Widespread Deployment Of Electric Vehicles Research Articles

3D Progression of Dead Li on Graphite Electrodes during Extended Fast Charging

Long charging times of commercial lithium-ion batteries (LIBs) constitute one of the critical remaining bottlenecks in the widespread deployment of electric vehicles (EVs). Therefore, there is a global push towards extreme fast charging (XFC)1in 10–15 minutes for 80% of the pack capacity. However, existing LIBs cannot achieve these XFC goals without significant capacity fade during cycling over battery lifetime.2 One of the key XFC degradation mechanisms is “Li plating,” which occurs when Li metal forms on the graphite electrode due the liquid electrolyte and solid-phase (graphite) mass transport limitations and sluggish charge-transfer kinetics of the Li intercalation reaction.3 This is mostly attributed to the formation of dead Li,4 which is the irreversibly plated Li that has become electronically disconnected from the graphite electrode. Since dead Li constitutes most of the XFC-related capacity loss, it is extremely important to unravel the link between its morphological evolution/progression, and battery capacity fade during extended XFC cycling.In this work, we report non-destructive, in-situ three-dimensional (3D) characterization of dead Li on graphite electrodes in full-cell LIBs during extended XFC using high-resolution (pixel size: ~ 7.8 µm; effective spatial resolution: ~25-30 µm) neutron micro-computed tomography (µCT). Specifically, we imaged batteries in the discharged state at the following cycle numbers at 6C charging rate: 10, 25, 50, and 150 cycles. Neutron µCT is promising for in-situ 3D Li detection on graphite electrodes because it provides strong contrast between Li and C (~ 71.87: 5.55 barns5). To leverage high-resolution neutron µCT for this work, we designed a custom neutron-friendly LIB coin-cell that provided exceptional neutron transmission at the graphite-separator-electrolyte interface as well as standard XFC performance at 6C between 3.0 and 4.1 V. We performed these imaging experiments at the MARS (CG-1D) beamline6 at the High Flux Isotope Reactor, Oak Ridge National Laboratory.Our results reveal 3D evolution of dead Li morphology from isolated plated deposits on only one edge of the graphite electrode after 25 cycles at 6C charging, to increasingly mossy-like covering the entire circumference of the graphite electrode after 150 cycles. Based on our imaging data, we did not observe any dead Li after only 10 XFC cycles. However, we noticed that dead Li started to nucleate around a partial edge of the graphite electrode after 25 XFC cycles, and then grew to the other edges after 50 XFC cycles, eventually forming a complete ring after 150 XFC cycles. At 150 XFC cycles, we also observed the dead Li ring outgrowths away from the edges of the graphite electrode onto the separator. In the extended XFC-cycled samples, 50 and 150 XFC cycles, we found significant dried out salt deposits on the separator, spacers, spring, and casing, which may be due to other parasitic reactions occurring at increased XFC cycling. Lastly, we determined quantitative relationships between the amount of dead Li in 3D, XFC cycling number, and XFC-related capacity loss of the battery.In conclusion, this work contributed to the advanced understanding of the 3D progression of dead Li and its relationship with XFC-related capacity loss on graphite electrodes during extended XFC cycling. We believe insights from this work will help develop improved 3D graphite electrode architectures that will minimize Li plating during repeated XFC cycling in fast-charged LIBs.

Regulation of multi-area power system load frequency in presence of V2G scheme.

The integration of renewable sources (RSs) and the widespread deployment of electric vehicles (EVs) has transitioned from a luxury to a necessity in modern power systems. This results from the sharp increase in electric power demand and public awareness of switching to green energy. However, in addition to load fluctuations and changes in system parameters, these RSs and EVs negatively impact the load frequency (LF). This work presents a LF control for a modern multi-area power system incorporating photovoltaic (PV) and EV chargers. The proposed controller primarily utilizes EV chargers within modern power systems. This approach offers the advantage of using the already present components instead of introducing new ones. The proposed controller comprises the ecological optimization approach (ECO) and the integral controller (I). Both of these components are designed for autonomous vehicle-to-grid (V2G) devices. The proposed control technique is applied to a three-area power system, where the V2G scheme is located in Area-1. Variations in the load, PV power generated, and system parameters are considered to evaluate the effectiveness of the proposed (I+ECO+V2G) controller for controlling the LF. To assess the performance of the proposed I+ECO+V2G system, a comparative analysis is conducted to compare its performance with both the I+ECO system and the standard I-controller. The simulation findings demonstrate that implementing the I+ECO and the proposed I+ECO+V2G strategies results in enhanced system stability and decreased LF fluctuations compared to the conventional I-control approach. Furthermore, while comparing the I+ECO control technique to the suggested control strategy I+ECO+V2G, it was seen that the latter reaches steady state values more quickly. The results validate the robustness and effectiveness of the proposed controller in mitigating the impacts of load disturbances, uncertainties, and nonlinearities within the system. These simulations were performed using MATLAB/SIMULINK. To validate the outcomes of the simulation results, an experimental setup consisting of a real-time dSPACE DS1103 connected to another PC via QUARC pid_e data acquisition card was used. The experimental findings have substantiated the accuracy of the simulation findings about the superiority of the I+ECO+V2G methodology compared to both the I+ECO and I-control methodologies concerning system performance and LF control.

The electric-thermal coupling simulation and state estimation of lithium-ion battery

The widespread deployment of electric vehicles is not solely associated with the electrification reform of the transport sector, but also plays an important role in the low-carbon transformation for urban energy systems. As the main energy supply, lithium-ion battery invariably affects the performance, cost and safety of the electric vehicles. Strengthening the energy management of lithium-ion battery system is of great importance and necessity to guarantee its efficiency and safety. In this paper, the prototype simulation systems of the multi-level batteries covering cell, module and pack are built within MATLAB/Simulink platform to estimate and predict their electrical and thermal properties under various environment and working conditions. Moreover, the battery states including state of charge, state of health and state of power are jointly estimated based on the equivalent circuit model and minimalist electrochemical model. The simulation and experimental results show that the prototype simulation system can not only describe the electrical and thermal properties of the battery, but also estimate the battery state with great accuracy, which guarantees the safe and stable operation of the lithium-ion battery system.

Spatially resolved generation profiles for onshore and offshore wind turbines: A case study of four Dutch energy transition scenarios

In line with the Dutch Climate Agreement, multiple energy transition scenarios have been constructed for 2030 and 2050. To various extents, they project a shift towards decentralized and intermittent renewable electricity generation (wind and solar) and widespread deployment of electric vehicles and heat pumps. These developments impose challenges regarding electricity supply-demand mismatch and grid congestion. In order to gain an understanding of when and where such problems are likely to occur, temporally and spatially resolved interpretations of the energy transition scenarios are required. This paper focuses on Dutch wind energy supply and shows construction of geodatabases of scenario-specific, hourly onshore and offshore wind electricity generation profiles on an individual turbine level. For the geographical distribution of turbine capacity, datasets on historically operational turbines, planned wind parks and suggested future turbine distributions are utilized. Turbine electricity generation profiles are constructed using a high resolution 3D meteorological dataset and power curves of commercially available turbine models. They are corrected for air pressure deviations and a multitude of loss factors, including wake effects. Compared to the present-day situation, yearly country-level electricity generation is projected to be a factor 16.6, 24.6 or 12.8 higher in 2050 when following the Regional, National or International Steering scenarios, respectively. In comparison to both the present-day and 2030 situation, onshore electricity generation is projected to be more evenly spread over different parts of the country in 2050. All offshore wind exploration areas considered in this research are projected to be completely utilized by 2050.

Optimal Siting and Sizing of Wireless EV Charging Infrastructures Considering Traffic Network and Power Distribution System

The main challenges in the widespread deployment of electric vehicles (EVs) are limited driving range, long charging downtime, lack of charging stations in some areas, and high battery cost. Dynamic wireless charging (DWC) technology can overcome some of these obstacles by recharging EV batteries remotely while vehicles move over the charging infrastructures. However, most studies have limited this technology to public EVs working in specific routes like electric buses and taxis routes. This paper presents a long-term stochastic scenario-based mathematical model for allocating and sizing DWC infrastructures considering Ev’s location-routing, power distribution system (PDS) losses, and transportation network traffic. The proposed long-term model allows all types of EVs to take advantage of the installed DWC infrastructures and facilitate EVs’ widespread use by overcoming conventional charging technologies problems. The optimization problem is structured in the form of a Mixed Integer Non-Linear Programming (MINLP) model. Simulation results with detailed analyzes are furnished to illustrate the characteristics and performance of the model in a coupled network combining transportation and power networks. The numerical analysis provides meaningful insight into the transportation system design based on DWC technology and the effect of EV routing on the charging infrastructure allocation. Simulation results show that using Location-Routing Problem (LRP) can save up to 45% of the total cost of the DWC system. In addition, the proposed model, along with the simulations performed, demonstrates the advantage of DWC technology in reducing the size of vehicle batteries.

Performance Optimization of High Ni (≥90%) Cathode Materials: Synthesis & Calcination

The widespread deployment of electric vehicles (EVs), powered by rechargeable lithium-ion batteries (LIBs), represents an opportunity to massively reduce fossil fuel consumption and CO2 emissions in the transportation sector. To increase the interest of the consumer, EVs should have a similar performance to that of internal combustion engine powered vehicles, such as a long driving range. However, the driving range of EV’s is limited by the battery performance, which is related to the properties of the cathode material in the battery. Therefore, the development of advanced cathodes is needed. Although pure LiNiO2 (LNO) was explored as a possible high-capacity candidate in the 1990s, it has since been recognized as inadequate due to its poor performance originating from structural instability. The current state-of-the-art cathodes for EV Li-ion batteries are derived from layered LiNiO2 (LNO) by substitution of Ni with other elements such as Mn, Co, and Al – so called LiNixMnyCozAl1-x-y-zO2 (NMCA). Typically, LNO is reported as a baseline material in order to measure improvements related to doping/coating strategies for NMCA-class cathodes. However, for the purpose of a full understanding of the role of Mn, Co, Al, and other dopants in NMCAs, the development of a truly state-of-the-art, LNO-based cathode should be accomplished. This presentation will focus on the various optimization strategies and parameters affecting the synthesis and performance of LNO and LNO-based, high-Ni cathodes synthesized at Argonne National Laboratory. Their potential applicability to battery applications will be reviewed.

The Proliferation of Solar Photovoltaics: Their Impact on Widespread Deployment of Electric Vehicles

Energy systems, in general, and power systems, in particular, are undergoing a changing landscape in terms of generation and demand portfolios. These transitions, although offering new economic and labor market opportunities, bring forth a suite of unprecedented technical implications and challenges in the interim. According to experts, our planet's population will reach 9.3 billion in the next four decades. A fundamental and granular outlook on global energy systems (prior to the COVID-19 outbreak) predicted that electricity consumption will double by 2050, with electrified transportation and higher living standards in buildings being among the principal components of this increase.

Model-Instructed Design of Novel Charging Protocols for the Extreme Fast Charging of Lithium-Ion Batteries Without Lithium Plating

Relatively long recharge time of high-energy density lithium-ion batteries (LIBs) is one of the major obstacles to the widespread deployment of electric vehicles (EVs). Excessive fast charge of LIBs can cause severe safety issues, the most significant of which is lithium plating. In addition to the consumption of cyclable lithium, dendrite-like lithium plating can cause short-circuiting within the cell, which could lead to catastrophic failure. In addition to the developments of advanced electrolytes, active materials and electrodes, the fast charging capability of LIBs can be improved by using optimized charging protocol. In this work, the Pseudo2D battery model is first used to screen some conventional charging protocols proposed for fast charging, including the constant-current constant-voltage charging (CCCV), constant-power constant-voltage charging (CPCV), multi-stage constant-current charging, and pulse current charging. A semi-optimal charging protocol is then developed by automatically modifying the charge current such that the cell is charged at close-to-maximum current without lithium plating. Based on the features of the semi-optimal protocol, two practical novel fast charging protocols are proposed which allow charging the cell at a higher rate without lithium plating. Through simulation, we show that the no-plating capacity after 10 min charge can be improved by up to ∼10.7% compared to CCCV charging for a 2.5 mAh cm−2 cell and ∼18.8% for a 4 mAh cm−2 cell, when the proposed charging protocols are used.

Energy and greenhouse gas emission reduction potentials resulting from different commuter electric bicycle adoption scenarios in Switzerland

To ensure long-term availability of mobility and to keep the effects of humanity on global climate limited, politics, research, and industry currently search for ways to reduce greenhouse gas (GHG) emissions caused by it. A potential reduction could be achieved by transitioning towards more efficient transport modes, optimally powered by renewable energy. Based on mobility (commutes), population, and weather data from Switzerland, we present a model to compute energy savings due to a (hypothetical) widespread deployment of electric bicycles. In different scenarios, we analyze the dependence of the commuting energy demand on users’ preferences about when to take the bike, such as an aversion to biking on cold and rainy days. Our study shows that GHG emission reductions of up to approx. 10% of the total emissions from diesel and gasoline are possible. In combination with a widespread deployment of electric vehicles, further savings of up to 17.5% could be achieved. In particular, the willingness to drive longer commutes by e-bike influences the potential GHG emission reductions, followed by the willingness to use the bicycle at temperatures below 10°C. Using these results, we identify the spatial energy and greenhouse gas savings potential and thus regions that are particularly suited for e-bike use. The identification of regions with high saving potentials allows for targeted marketing, transition-supporting incentives, or infrastructural changes to maximize the reduction of emissions.

Evaluation of a concept out-of-autoclave process for manufacturing carbon fibre reinforced polymer automotive parts

To comply with increasingly stringent exhaust-emission- and fuel-consumption-legislation imposed on internal-combustion-engine-powered vehicles; and to realise the widespread deployment of electric vehicles hindered by heavy energy storage systems, lightweight construction achieved through application of fibre reinforced polymers, most notably Carbon Fibre Reinforced Polymer, to the mass-produced automotive architecture has been predicted to increase in the coming years. However, this is difficult to envisage by the time-, labour-, energy- and waste-intensive part-manufacturing processes of present, including autoclave-processing and Out-of-Autoclave processes such as Resin Transfer Moulding. In this paper, an innovative concept Out-of-Autoclave process, based on the Prepreg Compression Moulding process, in which hot forming and curing of reinforced thermoset composite prepreg are integrated in one step, previously demonstrated to offer potential for reduced cycle-time, lead-time and financial cost; and greater environmental sustainability compared to current processes, was evaluated for manufacturing Carbon Fibre Reinforced Polymer automotive parts. Formability, springback, geometric accuracy and mechanical properties of parts were determined by macroscopic observation, scanning electron microscopy, flexural testing, tensile testing and fatigue testing. Springback-free parts exhibiting mechanical properties within approximately 80 % of benchmark autoclave-produced parts were produced. Compressive crash-box testing was then conducted across strain-rates of 0.02, 0.20 and 2.00 s-1, with crushed-length, specific energy-absorption and crush-force-efficiency superior to autoclave-produced parts. The concept process thus demonstrated a significant step towards realising the application of Carbon Fibre Reinforced Polymer to the mass-produced automotive architecture and to an array of applications including tensile and flexural strength-critical safety-cell body-parts, impact-energy-absorptive-critical crumple-zone body-parts and fatigue-critical suspension-parts.

Batteries and fuel cells for emerging electric vehicle markets

Today’s electric vehicles are almost exclusively powered by lithium-ion batteries, but there is a long way to go before electric vehicles become dominant in the global automotive market. In addition to policy support, widespread deployment of electric vehicles requires high-performance and low-cost energy storage technologies, including not only batteries but also alternative electrochemical devices. Here, we provide a comprehensive evaluation of various batteries and hydrogen fuel cells that have the greatest potential to succeed in commercial applications. Three sectors that are not well served by current lithium-ion-powered electric vehicles, namely the long-range, low-cost and high-utilization transportation markets, are discussed. The technological properties that must be improved to fully enable these electric vehicle markets include specific energy, cost, safety and power grid compatibility. Six energy storage and conversion technologies that possess varying combinations of these improved characteristics are compared and separately evaluated for each market. The remainder of the Review briefly discusses the technological status of these clean energy technologies, emphasizing barriers that must be overcome.

Data-driven decomposition analysis and estimation of link-level electric vehicle energy consumption under real-world traffic conditions

Electric vehicles (EVs) have great potential to reduce transportation-related fossil fuel consumption as well as pollutant and greenhouse gas (GHG) emissions, due to their use of renewable electricity as the sole energy source. Therefore, the wide-spread deployment of EVs is regarded asan attractive means to mitigate the environmental problems (e.g., air pollution and climate change) resulting from transportation activities. Government agencies are trying to promote EV deployment by allocating considerable funding as well as promulgating supportive policies. However, the mass adoption of EVs is still impeded by the limited charging infrastructure and all-electric-range (AER). All these lead to a critical research topic: the EV energy consumption analysis and estimation under real-world traffic conditions, which is fundamental to various types of EV-centred applications that aim at improving the EV energy efficiency and extending the AER. For example, eco-routing systems for EVs rely on accurate link-level energy consumption estimation to calculate the EV energy consumption costs of the different route options. In this work, to obtain an accurate link-level energy consumption estimation model for EVs, the energy consumption under real-world traffic congestion is decomposed based on two proposed impact factors: positive kinetic energy (PKE) and negative kinetic energy (NKE). Upon this decomposition, a data-driven model is built to estimate EV energy consumption on each roadway link considering real-world traffic conditions. Finally, the model performance is evaluated by comparing with the performance of baseline model adapted from existing models. The results show that the proposed EV link-level energy consumption estimation model outperforms the existing models in terms of accuracy, implying that it is quite promising in various on-board EV applications.

Feasibility assessment of a solar-powered charging station for electric vehicles in the North Central region of Bulgaria

The paper discusses the topical issue related to the prospects of widespread deployment of electric vehicles and their associated infrastructure in Bulgaria. The main problems hindering the development of electric vehicle transport are summarized and the current status of charging infrastructure in the country is discussed. An approach is proposed for analysis and evaluation of the financial feasibility of investment in a solar-powered charging station for electric vehicles in North Central region of Bulgaria.

A Comparative Analysis of Route‐Based Energy Management Systems for Phevs

AbstractPlug‐in hybrid electric vehicle (PHEV) development seems to be essential step on the path to widespread deployment of electric vehicles (EVs) as the zero‐emission solution for the future of transportation. Because of their larger battery pack in comparison to conventional hybrid electic vehicles (HEVs), they offer longer electric range which leads to a superior fuel economy performance. Advanced energy management systems (EMSs) use vehicle trip information to enhance a PHEV's performance. In this study, the performance of two optimal control approaches, model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A‐ECMS), for designing an EMS for different levels of trip information are compared. The resulting EMSs are fine‐tuned for the Toyota Prius plug‐in hybrid powertrain and their performances are evaluated by using a high‐fidelity simulation model in the Autonomie software. The results of simulation show that both MPC and A‐ECMS can approximately improve fuel economy up to 10% compared to the baseline Autonomie controller for EPA urban and highway drive cycles. Although both EMSs can be implemented in real time, A‐ECMS is 15% faster than MPC. Moreover, it is shown that the engine operating points are more sensitive to the battery depletion pattern than to different driving schedules.