This paper proposes a design methodology for inductor-based equalization circuits able to maximize their performance in terms of balancing current by taking into account relevant characteristics of both battery pack and power electronics components involved. Despite the proposed design methodology can be extended to all the inductor-based architectures, the coupled or multi-inductor configuration is considered as case study and a detailed analytical model is presented considering ideal and real conditions, which also include an efficiency model that takes into account for conduction and switching losses of the switches. Then, based on the overall model developed, the proposed design methodology allows for sizing the main components of the multi-inductor architecture in order to achieve the desired performance in terms of efficiency and balancing current. Experimental results are presented for demonstrating the goodness of the proposed design methodology, considering an optimized layout for the multi-inductor prototype.
Implementing battery electric buses (BEBs) in transit is considered a sustainable mobility means. However, various planning and operational challenges impede the full adoption of BEBs. Currently, BEB system optimization practice assumes that BEBs will deliver the same performance (battery capacity) over the entire service lifespan. However, BEBs' batteries will degrade over time, leading to a limited operating range. As such, the present study addresses the impact of BEBs' battery degradation on the BEB system infrastructure and component sizing optimization. The study develops a multi-stage optimization model to optimize BEB infrastructure and component sizing, considering battery degradation over the transit system service lifespan. The trade-off between adding charging infrastructure and/or battery replacement is assessed. The results show that adding more charging piles is more cost-effective than replacing the batteries for small hup-and-spoke transit networks. The results also show some insights to improve the adoption of BEBs in transit.
There is no question to the fact that electric vehicles (EVs) are the most viable solution to the climate change that the planet has long been combating. Along the same line, it is a salient subject to expand the availability of charging infrastructure, which quintessentially necessitates the optimization of the charger's locations. This paper proposes to formulate the optimal EV charger location problem into a facility location problem (FLP). As an effort to find an efficient method to solve the well-known nonpolynomial deterministic (NP)-hard problem, we present a comparative quantification among several representative solving techniques.
Permanent magnet synchronous motors (PMSMs) have been widely used in the aerospace, industrial control, and electric vehicles (EV) industries. A flatness-based control is proposed as an alternative approach to conventional field-oriented control (FOC) for PMSM drive applications due to its suitable dynamic performance and independence from the system operating point. Recently, different types of observers have been employed in flatness-control studies or the control method has been extended to saturated motors to improve its performance. In this paper, a trajectory planning approach for flatness-based control of PMSM drives is presented in order to deal with controller limits, which have received limited attention so far. The trajectory planning affects the response of indirectly controlled states. The proposed method ensures that the maximum allowable motor current is determined by the motor and driver's electrical ratings, as well as the switching voltages, which are limited by the DC-bus voltage (and sometimes, the state of charge of the DC battery), remain within limits to ensure control stability.
Direct current (dc) circuit breakers (DCCBs) play a key role in dc power system protection. This paper aims to discuss two practical challenges of DCCBs technology in terms of standards and potential markets. Apart from DCCBs technical limitations, e.g., low inertia of dc power systems and high cost of solid-state switches, lack of well-documented standards slows down the fast growth of DCCBs. In addition, as dc power systems cannot be developed without mature DCCBs, the DCCBs market is unclear and unappealing for entrepreneurs and investors. In this paper, existing standards/regulations of circuit breakers are highlighted, and the most cited standards for DCCBs are listed. Also, DCCBs market segmentation, potential markets, driving factors, and market of solid-state circuit breakers are discussed.
This paper focuses on the development of a tool that includes an automated testbed with controls, protection, and communications integrated into a real-time system to provide a platform to generate data sets for failure modes and effects analysis. This tool establishes a value for automation of data generation for different scenarios and addresses the gap of nonexistent field data for different applications and use cases. The features of this tool can further be expanded to include multiple power electronics models, communication protocols, and scaled system architectures. This general framework was evaluated for a DC fast charger system use case to provide quantitative solution for resiliency.
Governments in all continents are regulating and limiting the emissions generated by the transportation system. In this scenario, diesel engines will be out of all major markets between 2030 and 2040. In Brazil, the most prominent automotive market in South America, the introduction of the PL-8 regulations imposes the auto manufacturers to introduce new propulsion technologies starting in 2025. This paper studies the architecture selection and component sizing of an electric propulsion system for a light-commercial vehicle transformation from an internal diesel combustion (IC) engine to a full battery electric vehicle (BEV). The paper investigates four different driveline architectures and compares the results with the original IC vehicle regarding longitudinal performances (e.g., acceleration, maximum speed, and gradeability), energy consumption efficiency, CO2 emissions, and the total cost of ownership. In the end, the electric vehicle is evaluated as an investment by calculating its internal rate of return (IRR), payback (PB), and return on investment ( ROI). The longitudinal performances and energy consumption efficiency are estimated using a one-dimensional (1D) model developed using Matlab/Simulink. The total cost of ownership and the projected vehicle retail price are determined based on the system sizing defined in this study and cost models from the literature review.
Magnetic core and component characterization presents technical challenges, particularly with a focus on achieving higher frequency and voltage operation with advances in power electronics converters due to wide bandgap semiconductors. Accurate characterization plays a critical role in benchmarking the performance of existing core materials and construction for various component designs. In addition, characterization of fabricated components with application relevant excitation is critical for accurate assessment of technical figures of merit. Standardization of core and component characterization plays a foundational role in benchmarking and quantifying performance improvement of new magnetic materials, core constructions, and component designs. In this article, we discuss existing standards relevant for magnetic core characterization and several new standard development activities currently underway relevant for medium frequency and power applications. We also discuss the application of relevant measurements to selected magnetic core materials and components currently active within the Advanced Magnetics for Power and Energy Development (AMPED) Consortium. Example current transformer components are designed and tested as an example of standardized testing that is currently underway, to be expanded in the future to also include inductors and transformers amongst others.
This paper examines the potential of AutoML for predicting the range and State of Charge (SOC) of Electric Vehicles (EVs). Unlike traditional SOC estimation methods, such as Coulomb counting, Equivalent Circuit Models (ECM), or Machine Learning (ML)-based approaches, Range Estimation Algorithms (REA) consider route-specific factors to offer more precise battery depletion predictions. However, ML-based REAs can be complex and time-consuming to train, necessitating a deep understanding of Artificial Neural Networks (NN) architecture and optimization strategies. AutoML addresses this issue by automating the selection of the optimal NN architecture, hyperparameters, and data preprocessing techniques, making it more accessible for those with limited expertise to develop effective ML models. Our study centers on constructing SOC estimation and range estimation models using the AutoML library AutoGluon, developed by Amazon Web Services (AWS). Our findings indicate that while SOC estimation alone has limitations in predicting an EV's remaining range, REAs are specifically designed to overcome this challenge by building on SOC estimation to accurately forecast the remaining distance.
As more and more electrified vehicles connected to the electrical power grid, energy storage systems within power grids can enhance the grid inertia and power stability, reduce electricity generation costs, and improve the power quality. These systems can also save energy and reduce emissions. The purpose of this research is to propose an economic dispatch model for an energy storage system added to a conventional power grid. The objective function is constructed based on the minimum dispatching cost of the generators within the grid. By solving these formulations with convex optimization, we obtain economic dispatch results of the energy storage system satisfying the non-anticipative constraints. The results are compared to the optimal solution of economic dispatch problem applied to the same grid without the battery. The proposed method is tested on a modified version of IEEE 9 bus system with 4 generators optimizing power generation cost over 24 hours. The results show that addition of an energy storage system reduces the total cost.
Smart-Charging of Electric Vehicles (EVs) is able to provide frequency regulation capacity services to the System Operator (SO) upon an automation generation control (AGC) signal. While the amount of available regulation capacity is of-fered in the Day-Ahead Market (DAM), there is high uncertainty on the actual amount of reserves that will be called in the Real-Time Market (RTM). This work focuses on aiding EV smart-charging to offer a consistent and reasonable amount of regulation capacity, taking into account the impact of potential future instantaneous called regulation reserves while also maintaining simplicity. The work also analyzes the results of different charger types with different characteristics and shows that they play an important role on the regulation provision. Finally, it has been shown that even though the regulation income is inevitably reduced (up to 66%), the Energy Management System (EMS) can still successfully charge the EV s and simultaneously provide regulation reserves with remuneration.
The recent developments in the automotive industry denote a significant increase in embedded control units, algorithms and connectivity inside the vehicle and with its environment. All trends require a substantial increase in the virtualization of present development activities. Here, the deployed methods must be proven valid and optimized strategies for building and identifying real-time simulation models have to be developed. The context of the present work is the validation of low-frequency powertrain oscillations based on drivability-relevant properties at a full-vehicle level. An early powertrain validation at the subsystem level demands real-time modelling of the specimen, the test bed and the residual vehicle. The simulation models must be proven valid in the drivability-relevant frequency range of up to 30 Hz. Therefore, the reference test bed setup is presented together with the selected design of experiments. Then, a system identification process in the time and frequency domain is conducted to allow fast and reliable identification of the first torsional natural mode of the powertrain and the time characteristic of the dynos stated in dead time and group delay. In conclusion, the authors present a method for efficient drivability-related dynamometer characterization for blackbox-based modeling up to 1 kHz.
The next generation of civil aircraft aims for low to zero emissions by 2050. Fuel cell-powered electric propulsion system can offer the required performance and low emission levels for regional flights. The challenge is to reduce weight and complexity, while achieving high reliability. This paper focuses on the preliminary design of the air and thermal management of a nacelle-integrated fuel cell system. These subsystems are necessary for the efficient and reliable operation of the fuel cell stacks and contain critical components like air intake, air duct and heat exchanger, which need to be designed and optimized to achieve compact and lightweight solutions. This paper shows design and assessment work on various preliminary concepts of large air ducts and heat exchangers, which are recognized as performance critical and large volume components. These components need to be tailored to the design space inside engine's nacelle. Using parametric 3D modeling, several variations for compact heat exchangers are created and the sensitivity of key dimensions for aero-thermal performance properties is studied. Subsequently, CFD simulations are conducted and different design options are evaluated concerning pressure loss, drag, heat transfer and mass flow rate. Additional suitable components such as filter, compressor and pump are discussed and selected. Finally, two suitable design configurations for the air and thermal management of a nacelle-integrated fuel cell system are selected and evaluated.
Solid-state circuit breakers (SSCBs) utilize semiconductors to isolate the fast-developing direct-current (DC) faults within several microseconds. However, the ultra-fast interruption speed and the autonomous fault detection in SSCBs bring challenges to the existing practice of overcurrent coordination in a complex MW-Ievel system. The absence of mutual communication among SSCBs makes the system vulnerable to unnecessary interruptions while isolating a single-point fault. To tackle the above difficulties, an autonomous SSCB coordination strategy utilizing directional current selectivity and early fault detection and prevention technology is proposed in this paper. The strategy is demonstrated with an MW-level, multi-port solid-state switchgear containing more than five SSCBs. The application targets a DC fast charging station with both a grid-tied source and a battery-backed system on the supply side, and multiple DC extreme fast chargers (XFCs) on the load side. A total of five fault scenarios are simulated in the system analyses, of which the three most representative fault cases are tested with SSCB prototypes in lab experiments.
Power electronic (PE) converters are considered as essential components of electric vehicles and their reliability is receiving increasing attention by vehicle manufacturers. In this vein, remaining useful life (RUL) prediction of PE components is crucial for extending their lifetime and for avoiding unexpected downtime of the power-converter based electrical systems. In this paper, we developed a novel prognostic methodology, utilizing a distance-based health indicator and tracking it using an interacting multiple model (IMM), to estimate the RUL of a degraded system. The distance between the test vector and the cluster center of a self-organizing map (SOM) obtained from a trained healthy model is identified as the failure precursor. The time evolution of the precursor is estimated by an interacting multiple model (IMM) consisting of multiple particle filters or Kalman filters with a linear trend. Then the RUL estimate and the concomitant uncertainty of the estimate are evaluated by extrapolating the IMM state estimates until they cross a predefined failure threshold. The algorithms are tested on data from accelerated aging tests under various operating conditions. The results demonstrate that the proposed algorithms provide good RUL predictions for the degraded devices and the methodology is applicable to a variety of systems.
This paper proposes an Orthogonal Autoencoded Long-Short-Term Memory (OALSTM) network for long-term the State-Of-Charge (SOC) forecasting in Lithium-ion (Li-ion) battery cells. By leveraging the use of LSTMs in capturing temporal trends and orthogonal Autoencoder for extracting non-trivial robust latent features, OALSTM can achieve precise and accurate long-term SOC estimations near the end-of-life. One key contribution is learning orthogonal temporal encodings that generalize for long-term forecasting because it reduces the likelihood of false multicollinearity. Our results show that OALSTM outperforms other benchmark models for long-term SOC estimation of Li-ion battery cells under varying charging and discharging conditions.
For the fuel cell system, the power converters can be applied widely due to the ability can solve the incompatibility of the bus voltage. To improve the power converter's ability to resist internal and external disturbance and accelerate dynamic response, a novel robust control method is implemented for the two-phase interleaved boost converter (TIBC), which contains the outer voltage regulation loop and the inner current tracking loop in this paper. First, the Model predictive control (MPC) is used to track reference current in the inner loop. The active disturbance rejection control (ADRC) can be applied to regulate the output voltage and generate reference current for the inner loop during the process of outer loop design. Then, the stability of the control strategy is verified by Routh-Hurwitz criteria. Finally, the effectiveness of the proposed control method is verified by simulation results, which indicates that the strong robustness and the fast dynamic response can be realized by the proposed method, when compared with the traditional dual-loop PI controller.
Electric motors play a significant role in the power train system in Electric Vehicles (EV s). Therefore, their online condition monitoring is essential in ensuring the reliable operation of the entire powertrain. In EV applications, the motors work in a non-ideal environment and continuously varying operating conditions. Therefore, the fault diagnosis of the EV motors is challenging, and the fault diagnosis model must work in wide ranges of speeds and loads. In this paper, a short-time Fourier transform with varying window is proposed as the current signal processing of the motor along with a convolutional neural-based network for the detection of the interturn short circuit of permanent magnet synchronous motor. The proposed method is evaluated using a simulation dataset and a benchmark bearing fault dataset. by Matlab/Simulink® and the model is trained with the data in four speeds, and the model tests are carried out for a different operating speed.
With the increasing demand for electric vehicles, battery recycling is becoming a critical topic for lowering the environmental impact of wasted batteries. Hydrometallurgical recycling is a widely used method, and the cathode leaching process is essential for hydro metallurgical recycling. This work comprehensively compares leaching processes for LCO, NMC, and LFP cathodes using life cycle assessment. Different leaching processes use different acids, leaching temperatures, and reaction times. A score is calculated based on the environmental impact of the leaching process and the recycling rates of all valuable metals. This score can help identify the best leaching process for obtaining the most valuable metals with the least environmental impact. From this study, we conclude that 2M sulfuric acid with hydrogen peroxide is the best leaching solution for LCO, 2M sulfuric acid with hydrogen peroxide is the best leaching choice for NMC, and 3M sodium persulfate is the best leaching process for LFP.
Permanent magnet synchronous machines (PMSM) with neodymium iron boron (NdFeB) permanent magnets (PM) are typically used in applications, such as electric transportation, that require high power/torque density and efficiency. NdFeB is popular because of its exceptionally high remanent flux density (B-r) and high coercivity (H-c) at room temperature, but supply chain limitations of rare-earth elements ( REE), such as neodymium (Nd) and dysprosium (Dy), have recently caused significant REE price fluctuations. As a result, researchers are searching for ways to reduce or eliminate using NdFeB in PMSM designs. In the past, ferrite PMs were the next best option, but recently, manganese bismuth (MnBi) has emerged as another possible alternative to ferrites, with magnetic properties superior to ferrites but inferior to NdFeB at room temperature. MnBi is relatively new compared to commercial PMs and shows a unique trend of significantly increasing coercivity with increasing temperature. This makes rotor PMs significantly more susceptible to irreversible demagnetization at low temperatures. Reduced order models (ROMs), or meta-models, can estimate nonlinear machine phenomenon well enough for multi-objective optimization, but no literature has used this tool to characterize MnBi low temperature demagnetization. Furthermore, few sources explain how to work through the challenges of multiobjective optimization when using ROMs. Therefore, this paper presents a case study for limiting low temperature irreversible demagnetization in MnBi IPMSMs using ROMs and multiobjective optimization. The choices made and the tools available to produce a sufficiently accurate demagnetization ROM can be applied to a wide variety of nonlinear multiphysics phenomena.
