Article
Engineering, Chemical
Yun Gao, Wujun Ji, Xin Zhao
Summary: This study focuses on the SOC estimation model of electric vehicle batteries using the extended Kalman filter algorithm and backpropagation neural network. The results show that the model has high accuracy and robustness, with low average estimation errors under different working conditions.
Article
Energy & Fuels
Jinpeng Tian, Rui Xiong, Weixiang Shen, Jiahuan Lu
Summary: A method based on deep neural network is proposed for fast and accurate estimation of SOC for LiFePO4 batteries, with an error of less than 2.03% over the entire battery SOC range. By integrating the DNN with a Kalman filter, the robustness of SOC estimation against random noises and error spikes can be improved.
Review
Energy & Fuels
Yuefeng Liu, Yingjie He, Haodong Bian, Wei Guo, Xiaoyan Zhang
Summary: With the rapid growth in productivity, the demand for fossil fuels has increased, leading to research and development of new energy sources. Electric vehicles powered by lithium-ion batteries have become the mainstream in the automotive industry. Battery management systems are important for ensuring the safety and reliability of electric vehicle operation. Deep neural networks have been widely used in the field of battery state estimation, and this review classifies recent estimation methods based on deep learning and discusses future directions.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Environmental Sciences
Krishna Veer Singh, Rajat Khandelwal, Hari Om Bansal, Dheerendra Singh
Summary: This study focuses on identifying the optimal operating values for battery state of charge, motor power, and fuel converter to improve battery life and fuel economy without compromising vehicle performance. Adjusting these parameters results in significant improvements in driving range and battery life, with minimal cost.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Electrochemistry
Shiqiang Zhuang, Yuan Gao, Andi Chen, Tingyu Ma, Yang Cai, Min Liu, Yiming Ke
Summary: This paper investigates the problem of state of charge estimation of lithium-ion batteries and proposes an optimized method based on the Cubature Kalman Filter algorithm. The parameters of the circuit model are determined using experimental data, and the optimized algorithm is compared with other algorithms. Experimental results show that the accuracy of SOC estimation is significantly improved using the optimized CKF algorithm.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2022)
Article
Multidisciplinary Sciences
Rajakumar Sakile, Umesh Kumar Sinha
Summary: This paper proposes an extended nonlinear state observer (ENSO) to accurately estimate the state of charge (SOC) of lithium-ion batteries. Utilizing a two RC equivalent circuit model and ninth-order polynomial fitting curve, the ENSO ensures stability and convergence rate. Compared to conventional methods, the proposed observer demonstrates better dynamic performance and convergence capability.
ADVANCED THEORY AND SIMULATIONS
(2022)
Article
Thermodynamics
Jialu Qiao, Shunli Wang, Chunmei Yu, Xiao Yang, Carlos Fernandez
Summary: In this research, a novel dynamic migration model is proposed to better describe the dynamic characteristics of lithium-ion batteries under different aging states by adjusting battery parameters in realtime. A novel chaotic firefly-particle filtering method is proposed, which uses the behavior of fireflies in nature to simulate particle optimization and find a new optimal solution through chaotic mapping. Compared with traditional particle filtering algorithm, the proposed algorithm improves the state-of-charge and state-of-health estimation accuracy by 1.48% and 0.38% respectively under Hybrid Pulse Power Characterization condition, and by 0.67% and 0.63% respectively under Beijing bus dynamic stress test condition. The proposed battery model and algorithm are of great significance in improving the condition monitoring quality of the battery management system.
Article
Engineering, Chemical
Abraham Efraim Rodriguez-Mata, Emanuel Gomez-Vidal, Carlos Alexander Lucho-Constantino, Jesus A. Medrano-Hermosillo, Rogelio Baray-Arana, Pablo A. Lopez-Perez
Summary: This study presents an analytical design of a Non-Linear Logistic Observer (NLLO) to predict the state variables in a biodigester, and compares its performance to other state estimators. Physical sensors used for biodigester samples are not always practical due to their sensitivity to sampling type and external disturbances, hence the use of virtual sensors. The proposed NLLO observer, which incorporates online CH4 and CO2 measurements, has been experimentally validated and shown to recover all state variables.
Article
Green & Sustainable Science & Technology
Zeeshan Ahmad Khan, Prashant Shrivastava, Syed Muhammad Amrr, Saad Mekhilef, Abdullah A. Algethami, Mehdi Seyedmahmoudian, Alex Stojcevski
Summary: This study evaluates the performance of seven different online SOC estimation algorithms using experimental data. The extended Kalman filter and sliding mode observer performed the best in terms of estimation accuracy and computation time.
Article
Computer Science, Information Systems
Marco Pasetti, Salvatore Dello Iacono, Dario Zaninelli
Summary: This study proposes a method for estimating the state of charge and identifying the charging stage of batteries on light electric vehicles. The method utilizes the active power measured at the charging socket to estimate the state of charge, eliminating the need for direct current measurements or communication with the vehicle's battery management system.
Article
Green & Sustainable Science & Technology
M. Becherif, H-S Ramadan, A. Benmouna, S. Jemei
Summary: This study focuses on developing a hybrid Coulomb counting/impedance measurement approach to accurately determine the State-of-Charge (SOC) of battery electric vehicles. This approach aims to extend battery life and optimize battery management. The initial SOC is estimated using impedance measurement, and then Coulomb counting is used to further estimate the SOC. Experimental and theoretical verification validate the effectiveness of this proposed method.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Energy & Fuels
Minjoon Kim, Jaehyuk So
Summary: State of charge (SOC) and state of health (SOH) estimation is crucial in electric-vehicle battery-management systems. This paper presents a hardware-optimized algorithm for SOC and SOH estimation in a slave module and provides verification results using a field-programmable gate array (FPGA).
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Bachir Zine, Haithem Bia, Amel Benmouna, Mohamed Becherif, Mehroze Iqbal
Summary: Battery state of charge is crucial for the development of electric vehicles. This article introduces an efficient battery state-of-charge estimator based on Coulomb counting technique, validated through experiments for tracking the battery pack's state of charge reliability.
Article
Automation & Control Systems
Peihang Xu, Xiaoyi Hu, Benlong Liu, Tiancheng Ouyang, Nan Chen
Summary: This article proposes a hierarchical estimation model considering the current rate, using a fractional-order model for battery modeling, data-driven parameter identification, and a multiscale dual extended Kalman filter for battery states estimation. The experimental results show significant improvement in SOC and SOH estimation compared with traditional methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Transportation Science & Technology
Nikolaos Tsanakas, Joakim Ekstrom, Johan Olstam
Summary: This paper proposes a novel approach for generating Virtual Vehicle Trajectories (VVT) to improve the accuracy of emission estimations in emission modelling. The method is empirically evaluated and shows promising results compared to traditional approaches.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Economics
Araavind Sridhar, Samuli Honkapuro, Fredy Ruiz, Jan Stoklasa, Salla Annala, Annika Wolff, Antti Rautiainen
Summary: Demand Response (DR) is a potential tool to reduce stress in the electricity system caused by renewable energy, and this study examines the motivators of residential consumers towards Direct Load Control (DLC) DR. The analysis identifies consumer subgroups and the influence of socioeconomic and demographic characteristics on these subgroups. The findings have practical implications for energy flexibility among residential consumers and highlight the importance of policy considerations for widespread adoption of DR.
Article
Computer Science, Information Systems
Enrico Spateri, Fredy Ruiz, Giambattista Gruosso
Summary: Single-switch quasi-resonant DC inverters are commonly used in low-power induction-heating applications due to their cost-effectiveness. However, they face challenges in achieving soft-switching and have limited controllability. This article proposes a time-domain simulation strategy to analyze the behavior of induction heating systems with a quasi-resonant single-ended DC inverter using pulse frequency modulation and variable load. The simulation incorporates the non-linear behavior of the load, taking into account temperature dynamics, work-piece anisotropies, and current harmonic waveforms. The power regulation strategy based on switch turn-on time control is shown to ensure safe operation of the converter even with varying load.
Article
Automation & Control Systems
Lorenzo Sabug, Gian Paolo Incremona, Mara Tanelli, Fredy Ruiz, Lorenzo Fagiano
Summary: This paper investigates the simultaneous design of active attitude control and passive attitude compensation mechanism for a spacecraft to satisfy practically-motivated mission objectives and constraints. The expressions of these fitness-related metrics with respect to the design variables are not analytically available, due to the nontrivial interactions between the spacecraft components and the interactions with the environment. The proposed black-box optimization (BBO)-based approach combines learning and optimizing the objective and constraint functions by design of experiments, and it shows the capability to provide a design with the best tracking performance while satisfying ground station communication requirements and power budget.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Energy & Fuels
Yesid Bello, Juan Sebastian Roncancio, Toufik Azib, Diego Patino, Cherif Larouci, Moussa Boukhnifer, Nassim Rizoug, Fredy Ruiz
Summary: In this article, a reasonable energy-efficient non-linear model predictive control (NMPC) is developed for an electric two-wheeler vehicle, considering different driving profiles and driver preferences on the Paris-Brussels route. The NMPC algorithm is successfully implemented in a test bed, demonstrating practical parameters and energy estimation for the optimization problem. Additionally, the efficiency of the brushless DC motor (BLCD) is taken into account. The results show that the proposed strategy based on NMPC can significantly increase the chances of completing the journey and improve the vehicle's range by nearly 20%.
Article
Engineering, Electrical & Electronic
Eduardo Mojica-Nava, Fredy Ruiz, Eder Baron-Prada
Summary: In this study, a fully distributed dynamic transactive control method based on saddle-point dynamics is proposed to coordinate distributed energy resources in a distribution system. By introducing a predictive-sensitivity conditioning term, stability and optimality of the system are maintained. Simulation results validate the effectiveness of the proposed approach numerically.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Multidisciplinary Sciences
Juan Camilo Nustes, Danilo Pietro Pau, Giambattista Gruosso
Summary: This paper presents a dataset of a 3-phase Permanent Magnet Synchronous Motor controlled by a Field Oriented Control scheme. The dataset includes motor responses to different input signal shapes, allowing verification of control capabilities in real-life scenarios. The measured data provides potential for developing non-linear control approaches, such as Machine Learning and Neural Networks, to replace linear controllers in the Field Oriented Control scheme.
Article
Energy & Fuels
Araavind Sridhar, Samuli Honkapuro, Fredy Ruiz, Jan Stoklasa, Salla Annala, Annika Wolff, Antti Rautiainen
Summary: This study investigates the willingness of Finnish residential consumers to enroll their household loads in demand response (DR) programs, considering their preferences for financial gains and emission reductions. The findings show that heating and electric appliances have a higher level of consumer willingness to participate compared to electric vehicles (EVs). Consumers tend to prefer financial incentives over environmental incentives and expect compensations of 100 euro/year for appliances and EVs, and 200 euro/year for heating. The results have practical implications for energy flexibility in the residential sector and highlight the need for appropriate policy measures.
Article
Engineering, Multidisciplinary
Enrico Spateri, Fredy Ruiz, Giambattista Gruosso
Summary: This article presents a digital twin based on a hybrid ElectroThermal model for the design and management of thermoforming systems. The model is modular and can simulate various configurations of heating elements. Experimental verification demonstrates the validity of the model. The proposed digital twin has low computational complexity and enables advanced control strategy development and optimization analysis of system parameters.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2023)
Article
Energy & Fuels
Shahbaz Hussain, S. M. Suhail Hussain, Marziyeh Hemmati, Atif Iqbal, Rashid Alammari, Stefano Zanero, Enrico Ragaini, Giambattista Gruosso
Summary: The traditional power systems are evolving into smart grids, but cyberattacks on smart grids, especially False Data Injection (FDI) attacks, are increasing. This paper develops a real-time setup to simulate FDI attacks on GOOSE and SV protocols in order to evaluate their impact on the power grid. Although IEC 62351 provides cybersecurity guidelines for GOOSE and SV, they only cover communication or Information Technology (IT) level. Therefore, a novel hybrid security scheme based on sequence content resolver is proposed to tackle FDI attacks on GOOSE and SV at both IT and Operation Technology (OT) level.
PROTECTION AND CONTROL OF MODERN POWER SYSTEMS
(2023)
Article
Computer Science, Information Systems
Silvana Matrone, Emanuele Giovanni Carlo Ogliari, Alfredo Nespoli, Giambattista Gruosso, Alessandro Gandelli
Summary: The rapid increase in electric vehicle usage in the past decade has created a need for accurate prediction of energy consumption during charging. This paper proposes a Machine Learning model based on the K-Nearest Neighbors algorithm to forecast the duration of EV charging sessions. The model assigns events to classes based on their duration intervals and uses only the available information at the start of the charging event. Validation on a real-world dataset and a sensitivity analysis demonstrate the effectiveness of the model compared to benchmark models.
Article
Computer Science, Software Engineering
Juan Camilo Nustes, Danilo Pietro Pau, Giambattista Gruosso
Summary: This paper describes the software implementation of the Field Oriented Control (FOC) model applied to a 3-phase Permanent Magnet Synchronous Machine (PMSM) and its components. It also explains how the PMSM_FOC dataset was generated using a Simulink representation for both the motor and the control scheme when different speed targets were fed as input. The paper elaborates on the input signals designed for the dataset generation and proposes potential scenarios for future motor control development using the Simulink model.
Article
Computer Science, Information Systems
Michele Quercio, Francesco Galbusera, Emir Poskovic, Fausto Franchini, Luca Ferraris, Aldo Canova, Giambattista Gruosso, Ali Gokhan Demir, Barbara Previtali
Summary: This study aims to produce Fe2.9wt.%Si ferromagnetic material using laser powder bed fusion (L-PBF) for electromagnetic actuators. Through characterization of microstructure and magnetic properties, it is found that the samples produced using L-PBF process exhibit good magnetic properties, especially after annealing treatment, making them a promising material for use in electromagnetic actuators.
Article
Engineering, Electrical & Electronic
Francesco Giordano, Cesar Diaz-Londono, Giambattista Gruosso
Summary: This article presents a comprehensive framework for integrating electric vehicles (EVs) into the power grid, enabling bidirectional power flow. The framework optimizes power allocation based on EV travel energy forecasts and real-time management, and evaluates EV participation in ancillary services. The study demonstrates the potential of EVs to enhance grid stability and provide economic value.
IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Giambattista Gruosso, Cesar Diaz Londono, Luca Daniel, Paolo Maffezzoni
Summary: This paper presents a method that can inform network operators about critical Buses and critical injection scenarios by using complex-domain modeling and accelerating Monte Carlo simulations via parameter space partitioning to handle voltage unbalance in the case of many PV sources.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2024)
