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The National Renewable Energy Laboratory (NREL) has developed a transient air conditioning (A/C) system model using SINDA/FLUINT analysis software. It captures all the relevant physics of transient A/C system performance, including two-phase flow effects in the evaporator and condenser, system mass effects, air side heat transfer on the condenser/evaporator, vehicle speed effects, temperature-dependent properties, and integration with a simplified cabin thermal model. It has demonstrated robust and powerful system design optimization capabilities. Single-variable and multiple variable design optimizations have been performed and are presented. Various system performance parameters can be optimized, including system COP, cabin cool-down time, and system heat load capacity. This work presents this new transient A/C system analysis and optimization tool and shows some high-level system design conclusions reached to date.

The National Renewable Energy Laboratory (NREL) has initiated projects to investigate the benefits and design challenges of using heat pipe/two-phase flow technologies to provide sub-system cooling and thermal management in future advanced vehicles, hybrid electric vehicles, and heavy-duty vehicles. Projects initially focused on vehicle instrument panel (IP) heat pipe cooling and passenger seat thermal management, but will also investigate engine cooling, electric motor cooling, and battery cooling in the future. Experimental results have demonstrated IP surface temperature reductions of 20°C-30°C during maximum solar intensity environments of 525-800 W/m2 (typical of Golden, CO from January to April) compared to uncooled conditions. The heat pipe cooling effect in the IP also reduced windshield temperatures by 9°C-12°C compared to the non-cooled configuration in April 2001 testing.

Current vehicle climate control systems are dramatically overpowered because they are designed to condition the cabin air mass in a specified period of time. A more effective and energy efficient objective is to directly achieve thermal comfort of the passengers. NREL is developing numerical and experimental tools to predict human thermal comfort in non-uniform transient thermal environments. These tools include a finite element model of human thermal physiology, a psychological model that predicts both local and global thermal comfort, and a high spatial resolution sweating thermal manikin for testing in actual vehicles.

On-road fuel economy is known to vary significantly between individual trips in real-world driving conditions. This work introduces a methodology for rapidly simulating a specific vehicle’s fuel economy over the wide range of real-world conditions experienced across the country. On-road test data collected using a highly instrumented vehicle is used to refine and validate this modeling approach. Model accuracy relative to on-road data collection is relevant to the estimation of “off-cycle credits” that compensate for real-world fuel economy benefits that are not observed during certification testing on a chassis dynamometer.

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory’s Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation.

The goal of the automated mobility platforms (AMPs) initiative is to raise the bar of service regarding equity and sustainability for public mobility systems that are crucial to large facilities, and doing so using electrified, energy efficient technology. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for Passengers with Reduced Mobility [PRMs]) and sustainability—both of which are important metrics often overlooked during the engineering design process.

Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate.

This research focuses on the technical issues that are critical to the adoption of high-energy-producing lithium Ion batteries. In addition to high energy density / high power density, this publication considers performance requirements that are necessary to assure lithium ion technology as the battery format of choice for electrified vehicles. Presentation of prime topics includes: • Long calendar life (greater than 10 years) • Sufficient cycle life • Reliable operation under hot and cold temperatures • Safe performance under extreme conditions • End-of-life recycling To achieve aggressive fuel economy standards, carmakers are developing technologies to reduce fuel consumption, including hybridization and electrification. Cost and affordability factors will be determined by these relevant technical issues which will provide for the successful implementation of lithium ion batteries for application in future generations of electrified vehicles.

System Integration Process for Accelerated Development explains how the integration of simultaneous engineering processes into the higher-level strategic business process can help accelerate the conversion of an idea into a finished product, resulting in a competitive advantage.

Efficient and accurate ordinary differential equation (ODE) solvers are necessary for powertrain and vehicle dynamics modeling. However, current commercial ODE solvers can be financially prohibitive, leading to a need for accessible, effective, open-source ODE solvers designed for powertrain modeling. Rust is a compiled programming language that has the potential to be used for fast and easy-to-use powertrain models, given its exceptional computational performance, robust package ecosystem, and short time required for modelers to become proficient. However, of the three commonly used (>3,000 downloads) packages in Rust with ODE solver capabilities, only one has more than four numerical methods implemented, and none are designed specifically for modeling physical systems. Therefore, the goal of the Differential Equation System Solver (DESS) was to implement accurate ODE solvers in Rust designed for the component-based problems often seen in powertrain modeling.

The development of highly automated driving functions (AD) recently rises the demand for so called Fail-Operational systems for native driving functions like steering and braking of vehicles. Fail-Operational systems shall guarantee the availability of driving functions even in presence of failures. This can also mean a degradation of system performance or limiting a system’s remaining operating period. In either case, the goal is independency from a human driver as a permanently situation-aware safety fallback solution to provide a certain level of autonomy. In parallel, the connectivity of modern vehicles is increasing rapidly and especially in vehicles with highly automated functions, there is a high demand for connected functions, Infotainment (web conference, Internet, Shopping) and Entertainment (Streaming, Gaming) to entertain the passengers, who should no longer occupied with driving tasks.

This project’s objective was to generate experimental data to evaluate the impact of metals doped B20 on diesel particle filter (DPF) ash loading and performance compared to that of conventional petrodiesel. The effect of metals doped B20 vs. conventional diesel on a DPF was quantified in a laboratory controlled accelerated ash loading study. The ash loading was conducted on two DPFs – one using ULSD fuel and the other on B20 containing metals dopants equivalent to 4 ppm B100 total metals. Engine oil consumption and B20 metals levels were accelerated by a factor of 5, with DPFs loaded to 30 g/L of ash. Details of the ash loading experiment and on-engine DPF performance evaluations are presented in the companion paper (Part I). The DPFs were cleaned, and ash samples were taken from the cleaned material. X-ray Fluorescence (XRF), X-Ray Photoelectron Spectroscopy (XPS) and X-Ray Diffraction (XRD) were conducted on the ash samples.

The project objective was to generate experimental data to evaluate the impact of metals doped B20 on DPF ash loading and performance compared to that of conventional petrodiesel. Accelerated ash loading was conducted on two DPFs – one exposed to regular diesel fuel and the other to B20 containing metal dopants equivalent to 4 ppm B100 total metals (currently total metals are limited to 10 ppm in ASTM D6751, the standard for B100). Periodic performance evaluations were conducted on the DPFs at 10 g/L ash loading intervals. After the evaluations at 30 g/L, the DPF was cleaned with a commercial DPF cleaning machine and another round of DPF evaluations were conducted. A comparison of the effect of ash loading with the two fuels and DPF cleaning is presented. The metals doped B20 fuel resulted in ash that was similar to that deposited when exposed to ULSD (lube oil ash) and exhibited similar ash cleaning removal efficiency.

Biodiesel (i.e., mono-alkyl esters of long chain fatty acids derived from vegetable oils and animal fats) is a renewable diesel fuel providing life-cycle greenhouse gas emission reductions relative to petroleum-derived diesel. With the expectation that there would be widespread use of biodiesel as a substitute for ultra-low sulfur diesel (ULSD), there have been many studies looking into the effects of biodiesel on engine and aftertreatment, particularly its compatibility to the current aftertreatment technologies. The objective of this study was to generate experimental data to measure the effectiveness of a current technology diesel oxidation catalysts (DOC) to oxidize soy-based biodiesel at various blend levels with ULSD. Biodiesel blends from 0 to 100% were evaluated on an engine using a conventional DOC.