Article
Engineering, Multidisciplinary
A. N. Archana, T. Rajeev
Summary: This article proposes a novel approach for the proper placement of EV charging stations in a distribution network to maintain system performance and validates the efficacy of the approach through testing.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Balasundar C., Sundarabalan C. K., Jayant Sharma, Srinath N. S., Josep M. Guerrero
Summary: This article examines the impact of voltage disturbance on EV batteries and charging systems and proposes a fault ride-through capability system to improve voltage quality. Through the use of a three-phase controlled rectifier and a dc-dc converter in the charging system, and the implementation of a dynamic voltage restorer, the EV battery and charging system are protected from critical voltage sag levels. The performance of the proposed EV charging station is evaluated under different voltage sag conditions using the MATLAB/Simulink platform and validated through software-in-the-loop testing.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Energy & Fuels
Aggelos S. Bouhouras, Despoina Kothona, Paschalis A. Gkaidatzis, Georgios C. Christoforidis
Summary: This article addresses the issue of energy losses in distribution networks caused by electric vehicles. It proposes an optimized charging schedule based on particle swarm optimization to minimize energy losses and improve the voltage profile of the grid.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Energy & Fuels
Furkan Ahmad, Mohd Khalid, Bijaya Ketan Panigrahi
Summary: This study proposes a comprehensive framework to optimally place solar-powered charging stations in a distribution network, aiming to improve voltage profile, minimize power loss, and reduce cost. The methodology involves a stochastic approach to predict EV load demand and a Feed-forward neural network to evaluate solar power from PV plants. The impact of EV load demand on the distribution network is explored, and an improved chicken swarm optimization method is used to optimize charging station placement in the IEEE 33 bus system, proving its dominance over other algorithms.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Engineering, Electrical & Electronic
Nihith Koundinya Sistla Pavan Venkat Sai, Vignesh Shivkumar, Narayanan Krishnan, Gulshan Sharma, Tomonobu Senjyu
Summary: Recent advancements in EV battery technologies and government policies promoting EVs have led to a significant increase in the number of EVs on the road, altering the load profile of distribution systems. This paper addresses grid stability by determining the impact of EV charging stations on voltage profiles and proposes a novel algorithm to quantify voltage stability indices based on refueling and travel patterns of EVs in a region. Simulations and analysis were conducted on IEEE 69 and 118 bus distribution systems.
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Dae-Jin Kim, Byungki Kim, Changwoo Yoon, Ngoc-Duc Nguyen, Young Il Lee
Summary: This paper proposes a disturbance observer (DOB)-based model predictive voltage control (MPVC) method to improve the power quality of electric vehicle charging stations (EVCSs) with battery energy storage systems (BESSs) in distribution networks. The DOB estimates the EV charging loads and PV generation power to minimize voltage fluctuation. The proposed MPVC with DOB does not require communication system and considers parameter uncertainties.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Engineering, Electrical & Electronic
Qiang Fu, Wenjuan Du, Haifeng Wang, Xianyong Xiao
Summary: This study establishes a linearized model of a DC distribution system, considering the charging and discharging states. The research shows that if the EVCSs are in the charging state, the DC distribution system is more likely to be unstable, and the system stability is the worst when all the EVCSs are at maximum charging state. Furthermore, a method to quickly evaluate the stability of the DC distribution system is proposed.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Energy & Fuels
Mohd Rizwan Khalid, M. S. Jamil Asghar, Mohammad Saad Alam, Salman Hameed, Irfan A. Khan
Summary: This paper validates an off-board electric vehicle charging station that meets the IEEE 519-2014 power quality standard, with experimental results showing high efficiency and the effectiveness of multi-phase transformer technology in reducing harmonic distortion and improving power factor.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Energy & Fuels
Minfa Huang, Hong Shen, Juanjuan Peng, Lu Zhang
Summary: Realizing fault recovery in an AC/DC hybrid distribution network through the advantages of DC network is highly effective. Power transfer can be achieved in space and time by coordinating voltage source converter (VSC), network reconfiguration, and electric vehicle charging station (EVCS). The faults in the AC/DC hybrid distribution network are divided into five types based on their locations and the connection characteristics between outage areas and power supply. A mixed integer programming model is established to optimize the recovery process, considering the minimum weighted outage power of loads. The proposed strategy significantly reduces the total weighted outage loads compared to only using VSCs and reconfiguration.
Article
Green & Sustainable Science & Technology
Ziyao Ma, Lu Zhang, Yongxiang Cai, Wei Tang, Chao Long
Summary: The study analyzes the advantages of PV-ES-CS in hybrid AC/DC distribution networks, establishes a bi-level optimal allocation model, and uses Nash equilibrium to balance economics and resilience. By simulating typical scenarios and using genetic algorithms, optimal results for PV-ES-CS configuration in the network are obtained.
IET RENEWABLE POWER GENERATION
(2023)
Article
Thermodynamics
Zhonghao Zhao, Carman K. M. Lee, Jiage Huo
Summary: This study addresses the optimal deployment of electric vehicle charging stations in the transportation and power distribution networks, which is a critical issue for the mass adoption of EVs. A finite-discrete Markov decision process formulation is proposed in a reinforcement learning framework to solve the curse of dimensionality problem. The proposed approach, which utilizes a LSTM-based recurrent neural network with an attention mechanism, outperforms other baseline approaches in terms of solution quality and computational time.
Article
Engineering, Electrical & Electronic
Hoang Tien Nguyen, Dae-Hyun Choi
Summary: Coordination between DSO and CSO is crucial for stable and economical operation of distribution grids and EVCSs. We propose a privacy-preserving decentralized framework based on DRO and MPC to address uncertainties in active unbalanced distribution systems. Two scalable optimization approaches are presented to reduce computation time, and data privacy of EVs is preserved through decentralized DRO-based MPC. The efficiency and scalability of the proposed framework are analyzed using IEEE 37-bus and IEEE 123-bus systems.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Engineering, Electrical & Electronic
K. Kathiravan, P. N. Rajnarayanan
Summary: The conventional transportation system using fossil fuels emits greenhouse gases, causing environmental pollution. To meet the needs of the growing population, a new transportation system must be established. Electric Vehicles (EVs) have less environmental impact and will be the foundation of future transportation systems. However, the low specific energy of batteries requires frequent charging. Additionally, the placement of Electric Vehicle Charging Stations (EVCS) in the power grid for long-distance transportation leads to increased network losses, negatively impacting the grid. This research focuses on minimizing network losses by optimizing the placement of EVCS with Distributed Generation (DG).
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Jitendra Singh Bhadoriya, Atma Ram Gupta, Ashwani Kumar, Rohit Ray, Surita Maini
Summary: With the exponential growth of electric vehicles worldwide, integrating fast electric vehicle charging stations into the distribution system has become crucial. This research focuses on optimal placement of charging stations and allocation of distributed generation within the distribution system to address challenges such as high power loss and poor voltage profiles. The study employs a transient search optimization algorithm to optimize a multi-objective function and showcases its convergence characteristics through MATLAB simulations. This research contributes insights into mitigating negative impacts and utilizing distributed generation for enhanced distribution system performance.
ELECTRICAL ENGINEERING
(2023)
Article
Energy & Fuels
Madathodika Asna, Hussain Shareef, Munir Azam Muhammad, Leila Ismail, Achikkulath Prasanthi
Summary: This paper introduces an effective planning methodology for electric vehicle fast-charging stations using a multi-objective binary version of the atom search optimization algorithm with quantum operations. By incorporating non-dominated sorting and Pareto concepts, the algorithm shows improved search capability and efficiency, successfully solving multi-objective optimization problems for fast-charging station location planning.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Editorial Material
Green & Sustainable Science & Technology
Anton Rassolkin, Kari Tammi, Galina Demidova, Hassan HosseinNia
Article
Energy & Fuels
Milla Vehvilainen, Pekka Rahkola, Janne Keranen, Jenni Pippuri-Makelainen, Marko Paakkinen, Jukka Pellinen, Kari Tammi, Anouar Belahcen
Summary: This study simulated and analyzed the impact of e-retrofitting strategies on heavy-duty vehicles. The results show that a multi-speed transmission in an electric heavy-duty truck significantly improves its traction performance and gradeability, while having a minor effect on the efficiency and energy consumption of the electric powertrain.
Article
Chemistry, Multidisciplinary
Nilusha Jayawickrama, Risto Ojala, Jesse Pirhonen, Klaus Kivekas, Kari Tammi
Summary: This study focuses on implementing a vision-based architecture to monitor and detect trash or valuables in shared cars using a convolutional neural network. While achieving an accuracy of 91.43%, misclassifications were observed due to variations in ambient light levels and shadows. Improvements suggested for future research include expanding the training image dataset and enhancing the modularity of the camera unit.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Civil
Sanchari Deb, Kari Tammi, Xiao-Zhi Gao, Karuna Kalita, Pinakeswar Mahanta, Sam Cross
Summary: This study proposes a two-stage planning model to determine charging station locations using fuzzy inference and Bayesian network, and employs a multi-objective framework and hybrid algorithm to obtain the Pareto front. Fuzzy decision making is used to compare Pareto optimal solutions, validating the efficacy of the model.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Chao Yang, Xinyi Tu, Juuso Autiosalo, Riku Ala-Laurinaho, Joel Mattila, Pauli Salminen, Kari Tammi
Summary: Industry 4.0 and Industry 5.0 are two different stages of development, with the former focusing on technological upgrades and improving intelligence, and the latter emphasizing societal needs, values, and responsibility. Digital twin and extended reality are two cutting-edge technologies that play important roles in human-machine interaction. This paper presents an extended reality application framework for manufacturing services, and describes its responsiveness test and industrial applications for a digital twin-based smart crane.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Electrical & Electronic
Risto Ojala, Jari Vepsalainen, Jesse Pirhonen, Kari Tammi
Summary: Intelligent transportation and smart city applications are growing rapidly, and accurate sensor perception of vehicles is crucial. This paper proposes an automated calibration approach for partially connected vehicle environments, utilizing Global Navigation Satellite System positioning information to fit a direct transformation between image and ground plane coordinates. Experimental results show that the camera-based localization accuracy is adequate for many intelligent transportation applications.
IET INTELLIGENT TRANSPORT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Alvari Seppanen, Jari Vepsalainen, Risto Ojala, Kari Tammi
Summary: This article proposes semi-autonomous control strategies to assist in the teleoperation of mobile robots under unstable communication conditions. A short-term autonomous control system is used to assist in the semi-autonomous control strategies when teleoperation is compromised. Experimental results show that autonomous, delay-dependent, and control-dependent assist improves teleoperation compared to fully manual control.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2022)
Article
Engineering, Marine
Antti Ritari, Janne Huotari, Kari Tammi
Summary: This paper presents a multiperiod decision model for shipowners to select energy system components and plan for the adoption of alternative fuels. The model aims to minimize the total cost of ownership by considering retrofitting, component sizes, and fuel allocation. The findings show that retrofitting can significantly reduce the total cost compared to fuel switching alone, batteries contribute to cost reduction, optimal component installation period can be shorter than their maximum lifetime, hydrogen fuel is favored over fuel cells, and hybrid propulsion is the key design choice for short sea vessels.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT
(2023)
Article
Robotics
Alvari Seppanen, Risto Ojala, Kari Tammi
Summary: This letter presents a novel point cloud adverse weather denoising deep learning algorithm (4DenoiseNet), which performs about 10% better than previous methods in terms of intersection over union metric and is more computationally efficient.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Computer Science, Theory & Methods
Nilusha Jayawickrama, Enric Perarnau Olle, Jesse Pirhonen, Risto Ojala, Klaus Kivekaes, Jari Vepsaelaeinen, Kari Tammi
Summary: To address the issue of poor interior cleanliness in shared vehicles, researchers have developed a computer vision-based prediction model capable of detecting trash and valuables in a timely manner. By capturing and analyzing images of the vehicle interior using a stationary wide-angled camera and a convolutional neural network, the algorithm achieved an accuracy of 89% in predicting leftover item types and 91% in general trash and valuables classes. Additionally, an indoor air quality unit was implemented to monitor specific air pollutants. Future work will focus on integrating the two systems and expanding the dataset.
JOURNAL OF BIG DATA
(2023)
Article
Computer Science, Information Systems
Antti Ritari, Panagiotis Mouratidis, Kari Tammi
Summary: This paper proposes using geometric programs to optimize conceptual-stage vessel design, which is more efficient, reliable, and automated compared to traditional nonlinear optimization methods used in naval architecture. Specifically focused on battery-electric vessels, the study presents geometric program compatible models for different components, such as lithium-ion cells and power converters. The modeling approach is applied to calculate optimal battery sizing for a coastal bulk carrier, and the problem is solved in less than a second using open-source software tools on a standard desktop computer. Sensitivity analysis reveals the impact of specific parameters on the optimal solution.
Article
Computer Science, Software Engineering
Xinyi Tu, Juuso Autiosalo, Riku Ala-Laurinaho, Chao Yang, Pauli Salminen, Kari Tammi
Summary: Digital twins and eXtended Reality are two key technological drivers for the human-centric Industry 5.0 transformation. This study introduces TwinXR, a novel method that leverages ontology-based descriptions of digital twins in industrial XR applications. The TwinXR method enables efficient and scalable XR development, as well as unlocks the potential of digital twins for data interchange and system interoperation. The study applies the TwinXR method in two industrial XR applications and demonstrates its effectiveness, while also suggesting future work on Semantic Web, Knowledge Graph, and factory-level TwinXR-compatible applications.
FRONTIERS IN VIRTUAL REALITY
(2023)
Article
Computer Science, Information Systems
Jesse Pirhonen, Risto Ojala, Klaus Kivekas, Kari Tammi
Summary: This paper presents the design and proof-of-concept of a machine vision-enhanced adaptive cruise controller, which reduces abrupt behavior by detecting brake lights in front and ensures early speed reduction. The experiments validate the system's effectiveness in reducing abrupt behavior and improving ride comfort.
Article
Engineering, Electrical & Electronic
Joel Mattila, Riku Ala-Laurinaho, Juuso Autiosalo, Pauli Salminen, Kari Tammi
Summary: This paper investigates the utilization of digital twin documents in smart factory communication. A proof-of-concept simulation model of a smart factory was implemented, and it was found that decentralized peer-to-peer control is the most suitable for smart factories.
Article
Computer Science, Theory & Methods
Risto Ojala, Jari Vepsalainen, Kari Tammi
Summary: With the emergence of intelligent and connected transportation systems, roadside camera units can enhance driver perception and onboard safety systems. Using computer vision, road users can be detected and their presence can be transmitted to vehicles that cannot perceive them. A motion detection and classification approach called MoDeCla was proposed and found to be computationally lightweight, capable of real-time detection on an inexpensive single-board computer. The results from benchmark tests using manually labeled data showed that MoDeCla achieved detection speeds an order of magnitude faster than state-of-the-art object detectors with similar accuracy, although the placement of bounding boxes presented some errors.
JOURNAL OF BIG DATA
(2022)
