Article
Automation & Control Systems
Haoran Cui, Long Zhang, Xiaoxu Wang, Mingyong Liu, Binglu Wang
Summary: In this paper, a novel iterative nonlinear filter based on the variational Bayesian framework is proposed, which can achieve highly accurate state estimation and numerical stability in nonlinear systems.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Thermodynamics
Cristian Chinas-Palacios, Carlos Vargas-Salgado, Jesus Aguila-Leon, Elias Hurtado-Perez
Summary: This study proposes a model based on Artificial Neural Networks and Particle Swarm Optimization algorithm to estimate the biomass needed for a Biomass Gasification Plant to produce syngas to meet energy demand. The results show that the proposed model outperforms existing models in terms of MSE.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Thermodynamics
Mehdi Jamei, Ismail Adewale Olumegbon, Masoud Karbasi, Iman Ahmadianfar, Amin Asadi, Mehdi Mosharaf-Dehkordi
Summary: This study conducted a comprehensive data-intelligence analysis on various types of oil-based hybrid nanofluids using the Extended Kalman Filter-Neural network (EKF-ANN) approach, revealing that this model outperformed other methods in predicting thermal conductivity.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2021)
Article
Engineering, Multidisciplinary
Vivek Narayanan, Bhim Singh
Summary: In this paper, a cascaded non-identical second-order generalized integrator (CNISOGI) and a variable step size modified clipped least mean square (VSSMCLMS) adaptive filter are proposed for solving grid and load abnormalities in a microgrid. The CNISOGI filter estimates the positive sequence components from polluted grid voltages, achieving improved quality grid currents. It also enables seamless transition of the microgrid between different modes. The VSSMCLMS filter achieves load compensation by estimating the active fundamental component of the load currents with faster response and lesser weight oscillations, demonstrating its superiority over conventional filters.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2023)
Article
Electrochemistry
Qingrui Gong, Ping Wang, Ze Cheng, Ji'ang Zhang
Summary: This paper proposes a hybrid neural network model based on deep learning for estimating the state of charge (SOC) of lithium-ion batteries. The model consists of convolutional module, ULSAM module, and GRU module for feature extraction and SOC value output. Experimental results demonstrate that the model can accurately estimate SOC values under complex operating conditions.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2021)
Article
Computer Science, Interdisciplinary Applications
Dulguun Narmandakh, Christoph Butscher, Faramarz Doulati Ardejani, Huichen Yang, Thomas Nagel, Reza Taherdangkoo
Summary: This article presents the use of neural network models to predict the swelling potential of clay soils, including both natural and artificial soils. The models were trained using the Levenberg-Marquardt algorithm and validated with experimental data, showing that the feed-forward neural network trained with this algorithm is the most accurate.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Remote Sensing
Xu Lin, Xinghai Yang, Chihao Hu, Wei Li
Summary: Kalman smoothing algorithms are widely used in target tracking systems for offline data processing. The adaptive Kalman filter algorithm can reduce the impact of abnormal dynamic models on filter results to some extent. However, the complexity of selecting optimal adaptive factors makes it difficult to improve smoothing accuracy.
Article
Energy & Fuels
Sadia Rimsha, Sadia Murawwat, Muhammad Majid Gulzar, Ahmad Alzahrani, Ghulam Hafeez, Farrukh Aslam Khan, Azher M. Abed
Summary: This research proposes an equivalent circuit model based on the Kalman filtering method and a data-driven technique, the Deep Feed-Forward Neural Network, for accurate SOC estimation of electric vehicle batteries. By identifying lithium-ion battery parameters using a second-order RC equivalent circuit model, accurate SOC estimation is achieved through various filtering methods. Furthermore, the deep feed-forward neural network method is implemented to enhance the accuracy of SOC estimation.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Thermodynamics
Ligai Kang, Xiaoxue Yuan, Kangjie Sun, Xu Zhang, Jun Zhao, Shuai Deng, Wei Liu, Yongzhen Wang
Summary: This paper analyzes the characteristics of thermal load, constructs a thermal load forecasting model considering attenuation and delay on transmission, and proposes a feed-forward active operation optimization method for the CCHP system. Experimental results show that the performance of the CCHP system is better than that of a separate system.
Article
Energy & Fuels
Yue Miao, Zhe Gao, Shasha Xiao, Haoyu Chai
Summary: This study proposes an adaptive fractional-order unscented Kalman filter (UKF) for SOC estimation with unknown parameters and order, achieving higher accuracy. To tackle the reduced estimation accuracy caused by low order in adaptive estimation, the paper adopts the augmented vector method for initial value compensation (IVC) to reduce the impact of initial values. By considering the changing parameters of the LIB and aiming to enhance the adaptive SOC estimation, the study introduces an adaptive unscented Kalman filter (AUKF) algorithm based on IVC for joint estimation of state, parameters, and model order. Experimental results demonstrate the effectiveness of the AUKF algorithm with IVC in SOC estimation under different working conditions.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Haoyu Chai, Zhe Gao, Zhiyuan Jiao, Chuang Yang
Summary: An adaptive fractional-order cubature Kalman filter with initial value compensation is proposed to estimate the state of charge (SOC) of a lithium-ion battery. Firstly, a fractional-order battery model is built and the state equation is discretized. Then, the initial values of the states are considered as augmented states to be estimated, and an augmented state equation is established. Finally, linear Kalman filter and cubature Kalman filter are designed to estimate the coefficients in the measurement equation and the real-time SOC, model parameters, and orders.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Engineering, Mechanical
Chuang Yang, Zhe Gao, Yue Miao, Tao Kan
Summary: Two types of fractional-order cubature Kalman filters were designed to address the initial value influence problem in state estimation of nonlinear continuous-time fractional-order systems. The proposed methods effectively reduce the impact of initial value on the state estimation, as demonstrated in simulation examples.
NONLINEAR DYNAMICS
(2021)
Article
Engineering, Mechanical
Min Wan, Jia Dai, Wei-Hong Zhang, Qun-Bao Xiao, Xue-Bin Qin
Summary: This article presents an adaptive feed-forward method to compensate for dynamic friction in machine tool systems, reducing tracking errors of machine axes. The proposed method establishes a dynamic friction model and constructs an adaptive controller, which achieves high steady-state accuracy and adaptability.
MECHANISM AND MACHINE THEORY
(2022)
Article
Energy & Fuels
Richard Bustos, Stephen Andrew Gadsden, Pawel Malysz, Mohammad Al-Shabi, Shohel Mahmud
Summary: This paper presents a dual filter architecture using the Kalman filter and the sliding innovation filter to estimate the capacity and state of charge of a lithium-ion battery. The study shows that under normal operating conditions, both the dual-KF and dual-SIF perform similarly in terms of estimation accuracy. However, in rapidly changing dynamics and faulty conditions, the dual-SIF shows better convergence and robustness to disturbances.
Article
Energy & Fuels
Chunsheng Hu, Bohao Li, Liang Ma, Fangjuan Cheng
Summary: This paper proposes a method that combines an adaptive extended Kalman filter (AEKF) with a long short-term memory (LSTM) network to improve the accuracy of state of charge (SOC) estimation in lithium-ion batteries. Experimental results show that the proposed method exhibits strong robustness under inaccurate initial conditions and achieves significant improvement in SOC estimation accuracy by compensating for random input errors in advance.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Thermodynamics
Danijel Pavkovic, Mihael Cipek, Mario Hrgetic, Almir Sedic
ENERGY CONVERSION AND MANAGEMENT
(2018)
Article
Energy & Fuels
Danijel Pavkovic, Mihael Cipek, Zdenko Kljaic, Tomislav Josip Mlinaric, Mario Hrgetic, Davor Zorc
Article
Thermodynamics
Mihael Cipek, Danijel Pavkovic, Zdenko Kljaic, Tomislav Josip Mlinaric
Article
Chemistry, Analytical
Davor Kolar, Dragutin Lisjak, Michal Pajak, Danijel Pavkovic
Article
Forestry
Juraj Karlusic, Mihael Cipek, Danijel Pavkovic, Juraj Benic, Zeljko Situm, Zdravko Pandur, Marijan Susnjar
Article
Green & Sustainable Science & Technology
Juraj Karlusic, Mihael Cipek, Danijel Pavkovic, Zeljko Situm, Juraj Benic, Marijan Susnjar
Article
Energy & Fuels
Matija Krznar, Petar Piljek, Denis Kotarski, Danijel Pavkovic
Summary: The research explores a hybrid power unit for multirotor UAVs, combining an internal combustion engine and battery for increased energy density and longer flight autonomy. Results show successful integration of two energy sources, leading to improved performance and flight duration.
Article
Energy & Fuels
Matija Krznar, Danijel Pavkovic, Mihael Cipek, Juraj Benic
Summary: This paper presents the results of modeling, control system design and simulation verification of a hybrid-electric drive topology suitable for power flow control within UAVs. The overall control system features PID feedback control of the ICE rotational speed, PI feedback control of the BLDC generator voltage and current, and estimator-based feed-forward load compensators. The robustness to key process parameters variations is investigated by means of root-locus methodology and the effectiveness of the control system is verified through comprehensive computer simulations.
Article
Energy & Fuels
Juraj Benic, Juraj Karlusic, Zeljko Situm, Mihael Cipek, Danijel Pavkovic
Summary: This paper investigates the potential uses of a novel direct driven electro-hydraulic system for articulated forestry tractors, and compares its energy efficiency with the classical electro-hydraulic systems currently used. The analysis includes a detailed examination of the skidder rear plate mechanism and laboratory experiments to simulate real-life fuel consumption and savings. The main finding is that the direct driven hydraulic system can reduce fuel consumption up to five times, resulting in a return of investment period of approximately four years when retrofitted on skidders.
Article
Energy & Fuels
Danijel Pavkovic, Mihael Cipek, Filip Plavac, Juraj Karlusic, Matija Krznar
Summary: In order to meet stricter emissions regulations, road vehicles require additional technologies aimed at reducing emissions from the internal combustion engine (ICE). A favorable solution is a 48-V electrical architecture utilizing a low-voltage/high-power induction machine as an engine belt starter generator (BSG) in a P0 mild hybrid power train. This study aimed to design a vibration damping system for the belt transmission and test the control system using simulations for realistic operating regimes. The results showed effective attenuation of belt compliance-related vibrations and improved vehicle performance.
Article
Energy & Fuels
Matija Krznar, Danijel Pavkovic, Mihael Cipek
Summary: This paper discusses the control of direct-current to direct-current (DC/DC) energy conversion in a hybrid power-train structure for use in a skidder. Models and control strategies are derived through simulations and laboratory experiments, and practical rules for parallelized DC/DC converter operation are formulated to maximize overall power conversion efficiency.
Article
Engineering, Electrical & Electronic
Mihael Cipek, Danijel Pavkovic, Zdenko Kljaic
Summary: Hybrid electric propulsion is becoming popular in railway transportation due to its potential for fuel savings and reductions in greenhouse gas emissions. This paper proposes a real-time energy management control strategy for a hybrid locomotive, which achieves fuel consumption minimization while considering battery state-of-charge and powertrain constraints. Simulation analysis shows a significant fuel savings of 22.9% compared to conventional locomotives.
Proceedings Paper
Engineering, Electrical & Electronic
Matija Krznar, Danijel Pavkovic, Mihael Cipek, Davor Zorc, Davor Kolar, Denis Kotarski
PROCEEDINGS OF 18TH INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES (IEEE EUROCON 2019)
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
Yuliia Kozhushko, Danijel Pavkovic, Denys Zinchenko, Tetiana Karbivska, Volodymyr Sydorets, Oleksandr Bondarenko
2019 IEEE 39TH INTERNATIONAL CONFERENCE ON ELECTRONICS AND NANOTECHNOLOGY (ELNANO)
(2019)
Article
Environmental Sciences
Mihael Cipek, Josko Petric, Danijel Pavkovic
JOURNAL OF SUSTAINABLE DEVELOPMENT OF ENERGY WATER AND ENVIRONMENT SYSTEMS-JSDEWES
(2019)
Article
Thermodynamics
Pengcheng Zhao, Jingang Wang, Liming Sun, Yun Li, Haiting Xia, Wei He
Summary: The production of green hydrogen through water electrolysis is crucial for renewable energy utilization and decarbonization. This research explores the optimal electrode configuration and system design of compactly-assembled industrial electrolyzer. The findings provide valuable insights for industrial application of water electrolysis equipment.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
V. Baiju, P. Abhishek, S. Harikrishnan
Summary: Thermally driven adsorption desalination systems (ADS) have gained attention as an eco-friendly solution for water scarcity. However, they face challenges related to low water productivity and scalability. To overcome these challenges, integrating ADS with other desalination technologies can create a small-scale hybrid system. This study proposes integrating ADS with a Thermo Electric Dehumidification (TED) unit to enhance its performance.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
C. X. He, Y. H. Liu, X. Y. Huang, S. B. Wan, Q. Chen, J. Sun, T. S. Zhao
Summary: A decentralized centroid multi-path RC network model is constructed to improve the temperature prediction accuracy compared to traditional RC models. By incorporating multiple heat flow paths and decentralizing thermal capacity, a more accurate prediction is achieved.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Chaoying Li, Meng Wang, Nana Li, Di Gu, Chao Yan, Dandan Yuan, Hong Jiang, Baohui Wang, Xirui Wang
Summary: There is an urgent need to shift away from heavy dependence on fossil fuels and embrace renewable energy sources, particularly in the energy-intensive oil refining process. This study presents an innovative concept called the Solar Oil Refinery, which applies solar energy in oil refining. A solar multi-energies-driven hybrid chemical oil refining system that utilizes solar pyrolysis and electrolysis has been developed, significantly improving solar utilization efficiency, cracking rate, and hydrogen yield.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Chao Ma, Guanghui Wang, Dingbiao Wang, Xu Peng, Yushen Yang, Xinxin Liu, Chongrui Yang, Jiaheng Chen
Summary: This study proposes a bio-inspired fish-tail wind rotor to improve the wind power efficiency of the traditional Savonius rotor. Through transient simulations and orthogonal experiments, the key factors affecting the performance are identified. A response surface model is constructed to optimize the power coefficient, resulting in an improvement of 9.4% and 6.6% compared to the Savonius rotor.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Sina Bahmanziari, Abbas-Ali Zamani
Summary: This paper proposes a new framework for improving electrical energy harvesting from piezoelectric smart tiles through a combination of magnetic plucking, mechanical impact, and mechanical vibration force mechanisms. Experimental results demonstrate a significant increase in energy yield and average energy harvesting time compared to other mechanisms.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Nanjiang Dong, Tao Zhang, Rui Wang
Summary: This study establishes a multiobjective mixed-variable configuration optimization model for a comprehensive combined cooling, heating, and power energy system, and proposes an efficient generating operator to optimize this model. The experimental results show that the proposed algorithm performs better than other state-of-the-art algorithms.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Ahmed E. Mansy, Eman A. El Desouky, Tarek H. Taha, M. A. Abu-Saied, Hamada El-Gendi, Ranya A. Amer, Zhen-Yu Tian
Summary: This study aims to convert office paper waste into bioethanol through a sustainable pathway. The results show that physiochemical and enzymatic hydrolysis of the waste can yield a high glucose concentration. The optimal conditions were determined using the Box-Behnken design, and a blended membrane was used for ethanol purification.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Sven Klute, Marcus Budt, Mathias van Beek, Christian Doetsch
Summary: Heat pumps are crucial for decarbonizing heat supply, and steam generating heat pumps have the potential to decarbonize the industrial sector. This paper presents the current state, technical and economic data, and modeling principles of steam generating heat pumps.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Le Zhang, To-Hung Tsui, Yen Wah Tong, Pruk Aggarangsi, Ronghou Liu
Summary: This study investigates the effectiveness of a current-carrying-coil-based magnetic field in promoting anaerobic digestion of chicken manure. The results show that the applied magnetic field increases methane yield, decreases carbon dioxide production, and reduces the concentration of ammonia nitrogen. Microbial community analysis reveals the enrichment of certain methanogenic genera and enhanced metabolic pathways. Pilot-scale experiments confirm the technical effectiveness of the magnetic field assistance in enhancing anaerobic digestion of chicken manure.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Bo Chen, Ruiqing Ma, Yang Zhou, Rui Ma, Wentao Jiang, Fan Yang
Summary: This paper presents an advanced energy management strategy for fuel cell hybrid electric heavy-duty vehicles, focusing on speed planning and energy allocation. By utilizing predictive co-optimization control, this strategy ensures safe inter-vehicle distance and minimizes energy demand. Simulation results demonstrate the effectiveness of the proposed method in reducing fuel cell degradation cost and overall operation cost.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Fabio Fatigati, Roberto Cipollone
Summary: Organic Rankine Cycle-based microcogeneration systems that use solar sources to generate electricity and hot water can help reduce CO2 emissions in residential energy-intensive sectors. The adoption of a recuperative heat exchanger in these systems improves efficiency, reduces thermal power requirements, and saves on electricity costs.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Lipeng He, Renwen Liu, Xuejin Liu, Xiaotian Zheng, Limin Zhang, Jieqiong Lin
Summary: This research proposes a piezoelectric-electromagnetic hybrid energy harvester (PEHEH) for low-frequency wave motion and self-sensing wave environment monitoring. The PEHEH shows promising power output and the ability to self-power and self-sense the wave environment.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Shangling Chu, Yang Liu, Zipeng Xu, Heng Zhang, Haiping Chen, Dan Gao
Summary: This paper studies a distributed energy system integrated with solar and natural gas, analyzes the impact of different parameters on its energy utilization and emissions reduction, and obtains the optimal solution through an optimization algorithm. The results show that compared to traditional separation production systems, this integrated system achieves higher energy utilization and greater reduction in carbon emissions.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Qingpu Li, Yaqi Ding, Guangming Chen, Yongmei Xuan, Neng Gao, Nian Li, Xinyue Hao
Summary: This paper proposes and studies a piston-type thermally-driven pump with a structure similar to a linear compressor, aiming to eliminate the high-quality energy consumption of existing pumps and replace mechanical pumps. The coupling mechanism of working fluid flow and element dimension is analyzed based on force analysis, and experimental data analysis is used to determine the pump operation stroke. Theoretical simulation is conducted to analyze the correlation mechanism of the piston assembly. The research shows that the thermally-driven pump can greatly reduce power consumption and has potential for industrial applications.
ENERGY CONVERSION AND MANAGEMENT
(2024)
