Search Results (54)

Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle subsystems as data-driven entities. The simulation framework is developed in the MATLAB/Simulink environment and is based on a power dynamics approach, capturing nonlinear interactions and performance intricacies between different powertrain elements. This study investigates subsystem synergies and performance boundaries under a combined driving cycle composed of the NEDC, WLTP Class 3 and US06 profiles, representing urban, extra-urban and aggressive highway conditions. To emulate the real-world load-following strategy, a state transition power management and allocation method is synthesised. The proposed method dynamically governs the power flow between the fuel cell stack and the traction battery across three operational states, allowing the battery to stay within its allocated bounds. This simulation framework offers a near-accurate and computationally efficient digital counterpart to a commercial hybrid powertrain, serving as a valuable tool for educational and research purposes. Full article

Li ion batteries are emerging as the mainstream source for propulsion in the automotive industry. Subjecting a battery to extreme conditions of charging and discharging can negatively impact its performance and reduce its cycle life. Assessing a battery’s electrical and thermal behaviour is critical in the later stages of developing battery management systems (BMSs). The present study aims at the thermal modelling of a 3.3 Ah cylindrical 26650 lithium iron phosphate cell using ANSYS 2024 R1 software. The modelling phase involves iterating two geometries of the cell design to evaluate the cell’s surface temperature. The multi-scale multi-domain solution method, coupled with the equivalent circuit model (ECM) solver, is used to determine the temperature characteristics of the cell. Area-weighted average values of the temperature are obtained using a homogeneous and isotropic assembly. A differential equation is implemented to estimate the temperature due to the electrochemical reactions and potential differences. During the discharge tests, the cell is subjected to a load current emulating the Worldwide Harmonised Light Vehicles Test Procedure (WLTP). The results from the finite element model indicate strikingly similar trends in temperature variations to the ones obtained from the experimental tests. Full article

There is currently exponential growth in the electric vehicle market, which will require an increase in the electrical grid capacity to meet the associated charging demand. If, on the one hand, the introduction of energy generation from renewable energy sources can be used to meet that requirement, the intermittent nature of some of these sources will challenge the mandatory real-time equilibrium between generation and consumption. In order to use most of the energy generated via these sources, mechanisms are required to manage the charging of batteries in electric vehicles, according to the levels of generation. An effective smart charging process requires communication and/or control mechanisms between the supply equipment and the electric vehicle, enabling the adjustment of the energy transfer according to the generation levels. At this level, the ISO 15118 standard supports high-level communication mechanisms, far beyond the basic control solutions offered through the IEC 61851-1 specification. It is, thus, relevant to evaluate it in smart charging scenarios. In this context, this paper presents the development of a charge emulation system using the ISO 15118 communication protocol, and it discusses its application for demand response purposes. The system comprises several modules developed at both ends, supply equipment and electric vehicles, and allows the exchange of data during an emulated charging process. The system also includes human interfaces to facilitate interactions with users at both ends. Tests performed using the implemented system have shown that it supports a demand response when integrated with a photovoltaic renewable energy source. The dynamic adjustment to charging parameters, based on real-time energy availability, ensures efficient and sustainable charging processes, reducing the reliance on the grid and promoting the use of renewable energy. Full article

High-voltage heat release from batteries can cause safety issues for electric vehicles. Relevant scientific research work is carried out in the laboratory. The battery safety of laboratory experiments should not be underestimated. In order to evaluate the safety performance of batteries in the laboratory testing of driving conditions of electric vehicles, this paper simulated and compared the discharge characteristics of two common batteries (lithium iron phosphate (LFP) battery and nickel–cobalt–manganese (NCM) ternary lithium battery) in three different operating conditions. The operating conditions are the NEDC (New European Driving Cycle), WLTP (World Light Vehicle Test Procedure) and CLTC-P (China light vehicle test cycle) for normal driving of electric vehicles. LFP batteries have a higher maximum voltage and lower minimum voltage under the same initial voltage conditions, with a maximum voltage difference variation of 11 V. The maximum current of WLTP is significantly higher than NEDC and CLTC-P operating conditions (>20 A). Low current discharge conditions should be emulated in teaching simulation and experiments for safety reasons. The simulation data showed that the LFP battery had good performance in maintaining the voltage plateau and discharge voltage stability, while the NCM battery had excellent energy density and long-term endurance. Full article

Induction motors (IMs) must meet high reliability and safety standards in mission-critical applications, such as electric vehicles (EVs), where sensorless control strategies are fundamental. However, sensorless rotor speed estimation demands improvements to overcome filtering distortions, tuning complexities, and sensitivity to IM model mismatch. Algebraic methods offer inherent filtering capabilities and design flexibility to address these challenges without introducing additional dynamics into the control system. The objective of this paper is to provide an algebraic estimation strategy that yields an accurate rotor speed estimate for sensorless IM control. The strategy includes an algebraic estimator with single-parameter tuning and inherent filtering action. We propose an EV case study to experimentally evaluate and compare its performance with a typical drive cycle and a dynamic torque load that emulates a small-scale EV power train. The algebraic estimator exhibited a signal-to-noise ratio (SNR) of 43 dB. The closed-loop experiment for the EV case study showed average tracking errors below 1 rad/s and similar performance compared to a well-known sensorless strategy. Our results show that the proposed algebraic estimation strategy works effectively in a nominal speed range for a practical IM sensorless application. The algebraic estimator only requires single-parameter tuning and potentially facilitates IM model updates using a resetting scheme. Full article

In the context of inductive power transfer (IPT) for electric vehicle (EV) charging, the precise determination of the mutual inductance between the magnetic pads is of critical importance. The value of this inductance varies depending on the EV positioning, affecting the power transfer capability. Therefore, the precise determination of its value yields various advantages, particularly by contributing to the optimization of the charging process of the EV batteries, since it offers the possibility of adjusting the position of the vehicle depending on the level of misalignment. Within this framework, algorithms grounded in artificial intelligence (AI) techniques emerge as promising solutions. This research work revolves around the estimation of the mutual inductance in a wireless inductive power transfer system using a resonant converter topology, implemented in MATLAB/Simulink^® R2021b. The system output was developed to emulate the behavior of a battery charger. To estimate this parameter, an artificial neural network (ANN) was developed. Given the characteristics of the system, the features were chosen in a way that they could provide a clear indication to the ANN if the vehicle position changed, independently of the charging power. In the pursuit of creating a robust AI model, the training dataset contained approximately 1% of the available data. Upon the analysis of the results, it was verified that the largest estimation error observed was around 3%, occurring at the lowest charging power considered. Hence, it can be inferred that the proposed ANN exhibits the capability to accurately estimate the value of mutual inductance in this type of system. Full article

In the near future, the incorporation of shared electric automated and connected mobility (SEACM) technologies will significantly transform the landscape of transportation into a sustainable and efficient mobility ecosystem. However, these technological advances raise complex scientific challenges. Problems related to safety, energy efficiency, and route optimization in dynamic urban environments are major issues to be resolved. In addition, the unavailability of realistic and various data of such systems makes their deployment, design, and performance evaluation very challenging. As a result, to avoid the constraints of real data collection, using generated artificial datasets is crucial for simulation to test and validate algorithms and models under various scenarios. These artificial datasets are used for the training of ML (Machine Learning) models, allowing researchers and operators to evaluate performance and predict system behavior under various conditions. To generate artificial datasets, numerous elements such as user behavior, vehicle dynamics, charging infrastructure, and environmental conditions must be considered. In all these elements, symmetry is a core concern; in some cases, asymmetry is more realistic; however, in others, reaching/maintaining as much symmetry as possible is a core requirement. This review paper provides a comprehensive literature survey of the most relevant techniques generating synthetic datasets in the literature, with a particular focus on the shared electric automated and connected mobility context. Furthermore, this paper also investigates central issues of these complex and dynamic systems regarding how artificial datasets could be used in the training of ML models to address the repositioning problem. Hereby, symmetry is undoubtedly a crucial consideration for ML models. In the case of datasets, it is imperative that they accurately emulate the symmetry or asymmetry observed in real-world scenarios to be effectively represented by the generated datasets. Then, this paper investigates the current challenges and limitations of synthetic datasets, such as the reliability of simulations to the real world, and the validation of generative models. Additionally, it explores how ML-based algorithms can be used to optimize vehicle routing, charging infrastructure usage, demand forecasting, and other important operational elements. In conclusion, this paper outlines a series of interesting new research avenues concerning the generation of artificial data for SEACM systems. Full article

The ever-increasing number of plug-in electric vehicles (PEVs) requires appropriate electric vehicle grid integration (EVGI) for charging coordination to maintain grid stability and enhance PEV user convenience. As such, the widespread adoption of electric mobility can be successful. EVGI is facilitated through charging stations and empowers PEV users to manage their charging demand by using smart charging solutions. This makes PEV grids assets that provide flexibility to the power grid. The Internet of Things (IoT) feature can make smooth EVGI possible through a supporting communication infrastructure. In this regard, the selection of an appropriate communication protocol is essential for the successful implementation of EVGI. This study assesses the efficacy of the UK’s 4G network with TCP and 4G UDP protocols for potential EVGI operations. For this, an EVGI emulation test bed is developed, featuring three charging parking lots with the capacity to accommodate up to 64 PEVs. The network’s performance is assessed in terms of data packet loss (e.g., the data-exchange capability between EVGI entities) and latency metrics. The findings reveal that while 4G TCP often outperforms 4G UDP, both achieve latencies of less than 1 s with confidence intervals of 90% or greater for single PEV cases. However, it is observed that the high penetration of PEVs introduces a pronounced latency due to queuing delays in the network including routers and the base station servers, highlighting the challenges associated with maintaining efficient EVGI coordination, which in turn affects the efficient use of grid assets. Full article

Energy savings in electric rail transport are important in order to increase energy efficiency and reduce its carbon footprint. This can be achieved by storing and using the energy generated during regenerative braking. The system described in this paper consists of a supercapacitor energy storage system (SC ESS), a bidirectional DC/DC converter, and an algorithm to control the energy flow. The proper design of the algorithm is critical for maximizing energy savings and stabilizing the power grid, and it affects the lifetime of the SC ESS. This paper presents an energy flow control algorithm based on Pontryagin’s minimum principle that balances maximum energy savings with maximum SC ESS lifetime. The algorithm also performs SC ESS recharging while the rail vehicle stops on inclines to reduce the impact of its next acceleration on the power grid. To validate the algorithm, offline simulations are performed using real tram speed measurements. The results are then verified with a real-time laboratory emulation setup with HIL simulation. The tram and power grid are emulated with LiFePO4 batteries, while the SC ESS is emulated with a supercapacitor. The proposed algorithm controls a three-phase converter that enables energy exchange between the batteries and the supercapacitor. The results show that the proposed algorithm is feasible in real time and that it can be used under real operating conditions. Full article

Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs’ SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study’s results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications. Full article

This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling setpoints with controlled temperature increases to reduce the cooling load. The warehouse building encompasses office and storage spaces, and three cooling scenarios are implemented, i.e., exclusive storage area cooling, exclusive office area cooling, and cooling in both spaces, to expand the study’s potential applications. Next, the simulation data are utilized for training machine learning (ML)-based pipelines, predicting five subsequent hourly energy consumption values an hour before the setpoint adjustments, providing time to plan participation in demand response programs or prepare for charging electric vehicles. For each scenario, the performance of an Artificial Neural Network (ANN) and a tree-based ML algorithm are compared. Moreover, an expanding window scheme is utilized, gradually incorporating new data and emulating online learning. The results indicate the superior performance of the tree-based algorithm, with an average error of less than 3.5% across all cases and a maximum hourly error of 7%. The achieved accuracy confirms the method’s reliability even in dynamic scenarios where the integrated load of storage space and offices needs to be predicted. Full article

This article deals with the design of a simple predictive control algorithm applied to a bidirectional DC-DC power converter for the angular speed control of a DC motor. We used the dynamics of a DC motor but mathematically reduced them to arrive at a simple model that is ideal for our purpose, not only to meet the control objective but also to generate reliable data for further analysis. This predictive control approach is based on the discrete time mathematical model of a DC motor. A huge capacitor to emulate an electric vehicle battery was then successfully connected to our experimental platform. Due to the robustness of the proposed control algorithm, the same predictive control scheme provided sufficient information to monitor the battery’s state. On this basis, and due to the system’s efficiency, it was possible to configure a fault detection scheme in our electric car battery emulator using only classical statistical tools. A PIC18F252 microcontroller was used in our experimental platform to implement our predictive control algorithm. It was then appropriately coupled to the power electronics required by the DC-DC converter to drive the DC motor. Our experimental results proved the excellent performance of the control method and also of the health monitoring system. On the other hand, the main difficulty in achieving our main goal was the realization of discrete control, which had to be as simple as possible while maintaining the control objective and while also being capable of generating reliable data for the health monitoring stage. Thus, the primary contribution of this work was the development of the predictive control of the speed of a universal motor, followed by the modification of the experimental design to simulate an electric car battery and the introduction of a novel statistical method for fault detection. Full article

Electric vehicles (EVs) are experiencing explosive growth in public adoption, causing a major shift in research and development priorities by OEMs toward electrified powertrains. To verify EV drivetrain platforms and software models in the design phase, testbeds with specific capabilities are essential. Full-scale vehicle testbeds are expensive, bulky, dissipative, and not easily reconfigurable or movable, making scaled testbeds more attractive, especially for education and research institutes. To support this cause, this paper reports on the development of a small-scale, modular, hardware-in-the-loop (HIL) testbed platform for the drivetrain of EVs that is cost-effective, efficient, and easily movable and reconfigurable and allows integration of a battery pack. The testbed is comprised of two directly coupled electric machines. The first machine emulates the traction motor and is used to control vehicle speed according to a specified drive cycle. The second machine is used to impose a torque profile on the first machine’s shaft—based on the vehicle’s parameters and driving environment—and emulates a gearbox (if necessary). A systematic two-way scaling approach is adopted to downscale the parameters and driving environment of full-size EVs to a level that can be handled by the testbed and to upscale the test results obtained from the testbed to the full-size vehicle level. The power consumption of the testbed is limited to system losses. A case study involving a full-size EV was performed and the HIL simulation results were compared to the computer simulation results to verify the performance of the testbed. Full article

This paper proposes a practical solution to address the energy management issue in fuel cell hybrid electric vehicles (FCHEVs). This solution revolves around a powertrain system that contains a fuel cell (FC) as the main supply, a photovoltaic cell (PC) as the secondary energy source, and a battery bank (Batt) as backup storage to compensate for the FC’s low response rate. The energy in this hybrid powertrain system alternated between the designated elements and the load via a DC bus, and to maintain a stable output voltage, the DC link was adjusted using a nonlinear approach that is based on the flatness theory and the nonlinear autoregressive moving average (NARMA-L2) neuro-controller. As for the current regulation loops, the sliding mode technique was employed to attain the high dynamic of the reference signals produced by the energy manager loop. To validate the accuracy of the proposed energy management approach (EMA), a test bench was equipped with digital, electronic circuits and a dSPACE DS-1104 unit. This experimental bench contained a fuel cell emulator FC of 1200 W and 46 A, lithium-ion batteries of 24 V, and a solar source capable of 400 W. The obtained results, indeed, attested to the validity of the approach used, yielding a notable performance during multiple charge variations. This ultimately demonstrated that the management approach enhanced the efficiency of the hybrid powertrain. Full article

Flywheels are nowadays a solution for the dynamic charging of electric vehicles since they act as transient energy storage. The need for a top efficient reversible power converter for the flywheel system is crucial to assure high dynamic performance. The paper presents the design of a 50 kW highly efficient reversible three-phase DC–AC inverter involving the most recent silicon carbide metal oxide semiconductor field effect transistors, and its experimental validation on a home-made emulator. Highest efficiency in reversible mode, compactness, and thermal enhancement are the targeted objectives that have been achieved. The power converter prototype evaluated on an original pulse width modulation testing-bench is able to emulate the working of the flywheel system. High frequency pulse width modulation switching, speed cycle operating, and thermal losses are evaluated. In addition, an efficiency above 99% for the converter has been attained, enabling robust functioning of the flywheel system emulator to perform specific charging profiles for electric vehicles. Full article