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With the capability of avoiding failure in advance, failure prediction model is important not only to end users, but also to the service engineers in vehicle industry. This paper proposes an approach based on anomaly detection algorithms and telematic data to predict the failure of the engine air system with Turbo charger. Firstly, the relationship between air system and all obtained features are analyzed by both physical mechanism and data-wise. Then, the features including altitude, air temperature, engine output power, and charger pressure are selected as the input of the model, with the sampling interval of 1 minute. Based on the selected features, the healthy state for each vehicle is defined by the model as benchmark. Finally, the ‘Medium surface’ is determined for specific vehicle, which is a hyperplane with the medium points of the healthy state located at, to detect the minor weakness symptom (sub-health state).

Vehicle-to-everything (V2X) communication, primarily designed for communication between vehicles and other entities for safety applications, is now being studied for its potential to improve vehicle energy efficiency. In previous work, a 20% reduction in energy consumption was demonstrated on a 2017 Prius Prime using V2X-enabled algorithms. A subsequent phase of the work is targeting an ambitious 30% reduction in energy consumption compared to a baseline. In this paper, we present the Eco-routing algorithm, which is key to achieving these savings. The algorithm identifies the most energy-efficient route between an Origin-Destination (O-D) pair by leveraging information accessible through commercially available Application Programming Interfaces (APIs). This algorithm is evaluated both virtually and experimentally through simulations and dynamometer tests, respectively, and is shown to reduce vehicle energy consumption by 10-15% compared to the baseline over real-world routes.

This article analyzes the aerodynamic performance of Class 8 tractor-trailer geometries made available by the Environmental Protection Agency (EPA) using CFD simulation. Large Eddy Simulations (LES) were carried out with the CFD package, Simerics-MP+. A Sleeper tractor and a 53-foot box trailer configuration was considered. The configuration featured a detailed underbody, an open-grille under-hood engine compartment, mirrors, and the radiator and condenser. Multiple tractor-trailer variants were studied by adding aerodynamic surfaces to the baseline geometries. These include tank fairings and side extenders for the cabins, two types of trailer skirts, and a trailer tail. The effect of these devices towards reducing the overall vehicle drag was investigated. Mesh generation was carried out directly on the given geometry, without any surface modifications, using Simerics’ Binary-Tree unstructured mesher.

The thermal performance of an engine coolant system is efficient when the engine head temperature is maintained within its optimum working range. For this, it is desired that air should not be entrapped in the coolant system which can lead to localized hot spots at critical locations. However, it is difficult to eliminate the trapped air pockets completely. So, the target is to minimize the entrapped air as much as possible during the coolant filling and deaeration processes, especially in major components such as the radiator, engine head, pump etc. The filling processes and duration are typically optimized in an engine test stand along with design changes for augmenting the coolant filling efficiency. However, it is expensive and time consuming to identify air entrapped locations in tests, decide on the filling strategy and make the design changes in the piping accordingly.

According to the Annual Energy Outlook 2022 (AEO2022) report, almost 30% of the transport sector will still use internal combustion engines (ICE) until 2050. The transportation sector has been actively seeking different methods to reduce the CO2 emissions footprint of fossil fuels. The use of lower carbon-intensity fuels such as Renewable Diesel (RD) can enable a pathway to decarbonize the transport industry. This suggests the need for experimental or advanced numerical studies of RD to gain an understanding of its combustion and emissions performance. This work presents a numerical modeling approach to study the combustion and emissions of RD. The numerical model utilized the development of a reduced chemical kinetic mechanism for RD’s fuel chemistry. The final reduced mechanism for RD consists of 139 species and 721 reactions, which significantly shortened the computational time from using the detailed mechanism.

Simulation and testing are often done by different engineers in different departments of a company. This can lead to disconnects and unrealistic predictions, especially if the person doing simulations does not have an experimental background. On the other hand, experimental results can also include errors that result in misleading answers. It is important for the engineer doing either testing or simulation to have a good understanding for what results are plausible and what results might be suspect. This paper will provide examples where error crept into testing or simulation that could have been caught and corrected early if a good feel for “reasonable” results had been in place. The importance of understanding how a software package is analyzing the data will be explained, since settings buried deep within a menu structure can drive misleading results.

A physics-based spark ignition model was developed and integrated into a commercial CFD code. The model predicted the spark discharge process based on the electrical parameters of the secondary ignition circuit, tracked the spark motion as it was stretched by in-cylinder gas motion, and determined the resulting energy deposition to the gas. In concert with the existing kinetic solver in the CFD code, the resulting ignition and flame propagation processes were simulated. The model results have been validated against both imaging rig experiments of the spark in moving air and against engine experimental data. The model was able to replicate the key features of the spark and to capture the cyclic variability of high-dilution combustion when multiple engine cycles were simulated.

Diesel oxidation catalyst (DOC) is one of the critical catalyst components in modern diesel aftertreatment systems. It mainly converts unburned hydrocarbon (HC) and CO to CO2 and H2O before they are released to the environment. In addition, it also oxidizes a portion of NO to NO2, which improves the NOx conversion efficiency via fast SCR over the downstream selective catalytic reduction (SCR) catalyst. HC light-off tests, with or without the presence of NOx, has been typically used for DOC evaluation in laboratory. In this work, we aim to understand the influences of DOC light-off experimental conditions, such as initial temperature, initial holding time, HC species, with or without the presence of NOx, on the DOC HC light-off behavior. The results indicate that light-off test with lower initial temperature and longer initial holding time (at its initial temperature) leads to higher DOC light-off temperature.

The prediction accuracy of a three-way catalyst (TWC) model is highly associated with the ability of the model to incorporate the reaction kinetics of the emission process as a lambda function. In this study, we investigated the O2 and H2 concentration profiles of TWC reactions and used them as critical inputs for the development of a global TWC model. We presented the experimental data and global kinetic model showing the impact of thermal degradation on the performance of the TWC. The performance metrics investigated in this study included CH4, NOx, and CO conversions under lean, rich, and dithering light-off conditions to determine the kinetics of oxidation reactions and reduction/reforming/water-gas shift reactions as a function of thermal aging. The O2 and H2 concentrations were measured using mass spectrometry to track the change in the oxidation state of the catalyst and to determine the mechanism of the reactions under these light-off conditions.

Interaction between a diesel spray and piston plays a significant role in overall combustion and emissions performance in compression-ignition engines. It is essential to design the lip feature respective to spray targeting and the following charge motion for combustion systems that rely on spray-piston interaction strongly, such as a stepped-lip piston. This study used a numerical campaign using computational fluid dynamics (CFD) simulation to optimize a stepped-lip combustion system at a 22:1 compression ratio (CR) for both performance and emissions. This is a substantial step up in CR from the stock value of 17:1 for the same engine platform. A machine learning model was used to identify the best combination of features from a design space involving hundreds of potential piston designs and injector nozzle configurations. This study provides a discussion on the general combustion characteristics of the stepped-lip combustion system and the sensitivity of the design parameters.

Vehicle to Everything (V2X) communication has enabled on-board access to information from other vehicles and infrastructure. This information, traditionally used for safety applications, is increasingly being used for improving vehicle fuel economy [1-5]. This work aims to demonstrate energy consumption reductions in heavy/medium duty vehicles using an eco-driving algorithm. The algorithm is enabled by V2X communication and uses data contained in Basic Safety Messages (BSMs) and Signal Phase and Timing (SPaT) to generate an energy-efficient velocity trajectory for the vehicle to follow. An urban corridor was modeled in a microscopic traffic simulation package and was calibrated to match real-world traffic conditions. A nominal reduction of 7% in energy consumption and 6% in trip time was observed in simulations of eco-driving trucks.

A comprehensive 3D Computational Fluid Dynamics (CFD) with conjugate heat transfer (CHT) tool was developed in-house for a Tesla Model 3 electric motor. To accurately predict the power loss (heat generation) inside the electric motor, the electromagnetic process was solved to obtain the spatial-dependent power loss in the rotor, stator, and windings. CFD was utilized for simulating the coolant oil flow using the multiphase Volume of Fluid (VOF) approach and Finite Element Analysis (FEA) was used for simulating the thermal process within the solid domains. These three separate analysis modules (electromagnetic, fluid flow and thermal solid) were coupled strongly to enable two-way interactions. Thermal results obtained from the final converged simulations were compared to the test data obtained from the thermocouple measurements for the two most representative operating points of this e-motor and showed reasonable predictions with similar trend as observed in the test.

Commercial automotive diesel engine service and repair, post a diagnostic trouble code trigger, relies on standard troubleshooting steps laid down to identify or narrow down to a faulty engine component. This manual process is cumbersome, time-taking, costly, often leading to incorrect part replacement and most importantly usually associated with significant downtime of the vehicle. Current study aims to address these issues using a novel in-house simulation-based approach developed using a Digital Twin of the engine which is capable of conducting in-mission troubleshooting with real world vehicle/engine data. This cost-effective and computationally efficient solution quickly provides the cause of the trouble code without having to wait for the vehicle to reach the service bay. The simulation is performed with a one-dimensional fluid dynamics, detailed thermodynamics and heat transfer-based diesel engine model utilizing the GT-POWER engine performance tool.

Advanced diesel combustion systems continue to push the peak cylinder pressure limit of engines upward to allow high-efficiency combustion with high compression ratios (CR). The air-standard Otto and Diesel cycles indicate increased compression ratios lead to higher cycle efficiency. The study presented here describes the development and demonstration of a high-efficiency diesel combustion system. The study used both computational and experimental tools to develop the combustion system fully. Computational fluid dynamics (CFD) simulations were carried out to evaluate combustion with two combustion systems at a compression ratio of 22:1 with a Wave piston design (based on the production Volvo Wave piston). Analysis of combustion performance and emissions were performed to confirm the improvements these piston designs offered relative to the baseline combustion system for the engine. Companion single-cylinder engine (SCE) experiments were performed to validate the simulation results.

With the increasing level of electrification of on-road, off-road and stationary applications, use of larger lithium-ion battery packs has become essential. These packs require large capital investments on the order of millions of dollars and pose a significant risk of self-annihilation without rigorous safety evaluation and management. Testing these larger battery packs to validate design changes can be cost prohibitive. A reliable numerical simulation tool to predict battery thermal runaway under various abuse scenarios is essential to engineer safety into the battery pack design stage. A comprehensive testing & simulation workflow has been established to calibrate and validate the numerical modeling approach with the test data for each of the individual sub model - electrochemical, internal short circuit and thermal abuse model. A four-equation thermal abuse model was built and validated for lithium-ion 21700 form factor cylindrical cells using NCA cathodes.

A computational fluid dynamics (CFD) model was developed to explore gasoline compression ignition (GCI) combustion. Results were validated with single-cylinder engine (SCE) experiments. It was shown that the CFD model captured experimental results well. Cylinder pressure, heat release and emissions from the CFD model were also used to analyze the performance of GCI combustion with a current heavy-duty diesel engine platform. This work also provides detailed analysis on in-cylinder combustion and emissions using CFD. It was found that multiple injection strategy can deliver desirable fuel stratification profile that benefits both engine and emissions performance. A wave contoured piston was compared with a stepped-lip type piston for both GCI and Diesel combustion scenarios on the same engine platform. Stepped-lip pistons offer an opportunity to use multiple injection strategies to overcome high UHC emissions of GCI combustion when compared to wave pistons.

Two methods of sampling exhaust emissions are typically used for characterizing emissions from diesel engines: total dilution which uses a constant volume sampling (CVS) system and partial flow dilution which relies on proportionally diluting a small part from the main exhaust stream. The CVS dilutes the entire exhaust flow to a constant volumetric flowrate which allows for proportional sampling of the exhaust species during transient engine operation. For partial dilution sampling during transient engine operation, obtaining a proportional sample is more rigorous and dilution of the extracted sample must be continuously changed throughout the cycle in order for the extracted sample flowrate to be proportional to the continuously changing exhaust flow. Typically, regulated emissions measured using both methods for an engine platform have shown good correlation. The focus for this work was on the experimental investigation of the two methods for the measurement of unregulated species.

The Exhaust Composition Transient Operation LaboratoryTM (ECTO-LabTM) is a burner system developed at Southwest Research Institute (SwRI) for simulation of IC engine exhaust. The current system design requires metering and combustion of nitromethane in conjunction with the primary fuel source as the means of NOX generation. While this method affords highly tunable NOX concentrations even over transient cycles, no method is currently in place for dictating the speciation of nitric oxide (NO) and nitrogen dioxide (NO2) that constitute the NOX mixture. NOX generated through combustion of nitromethane is dominated by NO, and generally results in an NO2:NOX ratio of < 5 %. Generation of any appreciable quantities of NO2 is therefore dependent on an oxidation catalyst to oxidize a fraction of the NO to NO2.

Onboard sensing and external connectivity using Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) technologies allows a vehicle to "know" its future operating environment with some degree of certainty, greatly narrowing prior information gaps. The increased development of such connected and automated vehicle systems, currently used mostly for safety and driver convenience, presents new opportunities to improve the energy efficiency of individual vehicles [1, 2, 3, 4, 5]. Southwest Research Institute (SwRI) in collaboration with Toyota Motor North America and University of Michigan is currently working on improving energy consumption of a Toyota Prius Prime 2017 by 20%. This paper will provide an overview of the various algorithms that are being developed to achieve the energy consumption target. Custom tools such as a traffic simulator was built to model traffic flow in Fort Worth, Texas with sufficient accuracy.

The process of developing, parameterizing, validating, and maintaining models occurs within a wide variety of tools, and requires significant time and resources. To maximize model utilization, models are often shared between various toolsets and experts. Model integration is typically divided into two categories: model exchange and model co-simulation. Of these two categories, model co-simulation is typically regarded as the more complex and difficult to implement. Co-Simulation provides the ability to integrate models between different toolsets or incompatible versions of the same software. Additionally, it provides the capabilities for real-time simulations and hardware-in-the-loop test scenarios. This paper reviews some of the common co-simulation data communication methods including pipes and file input/output. The differences between serial and parallel, aka synchronous and asynchronous, communication patterns are also discussed.