Energetic Macroscopic Representation Research Articles

Nonintrusive Condition Monitoring of FCEV Using System-Level Digital Twin Model

High maintenance costs and short lifecycles are two main issues in fuel cell electric vehicle (FCEV) development. With the development of digital technology, the concept of digital twins provides new opportunities for monitoring and predicting the behavior of FCEV. However, the constitution of the digital model of FCEV faces many challenges caused by the multi-physical domain. In this paper, a digital model is built for condition monitoring of FCEV in both short-term and long-term. The built model reflects the interaction of multi-physicals domains by representing the FCEV using the energetic macroscopic representation (EMR) method. Moreover, by considering different road condition, the effect of different driving modes can be shown through the energy flow between the battery and the fuel cell. And the model can give a visible reflection of the electric, magnetic, mechanical, and chemical inside the vehicle. To further investigate the effect of all the above factors on the long-term operation status, the echo state network (ESN) is used to predict the degradation phenomenon of the proton exchange membrane fuel cells (PEMFC), which is used for prognostic purposes. At last, the digital twin test bench is shown with a real-time simulation platform of the OPAL-RT.

Analysis of the Timetable Impact on Energy Consumption of a Subway Line

This paper analyzes the timetable impact on the energy consumption of a subway line. In most timetable studies, simplified models are used and can lead to misestimation of the braking energy and thus the energy transfer between braking vehicles and accelerating vehicles. In this paper, specific attention is paid to the models of the vehicles, the traction power substation, and the rail supply network to enable an accurate estimation of the energy consumption. The energetic macroscopic representation formalism is used to organize the models of the subsystems so they have the right interactions. The developed model is validated by experimental tests on a real subway line. The error on the global energy consumption is lower than 2.2%. The model is then employed to examine the influence of the vehicle time interval on energy consumption. A 10-second adjustment in this time interval can result in a substantial 22% decrease in energy consumption for the analyzed real subway line.

OPTIMISATION OF HYBRID ELECTRIC VEHICLE ENERGY MANAGEMENT USING MACHINE LEARNING

The focus of the research is on the optimization of the hybrid electric vehicle energy management using machine learning. The objective of this research is to develop the algorithm by using Support Vector Machine (SVM) and to identify the optimal operation mode for SHEV based on the power demand use by using the algorithm. First of all, this research focus on the series hybrid electric vehicle and the vehicle used is an all-terrain vehicle (ATV). Suitable formula will be used to complete the modeling of the vehicle in Energetic Macroscopic Representation (EMR) form and the model constructed by using Matlab Simulink. Then, Support Vector Machine is used to optimize the energy management in the vehicle. The training data from New European Driving Cycle (NEDC) and Worldwide harmonized Light vehicles Test Cycles (WLTC) with 3 classes will put be inside the SVM to undergo training and then the optimal operation modes for each driving cycle will be obtained by using Linear SVM. Then, the obtained results which are the predicted operation mode using the driving cycles and are plotted in the graph. The pattern of the graph is analyzed and then the best predicted operation mode with the highest accuracy among all the driving cycles is chosen. The trained model of the driving cycle with the highest accuracy is used to predict the optimal operation mode for ATV so that to have higher efficiency in energy management by using the Classification Learner inside the Matlab.

Development of an Optimal Port Crane Trajectory for Reduced Energy Consumption

This paper is concerned with the development of an optimal load-handling trajectory for port cranes. The objective is to minimize load cycle time and reduce energy consumption. Energetic macroscopic representation formalism is used to model a port crane load-handling mechanism. The crane model developed includes the mathematical model, the crane’s local control system, and a MATLAB/Simulink model for simulation. The particle swarm optimization algorithm is used to find the set of pareto optimal crane trajectories given the variation in crane size, ship size, and wind speed. Experimental validation of the crane model is conducted by comparing it with a real-world crane. Simulation results show that the optimal crane load trajectory is 38% faster and more productive than the nonoptimal crane load trajectory. Furthermore, the results show that the optimal trajectory reduces the cranes’ peak power and energy consumption by 36% when compared with the nonoptimal trajectory.

Energetic macroscopic representation of a mechanical compression-assisted hybrid thermochemical cycle exploiting low grade heat for cold applications

This paper describes an original model based on The Energetic Macroscopic Representation (EMR) to develop a control structure of an innovative compression-assisted hybrid thermochemical cooler driven by low-grade thermal energy. Hybrid thermochemical cooler with mechanical compression have a wider operating temperature range than conventional thermochemical processes. This makes it possible to exploit lower grade heat sources and/or produce cold at lower temperatures. In addition, the ability to control compressor speed increases thermochemical process controllability to respond to load or source variations. Nodal modeling of each process component is developed according to the EMR formalism. A parametric identification and validation of this process model is then carried out using experimental data. The deviation from the experimental data is lower than 1bar for the reactor, condenser and evaporator pressure, lower than 0.1 for the reaction advancement and lower than 2°C for the reactor wall temperature. The EMR model is then inverted to obtain a process control law for maintaining a cold room at -18°C. Despite the variation in cooling demand from a minimum of 100W to a maximum of 700W, the compressor's control structure was able to regulate its speed of rotation to maintain the cold room temperature at -18±0.5°C for 24h.

Unified Environment for Real Time Control of Hybrid Energy System Using Digital Twin and IoT Approach.

Today, climate change combined with the energy crisis is accelerating the worldwide adoption of renewable energies through incentive policies. However, due to their intermittent and unpredictable behavior, renewable energy sources need EMS (energy management systems) as well as storage infrastructure. In addition, their complexity requires the implementation of software and hardware means for data acquisition and optimization. The technologies used in these systems are constantly evolving but their current maturity level already makes it possible to design innovative approaches and tools for the operation of renewable energy systems. This work focuses on the use of Internet of Things (IoT) and Digital Twin (DT) technologies for standalone photovoltaic systems. Based on Energetic Macroscopic Representation (EMR) formalism and the Digital Twin (DT) paradigm, we propose a framework to improve energy management in real time. In this article, the digital twin is defined as the combination of the physical system and its digital model, communicating data bi-directionally. Additionally, the digital replica and IoT devices are coupled via MATLAB Simulink as a unified software environment. Experimental tests are carried out to validate the efficiency of the digital twin developed for an autonomous photovoltaic system demonstrator.

Fuzzy controller hardware implementation for an EV's HESS energy management

The recent technological advances related to embedded systems, and the increased requirements of the Electric Vehicle (EV) industry, lead to the evolution of design and validation methodologies applied to complex systems, in order to design a product that respects the requirements defined according to its performance, safety, and reliability. This research paper presents a design and validation methodology, based on a hardware-in-the-loop (HIL) approach, including a software platform represented by Matlab/ Simulink and a real-time STM32 microcontroller used as a hardware platform. The objective of this work is to evaluate and validate an Energy Management System (EMS) based on Fuzzy Logic Controller (FLC), developed in C code and embedded on an STM32 microcontroller. The developed EMS is designed to control, in real-time, the energy flow in a hybrid energy storage system (HESS), designed in an active topology, made of a Li-ion battery and Super-Capacitors (SC). The proposed HESS model was organized using the Energetic Macroscopic Representation (EMR) and constructed on Matlab/Simulink software platform. The evaluation and validation of the developed algorithm were performed by comparing the HIL and simulation results under the New European Driving Cycle (NEDC).

Optimal allocation and energy management of a wind–hydrogen generation system equipped with the speed regulating differential mechanism

The hybrid drive wind turbine (WT) can be friendly connected to the power grid by using a speed regulating differential mechanism (SRDM) instead of partially or fully rated converters, which has been considered as a promising solution for the stable consumption of large-scale wind power generation. To further improve the on-grid performance of hybrid drive WTs, this paper develops a multi-source power generation scheme, in which a hydrogen storage system (HSS) is integrated for mitigating the wind power generation intermittencies. The overall architecture and kinematic principles of the proposed wind–hydrogen generation system, called SRDM-based WT with HSS, are first analyzed. Then, the graphical descriptions of mathematical models are finalized via the Energetic Macroscopic Representation method, by which the physical characteristics and energy flow relationships are revealed. To ensure the economical and stable operation of the proposed wind–hydrogen scheme, an effective optimal allocation framework, considering the uncertainties from wind power output and load demand, is presented to HSS, targeting the maximum annual revenue. The effects of several key HSS parameters on the capacity allocation results are also investigated. Moreover, aiming at the different system working modes, an energy management approach is synthesized to achieve the interaction analysis and power supervision between energy sources and storage elements. Finally, experimental and simulation case studies are demonstrated. Results illustrate the effectiveness of the proposed approaches and the optimal performance for uninterrupted on-grid operation of the proposed wind–hydrogen energy system.

Fuzzy logic-based energy management strategy on dual-source hybridization for a pure electric vehicle

This paper presents a fuzzy logic controller (FLC) based energy management strategy (EMS), combined with power filtering for a pure electric vehicle. The electrical power supply is provided by a hybrid energy storage system (HESS), including Li-Ion battery and supercapacitors (SCs), adopting a fully active parallel topology. The vehicle model was organized and constructed using the energetic macroscopic representation (EMR). The main objective of this work is to ensure an efficient power distribution in the proposed dual source, in order to reduce the battery degradation. To evaluate the impact of the developed design and the efficiency of the developed EMS, the proposed FLC strategy is compared to a classical EMS using SCs-filtering strategy and architecture based on battery storage model. To validate the proposed topology, simulation results are provided for the new European driving cycle (NEDC) using MATLAB/Simulink environment.

Dynamic fuel cell model improvement based on macroscopic energy representation

<p><span lang="EN-GB">In this paper, a new dynamic model is presented of the proton exchange membrane fuel cell (PEMFC), using energetic macroscopic representation (EMR). This model is developed for electric automotive applications, powered by a hybrid energy storage system (HESS). The PEMFC can be used as a primary source, the supercapacitors (SC) and/or batteries are the secondary sources. The model design enables the optimization of energy use and the reduction of equipment costs, by involving the fuel cell (FC) in the transient regimes. This model takes into consideration dynamic phenomena, such as double layer capacitance, charge transfer, energy losses caused by the propagation fuel and oxidant delay. The new developed model has been tested and validated using BAHIA bench of HELION/AREVA.</span></p>

Control optimization of automatic multistage transmission in cityelectric bus

The threatening ecological situation in modern cities overloaded with traffic makes it expedient to replace buses with dieselengines with electric buses. In order to increase the energy efficiency of a drive of electric bus and, thanks to this, to expand its power reserve, the paper investigates the feasibility of using a multistage automatic transmission (AT). For research, the Electron E19101 electric bus was taken and two of its variants were considered –a studied one with a six-speed AT and a basic one with a single-speed transmission. For each of the options, the traction characteristics of the electric drive for different positions of the accelerator pedal were formed. Comparative studies of the operation of both variants of the electric bus were carried out by means of computer simulation. For this, computer models of all subsystems of electric buses, as well as external influences that resist movement and determine the load on the electric drive system, were built using the Energetic Macroscopic Representation method in the Matlab/Simulink environment. The application of the specified method makes it possible to simulate the movement of electric bus during a long-term of the adapted FTP-75 standard urban transport cycle. For the studied variant, the AT control system was developed with the aim of current selection of the optimal value of the gearbox transmission ratio, for which the maximum value of the efficiency of the electric drive system is obtained in specific driving conditions. The research results showed a 3.35% advantage in energy costs for movement in the applied transport cycle of the electric bus with the multistage transmission over the single-stage one. However, as also shown, this advantage increases to 5.3% during the buses move in the similar transport cycle at low speeds, which occurs in today's traffic-congested cities. In addition, the use of theATmakes it possible to reduce the rated power of the electric bus motor, increase its maximum traction force, which will ensure the possibility of moving with a full load on a large slope, as well as significantly increase the maximum speed of movement, which is important for suburban transportation.

Joint coordination of optimal power management and energy storage system sizing for a full-scale marine current turbine considering microgrid integration constraint

The issues of variability and uncertainty of tidal speed result in severe challenges for standalone microgrid (as island grids for examples) operators. Indeed, to maintain the system power balance, existing classical generators which are associated with tidal power units must be able to frequently change their operating point, either up or down, to respond to the fast fluctuations of tidal power and consumption and sometimes exceed their maximal ramp capability. This in turn accelerates aging process and results in serious damage. The switching to a safer and more reliable system seems to be a promising and long-term solution. This paper proposes to associate a supercapacitor-based Energy Storage System (SC-ESS) to a tidal turbine to smooth the fast power production fluctuations. The association of this SC-ESS with the tidal turbine unit aims to meet requirements on the variation of injected tidal power in an islanded microgrid. An original smart power management strategy is proposed for the use of the ESS. Taking into account this strategy, an optimal design methodology based on simulations on the more severe encountered day cases is proposed. This methodology allows to determine a minimal sizing of the ESS to fulfil the grid requirements. The paper also proposes a complete modeling of the whole system behavior. The Energetic Macroscopic Representation (EMR) tool is used to realize the system model where the inversion-based control principle is then used to deduce the control schemes. This global methodology is applied to a test tidal turbine which is based on a real 1 MW prototype setup to the islanded grid of Ushant in France. Using real measured data, it is shown that a relatively small and low cost SCs pack is sufficient to accommodate the high tidal power fluctuations. A comparison is performed between the existing configuration where a resistor bank is used to shave the excessive power variation and the considered case where SCs pack is used. For one tidal cycle, it is shown that a reduction in energy loss of 18.2% is achieved, which presents a non-negligible gain for Ushant's Island microgrid.

Flexible Simulation of an Electric Vehicle to Estimate the Impact of Thermal Comfort on the Energy Consumption

The energy consumption of electric vehicles depends not only on the traction energy but also on the thermal comfort energy. Some studies lead to the estimation of this energy consumption from real measurements on different driving and climatic conditions. However, those results rely on a large number of vehicle tests, which is time-consuming. Moreover, the impacts of the different subsystems cannot be differentiated in such global studies. A flexible simulation tool can help to analyze the impact of the different parts of a vehicle. This article proposes a multiphysical parametrized model of an electric vehicle, including the traction and comfort subsystems. A flexible model of a Renault Zoe is developed due to the energetic macroscopic representation. This model is validated by experimental tests of the real vehicle. Then, the impact of the heating, ventilation, and air conditioning (HVAC) subsystem is studied for different driving cycles and climatic conditions. In very cold conditions, the use of the HVAC subsystem represents an increase of up to 248% of the total energy consumption, compared to summer conditions.

Dynamic Simulation Model of a Hybrid Electric ATV

The application of electric vehicles has its own disadvantages which can influence the critical performance of the vehicles such as the limited capacity of the battery that play a major role in the range of mileage covered. Hence, a hybrid electric All-Terrain-Vehicle (HEATV) is a best solution as the addition of gasoline-powered engine and electric generator can extend the range covered by an electric ATV. In order to determine the effectiveness of the hybrid configuration, a dynamic simulation model need to be developed by Energetic Macroscopic Representation Method (EMR) to analyze the performance of the ATV. The EMR method involve the causal ordering graph and action-reaction principle together with Inversion-based Control to develop a control structure of a HEATV. The simulation test will be run on the original and modifications of the ATV where the original one only depends on EM to drive the vehicle whereas the modified vehicle include the ICE and electric generator for charging process. The simulation results of the original ATV show the decreasing trend in terms of battery state-of-charge. However, after the modifications have been done the battery state-of-charge show the increasing trend and thus, extend the range of mileage covered by an ATV.

Real-time aTmega microcontroller-based simulator enabled hardware-in-the-Loop for fuzzy control dual-sources HESS

In this paper, a real-time simulation of a hybrid energy storage system (HESS), using a hardwarein- the-loop (HIL) platform is proposed. The HESS is in a semi-active configuration including Supercapacitors (SC) controlled by a chopper and a Li-ion battery. The model organization was performed using Energetic Macroscopic Representation (EMR). The energy flow management is provided by an energy management strategy (EMS) based on fuzzy logic controller (FLC), developed in C language for ARDUINO and uploaded into the aTmega microcontroller. The main objective of this work, is to evaluate, on the one hand the performances of the proposed architecture, by reducing the factors that impact the battery performances. On the other hand, the program and the platform (HIL) developed, through the comparison of results with those of the simulation, performed on MATLAB/SIMULINK under ECE-15 cycle.

EMR modelling of tidal turbines integrated into Orkney grid

The main challenges for renewable energy integration in electrical grid system are their variability and prediction. Tidal renewable energy is considered low variability in comparison to wind energy generation and due to tides regular periodicity, it is often quoted as predictable. This paper presents the modeling of Orkney's distribution network with the integration of tidal energy. This multi-physics model is based on the graphical formalism tool, Energetic Macroscopic Representation (EMR). This tool has been used to analyze the Tidal energy conversion (TEC) system based on the behavior of permanent magnet synchronous machine (PMSM). The daily, monthly and yearly variations of marine water speed in Orkney were observed. The EMR has been efficiently used in the representation of Orkney energy system integration of Orbital O2 tidal systems, and showing its effectiveness to adopt the control strategy. More precisely, the data of current water profile for 2009 has been chosen from 10 years big data to test the adopted deduced control technique. The methodology of this tool is described in details and the simulation results obtained in MATLAB environment are presented.

Improvement of switched structure semi‐active battery/supercapacitor hybrid energy storage system for electric vehicles

Among new configurations of battery/supercapacitor (SC) hybrid energy storage systems (HESSs) for electric vehicles (EVs), several can be united under the common name of ‘switched structure’. Their features are the ability to switch their structure during operation to direct power of a DC-bus from the battery or from the SC bank, as well as the use of a low-power DC–DC converter that connects both ESSs. Here, such a switched structure between battery/SC HESS was improved, which made it possible to obtain new operating modes. They expand the operating range of the SC bank, while the DC-bus voltage range remains relatively small. As a result, the installed capacity of the SC bank can be reduced by 50% without decreasing the available energy of the SC bank. An algorithm for the energy management system was developed according to the rule-based strategy that allows to choose one from the eight operating modes. The simulation of investigated EV operation with the proposed HESS was carried out during the transport cycle on a model developed through the energetic macroscopic representation approach. The simulation results are confirmed by experiments on a scaled-down experimental setup.

Modeling and Control of Fuel Cell/Supercapacitor Hybrid Source Based on Energetic Macroscopic Representation

In this paper, we present a dynamic simulator that uses a proton exchange membrane fuel cell (PEMFC). This source, is used as a primary source with a supercapacitor (SC) considered as a secondary source. This simulator integrates a PEMFC’s dynamic model, validated on the Bahia bench and a semi-empirical model of SC. The developed model is implemented using the macroscopic energetic representation tool (EMR). The energy management system (EMS) is based on rule-based power management, where a low-pass filter splits the power among sources (PEMFC and SCs) using a cut-off frequency of 0.2Hz, and a controller block that regulates the bus voltage deduced by the EMR’s inversion-based control approach.

Fuzzy Logic Controller for an EV’s Dual-Source Hybridization

This paper presents an energy management system (EMS) based on fuzzy logic control (FLC) strategy combined with power filtering. This strategy is developed for an Electric Vehicle (EV) hybrid energy storage systems (HESS). The proposed control and energy management strategy (EMS) aims to ensure an efficient power split guaranteeing that battery and supercapacitors (SC) provide the continuous and transient-power, respectively, adopting a pure electric vehicle fully-active parallel topology. In order to develop the studied system model, the Energetic Macroscopic Representation (EMR) approach is adopted. Considering SC’s control criterion, and battery root mean square RMS current reducing, an evaluation of the proposed EMS and developed model was conducted using MATLAB/SIMULINK simulation under New European Driving Cycle (NEDC) and compared to the classical only battery storage configuration.

A Modular Simulation Testbed for Energy Management in AC/DC Microgrids

This paper introduces a modular testbed to simulate AC/DC microgrids. The testbed is implemented in Matlab Simulink and is based on the energetic macroscopic representation (EMR) formalism. It is designed to be a tool to evaluate energy management strategies in AC/DC microgrids. The microgrid simulation model includes a photovoltaic generator, a fuel cell system, ultracapacitors, and batteries on the DC side. It includes voltage source converters (VSC) to couple the DC side with the AC side of the microgrid, which includes a variable AC load and a synchronous generator. Two case studies illustrate the use of the testbed. The model is implemented in Matlab Simulink and made openly available for the scientific community. Using this model, researchers can develop and evaluate energy management strategies in AC/DC microgrids.