.

关键词： Drivability calibration fuzzy logic algorithm Driver classification

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Control and Power Management of Battery-PEM Fuel Cell Hybrid Power System for Marine Propulsion Systems

Control and Power Management of Battery-PEM Fuel Cell Hybrid...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Jamma, Mustapha Bujlo, Piotr Ulleberg, Øystein Institute for Energy Technology Instituttveien 18 Kjeller2007 Norway

ISBN: (纸本)9781665471640

To reduce the air pollution and greenhouse gas emissions in harbors and coastal areas, the propulsion systems of maritime transport, and harbor operations, and services, should be driven by zero-emission power systems. Hence, the development of a zero-emission systems, such as, a hybrid battery-fuel cell system, with an appropriately designed control system is essential to accurately manage the energy between the proton-exchange membrane fuel cells (PEMFCs), batteries, and loads. In this work, a hybrid power system based on PEMFCs and lithium-ion batteries for marine propulsion systems is proposed. An energy management strategy (EMS) based on a fuzzy logic approach and rule-based technique is developed to stabilize the DC bus voltage at the required constant value, properly control the power distribution between the hybrid system components to meet the required power demand of the loads and minimize fuel cell degradation. The overall system is modeled under MATLAB/Simulink environment. Finally, the results are discussed, and the suitability of the suggested hybrid power system and EMS is justified. © 2023 IEEE.

关键词： energy management system fuzzy logic algorithm hybrid power system PEM fuel cell zero-emission mobility

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fuzzy-logic-Based Active Protection for Photovoltaic DC Power Plant

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IEEE TRANSACTIONS ON POWER DELIVERY 2020年第2期35卷 497-507页

作者： Jia, Ke Zhu, Rui Bi, Tianshu Zhao, Qijuan Wang, Congbo Xuan, Zhenwen North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China

The dc boosting and integration system has been increasingly used in photovoltaic power plants due to its flexible control and excellent performance. The dc boosting and integration system has complex topology and many photovoltaic branches. Because power electronic devices cannot withstand large fault currents, protection with fast operating speed is required. Thus, the starting element has to start quickly and sensitively, and the protective relays must identify the faulty section correctly based on the instantaneous value of the distorted transient fault current. Considering these, the conventional protection carries great potential for disoperation;therefore, a novel active protection is proposed. The starting elements start more sensitively, and the faulty region is correctly distinguished through identifying the fault information generated by active control. The effective fault information is derived from distorted transient instantaneous current data through the fuzzy logic algorithm. Simulation results reveal that the proposed protection can accurately operate within 1 ms when noise is up to 20 dB and transition resistance is up to 500 omega.

关键词： DC boosting and integration system fuzzy logic algorithm active control active protection

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Enhancing Identification of Meteorological and Biological Targets Using the Depolarization Ratio for Weather Radar: A Case Study of FAW Outbreak in Rwanda

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REMOTE SENSING 2024年第14期16卷 2509-2509页

作者： Maniraguha, Fidele Vodacek, Anthony Kim, Kwang Soo Ndashimye, Emmanuel Rushingabigwi, Gerard Univ Rwanda African Ctr Excellence Internet Things KN 67 StPOB 3900 Kigali Rwanda Rwanda Meteorol Agcy Technol & Informat Syst Div KN 96 StPOB 898 Kigali Rwanda Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Seoul Natl Univ Dept Agr Forestry & Bioresources Seoul 08826 South Korea Carnegie Mellon Univ Africa Dept Informat & Commun Technol Kigali 10000 Rwanda Univ Rwanda Dept Elect & Elect Engn EEE KN 67 StPOB 3900 Kigali Rwanda

Leveraging weather radar technology for environmental monitoring, particularly the detection of biometeors like birds, bats, and insects, presents a significant challenge due to the dynamic nature of their behavior. Unlike hydrometeor targets, biometeor targets exhibit arbitrary changes in direction and position, which significantly alter radar wave polarization upon scattering. This study addresses this challenge by introducing a novel methodology utilizing Rwanda's C-Band Polarization Radar. Our approach exploits the capabilities of dual-polarization radar by analyzing parameters such as differential reflectivity (ZDR) and correlation coefficient (RHOHV) to derive the Depolarization Ratio (DR). While existing radar metrics offer valuable insights, they have limitations in fully capturing depolarization effects. To address this, we propose an advanced fuzzy logic algorithm (FL_DR) integrating the DR parameter. The FL_DR's performance was rigorously evaluated against a standard FL algorithm. Leveraging a substantial dataset comprising nocturnal clear air radar echoes collected during a Fall Armyworm (FAW) outbreak in maize fields from September 2020 to January 2021, the FL_DR demonstrated a notable improvement in accuracy compared to the existing FL algorithm. This improvement is evident in the Fraction of Echoes Correctly Identified (FEI), which increased from 98.42% to 98.93% for biological radar echoes and from 87.02% to 95.81% for meteorological radar echoes. This enhanced detection capability positions FL_DR as a valuable system for monitoring, identification, and warning of environmental phenomena in regions similar to tropical areas facing FAW outbreaks. Additionally, it could be tested and further refined for other migrating biological targets such as birds, insects, or bats.

关键词： depolarization ratio (DR) fuzzy logic algorithm fall armyworm (FAW) detection weather radar FAW early warning system

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Risk-Aware Path Planning for Autonomous Vehicles

Risk-Aware Path Planning for Autonomous Vehicles

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作者： Gaeini, Shaghayegh Northern Illinois University

学位级别：M.S., Master of Science/Master of Surgery

Autonomous vehicles have been the subject of intense research leading to great improvements in safety, comfort, accessibility, efficiency, etc. Path planning which provides the autonomous vehicle with a smooth, collision-free, and safe path towards its destination, is a crucial part of autonomous vehicles (AVs). Various path planning techniques have been developed in the past decades that can successfully plan a smooth and safe path in various driving scenarios. Nevertheless, these algorithms perform poorly in critical situations where the vehicle needs to quickly make a decision. To address this issue, a couple of algorithms have been proposed to replace the AV’s conventional path planning algorithm in situations with high a risk of collision as none of the current algorithms are efficient enough to perform the complete path planning for the vehicle. This study proposes a novel risk-aware planning method based on trajectory generation in the Frenet framework which is capable of planning a smooth, safe, and collision-free path for the AV in normal and critical driving situations. This algorithm works based on generating and evaluating a set of potential trajectories using a proposed cost function based on the lateral deviation, time, speed limit, and risk of trajectories. This is determined using a novel risk assessment method based on fuzzy logic algorithms. Achieving a reliable path planning method that performs efficiently in complex environments such as highways, is a key factor. Therefore, a prediction algorithm has been developed in this study to estimate the position, velocity, and trajectory of obstacles in the surrounding environment of the ego vehicle. This thesis concludes that the proposed risk-aware path planning method builds safe, smooth, and efficient trajectories that follow the desired path, estimate the position and velocity of surrounding vehicles, and avoid collisions in various normal or high-risk scenarios.

关键词： Autonomous vehicles Frenet coordinates fuzzy logic algorithm Path planning Risk assessment system Trajectory generation

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Incorporating human factors into LCM using fuzzy TCI model

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TRANSPORTMETRICA B-TRANSPORT DYNAMICS 2021年第1期9卷 198-218页

作者： Li, Linbo Li, Yang Ni, Daiheng Tongji Univ Key Lab Rd & Traff Engn Minist Educ 4800 Caoan Rd Shanghai 201804 Peoples R China Univ Massachusetts Civil & Environm Engn Amherst MA 01003 USA

Incorporation of Human Factors (HF) into the mathematical car-following (CF) models has always been the research hotspot. Ignorance of such inclusion would inevitably hinder us from acquiring a comprehensive understanding of traffic flow phenomena. This paper proposed a novel CF model in order to bridge three existing research gaps: the demand for more inclusion of HF into the Longitudinal Control Model (LCM);the requirement for a more desirable underlying CF model for the Task Capability Interface (TCI) model;the ignorance of the fuzziness of human brains when modeling Task Difficulty (TD). Specifically, in order to imitate driver's natural or subjective uncertainty and ambiguity of his TD, the fuzzy logic approach is introduced, and the TD is then incorporated into the LCM. Thereafter, both numerical simulation and field-data validation have been performed. Results indicate that our proposed model is more capable of accommodating HF and exhibits better performance than its predecessor.

关键词： Car-following model human factors task capability interface model fuzzy logic algorithm

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fuzzy logic-based virtual capacitor adaptive control for multiple HESSs in a DC microgrid system

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年第May期107卷 78-88页

作者： Ni, Feng Yan, Linfang Liu, Ju Shi, Mengxuan Zhou, Jianyu Chen, Xia Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan Hubei Peoples R China State Grid Hubei Power Co Ltd Econ Res Inst Wuhan 430074 Hubei Peoples R China

In a DC microgrid, virtual capacitor droop control (CDC) can achieve power allocation according to different frequency characteristics of a hybrid energy storage system (HESS). However, the control performance may be easily affected by the line impedance. In this paper, an adaptive droop controller based on a fuzzy logic algorithm is proposed. By detecting the unbalanced power and the voltage deviation, the droop coefficient can be adaptively adjusted to mitigate the influence of the line impedance and achieve desirable power splitting in the HESS. Furthermore, the small-signal model of the HESS is established and the impact of the droop coefficient variation on the stability of the DC microgrid is exploited. The simulation results in PSCAD/EMTDC verify the effectiveness of the proposed adaptive droop controller. It has been proved that desirable power splitting can be obtained among multiple HESSs under different scenarios.

关键词： DC microgrid Virtual capacitor droop control fuzzy logic algorithm Adaptive control

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Bionic Robotic Fish Path Planning based on fuzzy logic

Bionic Robotic Fish Path Planning based on Fuzzy Logic

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第四届材料科学应用与能源材料国际研讨会

作者： Fan Gu Lingyan Hou Zihui Gao Tianye Han School of computer science Beijing Information Science and Technology University

With improvement and development of the science of robotics, the bionic robotic fish has attracted great attentions in recent years. Path planning is a key point in the field of bionic robotic fish control. This paper takes full account of underwater motion characteristics of bionic robotic fish, and puts forward path planning based on fuzzy logic algorithm aiming at problems of former algorithms such as deviation due to accuracy defect of shooting area and angle and tail motions;by referring to obstacle avoidance thinking and decision making process of people while walking, it makes further planning according to distance from obstacles to the robotic fish and the direction angle so as to realize accurate ball shooting.

关键词： Path Planning fuzzy logic algorithm Robotic Fish Membership Function

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SOC Balancing and Coordinated Control Based on Adaptive Droop Coefficient algorithm for Energy Storage Units in DC Microgrid

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ENERGIES 2022年第8期15卷 2943页

作者： Tian, Guizhen Zheng, Yuding Liu, Guangchen Zhang, Jianwei Inner Mongolia Univ Technol Coll Elect Power Hohhot 010080 Peoples R China Inner Mongolia Reg Key Lab Elect Power Convers Tr Hohhot 010080 Peoples R China

In order to achieve a state-of-charge (SOC) balance among multiple energy storage units (MESUs) in an islanded DC microgrid, a SOC balancing and coordinated control strategy based on the adaptive droop coefficient algorithm for MESUs is proposed. When the SOC deviation is significant, the droop coefficient for an energy storage unit (ESU) with a higher (or lower) SOC is set to a minimum value when discharging (or charging). The ESU with the higher (or lower) SOC is controlled to discharge (or charge) with the rated power, while the other ESU compensates for the remaining power when the demanded discharging (or charging) power is greater than the rated power of the individual ESU. Otherwise, when the demanded discharging (or charging) power is lower than the rated power of either ESU, the ESU with the higher (or lower) SOC releases (or absorbs) almost all the required power while the other ESU barely absorbs or releases power, thus quickly realizing SOC balancing. When the SOC deviation is slight, the fuzzy logic algorithm dynamically adjusts the droop coefficient and changes the power distribution relationship to balance the SOC accurately. Furthermore, a bus voltage recovery control scheme is employed to regulate the bus voltage, thus improving the voltage quality. The energy coordinated management strategy is adopted to ensure the power balance and stabilize the bus voltage in the DC microgrid. A simulation model is built in MATLAB/Simulink, and the simulation results demonstrate the effectiveness of the proposed control strategy in achieving fast and accurate SOC balance and regulating the bus voltage.

关键词： DC microgrid coordinated control energy management SOC balancing droop control fuzzy logic algorithm

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Sensor Fault Diagnosis Using an Indirect fuzzy Feedback Linearization and Decision Trees 12

Sensor Fault Diagnosis Using an Indirect Fuzzy Feedback Line...

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12th International Conference on ICT Convergence (ICTC) - Beyond the Pandemic Era with ICT Convergence Innovation

作者： TayebiHaghighi, Shahnaz Koo, Insoo Univ Ulsan Dept Elect Elect & Comp Engn Ulsan South Korea

ISBN: (纸本)9781665423830

Sensor fault diagnosis is a cutting-edge research area in recent years. In the paper, an indirect fuzzy feedback linearization technique is suggested for sensor fault diagnosis. The proposed scheme has three main steps as following: The preprocessing unit is the first step to design an indirect observer in which the feature extraction technique is used to extract the root means square (RMS) data from resampled fuel to air ratio signals. The normal RMS resampled data is modeled using the nonlinear data identification technique. The combination of autoregressive with external input, Laguerre input-output filter, and fuzzy algorithm, FAL, is suggested for normal resampled RMS data modeling. After modeling the data, the combination of the FAL modeling method, feedback linearization technique, and fuzzy estimator, FAL-FF, is suggested for RMS resampled data estimation. The fuzzy technique is used to estimate the nonlinear function in feedback linearization observer and improve the accuracy of estimation for data classification. Next, the residual resampled RMS fuel to air ratio data is generated using the difference between original and estimated data. Finally, the decision tree is used to classify the resampled RMS residual sensors data for fault diagnosis. Regarding the results, the accuracy of sensor fault diagnosis using the proposed scheme is 96.7%.

关键词： Sensor fault diagnosis machine learning autoregressive with external input modeling fuzzy logic algorithm feedback linearization observer decision tree

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