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Considering the current trend towards the electrification of commercial vehicles, the development of Beam eAxle solutions has become necessary. The utilization of an electric drive unit in heavy-duty solid axle-based commercial vehicles presents unique and demanding challenges. These include the necessity for elevated peak and continuous torque while meeting packaging constraints, structural integrity requirements, and extended service life. One such solution was developed by BorgWarner to address these challenges. This paper offers a comprehensive overview of the design and development process undertaken for this Dual Motor Beam eAxle system. This includes the initial comparison of various eAxle solutions, the specifications of components selected for this design, and the initial results from dyno and vehicle development.

The paper solves the problem of increasing the accuracy of measuring torque and use of an electronic torque meter as a feedback sensor of synchronous and induction machines of an electric traction drive. Relevance of the problem and methods for its solving by indirect means using simulation models of electric machines are considered. The theoretical development of the meter is based on the energy model of electric machines in the form of differential equations for active and reactive power balance. This eliminates the direct influence of instability and nonlinearity of inductive elements and takes into account electrical, magnetic, mechanical, and harmonic losses using the simplest algorithms. Key variables of the model in the form of total active power and angular velocity are measured directly, and the high nominal efficiency of traction machines (95%) provides a wide tolerance range for calculating total losses.

To better understand the technical challenges of commercial vehicle electrification, BorgWarner converted a production Internal Combustion Engine (ICE) medium duty truck into a fully electrified vehicle. The resulting vehicle includes a newly developed dual-motor rear Beam eAxle driven by a pair of high-performance silicon carbide (SiC) inverters, an 800V battery system, and a new thermal management system customized for the electric vehicle. This paper will detail the conversion process along with the key components involved in the build. The resulting performance of the fully electrified commercial vehicle will be presented in comparison to the original production vehicle. The primary aim is to outline what is entailed in an electric vehicle conversion and to share the learnings gained throughout this build and development process.

Accelerated adoption of electric propulsion system in mobility industry has stressed the time and iterations of product development cycle which was traditionally known to go over multiple iterations and phases. Current market demands a timely introduction of compelling products that brings high value to end user. Further, a growing emphasis over reducing mineral content using sustainable options and process, adds further complexity to multi-objective-optimization of electric drive systems. At BorgWarner our engineers use Digital-Twins, physics-based models which closely represent BorgWarner products in greater dept (physics) thus allowing an improved assessment of product design (components and systems) to target application at very early stage in product development. The spring success with Digital-Twin, BorgWarner furthered enhanced the model through introducing Artificial Intelligent (AI) and Machine Learning (ML) technologies in both modelling and virtual sensing.

The transition towards electrification in commercial vehicles has received more attention in recent years. This paper details the conversion of a production Medium-Duty class-5 commercial truck, originally equipped with a gasoline engine and 10-speed automatic transmission, into a battery electric vehicle (BEV). The conversion process involved the removal of the internal combustion engine, transmission, and differential unit, followed by the integration of an ePropulsion system, including a newly developed dual-motor beam axle that propels the rear wheels. Other systems added include an 800V/99 kWh battery pack, advanced silicon carbide (SiC) inverters, an upgraded thermal management system, and a DC fast charging system. A key part of the work was the development of the propulsion system controls, which prioritized drivability, NVH suppression, and energy optimization.

Wound rotor synchronous machines (WRSM) without rare-earth magnets are becoming more popular for traction applications, but their potential in drive performance has not yet been fully explored. This paper presents a Pulse Width Modulation (PWM) scheme optimization procedure to minimize motor and inverter losses. It leverages different PWM schemes with different PWM switching strategies and switching frequencies. First, a generic PWM-induced motor loss calculation tool developed by BorgWarner is introduced. This tool iteratively calculates motor losses with PWM inputs across the entire operating map, significantly improving motor loss prediction accuracy. The inverter losses are then calculated analytically using motor and wide-bandgap (WBG) switching device characteristics. By quantifying these various scenarios, the optimal PWM scheme for achieving the best system efficiency across the entire operating map is obtained.

Owing to their weight saving potential and improved flexural stiffness, metal-polymer-metal sandwich laminates are finding increasing applications in recent years. Increased use of such laminates for automotive body panels and structures requires not only a better understanding of their mechanical behavior, but also their formability characteristics. This study focuses on the formability of a metal–polymer-metal sandwich laminate that consists of AA5182 aluminum alloy as the outer skin layers and polypropylene (PP) as the inner core. The forming limit curves of Al/PP/Al sandwich laminates are determined using finite element simulations of Nakazima test specimens. The numerical model is validated by comparing the simulated results with published experimental results. Strain paths for different specimen widths are recorded.

Controller Area Network (CAN), the de facto standard for in-vehicle networks, has insufficient security features and thus is inherently vulnerable to various attacks. To protect CAN bus from attacks, intrusion detection systems (IDSs) based on advanced deep learning methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have been proposed to detect intrusions. However, those models generally introduce high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory requests, we exploit the use of Binarized Neural Network (BNN) and hardware-based acceleration for intrusion detection in in-vehicle networks. As BNN uses binary values for activations and weights rather than full precision values, it usually results in faster computation, smaller memory cost, and lower energy consumption than full precision models.

Tubular sections are found in many automotive structural components such as front rails, cross beams, and sub-frames. They are also used in other vehicular structures, such as buses and rails. In many of these components, smaller tubular sections may be joined together using an adhesive to build the required structure. For crash safety applications, it is important that the joined tube sections be able to provide high energy absorption capability and withstand the impact load before the adhesive bond failure occurs. In this study, single lap tubular joints between two aluminum tubes are investigated for their crush performance at both quasi-static and high impact speeds using finite element analysis. A crash optimized adhesive Betamate 1496 is considered. The joint parameters, such as adhesive overlap length, tube diameters and tube lengths, are varied to determine their effects on energy absorption, peak and mean loads, and tube deformation mode.

Semi-trucks, specifically class-8 trucks, have recently become a platform of interest for autonomy systems. Platooning involves multiple trucks following each other in close proximity, with only the lead truck being manually driven and the rest being controlled autonomously. This approach to semi-truck autonomy is easily integrated on existing platforms, reduces delivery times, and reduces greenhouse gas emissions via fuel economy benefits. Level 1 SAE fuel studies were performed on class-8 trucks operating with the Auburn Cooperative Adaptive Cruise Control (CACC) system, and fuel savings up to 10-12% were seen. Enabling platooning autonomy required the use of radar, global positioning systems (GPS), and wireless vehicle-to-vehicle (V2V) communication. Poor measurements and state estimates can lead to incorrect or missing positioning data, which can lead to unnecessary dynamics and finally wasted fuel.

Autonomy for multiple trucks to drive in a fixed-headway platoon formation is achieved by adding precision GPS and V2V communications to a conventional adaptive cruise control (ACC) system. The performance of the Cooperative ACC (CACC) system depends heavily on the reliability of the underlying V2V communications network. Using data recorded on precision-instrumented trucks at both ACM and NCAT test tracks, we provide an understanding of various effects on V2V network performance: Occlusions - non-line-of-sight (NLOS) between the Tx and Rx antenna may cause network signal loss. Rain - water droplets in the air may cause network signal degradation. Antenna position - antennas at higher elevation may have less ground clutter to deal with. RF interference - interference may cause network packet loss. GPS outage - outages caused by tree cover, tunnels, etc. may result in degraded performance. Road curvature - curves may affect antenna diversity.

In frontal collision of vehicles, the front bumper system is the first structural member that receives the energy of collision. In low speed impacts, the bumper beam and the crush cans that support the bumper beam are designed to protect the engine and the radiator from being damaged, while at high speed impacts, they are required to transfer the energy of impact as uniformly as possible to the front rails that contributes to the occupant protection. The bumper beam material today is mostly steels and aluminum alloys, but carbon fiber composites have the potential to reduce the bumper weight significantly. In this study, crash performance of bumper beams made of a boron steel, aluminum alloy 5182 and a carbon fiber composite with steel crush cans is examined for their maximum deflection, load transfer to crush cans, total energy absorption and failure modes using finite element analysis.

The new wireless rotor electrical machine has all the coils placed on the stator. The laminated wireless rotor serves as a magnetic field switcher. A wireless rotor is cold, and it facilitates machine cooling. The model of the wireless rotor machine includes electrical and mechanical components. The wireless rotor machines can find an application in practice because they have advantages over existing devices. In modeling, we use a new approach. We consider a motor as a natural control system with torque feedback. Such an approach simplifies analyses and adjustment of the operation of an electrical machine. First, we analyze an open-loop control system without torque feedback. In this case, by changing the rotor speed under external torque, we can receive the torque-speed motor characteristics. After some adjustments to the open-loop system, we can consider the close loop control system with torque feedback. We use a space vector representation for sinusoidal electric components.

The engine operating point (EOP), which is determined by the engine speed and torque, is an important part of a vehicle's powertrain performance and it impacts FC, available propulsion power, and emissions. Predicting instantaneous EOP in real-time subject to dynamic driver behaviour and environmental conditions is a challenging problem, and in existing literature, engine performance is predicted based on internal powertrain parameters. However, a driver cannot directly influence these internal parameters in real-time and can only accommodate changes in driving behaviour and cabin temperature. It would be beneficial to develop a direct relationship between the vehicle-level parameters that a driver could influence in real-time, and the instantaneous EOP. Such a relationship can be exploited to dynamically optimize engine performance.

A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.

There are many methods developed over the past decade to solve the problem of energy management control for hybrid electric vehicles. A novel method is introduced in this paper to address the same problem which reduces the problem to a set of physical equations and maps. In simple terms, this method directly calculates the actual cost or savings in fuel energy from the generation or usage of electric energy. It also calculates the local optimum electric power that yields higher electric fuel savings (EFS) or lower electric fuel cost (EFC) in the fuel energy that is spent for driving the vehicle (which in general does not take the system to the lowest engine Brake Specific Fuel Consumption (BSFC)). Based on this approach, a control algorithm is developed which attempts to approach the global optimum over a drive cycle.

Roll forming assemblies are generally neat and robust, but there are structural integrity risks involved too if the forming procedures and design features do not complement each other. Specifically, in solenoid valves, an imprecised roller crimping (or forming) adversely affect the desired magnetic performance of the valves. Furthermore, in-depth evaluations of the formed shape using hardware and lab testing are extremely challenging cost wise and time consuming as well. However, utilizing simulation technique such as finite element analysis (FEA) to understand the in-sights of roller formed assemblies of a solenoid valve (or other products) could be an effective way to minimize overall cost and time involved in the product development. Therefore, a three-dimensional non-linear FEA model of roller crimping simulation was established in ANSYS Workbench Mechanical, and the predicted results were correlated with real hardware data to prove the technique & process adopted.

As CO2 legislation tightens, the next generation of turbocharged gasoline engines must meet stricter emissions targets combined with increased fuel efficiency standards. Recent studies have shown that the following technologies offer significant improvements to the efficiency of turbocharged GDI engines: Miller Cycle via late intake valve closing (LIVC), low pressure loop cooled EGR (LPL EGR), port water injection (PWI), and cylinder deactivation (CDA). While these efficiency-improving technologies are individually well-understood, in this study we directly compare these technologies to each other on the same engine at a range of operating conditions and over a range of compression ratios (CR). The technologies tested are applied to a boosted and direct injected (DI) gasoline engine and evaluated both individually and combined.

As CO2 legislation tightens, the next generation of turbocharged gasoline engines must meet stricter emissions targets combined with increased fuel efficiency standards. Promising technologies under consideration are: Miller Cycle via late intake valve closing (LIVC), low pressure loop cooled exhaust gas recirculation (LPL EGR), port water injection (PWI), and cylinder deactivation (CDA). While these efficiency improving options are well-understood individually, in this study we directly compare them to each other on the same engine at a range of operating conditions and over a range of compression ratios (CR). For this purpose we undertake a comprehensive simulation of the above technology options using a GT-Power model of the engine with a kinetics based knock combustion sub-model to optimize the fuel efficiency, taking into account the total in-cylinder dilution effects, due to internal and external EGR, on the combustion.

The diesel engine with direct injection of hydrogen gas has clear advantages over the hydrogen engine with forced ignition of a hydrogen-air mixture. Despite of this, the concept of hydrogen-diesel engine has not investigated until now. In the paper, a detailed study of the working process of hydrogen-diesel engine carried out for the first time. Based on the results of the experimental studies and mathematical modeling, it has established that the behavior of thermo-physical processes in the combustion chamber of hydrogen-diesel engine, in a number of cases, differs fundamentally from the processes that take place in the conventional diesel engines. There have been identified the reasons for their difference and determined the values of the operating cycle parameters of hydrogen diesel engine, which provide the optimal correlation between the indicator values and the environmental performance.