TY - CHAP AU - Sabzi, Shahab AU - Vajta, László TI - Optimizing Electric Vehicle Charging considering Driver Satisfaction with Market Integration T2 - 2024 9th International Youth Conference on Energy (IYCE) PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway (NJ) SN - 9798350372380 T3 - International Youth Conference on Energy, IYCE, ISSN 2770-8500 PY - 2024 PG - 6 DO - 10.1109/IYCE60333.2024.10634927 UR - https://m2.mtmt.hu/api/publication/35294254 ID - 35294254 N1 - Conference code: 201994 Export Date: 16 September 2024 Correspondence Address: Sabzi, S.; Budapest University of Technology and Economics, Hungary; email: sabzi@iit.bme.hu AB - This paper presents a smart charging strategy for electric vehicles (EVs) by prioritizing driver satisfaction while integrating local electricity markets (LEMs). By developing the new optimization algorithm, we incorporate socio-demographic factors and real-Time data to align EV charging schedules with market signals. This approach maximizes driver satisfaction while minimizing energy costs, transforming EVs into flexible resources that support grid management. The proposed method leverages advanced machine learning techniques to predict optimal charging scenarios, balancing the demands of EV drivers and grid requirements. Simulation results demonstrate significant improvements in driver satisfaction and achieving grid efficiency, highlighting the dual benefits of enhanced user experience and operational dynamics within LEMs. This integration not only supports renewable energy integration but also provides substantial economic benefits, illustrating the potential for EVs to act as dynamic components of a resilient and sustainable energy system. © 2024 IEEE. LA - English DB - MTMT ER - TY - JOUR AU - Sabzi, Shahab AU - Vajta, László TI - Optimizing Electric Vehicle Charging Considering Driver Satisfaction through Machine Learning JF - IEEE ACCESS J2 - IEEE ACCESS VL - 12 PY - 2024 SP - 102167 EP - 102177 PG - 11 SN - 2169-3536 DO - 10.1109/ACCESS.2024.3431992 UR - https://m2.mtmt.hu/api/publication/35146327 ID - 35146327 N1 - Export Date: 7 August 2024 Funding details: U.S. Department of Commerce, DOC, BS123456 Funding text 1: This paragraph of the first footnote will contain support information, including sponsor and financial support acknowledgment. For example, \\u2018\\u2018This work was supported in part by the U.S. Department of Commerce under Grant BS123456.\\u2019\\u2019 AB - Electric vehicles (EVs) are essential to the modernization of transportation systems. However, optimizing EV charging to align with grid stability and renewable energy availability remains a challenge. To address this challenge, this study introduces a machine learning-based framework to optimize EV charging by considering driver satisfaction—a novel approach quantifying this multidimensional construct through socio-demographic attributes, State of Charge (SoC), proximity to charging stations, and variable charging fees. Driver satisfaction is defined as the extent to which the EV charging experience aligns with drivers’ expectations, integrating these key factors to influence decision-making and overall happiness with the charging service. Trained on a dataset from Hungarian EV users, the developed model predicts outcomes with high accuracy (87.9%), leading to an optimization algorithm that maximizes driver satisfaction while minimizing grid power purchase costs. Our results from a simulated smart grid demonstrate the model’s effectiveness, achieving an average charging satisfaction score of 98.5% compared to 69.54% from a traditional method. Additionally, the proposed method maintained the SoC of the EV fleet at a stable average around 50%, optimizing energy use and grid stability. By dynamically assigning EVs to charging stations and leveraging photovoltaic sources, our solution not only boosts driver satisfaction but also aids in the sustainable growth of smart grids. This research marks a significant step forward in the smart management of EV charging by introducing a driver-centric optimization model, filling a critical gap in current literature and offering insights into its application in enhancing urban mobility solutions. Authors LA - English DB - MTMT ER - TY - CHAP AU - Sabzi, Shahab AU - Vajta, László ED - Institute, of Electrical and Electronics Engineers TI - Machine Learning Based Electric Vehicle Drivers Charging Satisfaction Analysis and Prediction T2 - 2024 IEEE Conference on Technologies for Sustainability (SusTech) PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway (NJ) SN - 9798350394344 T3 - IEEE Conference on Technologies for Sustainability SusTech, ISSN 2640-6829 PY - 2024 SP - 383 EP - 389 PG - 7 DO - 10.1109/SusTech60925.2024.10553452 UR - https://m2.mtmt.hu/api/publication/35133156 ID - 35133156 N1 - et al.; IEEE; IEEE Coastal Los Angeles Section; IEEE Foothill Section; IEEE Oregon Section; IEEE San Fernando Valley Section Conference code: 200251 Export Date: 15 July 2024 Correspondence Address: Sabzi, S.; Budapest University of Technology and Economics, Hungary; email: sabzi@iit.bme.hu AB - In this paper, we develop a prediction model to assess electric vehicle (EV) drivers' charging satisfaction based on their socio-demographic characteristics. Our main focus was on the human side factors of charging behavior, but not the technical aspects, such as network related topics. We examined and predicted EV drivers' charging behavior based on socio-demographic factors, vehicle and charging station characteristics, and charging patterns. To understand the charging preferences and habits of EV drivers, we conducted a survey with 225 participants in Hungary. The effect of Several factors including age, driving experience, year of EV adoption, gender, education level, income level, state of charge (SoC), charging fees, and distance from charging stations on EV charging satisfaction was studied. A significant correlation was found between some of these factors and EV charging satisfaction. In addition, we used a feedforward neural network (FFNN) model based on TensorFlow and Keras frameworks to predict future EV drivers' charging satisfaction levels. We found that the findings of our study have practical implications for the design and planning of EV charging infrastructure and planning of EV charging sessions. In addition to providing insight into the factors affecting EV owners' charging behavior, they can also advise on the optimal design and placement of charging stations, as well as the best incentives for EV owners. Keywords- Electric vehicle, prediction, machine learning, data analysis, driver behavior, driver satisfaction © 2024 IEEE. LA - English DB - MTMT ER - TY - JOUR AU - Szántó, Mátyás AU - Vajta, László TI - Forecasting critical weather front transitions based on locally measured meteorological data JF - IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE J2 - IDŐJÁRÁS VL - 127 PY - 2023 IS - 4 SP - 459 EP - 471 PG - 13 SN - 0324-6329 DO - 10.28974/idojaras.2023.4.3 UR - https://m2.mtmt.hu/api/publication/34417880 ID - 34417880 N1 - Export Date: 21 December 2023 Correspondence Address: Szántó, M.; Department of Control Engineering and Information Technology, Műegyetem rkp. 3, Hungary; email: mszanto@iit.bme.hu Funding Agency and Grant Number: European Union [RRF-2.3.1-21-2022-00004] Funding text: The authors would like to thank Dr. Kalman Kovacs and the Data Supply Department of the Hungarian Meteorological Service for sharing the extensive local and medical meteorological and accident databases for the purpose of the research presented in this paper. The research presented here was supported by the the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory. AB - Certain types of medical meteorological phenomenontransitions can have a significant deteriorating effect on road safety conditions. Hence, a system that is capable of warning road users of the possibility of such conversions can prove to be utterly useful. Vehicles on different levels of automation (i.e., ones equipped with driver assistance systems – DAS) can use this information to adjust their parameters and become more cautious or warn the drivers to be more careful while driving. In this paper, we prove that identifying the critical type of weather front transition (i.e., no front to unstable cold front) is possible based on locally observable meteorological information. We present our method for classifying weather front transitions to non-critical versus critical types. Our developed machine learning model was trained on a dataset covering 10 years of meteorological data in Hungary, and it shows promising results with a recall value of 86%, and an F1-score of 60%. As the developed method will form the basis of a patent, we are omitting key components and parameters of our solution from this paper. LA - English DB - MTMT ER - TY - CHAP AU - Faghihi, T. AU - Sabzi, Shahab AU - Vajta, László TI - Energy Management Strategy based Charging Coordination for Electric Vehicle Integrated Distribution Grid T2 - 2023 International Conference on Future Energy Solutions (FES) PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway (NJ) SN - 9798350332308 PY - 2023 SP - 1 EP - 6 PG - 6 DO - 10.1109/FES57669.2023.10182883 UR - https://m2.mtmt.hu/api/publication/34094269 ID - 34094269 N1 - Export Date: 14 August 2023 Correspondence Address: Faghihi, T.; Budapest University of Technology and Economics, Hungary; email: tayebeh.faghihi@edu.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Szántó, Mátyás AU - Hidalgo, C. AU - Gonzalez, L. AU - Perez, J. AU - Asua, E. AU - Vajta, László TI - Trajectory Planning of Automated Vehicles Using Real-Time Map Updates JF - IEEE ACCESS J2 - IEEE ACCESS VL - 11 PY - 2023 SP - 67468 EP - 67481 PG - 14 SN - 2169-3536 DO - 10.1109/ACCESS.2023.3291350 UR - https://m2.mtmt.hu/api/publication/34069746 ID - 34069746 N1 - Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary Industry and Mobility Area, Basque Research and Technology Alliance, Tecnalia, Arteaga street, 25, Derio, Bizkaia, Spain Department of Electricity and Electronics, University of the Basque Country, Sarriena, s/n, Leioa, Vizcaya, Spain LA - English DB - MTMT ER - TY - CHAP AU - Szántó, Mátyás AU - Vajta, László ED - Nathanail, Eftihia G. ED - Gavanas, Nikolaos ED - Adamos, Giannis TI - Relationship and Differences Between Entrepreneurship and Research in the CrowdMapping Project for Crowdsourced Urban Data T2 - Smart Energy for Smart Transport PB - Springer Nature Switzerland CY - Cham SN - 9783031237218 T3 - Lecture Notes in Intelligent Transportation and Infrastructure, ISSN 2523-3440 PY - 2023 SP - 531 EP - 541 PG - 11 DO - 10.1007/978-3-031-23721-8_44 UR - https://m2.mtmt.hu/api/publication/33695726 ID - 33695726 N1 - Export Date: 2 October 2023 Correspondence Address: Szántó, M.; Department of Control Engineering and Information Technology, Hungary; email: mszanto@iit.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Szántó, Mátyás AU - Kobál, Sándor AU - Vajta, László AU - Horváth, Viktor Győző AU - Lógó, János Máté AU - Barsi, Árpád TI - Building Maps Using Monocular Image-feeds from Windshield-mounted Cameras in a Simulator Environment JF - PERIODICA POLYTECHNICA-CIVIL ENGINEERING J2 - PERIOD POLYTECH CIV ENG VL - 67 PY - 2023 IS - 2 SP - 457 EP - 472 PG - 16 SN - 0553-6626 DO - 10.3311/PPci.21500 UR - https://m2.mtmt.hu/api/publication/33609760 ID - 33609760 N1 - Funding Agency and Grant Number: European Union [RRF-2.3.1-21-2022-00004]; Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund [TKP2021, BME-NVA-02] Funding text: The research was supported by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory. The research reported in this paper is part of project no. BME-NVA-02, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021 funding scheme. AB - 3-dimensional, accurate, and up-to-date maps are essential for vehicles with autonomous capabilities, whose functionality is made possible by machine learning-based algorithms. Since these solutions require a tremendous amount of data for parameter optimization, simulation-to-reality (Sim2Real) methods have been proven immensely useful for training data generation. For creating realistic models to be used for synthetic data generation, crowdsourcing techniques present a resource-efficient alternative. In this paper, we show that using the Carla simulation environment, a crowdsourcing model can be created that mimics a multi-agent data gathering and processing pipeline. We developed a solution that yields dense point clouds based on monocular images and location information gathered by individual data acquisition vehicles. Our method provides scene reconstructions using the robust Structure-from-Motion (SfM) solution of Colmap. Moreover, we introduce a solution for synthesizing dense ground truth point clouds originating from the Carla simulator using a simulated data acquisition pipeline. We compare the results of the Colmap reconstruction with the reference point cloud after aligning them using the iterative closest point algorithm. Our results show that a precise point cloud reconstruction was feasible with this crowdsourcing-based approach, with 54\% of the reconstructed points having an error under 0.05 m, and a weighted root mean square error of 0.0449 m for the entire point cloud. LA - English DB - MTMT ER - TY - JOUR AU - Sabzi, Shahab AU - Vajta, László TI - Security and Energy Consumption Considerations of Electric Vehicles Integration in Smart Grids JF - U.Porto Journal of Engineering J2 - UPjeng VL - 9 PY - 2023 IS - 1 SP - 134 EP - 149 PG - 16 SN - 2183-6493 DO - 10.24840/2183-6493_009-001_001382 UR - https://m2.mtmt.hu/api/publication/32839439 ID - 32839439 LA - English DB - MTMT ER - TY - CHAP AU - Tayebeh, Faghihi AU - Sabzi, Shahab AU - Vajta, László ED - Kiss, Bálint ED - Szirmay-Kalos, László TI - Effects of electric vehicles and PV units on the distribution network, a modified IEEE 31 buses distribution network case study T2 - Proceedings of the Workshop on the Advances in Information Technology 2022 PB - OSZK CY - Budapest SN - 9789634218715 PY - 2022 SP - 151 EP - 156 PG - 6 UR - https://m2.mtmt.hu/api/publication/33538477 ID - 33538477 LA - English DB - MTMT ER -