Search Results

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).

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.

Exhaust manifold in engine is used to transfer the hot exhaust gas from cylinder head to the turbocharger with minimum pressure loss and to support the turbocharger assembly. This puts manifold under intense thermal and mechanical loading and makes the design very complex. While designing the manifold, resonance of the system must be avoided, and thermo-mechanical fatigue life expectations must be met. Different engine applications would call for multiple turbocharger configuration and orientations to be considered in the design layout for system level resonance assessment. This paper talks about the failure investigation of the manifold which was designed for High mount rear out (HMRO) turbo orientation and then used with high mount front out (HMFO) layout in road miller application resulted into the manifold failures.

With the constant strive towards increase in performance and corresponding stringent emission standards of modern IC engine, engine components such as the head, block and piston are subjected to higher thermal loads. An integrated simulation methodology is proposed where the head, the block and the piston are integral part of the analysis. The CFD – CHT methodology is used to simulate and predict the temperature of these engine components. The head and block are run in a steady-state conjugate heat transfer framework while the transient multiphase volume of fluid approach is used to determine piston temperatures. Combustion surfaces boundary conditions are derived from 3D CFD open-loop combustion simulation, while cooling and lubrication surface boundary condition are mapped from 1D system simulation or experimental data. The heat transfer boundary conditions are exchanged between the two simulations.

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.

Virtual sensing or estimation of emission species such as NOx at the engine exhaust using the appropriate engine measurables and leveraging it for control/diagnosis is a challenging task given the highly nonlinear and dynamic nature of the combustion process. This article presents development of virtual engine-out NOx (EONOx) sensor using two different supervised dynamic artificial neural network (ANN) topologies, namely, trained recurrent neural network (RNN) and wavelet neural network (WNN). The proposed RNN architecture is a single hidden layer neural network with permutations of feedback connections between the inter- and intra-layer nodes. The RNN resembles a nonlinear state-space model mapping select engine measurables and the engine-out NOx and is trained using a variant of real-time recurrent learning (RTRL) algorithm. The WNN architecture is a single hidden layer neural network comprising hidden layer nodes with wavelets as activation functions.

The lack of inherent security controls makes traditional Controller Area Network (CAN) buses vulnerable to Machine-In-The-Middle (MitM) cybersecurity attacks. Conventional vehicular MitM attacks involve tampering with the hardware to directly manipulate CAN bus traffic. We show, however, that MitM attacks can be realized without direct tampering of any CAN hardware. Our demonstration leverages how diagnostic applications based on RP1210 are vulnerable to Machine-In-The-Middle attacks. Test results show SAE J1939 communications, including single frame and multi-framed broadcast and on-request messages, are susceptible to data manipulation attacks where a shim DLL is used as a Machine-In-The-Middle. The demonstration shows these attacks can manipulate data that may mislead vehicle operators into taking the wrong actions.

As turbulence modeling has become an indispensable approach to perform flow simulation in a wide range of industrial applications, how to enhance the prediction accuracy has gained increasing attention during the past years. Of all the turbulence models, RANS is the most common choice for many OEMs due to its short turn-around time and strong robustness. However, the default setting of RANS is usually benchmarked through classical and well-studied engineering examples, not always suitable for resolving complex flows in specific circumstances. Many previous researches have suggested a small tuning in turbulence model coefficients could achieve higher accuracy on a variety of flow scenarios. Instead of adjusting parameters by trial and error from experience, this paper introduced a new data-driven method of turbulence model recalibration using adjoint solver, based on Generalized k-ω (GEKO) model, one variant of RANS.

Safety, fuel economy and uptime are key requirements for the operation of heavy-duty line-haul trucks within a fleet. With the penetration of connectivity and automation technologies, energy optimal and safe operation of the trucks are further improved through Advanced Driver Assistance System (ADAS) features and automated technologies as in truck platooning. Understanding the braking capability of the vehicle is very important for optimal ADAS and platooning control system design and integration. In this paper, the importance of tire connectivity and tire conditions on truck stopping distance are demonstrated through testing. The test data is further utilized to develop tire models for integration in an optimal vehicle automation for platooning. New ways to produce and use the tire related information in real-time optimal control of platooning trucks are proposed and the contribution of tire information in fuel economy is quantified through simulations.

The valve train is one of the most important part of engine , and its function is fresh charge inlet and exhaust exit according to order of engine based on intake and exhaust valve [1].The compression relief brake mechanism is one of the integrated brake technologies in Internal Combustion Engines (ICE) which not only reduces engine speed during downhill under overspeed condition by opening of one of the exhaust valves before the power stroke but also helpful to reduce brake pad wear by assisting in vehicle braking. The clearance between exhaust valve and piston during compression relief brake event is important aspect for overall valve train dynamic perspective. Valve motion study included this valve to piston clearance measurement in engine testing as mandate during product development phase.

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.

The internal combustion engine’s performance is affected by in-cylinder combustion processes and heat transfer rates through the combustion chamber walls. Hot spots may affect the reliability and durability of the engine components. Design of efficient and effective coolant systems requires accurate accounting of the heat fluxes into and out of the solid parts during the engine operation. The need to assess the engine’s performance early in the design process has motivated the use of a computational approach to predict such data. A more accurate representation of the engine’s operation is obtained by coupling the thermal, flow, and combustion analysis of the various components, such as the combustion chamber, ports, engine block, and its cooling system. Typically, a stand-alone CFD simulation does not capture the complex nature of the problem, and the manual transfer of data between multiple analyses may lead to an onerous or error-prone workflow requiring multiple user interventions.

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS).

Evaluation of the platooning legislative space suggests a limited near-term opportunity for autonomous vehicles as currently only nine states have platooning and autonomous favorable legislations. An extensive closed-loop vehicle model simulation was conducted to quantify two-truck platooning fuel economy entitlement benefits across all United States (US) interstate routes (I-xx) spanning over 40,000 miles as compared to a single truck. A simultaneous study was carried out to identify the density of Class 8 heavy-duty trucks on these interstates, using the Freight Analysis Framework (FAF) 4 database. These two studies were combined to ascertain interstates that foresee the least fuel consumption due to platooning and thus identifying states with the most platooning benefits. Identification of states with most platooning benefits provides realistic data to push for autonomous driving and platooning legislations.

Range-extended hybrids are an attractive option for medium- and heavy-duty commercial vehicle fleets because they offer the efficiency of an electrified powertrain with the driving range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy (DOE)-funded project for Medium- and Heavy-Duty Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric Class 6 truck with a range-extending engine designed for pickup and delivery application.

Traditionally, most charge air coolers (CACs) have been constructed using the Nocolok aluminum brazing process. The Nocolok process uses flux, some of which remains after the manufacturing process, and migrates through the intake tract to the engine during normal use. This migration and deposition on engine components can cause a variety of issues with engine operation. Currently the only alternative to Nocolok brazed CACs for engines sensitive to flux migration is vacuum brazing, which comes at a significant price increase. In the effort to reduce cost and increase efficiency, there is interest in whether a Nocolok brazed CAC with a reduced amount of flux residue can be successfully applied to flux-sensitive engines.

Fuel usage negatively impacts the environment and is a significant portion of operational costs of moving freight globally. Reducing fuel consumption is key to lessening environmental impacts and maximizing freight efficiency, thereby increasing the profit margin of logistic operators. In this paper, fuel economy improvements of a cab-over style 49T heavy duty Foton truck powered by a Cummins 12-liter engine are studied and systematically applied for the China market. Most fuel efficiency improvements are found within the vehicle design when compared to opportunities available at the engine level. Vehicle design (improved aerodynamics), component selection/matching (low rolling resistance tires), and powertrain electronic features integration (shift schedule/electronic trim) offer the largest opportunities for lowering fuel consumption.

This paper presents a thermal electric two-way coupled li-ion battery pack model for an all-electric VW motorsport racer. It starts from the hybrid pulse power characterization (HPPC) test data at different state of charge (SoC) and temperature levels. Such information is used for cell level battery equivalent circuit model (ECM) parameter identification. Multiple cell ECMs are connected in series to create a module ECM. Battery thermal performance of the module is simulated first by computational fluid dynamics (CFD) for the module. Then, a thermal reduced order model (ROM) is created out of the CFD solution. The thermal ROM is then two-way coupled with the battery module ECM to form a complete battery module model. Multiple module models are connected to create a battery pack model. The complete pack model is then exported into Simulink for validation and simulation.

This paper describes the methodology used to select an application-based fan that has optimum operating characteristics in terms of cooling air flow rate, fan power, and noise. The selected fan is then evaluated for structural strength. To evaluate different fans, complete rail coach under-hood simulations were carried out using steady-state 3D computational fluid dynamics (CFD) approach. These simulations considered an actual, highly non-uniform flow field. For each fan option, fan power, air flow rate, and surface acoustic power was evaluated. Pressure profiles on the fan blades were studied to assess the effect of non-uniform downstream air passage designs. Surface acoustic power was calculated using broadband noise source (BNS) model in ANSYS Fluent®. Surface pressure profiles over fan blades imported from 3D CFD were used in finite element analysis (FEA) in ANSYS. Analyses were carried out for blade linear and non-linear properties.

Advanced research in Spark-ignition (SI) engines has been focused on dilute-combustion concepts. For example, exhaust-gas recirculation is used to lower both fuel consumption and pollutant emissions while maintaining or enhancing engine performance, durability and reliability. These advancements achieve higher engine efficiency but may deteriorate combustion stability. One symptom of instability is a large cycle-to-cycle variation (CCV) in the in-cylinder flow and combustion metrics. Large-eddy simulation (LES) is a computational fluid dynamics (CFD) method that may be used to quantify CCV through numerical prediction of the turbulent flow and combustion processes in the engine over many engine cycles. In this study, we focus on evaluating the capability of LES to predict the in-cylinder flows and gas exchange processes in a motored SI engine installed with a transparent combustion chamber (TCC), comparing with recently published data.