Heavy-duty Electric Vehicles Research Articles

Energy efficiency and electrochemical characteristic assessment of PEMFC-supercapacitor hybridization via railway profile

Electric trains equipped with efficient electric motors and powered by high-capacity hydrogen fuel cells are a choice of the global transportation industry to accelerate the global transition to a low-carbon transportation system. The operation of distinct vehicle types in varying geographical regions is characterized by unique features. Concurrently, the continually fluctuating demand for electricity necessitates the utilization of fuel cells in conjunction with energy storage devices. Thus, this study centers on examining the role of the PEMFC-supercapacitor direct hybridization system within the authentic driving profile of the subway in the Bangkok metropolitan region. The supercapacitor within the hybridization system serves as an energy management device, enhancing the durability of fuel cells when subjected to diverse load demands associated with train propulsion. In order to validate this hypothesis, an MRT (Mass Rapid Transit) protocol was formulated to emulate and serve as the load demand for the investigation, and then the PEMFC-supercapacitor direct hybridization system was operated using the created profile. Subsequently, the hybridization system undergoes electrochemical characterizations and fuel cell performance tests for diagnostic purposes. Empirical findings validated that the supercapacitor operates as a filter, generating a stable voltage response to varying load requirements, and possesses the capability to assimilate energy originating from regenerative braking. The integration of supercapacitors in stabilizing the operational parameters of proton exchange membrane fuel cells (PEMFCs) has markedly prolonged their operational lifespan. This assertion is substantiated by voltage signal analysis, which demonstrated a negligible increase in surface resistance of merely 0.23%, as depicted by Nyquist plots. Furthermore, cyclic voltammetry revealed that the system attained a peak electrochemical surface area (ECSA) of 0.0089 (106 cm2/mg of Pt). The information presented in this article constitutes data that has been minimally explored in existing research. Specifically, the study concentrates on the practical driving pattern of electric trains. Consequently, experimental results were deemed advantageous for the advancement of heavy-duty electric vehicles.

Viscoelastic flow instabilities for enhanced heat transfer in battery pack cooling

Effective thermal management plays a critical role in ensuring peak performance for heavy-duty electric vehicle battery packs and high-performance electronics. To address the need for effective heat dissipation, liquid coolants are employed, which exhibit higher heat transfer performance compared to traditional air-cooled systems. However, the associated increased viscosity of such liquid coolants and constricted flow path lead to reduced mixing, thereby deteriorating the thermal performance of the system. Here, we explore the potential of utilizing viscoelastic liquid coolants for enhancing heat transfer from the heated walls of a constricted flow path with obstacles, modeling features of battery coolant channels. At low-Reynolds number regime, viscoelastic fluids exhibit instabilities which can disrupt the thermal boundary layer and enhance fluid mixing in the domain, thereby facilitating wall heat transfer. We employed computational fluid dynamics simulations, validated with experimental observations, to explore the effect of the flow geometry, Weissenberg number, solvent viscosity ratio, polymer extensibility, and Reynolds number on thermal performance. Our study indicates that the heat transfer is enhanced with flow instabilities in viscoelastic fluids, which can be generated exclusively in the flow channel with obstacles by increasing the Weissenberg number, reducing the solvent viscosity ratio, increasing the polymer extensibility, and increasing the Reynolds number. The enhancement in the heat transfer can exclusively be attributed to the elastic properties of the fluid in contrast to the inelastic shear-thinning fluids exhibiting no instability. The insights from the study can play an important role in the development of advanced, high-efficiency battery cooling systems, combining surface engineering and synthesis of optimized heat transfer liquids.

Research on load-resistant enhancement control optimization of composite energy power supply system based on deep reinforcement learning

Abstract To solve the problem of insufficient inertia in the power electronics components under the development trend of power electronics in the transmission system of heavy-duty electric vehicles, combined with the composite energy power supply mode and control method, voltage-based feedback channels and virtual capacitor branches are designed for both engine-generator sets and DCDC control loops. These innovations compensate for system inertia and introduce a TD3 deep reinforcement learning-based adaptive regulation component to manage electrical systems in response to DC voltage fluctuations. This approach enhances the stability of the entire system in the face of load disturbances. Strategy validations demonstrate that this method ensures stable system operation, significantly mitigates DC voltage fluctuations during sudden load changes and is highly adaptable without relying on system models.

Identification of the dynamic model of second-life lithium-ion cells through Subspace System Identification and similarity transformation

When lithium-ion batteries (LiBs) reach the end of their first useful life in electric vehicles (EVs), they can still be used in applications with lower power demands, a process known as second-life. However, to ensure that LiBs – or cells – removed from EVs operate safely, efficiently, and reliably in a second application, various tests and procedures must be applied to study their internal conditions. Some information about the conditions of the lithium-ion cells can be obtained from the parameters of its equivalent circuit model(ECM). However, the tests to obtain blackthe ECM parameters usually require taking the LiBs out of service and testing their cells in laboratory benches with special equipment, which is costly and time-consuming. Therefore, this work proposes a methodology to identify the ECM of lithium-ion cells based on Subspace System Identification (SSI) methods. This procedure can be executed online, while the LiBs are being used in the specified application. SSI methods have been widely adopted to identify the dynamic model of lithium-ion cells, but they return models without a physical meaning, called non-parametric models. This type of model cannot be used to address the internal conditions of a lithium cell, because its parameters do not have any physical meaning. To solve this problem, a novel method for estimating the lithium-ion cell physical parameters, in the ECM format, and from its non-parametric model, is proposed in this work. The procedure is based on similarity transformation techniques and special characteristics of the ECM. To validate the proposed method, nine samples of cells from the same manufacturer were studied. These cells had distinct degradation conditions and were removed from heavy-duty EVs at the end of their first life. The results showed that: (a) a good approximation between the identified model voltage output and the actual cells’ experimental voltage output was achieved, with root mean square error values as small as 2.26 mV; (b) it was possible to identify the ECM of the tested cells and their parameters using the proposed similarity transformation; and (c) the identified parameters can be used to detect the internal conditions of the cells.

Inverter With Paralleled Modules to Extend Current Capacity and Combat Motor Overvoltage in SiC-Based Adjustable Speed Drives

This article proposes a quasi-three-level (Q3L) PWM-based module-parallel inverter to address two major issues of deploying SiC MOSFETs in high-power cable-fed adjustable speed drives. Firstly, the maximum output power of SiC inverters is limited due to the lack of high current rating SiC devices, which cannot satisfy the demand of high-power applications such as heavy-duty electric vehicles and more electric aeroplanes. The second issue comes from the fast-switching speed of SiC MOSFETs, where power cables act like transmission lines under steep rising/falling voltages (high <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dv/dt$</tex-math></inline-formula> ), with back and forth voltage reflections that result in serious overvoltage oscillations at motor terminals. To address these issues, a SiC module-parallel inverter is adopted with elevated current capacity, where the phase voltage is maintained at the mid-point of a coupled inductor connected to the output nodes of each paralleled half-bridge module. By actively controlling the switching delay between the paralleled half-bridge-legs, Q3L PWM waveforms are generated at the inverter output nodes which can mitigate the motor overvoltage due to the voltage reflections through power cables. An active current control technique is also proposed to facilitate the current balance between the paralleled half-bridge-legs. The proposed Q3L PWM-based module-parallel inverter is experimentally verified using a SiC-based cable-fed motor drive system. The results show that the proposed approach can extend the current capabilities of SiC devices as well as mitigate the motor overvoltage, enabling the adoption of SiC devices in high-power motor drives.

A data-aided robust approach for bottleneck identification in power transmission grids for achieving transportation electrification ambition: a case study in New York state

As the enthusiasm for electric vehicles passes the range anxiety and other tests, large-scale transportation electrification becomes a prominent topic in research and policy discussions. In consequence, the public attention has shifted upstream and holistically towards the integration of large-scale transportation electrification to power systems. This paper proposes a method to identify bottlenecks in power transmission systems to accommodate large-scale and stochastic electric vehicles charging demands. First, a distributionally robust chance-constrained direct current optimal power flow model is developed to quantify the impacts of stochastic electric vehicles charging demands. Subsequently, an agent-based model with the trip-chain method is applied to obtain the spatiotemporal distributions of electric vehicles charging demands for both light-duty electric vehicles and medium and heavy-duty electric vehicles. The first two moments of those distributions are extracted to build an ambiguity set of electric vehicles charging demands. Finally, a 121-bus synthetic transmission network for New York State power systems is used to validate the future transportation electrification in New York State from 2025 to 2035. Results show that the large-scale transportation electrification in New York State will account for approximately 13.33 % to 16.79 % of the total load demand by 2035. The transmission capacity is the major bottleneck in supporting New York State to achieve its transportation electrification. To resolve the bottlenecks, we explore some possible solutions, such as transmission capacity expansion and distributed energy resources investment. Wind power shows an advantage over solar energy in terms of total operational costs due to better peak alignment between wind power and electric vehicles charging demand.

Spatiotemporal Operational Emissions Associated With Light-, Medium-, and Heavy-Duty Transportation Electrification

The spatiotemporal distribution of pollutants plays a significant role on their impact on ecosystems and human health. This paper presents a strategy for calculating spatiotemporal operational emissions of road transportation and the electric grid to quantify the impact of transportation electrification. Emissions from internal combustion engine vehicles and offset emissions from electric vehicles (EVs) are considered using actual transportation networks and travel data. The spatiotemporal charging demand for light-duty and medium- and heavy-duty EVs are estimated in a behaviorally-informed way and mapped to electrical buses within the power grid and an ac optimal power flow with unit commitment is solved. The methodology is demonstrated with ten scenarios simulated on a grid with 7000 electrical buses geographically sited in Texas, created with actual generator data for the 2020 grid, and a future case including anticipated generator updates by 2030. Results show overall transportation emissions reductions in daily operational emissions of up to 20–30% for all pollutants studied, outweighing the increase in emissions from the electric grid. Considering emissions on an hourly basis, up to approximately 1000% reduction in CO emissions is observed.

Online state of health estimation of lithium-ion batteries through subspace system identification methods

When Lithium-ion Batteries (LiBs) reach the end of their first life in electric vehicles (EVs), they can still be used in applications with lower power demands, a process known as second-life. However, to ensure that LiBs – or cells – removed from EVs operate safely, efficiently and reliably in a second application, several tests and procedures must be applied to study their internal conditions. Naturally, one of the most important parameters to be determined is the state of health (SoH). However, the available processes for determining the SoH of lithium-ion cells are limited by high costs, relatively long test times and the need for specific equipment, limiting the second-life market. Hence, this work proposes a methodology to estimate the SoH of lithium-ion cells, based on subspace system identification (SSI) methods, where the parameters estimated for the equivalent circuit model (ECM) of a given cell are associated with its SoH. To validate the proposed methodology, nine cell samples from the same manufacturer were considered, which were removed from heavy-duty EVs at the end of their first life. The obtained results showed that: (a) good approximations between the identified models and the actual cells were achieved, with root mean square error (RMSE) values as small as 1.32 mV; (b) SSI methods can be applied online, while the LiBs are still operating in the EV during their first life, eliminating the need of additional tests; and (c) there is a clear association between ECM parameters and the SoH, so it was possible to estimate the SoH of the samples with RMSE values varying from 2.11% to 3.34%. Therefore, the proposed methodology offers significant improvements when compared to the conventional capacity tests, including the possibility of estimating the SoH relatively fast, online and without the need for specific equipment.

Aging-Sensitive Fast-Charging for Heavy-Duty Electric Vehicles

To use lithium-ion cells on-board electric vehicles (EVs) safely and efficiently, a battery management system (BMS) is essential. It monitors the battery’s states and controls utilization by limiting e.g., current and temperature. Advanced BMS increasingly use electrochemical models to assess internal states of battery cells and apply usage constraints based on these [1]. The performance of an electrochemical BMS is closely tied to the performance of the underlying model and the accuracy of its parameters [2]. We employ a novel re-parametrization method to update electrochemically constrained optimal fast-charging procedure to minimize battery degradation. The parameters of electrochemical models are typically identified through a combination of physico-chemical characterization techniques and curve fitting [3]. Since model parameters change with aging [4], there is a need to re-parametrize electrochemical models on-board to conserve model accuracy. As basis for this re-parametrization, we propose a novel reference performance test that can be incorporated into a conventional over-night charge and performed intermittently.Optimal control has previously been demonstrated for fast-charging in EV applications [1]. However, electrochemical, and mechanical degradation need to be considered to allow optimal fast charging throughout batteries’ lifetimes. Khalik et al. [5] demonstrated how a charging procedure could be designed a-priori for the entire cycle life, considering a direct implementation of the solid-electrolyte-interphase growth and capacity fade due to the associated lithium consumption. Such approaches can extend battery lifetime, but neither yield optimal charging-procedures at every state-of-health nor handle unknown aging modes on the fly. Our proposed approach involves intermittent identification of electrochemical model parameters and subsequent update of the fast-charging protocol such that it adheres to electrochemical constraints throughout the entire battery lifetime.In this work, summarized in Figure 1, we present this methodology and show results from an experimental study testing the proposed approach vs. conventional charging procedures. The experimental study is performed on state-of-the art automotive cells used in heavy-duty electric vehicles.Figure caption: Comparison of a conventional electrochemically constrained fast-charging procedure with the aging-sensitive approach proposed in the present work.

Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States

While the market for medium- and heavy-duty battery-electric vehicles (MHD EVs) is still nascent, a growing number of these vehicles are being deployed across the U.S. This study used over 2.3 million miles of operational data from multiple types of MHD EVs across various regions and operating conditions to address knowledge gaps in total cost of ownership and operational range. First, real-world energy cost savings were determined: MHD fleets should experience energy cost savings each year from 2021 to 2035, regardless of vehicle platform, with the greatest savings seen in transit buses (up to USD 4459 annually) and HD trucks (up to USD 3284 annually). Second, to help fleets across various geographies throughout the U.S. assess the suitability of EVs for their year-round operating needs, operational range was modeled using the XGBoost algorithm (R2: 70%) given 22 input features relevant to vehicle efficiency. Finally, this paper recommends (1) that MHD fleets apply energy-saving practices to minimize the impacts of cold temperatures and high congestion levels on vehicle efficiency and range, and (2) that local hauling fleets select trucks with a nominal range nearly double the expected maximum daily range to account for range losses under local, urban driving conditions.

Developing a profile of medium- and heavy-duty electric vehicle fleet adopters with text mining and machine learning

The transportation sector must rapidly decarbonize to meet its emissions reduction targets. Medium- and heavy-duty decarbonization is lagging the light-duty sector due to technical and operational challenges and the choices made by medium- and heavy-duty fleet operators. Research investigating the procurement considerations of fleets has relied heavily on interviews and surveys, but many of these studies suffer low rates of participation and are difficult to generalize. To model fleet operators’ decision-making priorities, we apply a robust text analysis approach based on latent Dirichlet allocation to a broad corpus of fleet adoption studies. We find operational compatibility to be the most salient factor, followed by long-term economics, technological familiarity, and perceived reliability.

Magnetic Design for Three-Phase Dynamic Wireless Power Transfer With Constant Output Power

This paper details the design of a three-phase transmitter-receiver topology for use in a dynamic wireless power transfer (DWPT) system for heavy-duty electric vehicles. The proposed approach eliminates the power oscillation and voltage/current sharing problems that plague planar three-phase DWPT systems. Multi-objective optimization is employed to maximize magnetic coupling and minimize undesired positive–negative sequence coupling. Selected designs are simulated for a 200-kW/m DWPT system, verifying the elimination of power oscillation and phase imbalance with minimal impact on magnetic performance. A prototype DWPT system is designed, and low-ripple power output is demonstrated at 52-kW operation.

Development of a Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) Tool

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles. The main challenge is that light-duty tools do not estimate real-time vehicle mass, a factor that has a disproportionate impact on the energy consumption of large commercial vehicles. The Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) tool advances the state of the art in evaluating electrification potential and infrastructure requirements for fleets of commercial vehicles. In this work, the HEVII tool is demonstrated with non-uniformly sampled telematics data from an existing fleet to assess the suitability for electrification of each individual vehicle, determine optimal locations for charging infrastructure to support a fleet of EVs and analyze associated costs. Payload mass is predicted using sparse ground-truth data for all input drive cycles and an initial data analysis is conducted to assess the characteristics driving behaviors and energy consumption of the fleet using an adaptable vehicle model. Battery size requirements are determined by applying a novel charger placement algorithm to maximize routes that are viable for EVs and balance time delays with infrastructure development costs. This work details and demonstrates the different aspects of the HEVII tool, presenting preliminary results from an example use case.&lt;/div&gt;&lt;/div&gt;

Self-Tuning Multiport Resonant DC/DC Converter Based on Actively-Controlled Inductors for Hybrid Storage System Integration

In the field of electric mobility, in particular heavy-duty electric vehicles and long-haul trucks, the energy systems are susceptible to transients with different amplitudes and dynamics due to the high power demand. With the combination of high power and long distances, the available battery technologies become insufficient as a single energy source for such a system. Thus, the integration of hybrid energy storage systems (HESSs) with different energy capabilities, such as fuel cells, battery packs, and supercapacitors, is highly required. In this context, for leveraging HESSs in an efficient way, a resonant multiwinding transformer-based converter is proposed to interconnect these different energy storage systems (ESSs) since the resonant converters are able to operate at a wider range of switching frequencies and, hence, a wider soft-switching range. However, due to the multiple resonant circuits and possible parameter deviations, the control capability might be compromised. Thus, a multiport resonant dc/dc converter embedded with variable inductors is proposed to enhance the power flow management among these different ESSs with reduced conversion stages. Further, the proposed concept can also improve the immunity to parameter deviations on the resonant by compensating for possible variations at the resonant frequencies. Finally, simulations and experimental results of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{20} \;\text{kW}$</tex-math></inline-formula> four-port resonant dc/dc converter embedded with variable inductors are shown to prove the effectiveness of the proposed concept.

ANALYSIS OF SOME GEOMETRIC PARAMETERS OF THE INDUCTIVE COUPLER INTENDED FOR HIGH POWER WIRELESS SYSTEMS

One of the essential components of a wireless high-power transfer system for heavy-duty electric vehicles is the inductive coupler. The efficiency of the wireless power transfer system strongly depends on the geometric parameters of the inductive coupler, which in turn have an important effect on the electromagnetic parameters of the whole system. The transmitting (Tx) and receiving (Rx) coils of the inductive coupler proposed in this paper have a Double-D planar structure and are located on magnetic cores made of magnetic concrete (Tx) and ferrite (Rx), respectively. The study of the parameters of the inductive coupler was carried out with the help of the finite element method, using the Flux ® field calculation software package.

Well-to-wheel performance of internal combustion engine vehicles and electric vehicles – study for future Indian market

ABSTRACT Thanks to top-notch vehicle testing and internationally standardized inspections, the modes of transportation are cleaner and more efficient today. Though the development for conventional internal combustion engine vehicles (ICEVs) has almost saturated today, yet it retains the commercial/heavy-duty (trucks and buses) automobile market. Thus, it becomes of utmost importance to analyze the well-to-wheel efficiency for determining the overall energy consumption and greenhouse gas (GHG) emissions while transitioning toward sustainable transportation. Electric vehicles (EVs) do have environmental impacts that are directly related to the country’s electricity generation, as they may involve fossil burning where other alternative sources of energy are not adequately present. This paper presents the overall energy efficiencies and GHG emissions from commercial/heavy-duty ICEVs and EVs. The study also briefly compares the other vehicle segments like 2 Wheeler, 3 Wheeler, and cars. Finally, the results show that commercial/heavy-duty ICEVs are advantageous over heavy-duty EVs when the GHG emissions are considered as of today. The commercial/heavy-duty ICEVs emit over 405 g CO2e/km, and counter EVs produce 706 g CO2e/km. The GHG emissions from 2 Wheeler ICEV were 46 g CO2e/km and by the same segment, EV is 13 g CO2e/km. Moving toward the car segment, an ICEV car produces around 158 g CO2e/km and an EV of a similar model emits 67 g CO2e/km. Furthermore, the 3 Wheeler ICEV emits around 99 g CO2e/km and a 3 Wheeler EV produces lesser at 24 g CO2e/km. Moving further on well-to-wheel energy efficiency, ICEVs have a maximum overall energy efficiency of 28%. Since EVs use various sources of energy for charging the battery, the overall efficiency was found to be at least 21% and maximum up to 39%. Thus, on an average EVs presently have a well-to-wheel efficiency of 32% when electricity generation mix is used for charging.

A Review of Hybrid Energy Storage System for Heavy-Duty Electric Vehicle

The driving range of electric vehicles is one of the major concerns to be addressed today. The cruising range of electric vehicles mainly depends on the energy storage system (ESS). The current energy storage system for small electric vehicles is mainly batteries. But for heavy-duty electric vehicles as well as high-performance electric sports cars, a hybrid energy storage system (HESS) has offered a better solution. A hybrid energy storage system usually consists of two complementary storage devices which are coordinated through an energy management system; these devices could be batteries, supercapacitors, fuel cells flywheels and others where each has different advantages and disadvantages and is suitable for different application scenarios. This paper reviews major energy storage system combinations and presents different energy storage system topologies. At the same time, different control methods of the hybrid energy storage system are reviewed in this paper, although some of which are based on the storage system available at the grid side.

Grid Impact Analysis and Mitigation of En-Route Charging Stations for Heavy-Duty Electric Vehicles

This paper presents a comprehensive grid impact analysis design and corresponding mitigation strategies for heavy-duty electric vehicle (EV) charging stations. The charging load of heavy-duty charging station can reach several megawatts, which could induce adverse impacts on the distribution grid if not effectively mitigated. To analyze the impacts and provide corresponding solutions, we select four representative distribution systems—including both single-feeder cases and a multi-feeder case—and design thorough test metrics for the impact analysis. The charging load profiles used in the analysis are derived from realistic conventional heavy-duty vehicle travel data. Based on the analysis results, charging stations are placed at three different representative locations in each distribution system: best, good, and worst locations. Mitigation strategies using a combination of smart charger functionality, on-site photovoltaic (PV) generation, and on-site energy storage (ES) are proposed and tested. A sizing method is also proposed to find the optimal PV-ES-charger capacity that minimizes the capital cost.

Energy Efficiency Prediction Model of Heavy-duty Electric Vehicles Using Numerical Simulation

Energy Efficiency Prediction Model of Heavy-duty Electric Vehicles Using Numerical Simulation

A Framework to Analyze the Requirements of a Multiport Megawatt-Level Charging Station for Heavy-Duty Electric Vehicles

Widespread adoption of heavy-duty (HD) electric vehicles (EVs) will soon necessitate the use of megawatt (MW)-scale charging stations to charge high-capacity HD EV battery packs. Such a station design needs to anticipate possible station traffic, average and peak power demand, and charging/wait time targets to improve throughput and maximize revenue-generating operations. High-power direct current charging is an attractive candidate for MW-scale charging stations at the time of this study, but there are no precedents for such a station design for HD vehicles. We present a modeling and data analysis framework to elucidate the dependencies of a MW-scale station operation on vehicle traffic data and station design parameters and how that impacts vehicle electrification. This framework integrates an agent-based charging station model with vehicle schedules obtained through real-world vehicle telemetry data analysis to explore the station design and operation space. A case study applies this framework to a Class 8 vehicle telemetry dataset and uses Monte Carlo simulations to explore various design considerations for MW-scale charging stations and EV battery technologies. The results show a direct correlation between optimal charging station placement and major traffic corridors such as cities with ports, e.g., Los Angeles and Oakland. Corresponding parametric sweeps reveal that while good quality of service can be achieved with a mix of 1.2-megawatt and 100-kilowatt chargers, the resultant fast charging time of 35–40 min will need higher charging power to reach parity with refueling times.