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Electrified powertrains will play a growing role in meeting global fuel consumption and CO2 requirements. In support of this, FCA US has developed its first dedicated hybrid transmission (the eFlite® transmission), used in the Chrysler Pacifica Hybrid. The Chrysler Pacifica is the industry’s first electrified minivan. [2] The new eFlite hybrid transmission architecture optimizes performance, fuel economy, mass, packaging and NVH. The transmission is an electrically variable FWD transaxle with an input split configuration and incorporates two electric motors, both capable of driving in EV mode. The lubrication and cooling system makes use of two pumps, one electrically operated and one mechanically driven. The Chrysler Pacifica has a 16kWh lithium ion battery and a 3.6-liter Pentastar® engine which offers total system power of 260 hp with 84 MPGe, 33 miles of all electric range and 566 miles total driving range. [2] This paper’s focus is on the eFlite transmission.

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 advanced Optimization techniques help us in exploring the light weight architecture. This paper explains the process of designing a lightweight track bar bracket, which satisfies all durability performance targets. The mounting locations and load paths are critical factors that define the performance and help in the development of weight efficient structure. The process is to identify the appropriate bolt location through Design of Experiment (DOE) and topology based studies; followed by section and shape optimization that help to distribute material in a weight efficient manner across the structure. Load path study using topology optimization is performed to identify the load path for durability load cases. Further shape optimization is done using hyper study to determine the exact thickness of the webs and ribs. A significant weight reduction from the baseline structure is observed. This process may be applicable for all casting components.

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.

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.

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.

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.

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.

Threaded fasteners or bolted joints are used extensively in automotive assemblies. There are standard procedures to evaluate joint performance under block cycles or road loads. The deciding load case for such joint design is slippage analysis of the joint. There are studies done to evaluate the theoretical and experimental behavior of these joints. There are different ways of understanding the interaction between the bolt and the nut under different loading scenarios. However, none have provided a satisfactory method of quantifying bolt loosening or loss of clamp load under cyclic loading, where no slippage is observed. Under varying loads, initial relaxation of the joint is followed by a loss of clamping load. Below a critical value, complete loss of clamping load progresses very rapidly and this results in a loose joint.

In this paper, a control strategy for state of charge (SOC) allocation using navigation data for Hybrid Electric Vehicle (HEV) propulsion systems is proposed. This algorithm dynamically defines and adjusts a SOC target as a function of distance travelled on-line, thereby enabling proactive management of the energy store in the battery. The proposed approach incorporates variances in road resistance and adheres to geolocation constraints, including ultra-low emission zones (uLEZ). The anticipated advantages are particularly pronounced during scenarios involving extensive medium-to-long journeys characterized by abrupt topological changes or the necessity for exclusive electric vehicle (EV) mode operation. This novel solution stands to significantly enhance both drivability and fuel economy outcomes.

There is an increasing demand for reducing vehicle development process and minimizing cost due to tough competition in Automotive market. One of the major focus areas is minimizing the vehicle proto build that are required for physical testing during vehicle development. Tow hooks are key structural components for the vehicle, which are designed to withstand structural strength performance under various vehicles towing condition. Typical extreme load scenario for the vehicle can be towing fully loaded vehicle breaks down on uphill road or stuck in wet muddy condition. To exercise the tow hook structural development in early design phase, it is important to have reliable simulation process. This paper focuses on development of virtual test simulation process that replicates the tow hook system test behavior for the operating load. The study includes the detail modeling of clevis load applicator, tow hook, bolt joint and attached test bed plate for capturing the load path.

Damages (fracture) in metals are caused by material degradation due to crack initiation and growth due to fatigue or dynamic loadings. The accurate and realistic modeling of an inelastic behavior of metals is essential for the solution of various problems occurring in engineering fields. Currently, various theories and failure models are available to predict the damage initiation and the growth in metals. In this paper, the failure of aluminum alloy is studied using progressive damage and failure material model using Abaqus explicit solver. This material model has the capability to predict the damage initiation due to the ductile and shear failure. After damage initiation, the material stiffness is degraded progressively according to the specified damage evolution response. The progressive damage models allow a smooth degradation of the material stiffness, in both quasi-static and dynamic situations.

One of the advantages of hybrid vehicles is the ability to operate the engine more optimally at a low brake specific fuel consumption (BSFC) as compared to conventional vehicles. This ability of hybrid vehicles is a major factor contributing to the fuel economy improvement over conventional vehicles. Unlike conventional gasoline powertrains, hybrid powertrains allow engine to be switched off and use battery power to propel vehicles. In order to maintain battery state of charge neutral operation between the start and end of a drive cycle, the net electrical energy consumption from the battery requires to be zero. An optimization algorithm can be developed and calibrated in different ways to achieve net zero battery energy over the cycle. For instance, the engine can be operated at powers higher than the power of the drive cycle to charge the battery. This accumulated energy can be used for all-electric propulsion by turning off the engine.

The automotive industry is moving towards larger SUVs and also electrification is a need to meet the carbon neutrality target. As a result, we see an increase in overall gross vehicle weight (GVW), with the additional weight coming from the HV battery pack, electric powertrain, and other electrical systems. Tow-eye is an essential component that is provided with every vehicle to use for towing during an emergency vehicle breakdown. The tow-eye is usually connected to the retainer/sleeve available in the bumper system and towed using the recovery vehicle or other car with towing provision. Therefore, the tow-eye should meet the functional targets under standard operating conditions. This study is mainly for cars with bumper and tow-eye sleeves made of aluminum which is used in the most recent development of vehicles for weight-saving opportunities. Tow-eye systems in aluminum bumpers are designed to avoid any bending or buckling of the sleeve during towing for whatever the GVW loads.

Currently the On-Board Diagnostics (OBD) requirements enforced by government agencies do not cover electric vehicles. Although the California Air Resources Board (CARB) mandates all light and medium duty vehicles and heavy duty engine dynamometer certified engines equipped with fossil fuel-powered engines, including all hybrid vehicles, must follow the OBD requirements in California Code of Regulation (CCR) 1968.2 and 1971.1, Battery Electric vehicles (BEVs), are exempted from OBD requirements. The legislators, such as CARB, have started to make proposals for on-board systems to monitor electric propulsion system health. In addition, there may be customer needs to obtain standard vehicle service information and the Original Equipment Manufacturers (OEMs) may also have the desire for common diagnostic strategies across different vehicle applications to lower the development costs.

Vibrations affect vehicle occupants and should be prevented early in design process. Powertrain (PT) wiggle is one of the well-known issues. It is the 3rd order lateral vibration, forced by half shaft inner LH/RH plunging tripod joints [1,2]. Lateral PT resonance (7-15Hz) occurs at certain vehicle speed during acceleration and may excite lateral, pitch and roll PT modes. Typically, PT wiggle occurs in speed range of 5-25kph. Vibration is noticeable on driver and passenger seats mostly in lateral direction. The inner half shaft joints are the major source of vibration. Unfortunately, existing MBD tools like Adams [3] are missing detailed tripod joint representation because of complex mechanical interactions inside the joint. At least three sliding contacts between tripod rollers and joint housing, lubricant inside the can and combination of rotation and plunging make the modeling too complicated.

Modern engines have high torque outputs and have low RPM due to increased demand for fuel efficiency. Vibrations caused by such engines have to be mitigated. Decoupling the roll mode from the remaining five rigid body modes results in a response which is predominantly about the torque roll axis (TRA) and helps reduce vibrations. Therefore, placing the mounts on the TRA early in the design phase is crucial. Best NVH performance can be obtained by optimizing the powertrain mount parameters viz; Position, Orientation and Stiffness. Many times, packaging restricts the mounts to be placed about the TRA resulting in degradation in NVH performance. Assuming that the line through the engine mount (Body side) centers is the desired TRA, we propose a novel method of shifting the TRA by adding mass modifying the powertrain inertia such that the new TRA is parallel to and on top to the desired TRA. This in turn will decouple the roll mode and reduce vibrations.

Accurate battery state of charge (SOC) estimation is essential for safe and reliable performance of electric vehicles (EVs). Lithium-ion batteries, commonly used for EV applications, have strong time-varying and non-linear behaviour, making SOC estimation challenging. In this paper, a processor in the loop (PIL) platform is used to assess the execution time and memory use of different SOC estimation algorithms. Four different SOC estimation algorithms are presented and benchmarked, including an extended Kalman filter (EKF), EKF with recursive least squares filter (EKF-RLS) feedforward neural network (FNN), and a recurrent neural network with long short-term memory (LSTM). The algorithms are deployed to two different NXP S32Kx microprocessors and executed in real-time to assess the algorithms' computational load. The algorithms are benchmarked in terms of accuracy, execution time, flash memory, and random access memory (RAM) use.

Most of the applications of magnesium in lightweighting commercial cars and trucks are die castings rather than sheet metal, and automotive applications of magnesium sheet have typically been experimental or low-volume serial production. The overarching objective of this collaborative research project organized by the United States Automotive Materials Partnership (USAMP) was to develop new low-cost magnesium alloys, and demonstrate warm-stamping of magnesium sheet inner and outer door panels for a 2013 MY Ford Fusion at a fully accounted integrated component cost increase over conventional steel stamped components of no more than $2.50/lb. saved ($5.50/kg saved). The project demonstrated the computational design of new magnesium (Mg) alloys from atomistic levels, cast new experimental alloy ingots and explored thermomechanical rolling processes to produce thin Mg sheet of desired textures.

All-wheel Drive (AWD) is a mature technology and most automobile manufacturers offer this feature on their vehicles. Improved traction, enhanced vehicle stability, and better handling are some of the key characteristics of AWD vehicles which are achieved by distributing the appropriate level of torque to the front and rear axles. Accurately capturing the torque split between the two axles is essential for sizing of driveline components like gears, bearings, and shafts. Traditionally, the torque split is considered to be either 50-50%, or solely proportional to the static weight distribution between the two axles. Design decisions are made based on historical test data. In this paper a longitudinal vehicle dynamics model for AWD systems is proposed to understand the influence of various key factors such as dynamic weight transfer, compliance of driveline components, and changing tire radius on the torque split.