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With the proliferation of electric vehicles in the market, it has become important for Automotive OEMs (Original Equipment Manufacturers) to focus on delivering a higher driving range while also maximizing performance. One approach OEMs are actively considering in meeting this goal is to include a secondary drive axle disconnect into the powertrain which has the potential to improve the overall driving range by about 6-8.3% [4]. This paper outlines the need for a novel controls architecture to make the Powertrain controls software modular and to reduce the development time needed to provide robust powertrain control software. To do this, the electrified powertrain torque controls at STELLANTIS NV takes a decentralized controls architecture approach, by separating the axle disconnect controls subsystem (ADCS) from the primary path of torque controls. The ADCS takes in information such as the desired axle state and controls the axle disconnect actuators to achieve that state.

A variety of structures resonate when they are excited by external forces at, or near, their natural frequencies. This can lead to high deformation which may cause damage to the integrity of the structure. There have been many applications of external devices to dampen the effects of this excitation, such as tuned mass dampers or both semi-active and active dampers, which have been implemented in buildings, bridges, and other large structures. One of the active cancellation methods uses centrifugal forces generated by the rotation of an unbalanced mass. These forces help to counter the external excitation force coming into the structure. This research focuses on active force cancellation using centrifugal forces (CFG) due to mass imbalance and provides a virtual solution to simulate and predict the forces required to cancel external excitation to an automotive structure. This research tries to address the challenges to miniaturize the CFG model for a body-on-frame truck.

Vibration shaker testing is a great tool of validating the vibration fatigue performance of automotive components & systems. However, the representative vibration schedule requires a pre-knowledge of the acceleration history for the test object, which usually is not available until the later development phase of a vehicle program when physical properties are available. Sometimes, a generic vibration schedule developed from the worst-case loading profiles are used with risk of lacking correlation with later full vehicle durability test such as Road Test Simulator (RTS) or Proving Ground (PG) road test due to the higher loading amplitude. This paper proposes a virtual accelerometer approach to collect acceleration responses of a component from a virtual vehicle model. First, a multiple body dynamic model will be produced for virtual load calculation over a series of digitalized virtual proving ground road profiles.

Improvement of vehicle path-tracking performance not only affects the vehicle driving safety and comfort but is also essential for autonomous driving technology. The current research focuses on vehicle path-tracking control study and application of dual-motor SBW system. The preview driver model is developed by considering the lateral and yaw tracking. MPC (model predictive control) and LQR (linear quadratic regulator) path following controllers are developed to compare the tracking control performance. A steer-by-wire (SBW) system of dual-motor configuration is designed with permanent magnet synchronous motor (PMSM) control scheme. Finally, the proposed control methods are verified with different driving cases, which shows that the system can effectively achieve small tracking errors in the simulation, and also can be applied in the future autonomous driving or advanced driver assistance system to maintain the lateral and yaw errors within a safe range during path-tracking.

Automotive thermal systems are becoming complicated each year. The powertrain efficiency improvement initiatives are driving transmission and engine oil heaters into coolant network design alternatives. The initiatives of electrified and autonomous vehicles are making coolant networks even more complex. The coolant networks these days have many heat exchangers, electric water pumps and valves, apart from typical radiators, thermostat and heater core. Some of these heat exchangers, including cabin heaters deal with very small amount of coolant flow rates at different ambient conditions. This paper describes how viscosity can be a major reason for simulation inaccuracy, and how to deal with it for each component in the coolant network. Both experimental and computational aspects have been considered in this paper with wide range of ambient temperatures.

Recently, the automotive industry has experienced rapid growth in powertrain electrification, with more and more battery electric vehicles (BEV) and hybrid electric vehicles being launched. Lithium-ion batteries play an important role due to their high energy capacity and power density, however they experience high heat generation in their operation, and if not properly cooled it can lead to serious safety issues as well as lower performance and durability. In that way, good prediction of a battery behavior is crucial for successful design and management. This paper presents a 1D electrochemical model development of a 144 Ah prismatic rolled cell using the GT-Autolion software with a pseudo 2D approach. The model correlation is done at cell level comparing model results and test data of cell open circuit voltage at different temperatures and voltage and temperature profile under different C-rates and ambient temperatures.

The key hurdles to achieving wide consumer acceptance of battery electric vehicles (BEVs) are weather-dependent drive range, higher cost, and limited battery life. These translate into a strong need to reduce a significant energy drain and resulting drive range loss due to auxiliary electrical loads the predominant of which is the cabin thermal management load. Studies have shown that thermal sub-system loads can reduce the drive range by as much as 45% under ambient temperatures below −10 °C. Often, cabin heating relies purely on positive temperature coefficient (PTC) resistive heating, contributing to a significant range loss. Reducing this range loss may improve consumer acceptance of BEVs. The authors present a unified thermal management system (UTEMPRA) that satisfies diverse thermal and design needs of the auxiliary loads in BEVs.

This paper introduces a novel approach to modeling Torque Converter (TC) in conventional and hybrid vehicles, aiming to enhance torque delivery accuracy and efficiency. Traditionally, the TC is modelled by estimating impeller and turbine torque using the classical Kotwicki’s set of equations for torque multiplication and coupling regions or a generic lookup table based on dynamometer (dyno) data in an electronic control unit (ECU) which can be calibration intensive, and it is susceptible to inaccurate estimations of impeller and turbine torque due to engine torque accuracy, transmission oil temperature, hardware variation, etc. In our proposed method, we leverage an understanding of the TC inertia – torque dynamics and the knowledge of the polynomial relationship between slip speed and fluid path torque. We establish a mathematical model to represent the polynomial relationship between turbine torque and slip speed.

As more electric vehicles (BEV, HEV, PHEV, etc.) are adopted in the upcoming decades, it is becoming increasingly important to conduct vehicle-level thermal simulations under different drive-cycle conditions while incorporating the various subsystem thermal losses. Thermal management of the various heat sources in the vehicle is essential both in terms of ensuring passenger safety as well as maintaining all the subsystems within their corresponding safe temperature limits. It is also imperative that these thermal simulations include energy consumption prediction, while considering the effect of battery degradation both in terms of increased thermal losses as well as reduction in the vehicle’s range. For this purpose, a three-dimensional transient thermal analysis framework was coupled with an electrochemical P2D-based battery model and a vehicle dynamics model to test different scenarios and their effect on a hybrid vehicle’s range and the lithium-ion battery life.

A DC-to-AC main Power Inverter Module (PIM) is one of the key components in electrified powertrain systems. Accurate thermal modeling and temperature prediction of a PIM is critical to the design, analysis, and control of a cooling system within an electrified vehicle. PIM heat generation is a function of the electric loading applied to the chips and the limited heat dissipation within what is typically compact packaging of the Insulated Gate Bipolar Transistor (IGBT) module inside the PIM. This work presents a thermal modeling approach for a 3-phase DC/AC PIM that is part of an automotive electrified powertrain system. Heat generation of the IGBT/diode pairs under electric load is modeled by a set of formulae capturing both the static and dynamic losses of the chips in the IGBT module. A thermal model of the IGBT module with a simplified liquid cooling system generates temperature estimates for the PIM.

Material formability is a very important aspect in the automotive stamping, which must be tested for the success of manufacturing. One of the most important sheet metal formability parameters for the stamping is the edge tear-ability. In this paper, a novel test method has been present to test the aluminum sheet edge tear-ability with 3D digital image correlation (DIC) system. The newly developed test specimen and fixture design are also presented. In order to capture the edge deformation and strain, sample's edge surface has been sprayed with artificial speckle. A standard MTS tensile machine was used to record the tearing load and displacement. Through the data processing and evaluation of sequence image, testing results are found valid and reliable. The results show that the 3D DIC system with double CCD can effectively carry out sheet edge tear deformation. The edge tearing test method is found to be a simple, reliable, high precision, and able to provide useful results.

Automobile companies recognized the need to understand how customers use their vehicles. To this end, telediagnostic modules were installed on fleet vehicles, with the consent of the owner, to collect and store usage data. This data was uploaded to a server when the module was able to communicate via Wi-Fi. The volume of data is enormous. The size of a single vehicle file can be over six gigabytes, contain millions of records, and contain hundreds of millions of measurements. Each vehicle needs to be analyzed and the results from different vehicles combined to determine typical and extreme vehicle usage. With hundreds or thousands of vehicles to be analyzed, the analytic task is daunting. To analyze continuous data, like speed, frequency histograms have been used. It will be shown here that the cumulative percentile histogram provides better information and can display single and multiple vehicle usage patterns.

Draw beads are widely used in the binder of a draw die for regulating the restraining force and control the draw-in of a metal blank. Different sheet materials and local panel geometry request different local draw bead configurations. Even the majority of draw bead is single draw bead, the alternative double draw bead does have its advantages, such as less bending damage may be brought to the sheet material and more bead geometry features available to work on. In this paper, to measure the pulling force when a piece of sheet metal passing through a draw bead on an inclined binder, the AA5XXX and AA6XXX materials were tested and its strain were measured with a digital image correlation (DIC) system. Five different types of double bead configurations were tested. The beads are installed in a Stretch-Bend-Draw-System (SBDS) test device. The clearance between a male and a female bead is 10% thicker than the sheet material. A tensile machine was used to record the pulling force.

This research studies the transformation kinetics of austempered ductile iron (ADI) with and without nickel as the main alloying element. ADI has improved mechanical properties compared to ductile iron due to its ausferrite microstructure. Not only can austempered ductile iron be produced with high strength, high toughness and high wear resistance, the ductility of ADI can also be increased due to high carbon content austenite. Many factors influence the transformation of phases in ADI. In the present work, the addition of nickel was investigated based on transformation kinetics and metallography observation. The transformation fractions were determined by Rockwell hardness variations of ADI specimens. The calculation of transformation kinetics and activation energy using the “Avrami Equation” and “Arrhenius Equation” is done to describe effects of nickel alloy for phase reactions.

Automotive hybrid vehicle controls development is an increasingly complex and challenging task. Therefore, to adequately verify and validate the control algorithms prior to its deployment onto real world testing platforms a robust, scalable, low-maintenance simulation platform is most necessary. The currently available test properties pose major challenges in setup, accessibility, maintenance, complexity, and reusability. The aim of this paper is to present a systematic approach of the initial setup, the adaptation to a vehicle program, and the maintenance of a purely MATLAB/Simulink based simulation platform that alleviates the concerns highlighted above. The platform follows the approach of a level 1 virtualization platform for production intent application software components - without the Run-Time Environment (RTE), Basic Software (BSW), and Microcode Abstraction (MCAL) layers.

For many automotive systems it is required to calculate both the durability performance of the part and to rule out the possibility of collision of individual components during severe base shake vibration conditions. Advanced frequency domain methods now exist to enable the durability assessment to be undertaken fully in the frequency domain and utilizing the most advanced and efficient analysis tools (refs 1, 2, 3, 4, 5). In recent years new capabilities have been developed which allow hyper-sized models with multiple correlated loadcases to be processed. The most advanced stress processing (eg, complex von-Mises) and fatigue algorithms (eg, Strain-Life) are now included. Furthermore, the previously required assumptions that the loading be stationary, Gaussian and random have been somewhat relaxed. For example, mixed loading like sine on random can now be applied.

During development of new vehicles, CAE driven optimizations are helpful in achieving the optimal designs. In the early phase of vehicle development there is an opportunity to explore shape changes, gage reduction or alternative materials as enablers to reduce weight. However, in later phases of vehicle development the window of opportunity closes on most of the enablers discussed above. The paper discusses a simplified methodology for reducing the weight in design cycle for truck frames using parametric Design of Experiments (DOE). In body-on-frame vehicles, reducing the weight of the frame in the design cycle without down gaging involves introducing lightening holes or cutouts while still maintaining the fatigue life. It is also known that the lightening holes might cause stress risers and be detrimental to the fatigue life of the component. Thus the ability to identify cutout locations while maintaining the durability performance becomes very critical.

Battery state-of-charge (SOC) is critical information for the vehicle energy management system and must be accurately estimated to ensure reliable and affordable electrified vehicles (xEV). However, due to the nonlinear temperature, health, and SOC dependent behaviour of Li-ion batteries, SOC estimation is still a significant automotive engineering challenge. Traditional approaches to this problem, such as electrochemical models, usually require precise parameters and knowledge from the battery composition as well as its physical response. In contrast, neural networks are a data-driven approach that requires minimal knowledge of the battery or its nonlinear behaviour. The objective of this work is to present the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The method includes a description of data acquisition, data preparation, development of an FNN, FNN tuning, and robust validation of the FNN to sensor noise.

The automotive door structure experience various static and dynamic loading conditions while going through an opening and closing operation. A typical swing door is attached to the body with two hinges and a check strap. These mechanisms carry the loads while the door is opened. Similarly, while closing the door, the latch/striker mechanism along with the seal around the periphery of the door react all loads. Typically, computer aided engineering (CAE) simulations are performed considering a nominal manufacturing (or build) tolerance condition, that results in one loading scenario. But while assembling the door with the body, the build variations in door mechanisms mentioned above can result in different loading scenarios and it should be accounted for design evaluation. This paper discusses various build tolerances and its effect on door durability performances to achieve a robust door design.

The purpose of this paper is to provide a comparison of multiaxial fatigue limit models and their correlation to experimental data. This paper investigates equivalent stress, critical plane and invariant-based multiaxial fatigue models. Several methods are investigated and compared based on ability to predict multiaxial fatigue limits from data published in literature. The equivalent stress based model developed by Lee, Tjhung and Jordan (LTJ), provides very accurate predictions of the fatigue limit under multiaxial loading due to its ability to account for non-proportional loading. This accuracy comes from the model constant which is calculated based on multiaxial fatigue data. This is the only model investigated that requires multiaxial fatigue testing to generate the model parameters. All other models rely on uniaxial test results.