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Ali Amamou
Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, QC G8Z 4M3, Canada

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Review
Published: 13 June 2021 in Energies
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In recent years, the use of electric Autonomous Wheeled Mobile Robots (AWMRs) has dramatically increased in transport of the production chain. Generally, AWMRs must operate for several hours on a single battery charge. Since the energy density of the battery is limited, energy efficiency becomes a key element in improving material transportation performance during the manufacturing process. However, energy consumption is influenced by the navigation stages, because the type of motion necessary for the AWMR to perform during a mission is totally defined by these stages. Therefore, this paper analyzes methods of energy efficiency that have been studied recently for AWMR navigation stages. The selected publications are classified into planning and motion control categories in order to identify research gaps. Unlike other similar studies, this work focuses on these methods with respect to their implications for the energy consumption of AWMRs. In addition, by using an industrial Self-Guided Vehicle (SGV), we illustrate the direct influence of the motion planning stage on global energy consumption by means of several simulations and experiments. The results indicate that the reaction of the SGV in response to unforeseen obstacles can affect the amount of energy consumed. Hence, energy constraints must be considered when developing the motion planning of AWMRs.

ACS Style

Mohammad Mohammadpour; Lotfi Zeghmi; Sousso Kelouwani; Marc-André Gaudreau; Ali Amamou; Massinissa Graba. An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots. Energies 2021, 14, 3517 .

AMA Style

Mohammad Mohammadpour, Lotfi Zeghmi, Sousso Kelouwani, Marc-André Gaudreau, Ali Amamou, Massinissa Graba. An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots. Energies. 2021; 14 (12):3517.

Chicago/Turabian Style

Mohammad Mohammadpour; Lotfi Zeghmi; Sousso Kelouwani; Marc-André Gaudreau; Ali Amamou; Massinissa Graba. 2021. "An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots." Energies 14, no. 12: 3517.

Review
Published: 15 November 2020 in Sensors
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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.

ACS Style

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 Style

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 (22):6532.

Chicago/Turabian Style

Abdul 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.

Review
Published: 15 October 2020 in Sustainability
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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.

ACS Style

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 Style

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 (20):8541.

Chicago/Turabian Style

Massinissa 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.

Journal article
Published: 24 September 2020 in Energies
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This paper deals with the design of an energy management strategy (EMS) for an industrial hybrid self-guided vehicle (SGV), considering the size of a fuel cell (FC) stack and degradation of a battery pack. In this context, first, a realistic energy model of the SGV was proposed and validated, based on experiments. This model provided a basis for individual components analysis, estimating energy requirements, component sizing, and testing various EMSs, prior to practical implementation. Second, the performance of the developed FC/battery SGV powertrain was validated under three EMS modes. Each mode was studied by considering four different FC sizes and three battery degradation levels. The final results showed that a small FC as a range extender is recommended, to reduce system cost. It is also important to maintain the FC in its high efficiency zones with a minimum ON/OFF cycle, leading to efficiency and lifetime enhancement of FC system. Battery SOC have to be kept at a high level during SGV operation, to support the FC during SGV acceleration. In order to improve the SGV’s overall autonomy, it is also important to minimize the stop and go and rotational SGV motion with appropriate acceleration and deceleration rate.

ACS Style

Amin Ghobadpour; Ali Amamou; Sousso Kelouwani; Nadjet Zioui; Lotfi Zeghmi. Impact of Powertrain Components Size and Degradation Level on the Energy Management of a Hybrid Industrial Self-Guided Vehicle. Energies 2020, 13, 5041 .

AMA Style

Amin Ghobadpour, Ali Amamou, Sousso Kelouwani, Nadjet Zioui, Lotfi Zeghmi. Impact of Powertrain Components Size and Degradation Level on the Energy Management of a Hybrid Industrial Self-Guided Vehicle. Energies. 2020; 13 (19):5041.

Chicago/Turabian Style

Amin Ghobadpour; Ali Amamou; Sousso Kelouwani; Nadjet Zioui; Lotfi Zeghmi. 2020. "Impact of Powertrain Components Size and Degradation Level on the Energy Management of a Hybrid Industrial Self-Guided Vehicle." Energies 13, no. 19: 5041.

Journal article
Published: 29 June 2020 in International Journal of Hydrogen Energy
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The PEMFC maximum power is greatly influenced by subfreezing temperature and degradation phenomena. Therefore, a dependable model is required to estimate the power with respect to the variation of the operating conditions and state of health. Semi-empirical models are potent tools in this regard. Nonetheless, there is not much information about their cold environment reliability. This paper comprehensively compares the performance of some models (already tested in normal ambient temperature) in subfreezing condition to introduce the most reliable one for PEMFC cold start-up application. Firstly, seven models are compared regarding voltage losses and precision. Subsequently, the three most dependable ones are selected and experimentally compared at sub-zero temperature in terms of polarization curve estimation for three PEMFCs with different degradation levels. The results of this study indicate that the model introduced by Amphlett et al. has a superior performance compared to other ones regarding the characteristic's estimation in below-zero temperature.

ACS Style

A. Amamou; M. Kandidayeni; A. Macias; L. Boulon; S. Kelouwani. Efficient model selection for real-time adaptive cold start strategy of a fuel cell system on vehicular applications. International Journal of Hydrogen Energy 2020, 45, 19664 -19675.

AMA Style

A. Amamou, M. Kandidayeni, A. Macias, L. Boulon, S. Kelouwani. Efficient model selection for real-time adaptive cold start strategy of a fuel cell system on vehicular applications. International Journal of Hydrogen Energy. 2020; 45 (38):19664-19675.

Chicago/Turabian Style

A. Amamou; M. Kandidayeni; A. Macias; L. Boulon; S. Kelouwani. 2020. "Efficient model selection for real-time adaptive cold start strategy of a fuel cell system on vehicular applications." International Journal of Hydrogen Energy 45, no. 38: 19664-19675.

Original research paper
Published: 29 May 2018 in Fuel Cells
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Output power of a fuel cell (FC) stack can be controlled through operating parameters (current, temperature, etc.) and is impacted by ageing and degradation. However, designing a complete FC model which includes the whole physical phenomena is very difficult owing to its multivariate nature. Hence, online identification of a FC model, which serves as a basis for global energy management of a fuel cell vehicle (FCV), is considerably important. In this paper, two well‐known recursive algorithms are compared for online estimation of a multi‐input semi‐empirical FC model parameters. In this respect, firstly, a semi‐empirical FC model is selected to reach a satisfactory compromise between computational time and physical meaning. Subsequently, the algorithms are explained and implemented to identify the parameters of the model. Finally, experimental results achieved by the algorithms are discussed and their robustness is investigated. The ultimate results of this experimental study indicate that the employed algorithms are highly applicable in coping with the problem of FC output power alteration, due to the uncertainties caused by degradation and operation condition variations, and these results can be utilized for designing a global energy management strategy in a FCV.

ACS Style

Mohsen Kandidayeni; Alvaro Macias Fernandez; A. A. Amamou; L. Boulon; S. Kelouwani. Comparative Analysis of Two Online Identification Algorithms in a Fuel Cell System. Fuel Cells 2018, 18, 347 -358.

AMA Style

Mohsen Kandidayeni, Alvaro Macias Fernandez, A. A. Amamou, L. Boulon, S. Kelouwani. Comparative Analysis of Two Online Identification Algorithms in a Fuel Cell System. Fuel Cells. 2018; 18 (3):347-358.

Chicago/Turabian Style

Mohsen Kandidayeni; Alvaro Macias Fernandez; A. A. Amamou; L. Boulon; S. Kelouwani. 2018. "Comparative Analysis of Two Online Identification Algorithms in a Fuel Cell System." Fuel Cells 18, no. 3: 347-358.

Journal article
Published: 01 April 2018 in Applied Energy
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Cold start of proton exchange membrane fuel cells (PEMFCs) at sub-zero temperatures is perceived as one of the obstacles in their commercialization way in automotive application. This paper proposes a novel internal-based adaptive strategy for the cold start of PEMFC to control its operating current in real time in a way to maximize the generated heat flux and electrical power in a short time span. In this respect, firstly, an online parameter identification method is integrated into a semi-empirical model to cope with the PEMFC performances drifts during cold start. Subsequently, an optimization algorithm is launched to find the best operating points from the updated model. Finally, the determined operating point, which is the current corresponding to the maximum power, is applied to PEMFC to achieve a rapid cold start. It should be noted that the utilization of adaptive filters has escaped the attention of previous PEMFC cold start studies. The ultimate results of the proposed strategy are experimentally validated and compared to the most commonly used cold start strategies based on Potentiostatic and Galvanostatic modes. The experimental outcomes of the comparative study indicate the striking superior performance of the proposed strategy in terms of heating time and energy requirement.

ACS Style

A. Amamou; Mohsen Kandidayeni; L. Boulon; S. Kelouwani. Real time adaptive efficient cold start strategy for proton exchange membrane fuel cells. Applied Energy 2018, 216, 21 -30.

AMA Style

A. Amamou, Mohsen Kandidayeni, L. Boulon, S. Kelouwani. Real time adaptive efficient cold start strategy for proton exchange membrane fuel cells. Applied Energy. 2018; 216 ():21-30.

Chicago/Turabian Style

A. Amamou; Mohsen Kandidayeni; L. Boulon; S. Kelouwani. 2018. "Real time adaptive efficient cold start strategy for proton exchange membrane fuel cells." Applied Energy 216, no. : 21-30.

Journal article
Published: 01 March 2018 in Journal of Power Sources
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Proton exchange membrane fuel cells (PEMFCs) have become the center of attention for energy conversion in many areas such as automotive industry, where they confront a high dynamic behavior resulting in their characteristics variation. In order to ensure appropriate modeling of PEMFCs, accurate parameters estimation is in demand. However, parameter estimation of PEMFC models is highly challenging due to their multivariate, nonlinear, and complex essence. This paper comprehensively reviews PEMFC models parameters estimation methods with a specific view to online identification algorithms, which are considered as the basis of global energy management strategy design, to estimate the linear and nonlinear parameters of a PEMFC model in real time. In this respect, different PEMFC models with different categories and purposes are discussed first. Subsequently, a thorough investigation of PEMFC parameter estimation methods in the literature is conducted in terms of applicability. Three potential algorithms for online applications, Recursive Least Square (RLS), Kalman filter, and extended Kalman filter (EKF), which has escaped the attention in previous works, have been then utilized to identify the parameters of two well-known semi-empirical models in the literature, Squadrito et al. and Amphlett et al. Ultimately, the achieved results and future challenges are discussed.

ACS Style

M. Kandidayeni; A. Macias; A.A. Amamou; L. Boulon; S. Kelouwani; H. Chaoui. Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes. Journal of Power Sources 2018, 380, 92 -104.

AMA Style

M. Kandidayeni, A. Macias, A.A. Amamou, L. Boulon, S. Kelouwani, H. Chaoui. Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes. Journal of Power Sources. 2018; 380 ():92-104.

Chicago/Turabian Style

M. Kandidayeni; A. Macias; A.A. Amamou; L. Boulon; S. Kelouwani; H. Chaoui. 2018. "Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes." Journal of Power Sources 380, no. : 92-104.