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Due to the impact of human lifestyle on building energy consumption, the development of occupants’ behavior models is crucial for energy-saving purposes. In this regard, occupancy modeling is an effective approach to intend such a purpose. However, the literature reveals that existing occupancy models have limitations related to the representation of occupancy state duration and the integration of occupancy variability among individuals. Accordingly, this paper proposes an explicit differentiated duration probabilistic model to generate realistic daily occupancy profiles in residential buildings. The discrete-time Markov chain theory and the semi-parametric Cox proportional hazards model (Cox regression) are used to predict household occupancy profiles. The proposed model is able to capture occupancy states duration and integrate human behavior variability according to individuals’ characteristics. Moreover, a parametric analysis is employed to investigate these characteristics’ impact on the model performance and consequently, select the most significant input variables. A validation process is conducted by comparing the model performance with that of previous methods, presented in the literature. For this purpose, the $k$ cross-validation technique is utilized. Validation results demonstrate that the proposed approach is highly efficient in generating realistic household occupancy profiles.
Luis Rueda; Simon Sansregret; Brice Le Lostec; Kodjo Agbossou; Nilson Henao; Sousso Kelouwani. A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management Applications. IEEE Access 2021, 9, 38187 -38201.
AMA StyleLuis Rueda, Simon Sansregret, Brice Le Lostec, Kodjo Agbossou, Nilson Henao, Sousso Kelouwani. A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management Applications. IEEE Access. 2021; 9 ():38187-38201.
Chicago/Turabian StyleLuis Rueda; Simon Sansregret; Brice Le Lostec; Kodjo Agbossou; Nilson Henao; Sousso Kelouwani. 2021. "A Probabilistic Model to Predict Household Occupancy Profiles for Home Energy Management Applications." IEEE Access 9, no. : 38187-38201.
High penetration of selfish Home Energy Management Systems (HEMSs) causes adverse effects such as rebound peaks, instabilities, and contingencies in different regions of distribution grid. To avoid these effects and relieve power grid stress, the concept of HEMSs coordination has been suggested. Particularly, this concept can be employed to fulfill important grid objectives in neighborhood areas such as flattening aggregated load profile, decreasing electricity bills, facilitating energy trading, diminishing reverse power flow, managing distributed energy resources, and modifying consumers' consumption/generation patterns. This paper reviews the latest investigations into coordinated HEMSs. The required steps to implement these systems, accounting for coordination topologies and techniques, are thoroughly explored. This exploration is mainly reported through classifying coordination approaches according to their utilization of decomposition algorithms. Furthermore, major features, advantages, and disadvantages of the methods are examined. Specifically, coordination process characteristics, its mathematical issues and essential prerequisites, as well as players concerns are analyzed. Subsequently, specific applications of coordination designs are discussed and categorized. Through a comprehensive investigation, this work elaborates significant remarks on critical gaps in existing studies toward a useful coordination structure for practical HEMSs implementations. Unlike other reviews, the present survey focuses on effective frameworks to determine future opportunities that make the concept of coordinated HEMSs feasible. Indeed, providing effective studies on HEMSs coordination concept is beneficial to both consumers and service providers since as reported, these systems can lead to 5% to 30% reduction in electricity bills.
Farshad Etedadi Aliabadi; Kodjo Agbossou; Sousso Kelouwani; Nilson Henao; Sayed Saeed Hosseini. Coordination of Smart Home Energy Management Systems in Neighborhood Areas: A Systematic Review. IEEE Access 2021, 9, 36417 -36443.
AMA StyleFarshad Etedadi Aliabadi, Kodjo Agbossou, Sousso Kelouwani, Nilson Henao, Sayed Saeed Hosseini. Coordination of Smart Home Energy Management Systems in Neighborhood Areas: A Systematic Review. IEEE Access. 2021; 9 ():36417-36443.
Chicago/Turabian StyleFarshad Etedadi Aliabadi; Kodjo Agbossou; Sousso Kelouwani; Nilson Henao; Sayed Saeed Hosseini. 2021. "Coordination of Smart Home Energy Management Systems in Neighborhood Areas: A Systematic Review." IEEE Access 9, no. : 36417-36443.
Home Energy Management systems are in a rapid development curve, supported by the advancements in computational intelligence, smart appliances, and new smart-grid frameworks. These systems are a fundamental part to implement demand-side management strategies and to shift local energy demand to periods of lower consumption effectively. In this paper, we explore the application of distributed co-evolutionary optimization algorithms and an agent-based architecture to reduce the consumption profile signature of the heating system during the critical peak demand periods, by reducing costs and respecting the comfort constraints of the occupants. The proposed control architecture targets the typical baseboard space heating systems and electrical thermal storage systems, as these represent a large portion of the energy usage in Nordic countries and are commonly controlled by room independent thermostats, which could be easily replaced by smart devices running an algorithm as the one presented in this work. Results prove the strategy proposed getting a cost reduction of up to 23% and a peak-to-average ratio decrease of up to 25% for reference scenarios. Also, an emulation Simulink model is developed to recreate a house and the different heating loads studied in this paper and an experimental test bed is built to model a real ETS system, two different complexity degree RC models are proposed to describe such systems.
William Devia; Kodjo Agbossou; Alben Cardenas. An evolutionary approach to modeling and control of space heating and thermal storage systems. Energy and Buildings 2020, 234, 110674 .
AMA StyleWilliam Devia, Kodjo Agbossou, Alben Cardenas. An evolutionary approach to modeling and control of space heating and thermal storage systems. Energy and Buildings. 2020; 234 ():110674.
Chicago/Turabian StyleWilliam Devia; Kodjo Agbossou; Alben Cardenas. 2020. "An evolutionary approach to modeling and control of space heating and thermal storage systems." Energy and Buildings 234, no. : 110674.
Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.
Abdul Sajeed Mohammed; Ali Amamou; Follivi Kloutse Ayevide; Sousso Kelouwani; Kodjo Agbossou; Nadjet Zioui. The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review. Sensors 2020, 20, 6532 .
AMA StyleAbdul Sajeed Mohammed, Ali Amamou, Follivi Kloutse Ayevide, Sousso Kelouwani, Kodjo Agbossou, Nadjet Zioui. The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review. Sensors. 2020; 20 (22):6532.
Chicago/Turabian StyleAbdul Sajeed Mohammed; Ali Amamou; Follivi Kloutse Ayevide; Sousso Kelouwani; Kodjo Agbossou; Nadjet Zioui. 2020. "The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review." Sensors 20, no. 22: 6532.
Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned autonomous vehicles. While the operating durability depends on self-path design, planning energy-efficient paths become crucial. Thus, this paper has no concrete contribution but highlights the lack of energy consideration of SGV-system design in literature by presenting a review of energy-constrained global path planning. Then, an experimental investigation explores the long-term effect of battery level on navigation performance of a single vehicle. This experiment was conducted for several hours, a deviation between the global trajectory and the ground-true path executed by the SGV was observed as the battery depleted. The results show that the mean square error (MSE) increases significantly as the battery’s state-of-charge decreases below a certain value.
Massinissa Graba; Sousso Kelouwani; Lotfi Zeghmi; Ali Amamou; Kodjo Agbossou; Mohammad Mohammadpour. Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context. Sustainability 2020, 12, 8541 .
AMA StyleMassinissa Graba, Sousso Kelouwani, Lotfi Zeghmi, Ali Amamou, Kodjo Agbossou, Mohammad Mohammadpour. Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context. Sustainability. 2020; 12 (20):8541.
Chicago/Turabian StyleMassinissa Graba; Sousso Kelouwani; Lotfi Zeghmi; Ali Amamou; Kodjo Agbossou; Mohammad Mohammadpour. 2020. "Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context." Sustainability 12, no. 20: 8541.
Detailed occupancy information in buildings is useful to improve the performance of energy management systems in order to enable energy consumption savings and maintain occupants' comfort. Different technologies employed to provide occupancy information account for high-precision devices such as optical and thermal cameras, and environmental or specialized sensors like carbon dioxide (CO2) and passive infrared (PIR). Although the latter systems have lower accuracy, they have received significant interest due to their affordable and less-intrusive nature. Accordingly, various studies have been conducted to explore the various elements of these technologies. Nevertheless, the algorithmic aspect of the occupancy detection process has not been adequately taken into consideration. This paper presents an extensive review of the techniques that have been exploited to process the information provided by the sensors and carry out occupancy information detection. In this study, a complete set of comparison criteria, comprising the performance, the occupancy resolution, the type of sensors used, the type of buildings, and the energy saving potentials has been considered in order to perform an in-depth analysis of the occupancy detection systems. Through its examination, this paper elaborates significant remarks on occupancy detection algorithms in order to realize a method that is not only efficient in processing sensors’ data but also effective in providing accurate occupancy information.
Luis Rueda; Kodjo Agbossou; Alben Cardenas; Nilson Henao; Sousso Kelouwani. A comprehensive review of approaches to building occupancy detection. Building and Environment 2020, 180, 106966 .
AMA StyleLuis Rueda, Kodjo Agbossou, Alben Cardenas, Nilson Henao, Sousso Kelouwani. A comprehensive review of approaches to building occupancy detection. Building and Environment. 2020; 180 ():106966.
Chicago/Turabian StyleLuis Rueda; Kodjo Agbossou; Alben Cardenas; Nilson Henao; Sousso Kelouwani. 2020. "A comprehensive review of approaches to building occupancy detection." Building and Environment 180, no. : 106966.
An efficient participation of prosumers in power system management depends on the quality of information they can obtain. Prosumers actions can be performed by automated agents that are operating in time-changing environments. Therefore, it is essential for them to deal with data stream problems in order to make reliable decisions based on the most accurate information. This paper provides an in-depth investigation of data and concept drift issues in accordance with residential prosumer agents. Additionally, the adaptation techniques, forgetting mechanisms, and learning strategies employed to handle these issues are explored. Accordingly, an approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently. The suggested method is based on triggered adaptation techniques and performance-based forgetting mechanism. The results obtained in this study demonstrate that the proposed approach is capable of constructing efficient prosumer agents models with regard to the concept drift problem.
David Toquica; Kodjo Agbossou; Roland Malhamé; Nilson Henao; Sousso Kelouwani; David Camilo Toquica Cárdenas. Adaptive Machine Learning for Automated Modeling of Residential Prosumer Agents. Energies 2020, 13, 2250 .
AMA StyleDavid Toquica, Kodjo Agbossou, Roland Malhamé, Nilson Henao, Sousso Kelouwani, David Camilo Toquica Cárdenas. Adaptive Machine Learning for Automated Modeling of Residential Prosumer Agents. Energies. 2020; 13 (9):2250.
Chicago/Turabian StyleDavid Toquica; Kodjo Agbossou; Roland Malhamé; Nilson Henao; Sousso Kelouwani; David Camilo Toquica Cárdenas. 2020. "Adaptive Machine Learning for Automated Modeling of Residential Prosumer Agents." Energies 13, no. 9: 2250.
Cristina Guzman; Alben Cardenas; Kodjo Agbossou. Local Estimation of Critical and Off-Peak Periods for Grid-Friendly Flexible Load Management. IEEE Systems Journal 2020, 14, 4262 -4271.
AMA StyleCristina Guzman, Alben Cardenas, Kodjo Agbossou. Local Estimation of Critical and Off-Peak Periods for Grid-Friendly Flexible Load Management. IEEE Systems Journal. 2020; 14 (3):4262-4271.
Chicago/Turabian StyleCristina Guzman; Alben Cardenas; Kodjo Agbossou. 2020. "Local Estimation of Critical and Off-Peak Periods for Grid-Friendly Flexible Load Management." IEEE Systems Journal 14, no. 3: 4262-4271.
Enabling diagnosis capabilities of Appliance Load Monitoring (ALM) necessitates providing in-operation information of appliances’ behavior. Due to both appliances’ time-varying model parameters and operations, household aggregated consumption has a dynamic structure. Existing time-invariant load models, built of off-line datasets with static information, are not sufficient to capture the actual behavior of the power consumption. In fact, these models, generally obtained from exhaustive training phases are intended to satisfy load monitoring goals. Therefore, a time-variant load modeling is more practical to capture such a dynamic property of the power consumption. Accordingly, this paper presents an adaptive on-line appliance-level load modeling approach, to design a load monitoring structure for diagnosis purposes. By using the aggregated power consumption of individual households, our proposed structure results in an autonomous household database construction. The modeling procedure begins with a designed recurrent pattern recognition system that is capable of detecting and maintaining load models. This load model structure is determined by using a hidden Markov model (HMM) with dynamic parameters, that are extracted from aggregated signal and trained within an on-line learning process. Our proposed approach can detect time-varying power consumption behavior and estimate the robust load models of appliances. Additionally, our novelty in employing a set of straightforward algorithms, suggests the practicality of our database construction approach.
Sayed Saeed Hosseini; Sousso Kelouwani; Kodjo Agbossou; Alben Cardenas; Nilson Henao. Adaptive on-line unsupervised appliance modeling for autonomous household database construction. International Journal of Electrical Power & Energy Systems 2019, 112, 156 -168.
AMA StyleSayed Saeed Hosseini, Sousso Kelouwani, Kodjo Agbossou, Alben Cardenas, Nilson Henao. Adaptive on-line unsupervised appliance modeling for autonomous household database construction. International Journal of Electrical Power & Energy Systems. 2019; 112 ():156-168.
Chicago/Turabian StyleSayed Saeed Hosseini; Sousso Kelouwani; Kodjo Agbossou; Alben Cardenas; Nilson Henao. 2019. "Adaptive on-line unsupervised appliance modeling for autonomous household database construction." International Journal of Electrical Power & Energy Systems 112, no. : 156-168.
Previous studies have shown that electric water heaters (EWH) have strong potential in demand-side management applications, more precisely because they offer energy storage capability and then can be employed as shift loads. However, the challenge of EWH curtailment strategies is to minimize the impact on the hot water availability while shaving the peak of consumption during critical periods. The success of such strategies depends highly on the knowledge of the consumption behavior of each user. Thus, appropriated modeling and consumption analysis could yield better management strategies. This study proposes an electric water heater control strategy based on dynamic programming and power consumption profile classification. An adaptive clustering process allows recognizing the clients who contribute to the highest power consumption during the peak periods. The analysis and simulation indicate that an appropriate control on the group of users could be implemented to reduce peak demand and to meet the hot water demand. A K-means clustering algorithm has been used for cluster analysis. The silhouette method has been applied to estimate the appropriate number of clusters.
Maria Alejandra Zuniga Alvarez; Kodjo Agbossou; Alben Cardenas; Sousso Kelouwani; Loic Boulon. Demand Response Strategy Applied to Residential Electric Water Heaters Using Dynamic Programming and K-Means Clustering. IEEE Transactions on Sustainable Energy 2019, 11, 524 -533.
AMA StyleMaria Alejandra Zuniga Alvarez, Kodjo Agbossou, Alben Cardenas, Sousso Kelouwani, Loic Boulon. Demand Response Strategy Applied to Residential Electric Water Heaters Using Dynamic Programming and K-Means Clustering. IEEE Transactions on Sustainable Energy. 2019; 11 (1):524-533.
Chicago/Turabian StyleMaria Alejandra Zuniga Alvarez; Kodjo Agbossou; Alben Cardenas; Sousso Kelouwani; Loic Boulon. 2019. "Demand Response Strategy Applied to Residential Electric Water Heaters Using Dynamic Programming and K-Means Clustering." IEEE Transactions on Sustainable Energy 11, no. 1: 524-533.
This paper presents a bifuel hydrogen–gasoline internal combustion engine (ICE) as an effective strategy for extending the electric vehicle's ranges. The electric power produced by the proposed ICE linked with a generator is a nonlinear function of the engine speed and the proportions of hydrogen and gasoline mixed fuel can be approximated around operating conditions. This nonlinear function is approximated by the Taylor series and a comparative study between the obtained results and the experimental data showed the effectiveness of the proposed approach. Furthermore, we observed that the Taylor series approach can achieve less than 7% error, while the modeling with an artificial neural network or a recursive least square method results in more than 8% error. To enable the ICE operation with maximum efficiency, a nonlinear optimization method is used. The proposed maximum efficiency tracking approach is compared with that of the most used industrial methods based on constant speed. The results show that the proposed approach can result in more than 7% of saving in energy, compared to that of the industrial method.
Mohamed Rebai; Sousso Kelouwani; Yves Dube; Kodjo Agbossou. Low-Emission Maximum-Efficiency Tracking of an Intelligent Bi-Fuel Hydrogen–Gasoline Generator for HEV Applications. IEEE Transactions on Vehicular Technology 2018, 67, 9303 -9311.
AMA StyleMohamed Rebai, Sousso Kelouwani, Yves Dube, Kodjo Agbossou. Low-Emission Maximum-Efficiency Tracking of an Intelligent Bi-Fuel Hydrogen–Gasoline Generator for HEV Applications. IEEE Transactions on Vehicular Technology. 2018; 67 (10):9303-9311.
Chicago/Turabian StyleMohamed Rebai; Sousso Kelouwani; Yves Dube; Kodjo Agbossou. 2018. "Low-Emission Maximum-Efficiency Tracking of an Intelligent Bi-Fuel Hydrogen–Gasoline Generator for HEV Applications." IEEE Transactions on Vehicular Technology 67, no. 10: 9303-9311.
Some electric vehicles include a fuel cell stack and a battery pack. A power allocation strategy is required in order to provide the cruise power demand efficiently. We propose an active power control strategy based on the optimal control theory. The defined cost function takes into account the driving power requests as well as the fuel cell stack power magnitude. This function can be easily extended in order to include additional energy costs. The optimal solution represents the best tradeoffs among all different cost function components. For realtime implementation, we propose a single step optimization strategy. The simulation of this system with the proposed optimal strategy shows a reduction of the battery pack discharge rates and the total energy used for the different driving cycles.
Ali Amamou; Marwa Ziadia; Sousso Kelouwani; Kodjo Agbossou; Yves Dube. Fuel-Cell and Battery Hybrid Source Optimal Power Management for Electric Mobility. 2018 IEEE Vehicle Power and Propulsion Conference (VPPC) 2018, 1 -5.
AMA StyleAli Amamou, Marwa Ziadia, Sousso Kelouwani, Kodjo Agbossou, Yves Dube. Fuel-Cell and Battery Hybrid Source Optimal Power Management for Electric Mobility. 2018 IEEE Vehicle Power and Propulsion Conference (VPPC). 2018; ():1-5.
Chicago/Turabian StyleAli Amamou; Marwa Ziadia; Sousso Kelouwani; Kodjo Agbossou; Yves Dube. 2018. "Fuel-Cell and Battery Hybrid Source Optimal Power Management for Electric Mobility." 2018 IEEE Vehicle Power and Propulsion Conference (VPPC) , no. : 1-5.
Building modeling and consumption analysis take vast importance in smart grid applications. Particularly the models of residential buildings in Nordic countries, where multi-layer slabs are mandatory to meet energy efficiency requirements, the modeling task becomes challenging. In fact, the complexity of the detailed model rises as the number of layers and thermal zones increases. This situation limits in some cases the implementation of model-based predictive control where the computation time for the execution of the model must be as short as possible to achieve optimized loop time performance. In this perspective, this paper proposes the development of a modular, accurate and flexible real-time multi-zone and multi-layer building emulation system representing the dynamic thermal-electric behavior of residential buildings. The proposed Hardware Implementation Architecture (HIA) includes space and water heating systems, as these are main consuming loads in Nordic countries. The emulation system can perform in accelerated simulation or in real-time modes, for one building or for a virtual park of buildings. Experimental validation using measurement data of occupied Canadian buildings with different insulation characteristics and using a Hardware-in-the-loop configuration has permitted to corroborate the usefulness of the proposed emulation system.
Cristina Guzman; Kodjo Agbossou; Alben Cardenas. Real-Time Emulation of Residential Buildings by Hardware Solution of Multi-Layer Model. IEEE Transactions on Smart Grid 2018, 10, 4037 -4047.
AMA StyleCristina Guzman, Kodjo Agbossou, Alben Cardenas. Real-Time Emulation of Residential Buildings by Hardware Solution of Multi-Layer Model. IEEE Transactions on Smart Grid. 2018; 10 (4):4037-4047.
Chicago/Turabian StyleCristina Guzman; Kodjo Agbossou; Alben Cardenas. 2018. "Real-Time Emulation of Residential Buildings by Hardware Solution of Multi-Layer Model." IEEE Transactions on Smart Grid 10, no. 4: 4037-4047.
This paper investigates a non-intrusive approach of retrieving electric space heater (ESH) power profiles from a residential aggregated signal. In cold-climate regions with heating appliances controlled by electronic thermostats, an accurate non-intrusive recognition of power profiles is a challenging task. Accordingly, a robust disaggregation approach based on the difference factorial hidden Markov model (DFHMM) and the Kronecker operation is contributed. The proposed method aims to uncover the underlying stochastic tow-state models of ESHs using their common prior knowledge. The major advantage of the developed load-monitoring architecture consists of modeling simplicity and inference as well as load-detection efficacy in the presence of perturbations from other unknown loads. The experimental results prove the effectiveness of the method in manipulating the challenging case of multiple two-state loads with a high event overlapping probability.
Nilson Henao; Kodjo Agbossou; Sousso Kelouwani; Sayed Saeed Hosseini; Michael Fournier. Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach. Energies 2018, 11, 88 .
AMA StyleNilson Henao, Kodjo Agbossou, Sousso Kelouwani, Sayed Saeed Hosseini, Michael Fournier. Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach. Energies. 2018; 11 (1):88.
Chicago/Turabian StyleNilson Henao; Kodjo Agbossou; Sousso Kelouwani; Sayed Saeed Hosseini; Michael Fournier. 2018. "Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach." Energies 11, no. 1: 88.
Fatima Amara; Kodjo Agbossou; Yves Dubé; Sousso Kelouwani; Alben Cardenas; Jonathan Bouchard. Household electricity demand forecasting using adaptive conditional density estimation. Energy and Buildings 2017, 156, 271 -280.
AMA StyleFatima Amara, Kodjo Agbossou, Yves Dubé, Sousso Kelouwani, Alben Cardenas, Jonathan Bouchard. Household electricity demand forecasting using adaptive conditional density estimation. Energy and Buildings. 2017; 156 ():271-280.
Chicago/Turabian StyleFatima Amara; Kodjo Agbossou; Yves Dubé; Sousso Kelouwani; Alben Cardenas; Jonathan Bouchard. 2017. "Household electricity demand forecasting using adaptive conditional density estimation." Energy and Buildings 156, no. : 271-280.
François Martel; Yves Dubé; Joris Jaguemont; Sousso Kelouwani; Kodjo Agbossou. Preemptive degradation-induced battery replacement for hybrid electric vehicles in sustained optimal extended-range driving conditions. Journal of Energy Storage 2017, 14, 147 -157.
AMA StyleFrançois Martel, Yves Dubé, Joris Jaguemont, Sousso Kelouwani, Kodjo Agbossou. Preemptive degradation-induced battery replacement for hybrid electric vehicles in sustained optimal extended-range driving conditions. Journal of Energy Storage. 2017; 14 ():147-157.
Chicago/Turabian StyleFrançois Martel; Yves Dubé; Joris Jaguemont; Sousso Kelouwani; Kodjo Agbossou. 2017. "Preemptive degradation-induced battery replacement for hybrid electric vehicles in sustained optimal extended-range driving conditions." Journal of Energy Storage 14, no. : 147-157.
Mauricio Higuita Cano; Kodjo Agbossou; Sousso Kelouwani; Yves Dubé. Experimental evaluation of a power management system for a hybrid renewable energy system with hydrogen production. Renewable Energy 2017, 113, 1086 -1098.
AMA StyleMauricio Higuita Cano, Kodjo Agbossou, Sousso Kelouwani, Yves Dubé. Experimental evaluation of a power management system for a hybrid renewable energy system with hydrogen production. Renewable Energy. 2017; 113 ():1086-1098.
Chicago/Turabian StyleMauricio Higuita Cano; Kodjo Agbossou; Sousso Kelouwani; Yves Dubé. 2017. "Experimental evaluation of a power management system for a hybrid renewable energy system with hydrogen production." Renewable Energy 113, no. : 1086-1098.
Mauricio Higuita Cano; Mohamed Islam Aniss Mousli; Sousso Kelouwani; Kodjo Agbossou; Mhamed Hammoudi; Yves Dubé. Improving a free air breathing proton exchange membrane fuel cell through the Maximum Efficiency Point Tracking method. Journal of Power Sources 2017, 345, 264 -274.
AMA StyleMauricio Higuita Cano, Mohamed Islam Aniss Mousli, Sousso Kelouwani, Kodjo Agbossou, Mhamed Hammoudi, Yves Dubé. Improving a free air breathing proton exchange membrane fuel cell through the Maximum Efficiency Point Tracking method. Journal of Power Sources. 2017; 345 ():264-274.
Chicago/Turabian StyleMauricio Higuita Cano; Mohamed Islam Aniss Mousli; Sousso Kelouwani; Kodjo Agbossou; Mhamed Hammoudi; Yves Dubé. 2017. "Improving a free air breathing proton exchange membrane fuel cell through the Maximum Efficiency Point Tracking method." Journal of Power Sources 345, no. : 264-274.
This paper addresses the energy management strategy (EMS) for a fuel cell hybrid electric vehicle (FC-HEV). The fuel cell system (FCS) is a multi-physics system, and consequently, its energetic performances depend on the degradation and on the operating conditions. The maximum power (MP) and the maximum efficiency (ME) points of the FCS are unique but they move with operating condition variations. Thus, developing an extremum seeking process (ESP) for both MP and ME tracking is a challenging task. In the ESP, models are identified online by using an adaptive recursive least square (ARLS) method to seek a variation in the FCS performances. Then an optimisation algorithm is used on the updated model to find the MP and the ME points. The ESP is incorporated into a hysteresis power splitting control (HPSC). A MP mode or a ME mode can be set based on the energy storage level (battery pack). The effectiveness of the proposed MP- and ME-ESP EMS is demonstrated by conducting experimental studies on two FCSs with different levels of degradation. It was demonstrated that the classical EMS based on maps are not valid when the operating parameters vary because of the level of degradation change.
Khalid Ettihir; Loïc Boulon; Kodjo Agbossou. Energy management strategy for a fuel cell hybrid vehicle based on maximum efficiency and maximum power identification. IET Electrical Systems in Transportation 2016, 6, 261 -268.
AMA StyleKhalid Ettihir, Loïc Boulon, Kodjo Agbossou. Energy management strategy for a fuel cell hybrid vehicle based on maximum efficiency and maximum power identification. IET Electrical Systems in Transportation. 2016; 6 (4):261-268.
Chicago/Turabian StyleKhalid Ettihir; Loïc Boulon; Kodjo Agbossou. 2016. "Energy management strategy for a fuel cell hybrid vehicle based on maximum efficiency and maximum power identification." IET Electrical Systems in Transportation 6, no. 4: 261-268.
The aim of this work is to develop a prediction method for renewable energy sources in order to achieve an intelligent management of a microgrid system and to promote the utilization of renewable energy in grid connected and isolated power systems. The proposed method is based on the multi-resolution analysis of the time-series by means of Wavelet decomposition and artificial neural networks. The analysis of predictability of each component of the input data using the Hurst coefficient is also proposed. In this context, using the information of predictability, it is possible to eliminate some components, having low predictability potential, without a negative effect on the accuracy of the prediction and reducing the computational complexity of the algorithm. In the evaluated case, it was possible to reduce the resources needed to implement the algorithm of about 29% by eliminating the two (of seven) components with lower Hurst coefficient. This complexity reduction has not impacted the performance of the prediction algorithm.
Boubacar Doucoure; Kodjo Agbossou; Alben Cardenas. Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data. Renewable Energy 2016, 92, 202 -211.
AMA StyleBoubacar Doucoure, Kodjo Agbossou, Alben Cardenas. Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data. Renewable Energy. 2016; 92 ():202-211.
Chicago/Turabian StyleBoubacar Doucoure; Kodjo Agbossou; Alben Cardenas. 2016. "Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data." Renewable Energy 92, no. : 202-211.