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This paper investigates the design of a two-layer energy-management system for a fuel cell hybrid electric vehicle (HEV). The first layer (upper layer) deals with the vehicle energy consumption, whereas the second layer (lower layer) deals with the power splitting between the fuel cell and the battery. The upper layer aims at providing the globally optimal energy consumption profile by considering the road-trip information and the vehicle dynamics. This energy profile is independent of the number and type of power sources on the vehicle. Therefore, it can be used to assist the real-time power splitting algorithm implemented into the lower layer. This layer design goal is mainly to share the vehicle power demand between the fuel cell and the battery while minimizing the hydrogen consumption. In addition, the splitting method takes into account the fuel cell efficiency map and the hydrogen/electricity relative pricing while imposing a smooth behavior on the fuel cell. This smooth behavior is desirable to preserve the fuel cell life and reduce the oxygen starvation phenomenon. The proposed energy-management system has been successfully implemented and validated on an HEV test bench. The experiments and simulations using several standard driving cycles suggest that the approach can reduce the hydrogen consumption up to 10% compared to a rule-based method and a depleting-sustaining method while preserving at the same time the battery pack from overdischarging. [ABSTRACT FROM PUBLISHER]

Presents a study which examined the configuration of class D amplifiers or inverters with field effect transistors for piezoelectric transducers. Operating principles of the inverters; Results; Conclusion.

Summary: This paper investigates the improvement of synchronization technique for single‐phase inverter. Specifically, the paper proposes a modified structure of second‐order generalized integrator with frequency‐locked loop (SOGI‐FLL) with FLL gain normalization. The proposed structure enhances the frequency detection, which makes it a powerful technique under distorted grid voltage. The validation of the proposed synchronization method includes simulations and experimental tests using Xilinx field programmable gate array (FPGA) as the target device. Moreover, time domain simulations using the direct power control (DPC) with the proposed structure are performed. The decoupled active and reactive powers are controlled using the artificial neural networks (ANNs) trained by the mean of a metaheuristic algorithm. In this paper, the grey wolf optimizer (GWO) is proposed to train the multilayer perceptron (MLP). The proposed approach shows better generation of synchronization signals and smooth power quality, making it suitable for grid‐tied and microgrids (MGs) power systems control. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates a hydrogen-based genset maximum efficiency tracking problem in the context of electric vehicle range extension. This genset is cheaper than fuel cells and has the desirable property of being greenhouse gas emission free in addition to being less pollutant than the conventional gasoline based gensets. Using Taylor's series, a parametric efficiency model is built iteratively. This model is used by a nonlinear optimization method which searches for the optimal operating conditions for a maximum achievable efficiency. The root-mean-square-error between experimental data and the model is less than 5 × 10−4. The hardware-in-the-loop simulation demonstrated that the proposed tracking approach is effective. In addition, it can improve the hydrogen-based genset efficiency up to 7.15% compared to the commonly used industrial method based on a constant speed drive approach. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate an adaptive energy management and power splitting system for a fuel cell hybrid electric vehicle. The battery pack is the main power source whereas the fuel cell is considered as a range extender that cannot sustain alone the vehicle traction power. In addition, the fuel cell contributes to reduce the battery pack degradation by limiting its depth-of-discharge (DoD). This energy management system is based on a two layer architecture in which the upper layer computes the anticipated end-of-trip DoD using online mass estimation. The lower layer is designed to split the driver power demand by minimizing a cost function which includes the hydrogen/electricity cost ratio. Therefore, the best trade-off between reducing battery pack degradation and using cost effective energy is provided. Furthermore, the system allows the fuel cell to operate at its maximum efficiency. Comparative study results indicate that using online mass estimation improves the overall fuel consumption efficiency whilst contributing at the same time to DoD reduction. [Copyright &y& Elsevier]

Abstract: A free air breathing fuel cell thermal model is developed. This proton exchange membrane fuel cell (PEMFC) has been selected as the basis for the study due to its use in automotive applications. The blowers integrated to the stack provide the required air flow for hydrogen oxidation as well as the fluid for the stack thermal regulation. Hence, their controls are a key point for keeping the system to maximum efficiency. Using well-known fuel cell electrochemistry, a dynamic thermal model near freezing temperature, which includes the stack physical parameters, is developed and validated. In addition to these parameters, only the inlet and outlet air temperatures are used to derive the model. Experimental validation with a real 1 kW free air breathing PEMFC has demonstrated that the model can reasonably track the stack internal temperature with a maximum deviation between the observed and the estimated temperatures of 5%. Therefore, the proposed method will allow the development of efficient blower management systems for PEMFC efficiency improvement. [Copyright &y& Elsevier]

The prediction of the occupancy in buildings is essential to design efficient energy control strategies that optimize consumption and reduce losses while guaranteeing the comfort of the occupants. For this reason, many works address the problem of detecting, estimating, and predicting buildings' occupancy using different techniques, devices, and technologies. The occupancy prediction process can be described in four stages: data acquisition, modeling, evaluation, and testing, which are closely related. This paper reviews the most relevant recent literature on building occupancy estimation and prediction, analyzing the key aspects of its stages. A detailed description of the variables and design considerations is presented, including measurement methods, sensor selection, modeling techniques, evaluation metrics, and different applications. Through its examination, this paper elaborates significant remarks on the interaction between the stages, providing an overview of the suitable design of the occupancy prediction process. Finally, current and future trends are discussed. • A systematic review of the occupancy estimation and prediction process is presented. • Data acquisition, modeling, evaluation, and testing are the four general stages. • The importance of sensor fusion in overcoming individual limitations is presented. • Occupancy detection methods include deterministic, stochastic, and machine learning. • Some potential future research directions are discussed. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the Proton Exchange Membrane Fuel Cell (PEMFC) Cold Startup problem within the specific context of the Plugin Hybrid Electric Vehicles (PHEV). A global strategy which aims at providing an efficient method to minimize the energy consumption during the startup of a PEMFC is proposed. The overall control system is based on a supervisory architecture in which the Energy Management System (EMS) plays the role of the power flow supervisor. The EMS estimates in advance, the time to start the fuel cell (FC) based upon the battery energy usage during the trip. Given this estimation and the amount of additional energy required, the fuel cell temperature management strategy computes the most appropriate time to start heating the stack in order to reduce heat loss through the natural convection. As the cell temperature rises, the PEMFC is started and the reaction heat is used as a self-heating power source to further increase the stack temperature. A time optimal self-heating approach based on the Pontryagin minimum principle is proposed and tested. The experimental results have shown that the proposed approach is efficient and can be implemented in real-time on FC-PHEVs. [Copyright &y& Elsevier]

An electrolyzer and a fuel cell have been integrated in a small-scale stand-alone renewable energy system to demonstrate that hydrogen can be used for long-term stationary energy storage. The economic and environmental performance of such a system is strongly related to the ability of the electrolyzer to convert electrical energy to hydrogen and the ability of the fuel cell to convert hydrogen back to electrical energy, which together define the round-trip efficiency of the hydrogen storage system. One promising way to improve the efficiency as well as to decrease the capital costs of the fuel cell is to recuperate the oxygen from the electrolyzer and use it as the fuel cell oxidant instead of compressed air. This paper presents the modifications made to the system in order to implement oxygen recuperation. The round-trip system efficiency was found to be 18% with oxygen recuperation and 13.5% without it. [Copyright &y& Elsevier]

• Providing an overview on predicting the mechanical performance of elastomer components subjected the combined effects of multiple sources of degradation. • Identification of multiple causes leading to the breakdown of elastomer materials and find the way to improve their performance. • Predicting models for thermal, thermo-oxidation and ozone ageing of elastomers are discussed. • Residual analysis, hypothesis testing, and cross-validation techniques are suggested to check the accuracy of existing experimental data. In this article, we review recent advances in elastomer degradation, focusing on four fundamental mechanisms: thermal degradation, ozone degradation, thermo-oxidative degradation, and photo-oxidative degradation. Here, we review the many causes of elastomer deterioration and provide an overview of the models and prediction methods used to estimate the lifespan of elastomers. Understanding degradation processes is crucial to improving the stability, durability, and reliability of products made of elastomers. External influences must be considered to accurately forecast elastomer lifetimes. Elastomers may alter significantly when exposed to high temperatures and ozone. Alternatively, oxygen and UV radiation may cause thermo-oxidative and photo-oxidative deterioration. Thus, while estimating elastomer longevity, these external influences must be included. This article examines thermal aging, ozone aging (surface cracking rate, ozone diffusion kinetics), and thermo-oxidative degradation models. Including diffusion of oxygen molecules, elastomer – oxygen interaction, oxidation state – mechanical performance relationship. One of the strengths of this article is that it focuses on both the chemical and physical aspects of elastomer degradation, providing a comprehensive understanding of the subject. The article includes models such as Zhurkov's law and Eyring's formula, which improve the accuracy of predictions. This article stands out for its focus on predicting the mechanical performance of rubber components subjected to aging and the combined effects of multiple sources of degradation. It also highlights the need for more accurate and practical approaches to predicting the service life of elastomer-based components. This document targets experts in science, engineering, materials science, and industry. It discusses elastomer degradation parameters to assist in constructing realistic models for predicting their lifespan in various scenarios. [ABSTRACT FROM AUTHOR]

Load forecasting could play a crucial role in energy management and control of buildings in residential neighborhoods. In these areas, electricity demand is influenced by different phenomena accounting for climate conditions and comfort preferences. The uncertain nature of these circumstances results in power profiles with diverse patterns. Under this condition, overall load prediction is suggested by utilizing Cluster-based Aggregate Forecasting (CBAF). Accordingly, this paper proposes a unified approach to such a practice. The proposed scheme employs an unsupervised machine-learning algorithm to develop a time-series clustering scheme that performs the classification task through the k-medoids-based clustering incorporating the Dynamic Time Warping (DTW) algorithm. Subsequently, a consensus is achieved among the resultant clusters where the Jaccard similarity index adjudges the similarity measurement. The Additive Gaussian Process (AGP), a powerful non-parametric forecasting technique, is exploited to predict aggregated load at each cluster level. With low complexity and high scalability, AGP is particularly utilized to provide effective forecasting. Numerical simulations on synthetic as well as real datasets have been carried out to illustrate the effectiveness of the proposed methodology. Additionally, two comparative studies are carried out with forecasts without clustering and with the benchmark non-parametric models employing a cluster-based technique. The proposed method demonstrates significant improvement in forecasting accuracy for both datasets by reducing the error metrics and achieving 7% improvement in the coefficient of determination (R 2) value as compared to the aggregated load forecast achieved without clustering. The comparative study demonstrates that the proposed method with AGP can forecast the total residential load more accurately than other benchmark models with an improvement of 26% and 21% in R 2 , respectively, for both datasets. [ABSTRACT FROM AUTHOR]

This work investigates the design and validation of a fuel cell management system (FCMS) which can perform when the fuel cell is at water freezing temperature. This FCMS is based on a new tracking technique with intelligent prediction, which combined the Maximum Efficiency Point Tracking with variable perturbation-current step and the fuzzy logic technique (MEPT-FL). Unlike conventional fuel cell control systems, our proposed FCMS considers the cold-weather conditions, the reduction of fuel cell set-point oscillations. In addition, the FCMS is built to respond quickly and effectively to the variations of electric load. A temperature controller stage is designed in conjunction with the MEPT-FL in order to operate the FC at low-temperature values whilst tracking at the same time the maximum efficiency point. The simulation results have as well experimental validation suggest that propose approach is effective and can achieve an average efficiency improvement up to 8%. The MEPT-FL is validated using a Proton Exchange Membrane Fuel Cell (PEMFC) of 500 W. [ABSTRACT FROM AUTHOR]

This study aims at highlighting the impact of the sizing of a hybrid multi-stack fuel cell – battery system on its behavior. Using a rule based energy management strategy, the influence of the hybridization rating is studied. The system's fuel consumption, operation duration, start/stop cycles, ampere-hour throughput and state of charge deviation are evaluated using an Energetic Macroscopic Representation (EMR) based dynamic model. The simulation results predict and expected superiority in both fuel economy and system degradation for systems with a high hybridization ratings whereas the use of systems with low hybridization ratings results in a reduced battery depth of discharge. [ABSTRACT FROM AUTHOR]

This work evaluates the performance of a plug-in hybrid electric vehicle (PHEV) energy management process that relies on the active management of the degradation of its energy carriers – in this scenario, a lithium-ion battery pack and a polymer electrolyte membrane fuel cell (PEMFC) – to produce a near economically-optimal vehicle operating profile over its entire useful lifetime. This solution is obtained through experimentally-supported PHEV models exploited by an optimal discrete dynamic programming (DDP) algorithm designed to efficiently process vehicle usage cycles over an extended timescale. Our results demonstrate the economic and component lifetime gains afforded by our strategy when compared with alternative rule-based PHEV energy management benchmarks. [ABSTRACT FROM AUTHOR]

This work analyses the economical dynamics of an optimized battery degradation management strategy intended for plug-in hybrid electric vehicles (PHEVs) with consideration given to low-cost technologies, such as lead-acid batteries. The optimal management algorithm described herein is based on discrete dynamic programming theory (DDP) and was designed for the purpose of PHEV battery degradation management; its operation relies on simulation models using data obtained experimentally on a physical PHEV platform. These tools are first used to define an optimal management strategy according to the economical weights of PHEV battery degradation and the secondary energy carriers spent to manage its deleterious effects. We then conduct a sensitivity study of the proposed optimization process to the fluctuating economic parameters associated with the fuel and energy costs involved in the degradation management process. Results demonstrate the influence of each parameter on the process's response, including daily total operating costs and expected battery lifetime, as well as establish boundaries for useful application of the method; in addition, they provide a case for the relevance of inexpensive battery technologies, such as lead-acid batteries, for economy-centric PHEV applications where battery degradation is a major concern. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The residential sector's substantial electricity consumption, driven by heating demands during winter, necessitates optimal energy consumption strategies in the era of decarbonization. To address this challenge, this paper introduces a synthetic dataset specifically tailored to simulate energy consumption in residential apartment buildings. Focusing on the interplay of cold weather conditions and the effects of aging factors, the dataset comprehensively encompasses key variables, including indoor temperature, energy consumption, outdoor temperature, outdoor humidity and solar radiation. It underscores the considerable impact of building aging on energy consumption patterns. The dataset's significance extends across various domains, particularly in the realms of energy forecasting and thermal modelling. It serves as a robust foundation for predicting future consumption patterns, optimizing resource allocation, and refining energy efficiency strategies. The inclusion of indoor temperature data facilitates an in-depth thermal modelling approach, shedding light on intricate relationships that influence building performance in cold climates. Beyond traditional, the dataset proves invaluable in nonlinear modelling and machine learning. It emerges as a key tool for algorithm training, enhancing forecast precision, and supporting well-informed decision-making. The introduction of a temporal dimension by accounting for aging factors allows for the exploration of evolving building components over time, a critical consideration for sustainable energy management and building maintenance strategies. The dataset was meticulously generated by creating geometry using SketchUp and conducting energy modelling and simulations via the OpenStudio platform, which integrates the Energy Plus modelling engine to enhance accuracy. In summary, this synthetic dataset generation provides valuable insights into energy consumption in residential buildings exposed to cold weather conditions and the influences of aging. Its multifaceted applications across forecasting, modelling, management, and planning underscore its potential to advance sustainable and efficient energy practices., (© 2024 The Author(s).)

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to overlook residential scenarios related to geographically-specific regions and existent practices to face unexplored situations. This paper presents applied research on NILM in Quebec residences to reveal its barriers to feasible implementations. It commences with a concise discussion about a successful NILM idea to highlight its essential requirements. Afterward, it provides a comparative statistical analysis to represent the specificity of the case study by exploiting real data. Subsequently, this study proposes a combinatory approach to load identification that utilizes the promise of sub-meter smart technologies and integrates the intrusive aspect of load monitoring with the non-intrusive one to alleviate NILM difficulties in Quebec residences. A load disaggregation technique is suggested to manifest these complications based on supervised and unsupervised machine learning designs. The former is aimed at extracting overall heating demand from the aggregate one while the latter is designed for disaggregating the residual load. The results demonstrate that geographically-dependent cases create electricity consumption scenarios that can deteriorate the performance of existing NILM methods. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly in Quebec houses.