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Commercial vehicle powertrain is called to respect a challenging roadmap for CO2 emissions reduction, quite complex to achieve just improving technologies currently on the market. In this perspective alternative solutions are gaining interest, and the use of green H2 as fuel for ICE is considered a high potential solution with fast and easy adoption. NOx emission is still a problem for H2 ICE and can be managed operating the engine with lean air fuel ratio all over the engine map. This combustion strategy will challenge the boosting system as lean H2 combustion will require quite higher air flow compared to diesel for the same power density in steady state. Similar problem will show up in transient response particularly when acceleration starts from low load and the exhaust gases enthalpy is very poor and insufficient to spin the turbine. The analysis presented in this paper will show and quantify the positive impact that a supercharger has on both the above mentions problems.

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.

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.

Improving fatigue resistance is a key factor to design components for advanced vehicle transmissions. The selection of materials and heat treatment plays a crucial role in controlling fatigue performance of power transmission components such as gears and shafts. Traditional, low frequency fatigue testing, used for identifying fatigue limit or generating S-N curve for multiple sets of material parameters is highly time consuming and expensive. Hence, it is necessary to develop the capability to predict fatigue performance of materials at different loading conditions with limited amount of data for instance the hardness and inclusion size. In the present work, we have evaluated behavior of the carburized steel subjected to axial and torsional cyclic loading conditions at low frequencies.

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.

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.

Electrification is one of the megatrends across the industries, like electric vehicles, electric aircraft, etc. which needs advancement in power electronics component technology. As technology advances in miniaturization of power electronics, thermal-management issues threaten to limit the performance of these devices. These may force designers to derate the device performance and ultimately these compromise in design may increase the size & weight of the application. One of the technologies capable of accomplishing these goals employs a class of materials know as metal foam. Metal foams are lightweight cellular materials inspired by nature. The main application of metal foams can be grouped into structural and functional and are based on several excellent properties of the material. Structural applications take advantage of the light-weight and specific mechanical properties of metal foam.

Commercial vehicles are moving in the direction of improving brake thermal efficiency while also meeting future diesel emission requirements. This study is focused on improving efficiency by replacing the variable geometry turbine (VGT) turbocharger with a high-efficiency fixed geometry turbocharger. Engine-out (EO) NOX emissions are maintained by providing the required amount of exhaust gas recirculation (EGR) using a 48 V motor driven EGR pump downstream of the EGR cooler. This engine is also equipped with cylinder deactivation (CDA) hardware such that the engine can be optimized at low load operation using the combination of the high-efficiency turbocharger, EGR pump and CDA. The exhaust aftertreatment system has been shown to meet 2027 emissions using the baseline engine hardware as it includes a close coupled light-off SCR followed by a downstream SCR system.

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.

Commercial vehicles require fast aftertreatment heat up in order to move the selective catalyst reduction (SCR) into the most efficient temperature range to meet upcoming NOx regulations. Heavy duty cylinder deactivation (CDA) is an important technology to meet these regulations. One of the challenges with implementing CDA in the heavy-duty market is to ensure acceptable engine and vehicle vibration. The purpose of this paper is to mitigate CDA vibration on a vehicle to acceptable levels. Emphasis was placed at the idle operating condition. Idle is the most challenging operating mode to enable, as deactivating cylinders reduces the frequency of the forcing function due to engine firing, which leads to a need to isolate these lower frequencies. A focused modal analysis of the engine (source), frame (path), and cabin (path/receiver) was used to characterize the vehicle system.

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.