DC Fast Charging Research Articles

EIS as a Rapid Performance Diagnostic Tool in Automotive Li-Ion Batteries

The influence of operating conditions such as temperature and state of charge on the performance of a high-power commercial lithium-ion pouch cell is explored using electrochemical impedance spectroscopy. State-of-charge and temperature ranges were selected based on relevance to automotive applications, such as DC Fast Charging, cold start, and high rate discharge events. Typical vehicle operation varies on the order of a few seconds and is therefore comparable with impedance values at frequencies higher than 0.1 Hz. Therefore, vehicle designers can utilize EIS as a tool to probe available power under standard operating conditions, as well as low temperatures and SOC in the vehicle at the battery cell level, offering the ability to decrease the amount of battery pack or vehicle level testing. Here, the full cell impedance is presented and analyzed. Clear trends in the high frequency resistance, charge transfer resistance, and mass transport resistances as a function of state of charge and temperature are evident.

An integrated multi-objective optimization and multi-criteria decision-making model for optimal planning of workplace charging stations

This study addresses the optimal planning of electric vehicle charging infrastructure at workplaces. As the optimal planning for a given workplace can involve various criteria that comprise conflicting single objectives, this study proposes a new integrated multi-objective optimization and multi-criteria decision-making (MCDM) model for determining the most suitable electric vehicle supply equipment (EVSE) configuration. This approach combines the advantage of multi-objective optimization, which yields Pareto solutions, with an improved MCDM model. The latter is used to evaluate the Pareto frontier to find the best performing solution by enabling the station owners to use linguistic variables for weighting the decision-making variables. The conventional weighted aggregated sum product assessment (WASPAS) method is improved by introducing the Dombi Bonferroni functions in the proposed model making it more flexible as compared to its counterparts. In the final step, the selected solutions are ranked by reapplying the MCDM model. A case study is performed based on collected charging data from a workplace. To validate the proposed model, a comparison against four alternative MCDM models is performed. It is demonstrated that the proposed model yields very close ranking order as the alternative approaches. Among five EVSE options, DC fast charging is found to be the best while AC Level-2 EVSE (19.2/22 kW) is found to be the least attractive option. Sensitivity analysis shows the robustness of the ranking results in response to changing weightings of the model coefficients.

A Consensus Algorithm for Multi-Objective Battery Balancing

Batteries stacks are made of cells in certain series-parallel arrangements. Unfortunately, cell performance degrades over time in terms of capacity, internal resistance, or self-discharge rate. In addition, degradation rates are heterogeneous, leading to cell-to-cell variations. Balancing systems can be used to equalize those differences. Dissipative or non-dissipative systems, so-called passive or active balancing, can be used to equalize either voltage at end-of-charge, or state-of-charge (SOC) at all times. While passive balancing is broadly adopted by industry, active balancing has been mostly studied in academia. Beyond that, an emerging research field is multi-functional balancing, i.e., active balancing systems that pursue additional goals on top of SOC equalization, such as equalization of temperature, power capability, degradation rates, or losses minimization. Regardless of their functionality, balancing circuits are based either on centralized or decentralized control systems. Centralized control entails difficult expandability and single point of failure issues, while decentralized control has severe controllability limitations. As a shift in this paradigm, here we present for the first time a distributed multi-objective control algorithm, based on a multi-agent consensus algorithm. We implement and validate the control in simulations, considering an electro-thermal lithium-ion battery model and an electric vehicle model parameterized with experimental data. Our results show that our novel multi-functional balancing can enhance the performance of batteries with substantial cell-to-cell differences under the most demanding operating conditions, i.e., aggressive driving and DC fast charging (2C). Driving times are extended (>10%), charging times are reduced (>20%), maximum cell temperatures are decreased (>10 °C), temperature differences are lowered (~3 °C rms), and the occurrence of low voltage violations during driving is reduced (>5×), minimizing the need for power derating and enhancing the user experience. The algorithm is effective, scalable, flexible, and requires low implementation and tuning effort, resulting in an ideal candidate for industry adoption.

High Penetration of Electric Vehicles Could Change The Residential Power System: Public dc Fast Chargers Will Not Be Enough

In the near future, the massive production and deployment of electric vehicles (EVs) will need to be matched by the availability of an equivalently large number of dc fast-charging capabilities. This article explores the possibilities that public dc fast-charging systems will not be enough to satisfy the demand for a convenient network of EV-charging stations, creating the need for a residential dc fast charger that has the potential to become the enabling block for dc residential systems. DC residential systems also present a natural solution for the integration of solar panels and battery storage. DC fast chargers can efficiently and easily integrate solar energy produced and currently stored on the ac power grid, forming the backbone for a dc residential grid. Furthermore, the infrastructure needed for EV dc fast chargers can easily accept a future migration of other natural dc systems, such as modern air conditioning (A/C), heat pumps, refrigeration, lighting, and, ultimately, the other remaining appliances.

Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning

AbstractA key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, the validation process can be accelerated. However, questions on the validity of projecting models trained on limited data or simple cycling profiles, such as constant current cycling, to real‐world scenarios with complex loads remains. Here, we present the ability to predict performance with less than 1.2 % mean absolute percent error when trained on cells aged using complex electric vehicle discharge profiles, and either AC Level 2 charge or DC Fast charge profiles, using only the first 45 cycles, namely 5 % of the total testing time. While error is low across the projections, this study also highlights that battery lifetime analysis using only cycling data may not extrapolate safely to certain real‐world conditions due to the impact of calendar degradation.

Autonomous Operation of the DC Fast-Charging Station

This article develops, evaluates, and verifies a supervisory controller (SC) to transfer from the grid-connected mode and operate a battery-enhanced electric vehicles’ (EVs) dc fast charging (DCFC) station in the autonomous mode, when the supply grid is not available. The SC is designed based on the supervisory control theory (SCT) of discrete event systems and is based on a rigorous mathematical process; nonblocking, i.e., avoids entering an operational deadlock scenario that leads to the system collapse; minimally restrictive with respect to the station's discrete behavior; modular; and scalable. The SC also operates the station in the grid-connected mode and provides a seamless transition between the two modes and thus the DCFC station is also synonymous with a dc-microgrid. The SC is implemented on an industrial programmable logic controller and its performance is verified in a real-time hardware-in-the-loop environment using an OPAL-RT testbed.

Impact of Power Profile on the Estimation of Second Life Batteries Remaining Useful Life

<div class="section abstract"><div class="htmlview paragraph">Second-life batteries (SLBs, automotive batteries that have lost their usefulness for vehicular applications) can provide low-cost environment-friendly solutions for grid-connected systems. The estimation of the remaining useful life (RUL) of SLBs is a fundamental step for the development of appropriate business models. This paper aims at unveiling correlations between the SLB's power profile and aging performance by defining appropriate metrics. A widely accepted empirical degradation model, that can predict calendar and cycling aging, is considered for this study. Several grid-connected power profiles are analyzed, such as peak shaving for DC-fast charge stations and frequency regulation. The results of this analysis show a correlation between the SLB's replacement rate with the minimum daily SoC.</div></div>

Design Space Optimization of a Three-Phase LCL Filter for Electric Vehicle Ultra-Fast Battery Charging

State-of-the-art ultra-fast battery chargers for electric vehicles simultaneously require high efficiency and high power density, leading to a challenging power converter design. In particular, the grid-side filter, which ensures sinusoidal current absorption with low pulse-width modulation (PWM) harmonic content, can be a major contributor to the overall converter size and losses. Therefore, this paper proposes a complete analysis, design and optimization procedure of a three-phase LCL filter for a modular DC fast charger. First, an overview of the basic LCL filter modeling is provided and the most significant system transfer functions are identified. Then, the optimal ratio between grid-side and converter-side inductance is discussed, aiming for the maximum filtering performance. A novel design methodology, based on a graphical representation of the filter design space, is thus proposed. Specifically, several constraints on the LCL filtering elements are enforced, such that all feasible design parameter combinations are identified. Therefore, since in low-voltage high-power applications the inductive components typically dominate the overall filter volume, loss and cost, the viable LCL filter design that minimizes the total required inductance is selected. The proposed design procedure is applied to a 30 kW, 20 kHz 3-level unidirectional rectifier, employed in a modular DC fast charger. The performance of the selected optimal design, featuring equal grid-side and converter-side 175 μμH inductors and 15 μμF capacitors, is verified experimentally on an active front-end prototype, both in terms of harmonic attenuation capability and current control dynamics. A current total harmonic distortion (THD) of 1.2% is achieved at full load and all generated current harmonics comply with the applicable harmonic standard. Moreover, separate tests are performed with different values of grid inner impedance, verifying the converter control stability in various operating conditions and supporting the general validity of the proposed design methodology.

Are Individuals’ stated preferences for electric vehicles (EVs) consistent with real-world EV ownership patterns?

Previous studies on factors affecting electric vehicle (EV) adoption mainly rely on individual stated preference surveys or aggregate market share analyses. This paper applies both approaches to study EV adoption patterns in the state of Virginia to compare and contrast findings from the two methods. An individual-level vehicle fuel type choice model is developed based on a 2018 stated preference survey of 837 Virginia drivers. A county-level EV ownership model is developed using Department of Motor Vehicles’ 2012–2016 vehicle registration data. Results show several consistent findings: 1) being male and having higher educational attainment have positive effects on EV adoption; 2) availability of DC fast charging stations is positively associated with EV adoption, particularly for battery electric vehicles (BEVs). However, the age effects are found to be inconsistent between the two study approaches: older individuals state negative preferences for EVs whereas counties with greater share of older populations are associated with more EV registrations. Additionally, the individual vehicle choice model complements the county-level EV ownership analysis by examining the effects of various vehicle-related attributes: 1) model results show positive effects of EV purchase incentives but negligible effects of EV annual use fees on EV preferences; 2) battery range is found to be significant for the utility of BEVs, but not for plug-in hybrid EVs; and 3) EV owners place greater importance on battery range and DC fast charging stations than non-EV owners. The combined analysis confirms several influential factors of EV adoption, and identifies instances when stated preference/interest are not consistent with real-world ownership patterns, which should be explicitly considered in EV policy making.

Investigation through analytical studies and experimental validation of ladder type resonant converter based constant current charger (>20 kW) for charging applications

As the world is moving towards green transportation, an effective charging infrastructure is needed to meet the demand of electric vehicles (EVs) with different power ratings. High power DC charging will facilitate EVs to charge quickly and reduce the wait time of a customer. As present charging infrastructure is limited to low power and is not compatible with different rated modular battery packs of EVs, this paper demonstrates Resonant Converter (RC) based high power constant current DC Fast charger. Owing to its inherent sensitivity, RCs suffer and fail to maintain rated load current when subjected to deviation in switching frequency and this has been the limiting feature for which they are not explored much in the automobile sector. In light of it, a topology study of about 25 RCs under lumped T, π, Ladder, and branch type structures is reported and examined the frequency dependence of different characteristic parameters in a range of frequencies encompassing the resonant frequency. This investigation conceived a novel RC that has adequate immunity in dealing with frequency sensitivity and also capable of pumping high DC power. The proposed converter is verified with a prototype and results are in close agreement with design.

A Supervisory Control for Resilient Operation of the Battery-Enabled DC Fast Charging Station and the Grid

This paper presents and evaluates a discrete-event control-based Supervisory Controller (SC) for a grid-connected, battery-enabled, DC Fast-Charging (DCFC) station for Electric Vehicles' (EVs). The SC enhances operational resiliency of the DCFC station and its host grid by decoupling the dynamics and limiting the steady-state power between the station and its host grid. The charging station includes multiple DCFC units, a Battery Energy Storage System (BESS), a solar-PV unit, and a grid-interface Voltage-Sourced Converter (VSC). The SC (i) limits the station-imported power from the grid based on the grid-imposed requirements, (ii) dynamically dispatches power flow among the grid, PV unit, DCFC units, and BESS to meet charging needs of EVs and when applicable charges BESS or exports power to the grid, (iii) protects the BESS by enforcing its state of charge upper and lower limits and the maximum charge/discharge rates, and (iv) enables the VSC to provide grid ancillary services, if required. Simulation results in PLECS software platform are given to assess the dynamic performance of the proposed SC in response to various discrete events/transitions. The SC algorithm is implemented on a Programmable Logic Controller (PLC) and verified based on real-time Hardware-In-the-Loop approach using an OPAL-RT platform.

Analysis of Electric and Hybrid Vehicle Usage in Proximity to Charging Infrastructure in Indiana

This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate the adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.

Optimal fast charging station locations for electric ridesharing with vehicle-charging station assignment

Electrified shared mobility services need to handle charging infrastructure planning and manage their daily charging operations to minimize total charging operation time and cost. However, existing studies tend to address these problems separately. A new online vehicle-charging assignment model is proposed and integrated into the fast charging location problem for dynamic ridesharing services using electric vehicles. The latter is formulated as a bi-level optimization problem to minimize the fleet’s daily charging operation time. A surrogate-assisted optimization approach is proposed to solve the combinatorial optimization problem efficiently. The proposed model is tested on a realistic flexible bus service in Luxembourg. The results show that the proposed online charging policy can effectively reduce the charging delays of the fleet compared to the state-of-the-art methods. With 10 additional DC fast chargers installed, charging operation time can be reduced up to 27.8% when applying the online charging policy under the test scenarios.

In-route inductive versus stationary conductive charging for shared automated electric vehicles: A university shuttle service

In-route inductive charging technology, as applied to automated electric vehicles, can help realize a fully automated system of both vehicles and chargers. This study presents a planning optimization analysis for fixed-route automated shuttles supported by in-route inductive charging technology. A techno-economic feasibility of inductive charging was assessed in comparison with stationary charging, including Level 2 AC chargers, and DC fast chargers (DCFCs). This analysis considered both present-day and future vehicle operations and overall system costs. A real project with two circulator Navya Arma shared automated electric vehicles (SAEVs) at the University of Michigan was investigated using real-world collected energy and travel data. The outcomes show that the proper design of quasi-dynamic inductive chargers at designated stops allows SAEVs to realize unlimited driving range and be cost-competitive to DCFC technology. Considering present-day costs and vehicles, low-speed SAEVs can realize charge-sustaining operation at a minimum cost either by implementing a 50-kW inductive charger at two stops with one segment per position and a 29-kWh onboard battery, or by installing a 100-kW inductive charger at one stop with one segment per position and a 28-kWh onboard battery. Considering future costs and vehicles, either a 40-kW charger at one stop with a 29-kWh battery or a 50-kW charger at the north stop with a 14-kWh battery would enable charge-sustaining operation. In addition, quasi-dynamic inductive solution can reduce the onboard battery by about 15% while providing unlimited driving range, but stationary scenarios require about 112% additional battery capacity to support a 12-h driving range.

A Comprehensive Review of DC Fast-Charging Stations With Energy Storage: Architectures, Power Converters, and Analysis

Electric vehicle (EV) adoption continues to rise, yet EV sales still represent a small portion of vehicle sales in most countries. An expansion of the dc fast-charging (DCFC) network is likely to accelerate this revolution toward sustainable transportation, giving drivers more flexible options for charging on longer trips. However, DCFC presents a large load on the grid, which can lead to costly grid reinforcements and high monthly operating costs-adding energy storage to the DCFC station can help mitigate these challenges. This article performs a comprehensive review of DCFC stations with energy storage, including motivation, architectures, power electronic converters, and detailed simulation analysis for various charging scenarios. The review is closely tied to current state-of-the-art technologies and covers both academic research contributions and real energy storage projects in operation around the world.

Demand-Aware Provisioning of Electric Vehicles Fast Charging Infrastructure

The concept of smart city strives for greener technology to reduce carbon emission to ameliorate the global warming. Following this footprint, the transportation sector is experiencing a paradigm shift and the transition to electric vehicles (EVs) has prodigious plausibility in reducing carbon emission. However, the anticipated EV penetration is hindered by several challenges, among them are their shorter driving range, slower charging rate and the lack of ubiquitous availability of charging locations, which collectively contribute to range anxieties for EVs' drivers. Meanwhile, the expected immense EV load onto the power distribution network may degrade the voltage stability. To reduce the range anxiety, we present a two-stage solution to provision and dimension a DC fast charging station (CS) network for the anticipated energy demand and that minimizes the deployment cost while ensuring a certain quality of experience for charging e.g., acceptable waiting time and shorter travel distance to charge. This solution also maintains the voltage stability by considering the distribution grid capacity, determining transformers’ rating to support peak demand of EV charging and adding a minimum number of voltage regulators based on the impact over the power distribution network. We propose, evaluate and compare two CS network expansion models to determine a cost-effective and adaptive CSs provisioning solution that can efficiently expand the CS network to accommodate future EV charging and conventional load demands. We also propose two heuristic methods and compare our solution with them. Finally, a custom built Python-based discrete event simulator is developed to test our outcomes.

A dynamic charging strategy with hybrid fast charging station for electric vehicles

The popularity of electric vehicles (EV) have been on the rise with technological advancements and environmental concerns. The charging time and charging demand are important challenges for EV adaptation. In order to address these challenges, a DC fast charging technology with a dynamic energy management system is proposed in this study. However, DC fast chargers require high power demand periods to reduce the charging time. This, in turn, will cause negative effects on the grid such as stability, resilience, and efficiency problems. The purpose of the study is to evaluate a hybrid DC fast charging station with the aim of reducing peak demand during charging periods. The proposed energy management algorithm together with the dynamic data use provides more reliable results on such systems operations. With the proposed control algorithm, both peak demand from the grid is substantially reduced by 45% and the battery life span is extended thanks to more controlled charge/discharge coordination.

Conic Optimization for Electric Vehicle Station Smart Charging With Battery Voltage Constraints

This article proposes a new convex optimization strategy for coordinating electric vehicle charging, which accounts for battery voltage rise and the associated limits on maximum charging power. Optimization strategies for coordinating electric vehicle charging commonly neglect the increase in battery voltage, which occurs as the battery is charged. However, battery voltage rise is an important consideration since it imposes limits on the maximum charging power. This is particularly relevant for dc fast charging, where the maximum charging power may be severely limited, even at the moderate state of charge levels. First, a reduced-order battery circuit model is developed, which retains the nonlinear relationship between the state of charge and maximum charging power. Using this model, limits on the battery output voltage and battery charging power are formulated as the second-order cone constraints. These constraints are integrated with a linearized power flow model for three-phase unbalanced distribution networks. This provides a new multiperiod optimization strategy for electric vehicle smart charging. The resulting optimization is a second-order cone program and, thus, can be solved in polynomial time by standard solvers. A receding horizon implementation allows the charging schedule to be updated online, without requiring prior information about when vehicles will arrive.

Determining the optimal electric vehicle DC-charging infrastructure for Western Australia

Take-up rates of electric vehicles (EV) are increasing and are predicted to accelerate rapidly. Public EV charging networks will be required to support future EV fleets. If unplanned, public charging networks are highly likely to be suboptimal. Planners need to understand and plan for future EV charging infrastructure requirements, particularly public DC fast charging networks, as both the upfront investment costs and the consequences of misallocation are high. However, the task of determining the optimal locations and allocations (types and numbers) of public EV charging infrastructure is complicated as it requires knowledge of many variables. These include EV driver behaviors, driving patterns, predicting evolutionary changes in EV and EV charging technologies, future EV take-up rates, and what investment may or may not occur in the absence of government funding support.

Lifetime Optimization for a Grid-Friendly DC Fast Charge Station With Second Life Batteries

Abstract Electrified vehicle (EV) batteries that have reached the automotive end of life are providing a low-cost energy storage solution for grid-connected systems, such as DC fast charge stations (DCFCs). There are several challenges associated with the integration of second life batteries (SLBs) in power systems, such as the definition of a systematic approach for the concurrent optimization of performance and lifetime with the aim of minimizing the investment and operating costs. This paper proposes the application of automotive SLBs to DCFC stations where high-power grid connection is not available or feasible. The SLBs are charged using a low-power grid connection and then provide DCFC power to the EVs. An optimal control problem has been formulated to identify the energy management control (EMC) strategy that allows minimizing the replacement rate of the SLBs, while ensuring the EV load request is match.