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Spatio-temporal dynamics of Hendra virus in Australia reveal stable maintenance of diverse viral clades among Pteropus bats
Hendra virus (HeV) was discovered in 1994 in Australia. Limited genomic data have hindered comprehensive understanding of HeV’s evolutionary dynamics. Here, in this work, we recovered 48 HeV genomes from bats and 9 from horses from Australia between 2016 and 2020, revealing four distinct clades. Each clade was distributed over a large spatial area with multiple clades co-circulating within a single bat roost on the same day and over consecutive years. The diversity and temporal stability of co-circulating clades suggest that viral dynamics are driven by episodic shedding of existing lineages maintained at the population level, rather than immune-driven strain-replacement dynamics. HeV isolates of different clades displayed variation in phenotypic properties but minimal antigenic differences. We provide an overview of evolutionary dynamics, phenotypic properties and assessment of countermeasures for HeV, and provide insights into the processes that maintain virus diversity in bats and influence the potential for viral emergence.
Lower-Energy Energy Storage System (LEESS) Component Evaluation
Alternate hybrid electric vehicle (HEV) energy storage systems (ESS) such as lithium-ion capacitors (LICs) and electrochemical double-layer capacitor (EDLC) modules have the potential for improved life, superior cold temperature performance, and lower long-term cost projections relative to traditional battery storage systems. If such lower-energy ESS (LEESS) devices can also be shown to maintain high HEV fuel savings, future HEVs designed with these devices could have an increased value proposition relative to conventional vehicles. NREL's vehicle test platform is helping validate the in-vehicle performance capability of alternative LEESS devices and identify unforeseen issues. NREL created the Ford Fusion Hybrid test platform for in-vehicle evaluation of such alternative LEESS devices, bench testing of the initial LIC pack, integration and testing of the LIC pack in the test vehicle, and bench testing and installation of an EDLC module pack. EDLC pack testing will continue in FY15. The in-vehicle LIC testing results suggest technical viability of LEESS devices to support HEV operation. Several LIC configurations tested demonstrated equivalent fuel economy and acceleration performance as the production nickel-metal-hydride ESS configuration across all tests conducted. The lowest energy LIC scenario demonstrated equivalent performance over several tests, although slightly higher fuel consumption on the US06 cycle and slightly slower acceleration performance. More extensive vehicle-level calibration may be able to reduce or eliminate these performance differences. The overall results indicate that as long as critical attributes such as engine start under worst case conditions can be retained, considerable ESS downsizing may minimally impact HEV fuel savings.
Gas Turbine Heavy Hybrid Powertrain Variants. Opportunities and Potential for Systems Optimization
Widespread use of alternative hybrid powertrains is currently inevitable, and many opportunities for substantial progress remain. Hybrid electric vehicles (HEVs) have attracted considerable attention due to their potential to reduce petroleum consumption and greenhouse gas emissions in the transportation sector. This capability is mainly attributed to (a) the potential for downsizing the engine, (b) the potential for recovering energy during braking and thus recharging the energy storage unit, and (c) the ability to minimize the operation of the engine outside of its most efficient brake specific fuel consumption (BSFC) regime. Hybridization of the Class 8, heavy-duty (HD) powertrain is inherently challenging due to the expected long-haul driving requirements and limited opportunities for regenerative braking. The objective of this project is to develop control strategies aiming at optimizing the operation of a Class 8 HEV that features a micro-turbine as the heat engine. The micro-turbine application shows promise in fuel efficiency, even when compared to current diesel engines, and can meet regulated exhaust emissions levels with no exhaust after-treatment system. Both parallel and series HEV variants will be examined to understand the merits of each approach of the micro-turbine to MD advanced powertrain applications. These powertrain configurations enable new paradigms in operational efficiency, particularly in the Class 8 truck fleet. The successful development of these HEV variants will require a thorough technical understanding of the complex interactions between various energy sources and energy consumption components, for various operating modes. PACCAR will be integrating the first generation of their series HEV powertrain with a Brayton Energy micro-turbine into a Class 8 HD truck tractor that has both regional haul and local pick-up and delivery (P&D) components to its drive cycle. The vehicle will be deployed into fleet operation for a demonstration period of six (6) months to assess real world operating benefits of the advanced powertrain. A parallel variant of the micro-turbine powertrain will be built and sent to the ORNL Vehicle Systems Integration Laboratory.
Life Cycle Assessment of Vehicle Lightweighting: A Physics-Based Model To Estimate Use-Phase Fuel Consumption of Electrified Vehicles
Assessing the life-cycle benefits of vehicle lightweighting requires a quantitative description of mass-induced fuel consumption (MIF) and fuel reduction values (FRVs). We have extended our physics-based model of MIF and FRVs for internal combustion engine vehicles (ICEVs) to electrified vehicles (EVs) including hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). We illustrate the utility of the model by calculating MIFs and FRVs for 37 EVs and 13 ICEVs. BEVs have much smaller MIF and FRVs, both in the range 0.04-0.07 L e /(100 km 100 kg), than those for ICEVs which are in the ranges 0.19-0.32 and 0.16-0.22 L/(100 km 100 kg), respectively. The MIF and FRVs for HEVs and PHEVs mostly lie between those for ICEVs and BEVs. Powertrain resizing increases the FRVs for ICEVs, HEVs and PHEVs. Lightweighting EVs is less effective in reducing greenhouse gas emissions than lightweighting ICEVs, however the benefits differ substantially for different vehicle models. The physics-based approach outlined here enables model specific assessments for ICEVs, HEVs, PHEVs, and BEVs required to determine the optimal strategy for maximizing the life-cycle benefits of lightweighting the light-duty vehicle fleet.
Subcooled Boiling Heat Transfer for Cooling of Power Electronics in Hybrid Electric Vehicles
At present, single-phase liquid, forced convection cooled heat sinks with fins are used to cool power electronics in hybrid electric vehicles (HEVs). Although use of fins in the cooling channels increases heat transfer rates considerably, a second low-temperature radiator and associated pumping system are still required in HEVs. This additional cooling system adds weight and cost while decreasing the efficiency of HEVs. With the objective of eliminating this additional low-temperature radiator and pumping system in HEVs, an alternative cooling technology, subcooled boiling in the cooling channels, was investigated in the present study. Numerical heat transfer simulations were performed using subcooled boiling in the power electronics cooling channels with the coolant supplied from the existing main engine cooling system. Results show that this subcooled boiling system is capable of removing 25% more heat from the power electronics than the conventional forced convection cooling technology, or it can reduce the junction temperature of the power electronics at the current heat removal rate. With the 25% increased heat transfer option, high heat fluxes up to 250 W/cm(2) (typical for wideband-gap semiconductor applications) are possible by using the subcooled boiling system.
Personal Fuel Economy in Conventional and Hybrid Electric Vehicles: Integrating Driving Cycle Predictions with Power Distribution Optimization
Improving fuel efficiency and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel efficiency in laboratory controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This study uses standard driving cycles as a base for comparison, while aiming to develop fuel efficiency estimates based on personalized driving cycles that can inform consumers’ vehicle purchase and use decisions. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: aggressive, normal, or calm. Then the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate personal fuel efficiency for conventional vehicles and HEVs. To optimize personal fuel efficiency estimates, the Equivalent Consumption Minimization Strategy (ECMS) is applied. Depending on the driving style (aggressive, normal, or calm) and the driving scenario (city or highway driving), conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel efficiency was 13% lower in calm city driving, but almost 34% higher for aggressive highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on application of case-based reasoning and used to tune the power distribution in HEVs, their fuel consumption can improve by up to 12% in city driving. Implications and limitations of the findings are discussed in the paper.
Hybrid electric vehicle and method of operating engine of the same
A hybrid electric vehicle (HEV) for multiple operation modes includes: a gasoline diffusion flame (GDF) combustion engine configured to perform gasoline diffusion flame combustion; a motor-generator operatively connected to the GDF combustion engine and configured to selectively drive the HEV with electric power of a battery or generate electric power to charge the battery; and a multi-mode controller including a processor and configured to receive operating conditions of the GDF combustion engine and the motor-generator and define a plurality of mode operating regions based on the received operating conditions. In particular, the plurality of mode operating regions includes: an electric vehicle (EV) only mode operating region, a GDF mode operating region where the GDF combustion engine operates and drives the HEV while the motor-generator stops, and a GDF+EV mode operating region where the motor-generator assists the operation of the GDF combustion engine to drive the HEV.
Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles. A Survey
The growing necessity for environmentally benign hybrid propulsion systems has led to the development of advanced power management control algorithms to maximize fuel economy and minimize pollutant emissions. This paper surveys the control algorithms for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) that have been reported in the literature to date. The exposition ranges from parallel, series, and power split HEVs and PHEVs and includes a classification of the algorithms in terms of their implementation and the chronological order of their appearance. Remaining challenges and potential future research directions are also discussed.
Hybrid Vehicle
This article explains operating principles of Hybrid Electric Vehicles (HEVs), goes over examples of HEV architecture, and provides information about fuel economy and emissions improvements resulting from producing HEVs compared to similar conventional vehicles.
Global optimal energy management control strategies for connected four‐wheel‐drive hybrid electric vehicles
This study presents a novel decentralised hierarchical global energy management control strategy for a group of connected four‐wheel‐drive hybrid electric vehicles (HEVs) in urban road conditions. In the higher level controller, signal phase and timing information and the optimal cruising velocity are combined to generate the target velocities for the HEVs. A model predictive control framework that focuses on the tracking of the target velocity and the associated desired control variable for every individual vehicle is proposed for the prediction of the optimal velocity that compromises fuel economy, mobility and safety. In the lower level controller, a dynamic programming problem is formulated that utilises the predicted velocity for the global energy management optimisation of every individual HEV. Simulation results validate the advantages of the proposed higher and lower level controllers.
A Consumer-Oriented Control Framework for Performance Analysis in Hybrid Electric Vehicles
Hybrid electric vehicles (HEVs) have attracted considerable attention due to their potential to reduce fuel consumption and emissions. Our objective with this paper is to enhance our understanding of the associated tradeoffs among the HEV subsystems, e.g., the engine, the motor, and the battery, and investigate the related implications for fuel consumption and battery capacity and lifetime. Addressing this problem can provide insights on how to prioritize these objectives based on consumers needs and preferences. The results of the proposed optimization approach can also be used to investigate the implications for HEV costs related to ownership and warranty.
Integrated Optimization of Powertrain Energy Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles
Hybrid Electric Vehicles (HEVs) and autonomous vehicles have been widely studied recently for on-road transportation. In the study of autonomous HEVs, the control of the vehicle's external dynamics and powertrain dynamics are often treated separately. Optimizing these two problems together can significantly improve fuel economy. In this paper, an autonomous HEV following a leader is considered. First, the augmented model to integrate the abovementioned dynamics is presented. Second, the optimization problem is defined to find the optimum fuel consumption of the follower in pursuit of a leader in a drive cycle. A customized control strategy based on Approximate Dynamic Programming (ADP) is then explored in which the optimal cost-to-go at each time step is approximated using neural networks. Also, the accuracy of the optimization solution is enhanced by applying the concept of the reachable sets. At last, three case studies show that the examined integrated control strategy outperforms the one with the separated optimization method by an additional 7.4%, 4.6%, and 11.8% improvement in fuel consumption, respectively.
Navigation Application Programming Interface Route Fuel Saving Opportunity Assessment on Large-Scale Real-World Travel Data for Conventional Vehicles and Hybrid Electric Vehicles
The green-routing strategy instructing a vehicle to select a fuel-efficient route benefits the current transportation system with fuel-saving opportunities. This paper introduces a navigation application programming interface (API), route fuel-saving evaluation framework for estimating fuel advantages of alternative API routes based on large-scale, real-world travel data for conventional vehicles (CVs) and hybrid electric vehicles (HEVs). Navigation APIs, such as Google Directions API, integrate traffic conditions and provide feasible alternative routes for origin-destination pairs. This paper develops two link-based fuel-consumption models stratified by link-level speed, road grade, and functional class (local/non-local), one for CVs and the other for HEVs. The link-based fuel-consumption models are built by assigning travel from many global positioning system driving traces to the links in TomTom MultiNet and road grade data from the U.S. Geological Survey elevation data set. Fuel consumption on a link is computed by the proposed model. This paper envisions two kinds of applications: (1) identifying alternate routes that save fuel, and (2) quantifying the potential fuel savings for large amounts of travel. An experiment based on a large-scale California Household Travel Survey global positioning system trajectory data set is conducted. The fuel consumption and savings of CVs and HEVs are investigated. At the same time, the trade-off between fuel saving and travel time due to choosing different routes is also examined for both powertrains.
Evaluating an Eco-Cooperative Automated Control System
The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.
Comprehensive Total Cost of Ownership Quantification for Vehicles with Different Size Classes and Powertrains
In order to accurately compare the costs of two vehicles, the total cost of ownership (TCO) should consist of all costs related to both purchasing and operating the vehicle. This TCO analysis builds on previous work to provide a comprehensive perspective of all relevant vehicle costs of ownership. In this report, we present what we believe to be the most comprehensive explicit financial analysis of the costs that will be incurred by a vehicle owner. This study considers vehicle cost and depreciation, financing, fuel costs, insurance costs, maintenance and repair costs, taxes and fees, and other operational costs to formulate a holistic total cost of ownership and operation of multiple different vehicles. For each of these cost parameters that together constitute a comprehensive TCO, extensive literature review and data analysis were performed to find representative values in order to build a holistic TCO for vehicles of all size classes. The light- and heavy-duty vehicles selected for analysis in this report are representative of those that are on the road today and expected to be available in the future. Important additive analyses in this study include systematic analysis of vehicle depreciation, in-depth examination of insurance premium costs, comprehensive maintenance and repair estimates, analysis of all relevant taxes and fees, and considerations of specific costs applicable to commercial vehicles. We find that cars depreciate faster than light trucks and that older plug-in electric vehicles have a greater depreciation rate than newer electric vehicles. Light-duty vehicle (LDV) insurance costs show comparable costs for different powertrains, and lower costs for larger size classes. Medium- and heavy-duty vehicle (MHDV) insurance costs vary significantly by vocation. Electric and electrified powertrains have lower maintenance and repair costs than internal combustion engine (ICE) powertrains for all vehicle sizes, relative to vehicle price. MHDV maintenance and repair costs depend heavily on vocation and duty cycle. LDV taxes and fees are comparable across powertrain types and size classes, though marginally higher registration fees exist for alternative fuel vehicles. MHDV fees depend on the vocation, weight rating, and state. Many electric tractor trailers would be affected by additional battery weight, reducing the available payload capacity, and this cost can be substantial. Electric vehicle charging for commercial vehicles can be time-consuming; labor rates can cause this cost to dominate TCO. With improved knowledge of each of the cost components, we calculate a lifetime TCO for comparison across vehicles of different types and attributes. For a simulated small sport utility vehicle in 2025, modeled using Autonomie, the hybrid electric vehicle (HEV) has the lowest cost, followed by the conventional ICE vehicle. For MHDV, TCO can be drastically different depending on the vocation. Long-haul vehicles typically have the lowest per-mile costs. Excluding labor costs, the class 4 delivery has a comparable TCO to the day cab. Vocational trucks, refuse trucks, and transit buses have a high per-mile cost of ownership due to maintenance and insurance. For all of these vehicles, the cost of operating the vehicle is heavily weighted by the labor of the driver, followed by the fuel costs. While the HEV begins as the lowest cost powertrain for passenger vehicles, fuel cells are forecast to reach cost parity by 2030 when hydrogen prices reach $\$ 5$/kg while battery electric vehicles (BEV) reach cost parity by 2035 at a battery cost of $\$ 98$ per usable kWh of capacity, with these two technologies being the lowest cost in 2050. For the class 8 day cab tractor, the HEV and ICE vehicle begin as the lowest cost powertrains, and the 250-mile BEV reduces in cost from the most expensive to the least expensive by 2030.
Alternative Fuel Vehicle Usage and Owner Demographics in New York State
With mounting concerns over climate change and the environmental impact of fossil fuels, the United States has witnessed a growing interest in alternative fuel vehicles (AFVs). In 2021, approximately 1.5 million battery EVs (BEVs), 0.8 million plug-in hybrid EVs (PHEVs), and 5.5 million hybrid EVs (HEVs) were registered in the United States. In the state of New York, a total of 51,900 BEVs, 44,600 PHEVs, and 221,600 HEVs were registered in 2021. The current report presents the results of an analysis of AFV adoption patterns in New York State and the rest of the United States based on data from the 2017 National Household Travel Survey (NHTS). Overall, the report reveals the demographics and mobility factors (e.g., household income, homeownership, and trip length) that contribute to the adoption of AFVs. This study provides insights that can inform policy decisions aimed at promoting sustainable transportation solutions.The 2017 NHTS data showed that the percentage of households owning at least one AFV is lower in New York City compared with that in other regions of New York State. From the NHTS samples, of the 25 households that owned at least one BEV in New York State, 15 households (60%) lived within a 5-mile radius, based on the great circle distance, of the nearest charging station, and 23 households (92%) lived within a 10-mile radius of the nearest charging station. Furthermore, among the 40 households in New York State that own at least one PHEV, 48% (19 households) lived within a 5-mile radius of the closest EV charging station, and 83% (33 households) lived within a 10-mile radius of the nearest charging station. The rest of the United States had a higher percentage of households that own at least one AFV compared with that of New York State. A comparison was made between EV adoption levels using NHTS and EValuateNY, which is a tool that gathers statistics on the electric car market in New York State. The estimates obtained from New York State household samples in NHTS were slightly lower than the data provided by EValuateNY. In New York State and the rest of the United States, households with higher incomes tended to have a higher proportion of AFV ownership compared with those with lower incomes. For example, households in New York State earning $\$ $150,000 or more had an approximately 6% share of owning at least one AFV, which was markedly higher than those earning less than $\$ $100,000 (less than 3%). Additionally, homeowners in New York State and the rest of the United States also exhibited a significantly higher share of AFV ownership compared with that of renters. In New York State, households that own at least one AFV tended to travel farther and had longer travel times compared with their counterparts without an AFV. In terms of households with at least one AFV, households with HEVs tended to have more person trips, longer person miles of travel, and more vehicle miles traveled, resulting in longer travel times than that of households with BEVs or PHEVs. Notably, households with AFVs had a slightly lower share of family and personal business trips but a higher share of social and recreational trips compared with households without AFVs. Additionally, households with at least one AFV tended to have a slightly higher share of walking trips than their counterparts without an AFV. However, the comparisons were not statistically significant. These travel patterns observed in New York State were consistent with those observed in other regions of the United States.
A free-trailing vane flow direction indicator employing a linear output Hall effect transducer
The Hall effect vane (HEV) was developed to measure flow angularity in the NASA 40-by-80-foot and 80-by-120-foot wind tunnels. This indicator is capable of sensing flow direction at air speeds from 5 to 300 knots and over a + or - 40 deg angle range with a resolution of 0.1 deg. A free-trailing vane configuration employing a linear output Hall effect transducer as a shaft angle resolver was used. The current configuration of the HEV is designed primarily for wind tunnel calibration testing; however, other potential applications include atmospheric, flight or ground research testing. The HEV met initial design requirements.