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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.

In the light duty diesel segment, the need persists for an advanced control system to monitor the health of an aftertreatment system throughout a vehicle’s life in order to maintain compliance with ever tightening emissions levels. In on-board diagnostics (OBD), every diagnostic is validated during development stages to detect when a system under monitoring of that diagnostic has failed. This necessitates the need to create parts which represent a failure that would be observed on the vehicle. These failed parts, referred to as limit or threshold parts, are developed through a limit part creation process. Although there are commonalities amongst Original Equipment Manufacturers (OEM), each OEM has their own detection logic which will require a unique and specific limit part. Various methods exist for creating these limit parts, and each method produces a different combination of ability to detect the failure and its associated tailpipe emissions.

Three-dimensional transient numerical simulations are conducted to study the oil flow in a four-cylinder internal-combustion engine while it operates without its oil filler cap on. The emphasis of the study is on analyzing the consequential oil ejection through the oil-cap open boundary. Navier-Stokes equations are solved together with the multiphase Volume of Fluid (VOF) model and the k-ϵ turbulence model. The engine crank shaft is mechanically connected to two cam shafts through a chain, which operates below the oil-filler duct. A baffle is located between the chain and the duct, shielding the latter to minimize oil ejection and potential spills. The chain geometry and dynamics are captured accurately through volume remesh and conformal mapping techniques. The motion of the four pistons, crank shaft, and two cam shafts is also considered. Retaining all these mechanical and geometrical details in the simulations is essential to obtain accurate oil ejection results.

Cargo box is one of the indispensable structures of a pickup truck which makes it capable of transporting heavy cargo weights. This heavy cargo weight plays an important role in durability performance of the box structure when subjected to road load inputs. Finite element representation for huge cargo weight is always challenging, especially in a linear model under dynamic proving ground road load durability analysis using a superposition approach. Any gap in virtual modeling technique can lead to absurd cargo box modes and hence durability results. With the existing computer aided engineering (CAE) approach, durability results could not correlate much with physical testing results. It was crucial to have the right and robust CAE modeling technique to represent the heavy cargo weight to provide the right torsional and cargo modes of the box structure and in turn good durability results.

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.

Stringent requirements for high fuel economy and energy efficiency mandate using increasingly complex vehicle thermal systems in most types of electrified vehicles (xEVs). Enabling the maximum benefits of such complex thermal systems under the full envelope of their operating modes demands designing complex thermal control systems. This is becoming one of the most challenging problems for electrified vehicles. Typically, the thermal systems of such vehicles have several modes of operation, constituting nonlinear multiple-input/multiple-output (MIMO) dynamic systems that cannot be efficiently controlled using classical or rule based strategies. This paper covers the different steps towards the design of a model-based control (MBC) strategy that can improve the overall performance of xEV thermal control systems. To achieve the above objective, the latter MBC strategy is applied to control cooling of the cabin and high voltage battery.

A critical market driver for rear axle lubricants continues to be the improved fuel efficiency, which is related to improvements in power transfer efficiency. Power transfer efficiency improvements are achieved with a reduction in the kinematic viscosity (KV) of rear axle lubricants. General Motors (GM) recently reduced the recommended viscosity grade for their rear axle lubricants from the Society of Automotive Engineers standard (SAE) 75W-90 to SAE 75W-85. This reduction in viscosity continues to require the optimization of rear axle lubricants to ensure durability. Lubricants that form thick elastohydrodynamic (EHD) films and are shear stable even when lower kinematic viscosities are required. This work depicts how a rear axle lubricant was developed and improved with the proper selection of base oil and polymer. This newly developed SAE 75W-85 rear axle fluid was incorporated as factory fill in 2019 in T1 LDPU-GMC Sierra and Chevrolet Silverado 1500 series pickup trucks.

Autoignition delay times of two full blend gasoline fuels (high and low RON) were explored in a rapid compression machine. CO2 dilution by mass was introduced at 0%, 15%, and 30% levels with the O2:N2 mole ratio fixed at 1:3.76. This dilution strategy is used to represent exhaust gas recirculation (EGR) substitution in spark ignition (SI) engines by using CO2 as a surrogate for major EGR constituents(N2, CO2, H2O). Experiments were conducted over the temperature range of 650K-900K and at 10 bar and 20 bar compressed pressure conditions for equivalence ratios of (Φ =) 0.6-1.3. The full blend fuels were admitted directly into the combustion chamber for mixture preparation using the direct test chamber (DTC) approach. CO2 addition retarded the autoignition times for the fuels studied here. The retarding effect of the CO2 dilution was more pronounced in the NTC region when compared to the lower and higher temperature range.

Accurate prediction of in-vehicle powertrain bounce mode is necessary to ensure optimum responses are achieved at driver’s touch points during 4post shake or rough road shake events. But, during the early stages of vehicle development, building a detailed vehicle finite element (FE) model is not possible and often powertrain bounce modes are computed assuming the powertrain to be a stand-alone unit. Studies conducted on FE models of a large SUV with body on frame architecture showed that the stand-alone approach overestimates the powertrain bounce mode. Consequently, there is a need for a simplified version of vehicle model which can be built early on to compute powertrain modes. Previously, representing all the major components as rigid entities, simplified unibody vehicle models have been built to compute powertrain modes. But such an approach would be inaccurate here, for a vehicle with body on frame architecture due to the flexible nature of the frame (even at low frequencies).

This paper will communicate an in-depth investigation uncovering contributing factors defining the desired and undesired acoustic signature of a V6 Vehicle. A transfer path analysis tool is exercised to rank improvement opportunities. These results are used to drive design improvements with the goal of achieving best-in-class sound quality when executed as a system. A cohesive powertrain-vehicle-level acoustic improvement package is executed, improving air induction, intake manifold, both structure and air-core, exhaust-radiated and under-hood-acoustic encapsulation. The acoustic package was validated by jury testing to provide significant refinement enhancement improving predicted 3rd party scores.

As the move to decrease physical prototyping increases the need to virtually prototype vehicles become more critical. Assessing NVH vehicle targets and making critical component level decisions is becoming a larger part of the NVH engineer’s job. To make decisions earlier in the process when prototypes are not available companies need to leverage more both their historical and simulation results. Today this is possible by utilizing a hybrid modelling approach in an NVH Simulator using measured on road, CAE, and test bench data. By starting with measured on road data from a previous generation or comparable vehicle, engineers can build virtual prototypes by using a hybrid modeling approach incorporating CAE and/or test bench data to create the desired NVH characteristics. This enables the creation of a virtual drivable model to assess subjectively the vehicles acoustic targets virtually before a prototype vehicle is available.

For flows around a tire rotating over a ground plane, the Reynolds number is probably the most important parameter influencing the transition mechanism leading to flow separation from the tire surface, as it determines the viscous response of the boundary layer in the vortex-wall interaction. The present work investigates the effects of Reynolds number on an isolated tire model using a commercial Computational Fluid Dynamics (CFD) code. It validates the baseline simulation for this purpose against the Particle Image Velocimetry (PIV) data from Stanford University got using a Toyota Formula 1 race car tire model. Time-resolved velocity fields and vortex structures from the PIV data are used to correlate local and global flow phenomena to identify unsteady boundary-layer separation and the subsequent flow structures. The study will highlight the pre to post critical flow regimes where the aero coefficients and vortex structure will be studied.

Tire modeling has been an area of major research in automotive industries as the tires cause approximately 25% of vehicle drag. With the fast-paced growth of computational resources, Computational Fluid Dynamics (CFD) has evolved as an effective tool for aerodynamic design and development in the automotive industry. One of the main challenges in the simulation of the aerodynamics of tires is the lack of a detailed and accurate experimental setup with which to correlate. In this study, the focus is on the prediction of the aerodynamics associated with an isolated rotating Formula 1 tire and brake assembly. Literature has indicated differing mechanisms explaining the dominant features such as the wake structures and unsteadiness. Limited work has been published on the aerodynamics of a realistic tire geometry with specific emphasis on advanced turbulence closures such as the Detached Eddy Simulation (DES).

Strength and fatigue assessment of chassis components are essentially influenced by the material used and manufacturing processes chosen. The manufacturing process of chassis components decides the variation in the mechanical properties of the component, which has an impact on the strength/fatigue performance. Investigating the design concerning the manufacturing processes is vital to the industry. Standard computer aided engineering (CAE) procedures for validating the alloy wheels usually consider the material properties as homogeneous. There was a gap between test results and CAE durability prediction (as per standard procedure). Incorporating the manufacturing process related characteristics with the strength simulation will be a viable solution to reduce this gap. This study was intended at developing a procedure for the strength analysis of an alloy wheel by considering the manufacturing process.

Euro NCAP committee has created the Mobile Progressive Deformable Barrier (MPDB) “Compatibility” test that could change the way we design the vehicle front structure for impact [4]. To assist the crashworthy design development activity for this new mode of impact test, CAE barrier models [2] have been developed and used by vehicle safety CAE engineers. These impact models are designed to generate the barrier deformation data essential for evaluation of the scores of the two rating parameters of “Standard Deviation”, “Bottom-Out” for the MPDB impact event. In test, a physical 3-D scanner measures the barrier deformation depth and draws contour plot necessary for determining above two rating parameters. For model results assessment, a virtual scanner, which can emulate the measurement accuracy of the physical scanner is required.

This paper focuses on the conjugate heat transfer analysis of an I4 engine, and discusses optimization of the coolant passages in engine coolant jackets. Direct Optimization approach integrates an optimizer with the numerical solver. This method of optimization is compared with a metamodel-based optimization in which a metamodel is generated to aid in finding an optimal design. The direct optimization and metamodel approaches are compared in terms of their accuracy, and execution time.

In this study we collect and analyze data on how hands-free automated lane centering systems affect the controllability of a hazardous event during an operational situation by a human operator. Through these data and their analysis, we seek to answer the following questions: Is Level 2 and Level 3 automated driving inherently uncontrollable as a result of a steering failure? Or, is there some level of operator control of hazardous situations occurring during Level 2 and Level 3 automated driving that can reasonably be expected, given that these systems still rely on a driver as the primary fall back. The controllability focus group experiments were carried out using an instrumented MY15 Jeep® Cherokee with a prototype Level 2 automated driving system that was modified to simulate a hands-free steering system on a closed track with speeds up to 110kph. The vehicle was also fitted with supplemental safety measures to ensure experimenter control.

The complexities involved in an optimization problem at a system level require knowledge base that has information on different approaches and customization of these approaches to a specific class of the optimization problems. One approach that is commonly used is the metamodel based design optimization. The metamodel is 1) a conceptual model for capturing, in abstract terms, essential characteristics of a given optimization problem, and 2) a schema of sufficient formality to enable the problem modeled to be serialized to statements in a concrete optimization language [1]. Optimization is performed based on this metamodel. This metamodel approach has been proven effective and accurate in providing the global optimum. Depending upon the computational hardware availability in an organization, the metamodel based optimization could be much faster way of achieving the optimized solution. However, the accuracy of the optimization is highly dependent on the quality of metamodel generated.

The Press-in-Place (PIP) gasket is a static face seal with self-retaining feature, which is used for the mating surfaces of engine components to maintain the reliability of the closed system under various operating conditions. Its design allows it to provide enough contact pressure to seal the internal fluid as well as prevent mechanical failures. Insufficient sealing pressure will lead to fluid leakage, consequently resulting in engine failures. A test fixture was designed to simulate the clamp load and internal pressure condition on a gasket bolted joint. A sensor pad in combination with TEKSCAN equipment was used to capture the overall and local pressure distribution of the PIP gasket under various engine loading conditions. Then, the test results were compared with simulated results from computer models. Through the comparisons, it was found that gasket sealing pressure of test data and CAE data shows good correlations in all internal pressure cases when the bolt load was 500 N.

This paper will focus on automotive development highlights of FCA US LLC magnesium intensive closures components. Deploying lightweight materials is one of many key strategies that has been implemented to reduce vehicle mass and improve overall fuel economy while maintaining rigorous functional objective performance. This paper will outline some basic design and manufacturing considerations for magnesium closures. The development of the 2017 Chrysler Pacifica liftgate and 2018 Jeep® Wrangler swing gate along with the two generations of magnesium spare tire brackets will be the focus.