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Wire shorts on an in-vehicle controller area network (CAN) impact the communication between electrical control units (ECUs), and negatively affects the vehicle control. The fault, especially the intermittent fault, is difficult to locate. In this paper, an equivalent circuit model for in-vehicle CAN bus is developed under the wire short fault scenario. The bus resistance is estimated and a resistance-distance mapping approach is proposed to locate the fault. The proposed approach is implemented in an Arduino-based embedded system and validated on a vehicle frame. The experimental results are promising. The approach presented in this paper may reduce trouble shooting time for CAN wire short faults and may enable early detection before the customer is inconvenienced.

Development of the next generation internal combustion engines and automatic transmissions for automotive applications is a mandatory powertrain engineering activity required now and in the coming years to meet forthcoming global emissions regulations. This paper details a preliminary investigation into possible synergies for fuel consumption reduction considering emerging automotive technologies integrated into the next generation combustion engine and automatic transmission architectures. A range of hypothetical gasoline engines were created and paired with a generalized set of step gear automatic transmissions designed to meet the performance requirements of high volume longitudinal full size truck application. These designs were then run through a design of experiments orthogonal array for prediction of fuel consumption on the WLTP test schedule and stand still acceleration to 100 kph.

New seal cross-section development is a very tedious and time consuming process if conventional analysis methods are used, as it is very difficult to predict the dimensions of the seal that will satisfy the sealing performance targets. In this study, a generic cross-section is defined and the design constraints are specified. Isight then runs the FEA model, utilizing a custom python script for post-processing. Isight then updates the dimensions of the seal and continues running analyses. Isight was run using two different design exploration techniques. The first was a design of experiments (DOE) to discover how the seal’s response varies with its dimensions. Then, after the analyst examined the results, Isight was run in optimization mode focusing on feasible design areas as determined from the DOE. Thus, after the initial model setup, the user can run the analyses in the background and only needs to interact with the program after Isight has determined a list of feasible designs.

The quality of material model input files for finite element analysis (FEA) is a fundamental factor governing the fidelity and accuracy of simulations at a sub-system or a vehicle level, dictating an investment of due diligence in developing and validating the material models. Several material models conventionally employed for FEA typically allow accounting for only uniaxial tensile behavior of the material; however, the models may be required to predict component-level response in a complex loading scenario. Therefore in developing LSDYNA material input files for such models, it becomes critical to validate their performance in alternative loading scenarios. For out-ofplane loading, typically a three or four-point bending load-case is used for validation. Simulating three point bending (TPB), particularly in the quasi-static regime, requires detailed representation of the moving pin impacting the specimen, and sliding of the specimen on the stationary pins.

The electric vehicle on-board charger (OBC) is responsible for converting AC grid energy to DC energy to charge the battery pack. This paper describes the development of GM’s second generation OBC used in the 2016 Chevrolet Volt. The second generation OBC provides significant improvements in efficiency, size, and mass compared to the first generation. Reduced component count supports goals of improved reliability and lower cost. Complexity reduction of the hardware and diagnostic software was undertaken to eliminate potential failures.

Mahalanobis Distance (MD) is gaining momentum in many fields where classification, statistical pattern recognition, and forecasting are primary focus. It is a multivariate method and considers correlation relationships among parameters for computing generalized distance measure to separate groups or populations. MD is a useful statistic in multivariate analysis to test that an observed random sample is from a multivariate normal distribution. This capability alone enables engineers to determine if an observed sample is an outlier (defect) that falls outside the constructed (good) multivariate normal distribution. In Mahalanobis-Taguchi System (MTS), MD is suitably scaled and used as a measure of severity in abnormality assessment. It is obvious that computed MD depends on values of parameters observed on a random sample. All parameters may not equally impact MD. MD could be highly sensitive with respect to some parameters and less sensitive to some other parameters.

Several application developers are currently faced with the problem of moving a complex system from a single-core to a multicore platform. The problem encompasses several issues that go from modeling issues (the need to represent the system features of interest with sufficient accuracy) to analysis and optimization techniques, to the selection of the right formulations for constraints that relate to time. We report on the initial findings in a case study in which the application of interest is a fuel injection system. We provide an analysis on the limitations of AUTOSAR and the existing modeling tools with respect to the representation of the parameters of interest for timing analysis, and we discuss applicable optimization methods and analysis algorithms.

A GT-Power Fast Run Model simplified from detail model for HIL is verified with a bench test using the dSPACE Simulator. Firstly, the conversion process from a detailed model to FRM model is briefly described. Then, the spark timing, fuel pulse with control for FAR, and torque level control are developed for proof of concept. Moreover a series of FRM/Simulink co-simulation and HIL tests are conducted. In the summary, the test results are presented and compared with GT detailed model simulations. The test results show that the FRM/dSPACE HIL stays consistent in most variables of interest under 0.7-0.9 real-time factor condition between 1000 - 5000 RPM. The same steady-state can be reached by RCP controllers or with GT-Power internal controllers. The transient states are close using different control algorithm. The main purpose of HIL application is achieved, despite inconsistencies in performance data like fuel consumption.

A permanent magnet synchronous motor (PMSM) motor is used to design the propulsion system of GM’s Chevrolet Bolt battery electric vehicle (BEV). Magnets are buried inside the rotor in two layer ‘V’ arrangement. The Chevrolet Bolt BEV electric machine rotor design optimizes the magnet placement between the adjacent poles asymmetrically to lower torque ripple and radial force. Similar to Chevrolet Spark BEV electric motor, a pair of small slots are stamped in each rotor pole near the rotor outer surface to lower torque ripple and radial force. Rotor design optimizes the placement of these slots at different locations in adjacent poles providing further reduction in torque ripple and radial force. As a result of all these design features, the Chevrolet Bolt BEV electric motor is able to meet the GM stringent noise and vibration requirements without implementing rotor skew, which (rotor skew) lowers motor performance and adds complexity to the rotor manufacturing and hence is undesirable.

GM has developed an all-new gasoline-electric hybrid powertrain for the model year 2016 Chevrolet Malibu Hybrid vehicle, which was designed to achieve excellent fuel economy, performance, and drive quality. The powertrain shares the transmission architecture with the 2016 Chevrolet Volt extended range electric vehicle, but includes changes to optimize the system for engine driven charge sustaining operation in the range of conditions represented by the US EPA 5 cycle fuel economy tests. In this paper, we describe the Malibu Hybrid propulsion system features and components, including the battery pack, transaxle, electric motors and power electronics, engine, and thermal system. The modifications between the Volt and Malibu Hybrid propulsion systems are discussed and explained as resulting from the differences between the primarily electric and gasoline powered applications.

Building on the experience of the Chevrolet Spark EV battery electric vehicle, General Motors (GM) has developed a propulsion system with increased capability for its next generation Chevrolet Bolt EV. It propels a new larger electric vehicle with significantly greater electric driving range. Through extensive analysis the primary propulsion system components, which include the drive unit, traction electric motor, power electronics, energy storage, and on-board charging module, were optimized individually and as an integrated system to deliver improvements in propulsion system energy, power, torque and efficiency. The results deliver outstanding EV range and fun-to-drive acceleration performance.

Response Surface Models are often used as a surrogate for expensive black-box functions during optimization to reduce computational cost. Often, the CAE analysis models are highly nonlinear and multi-modal. A response surface approximation of such analysis as a result is highly multi-modal; i.e. it contains multiple local optima. A gradient-based optimizer working with such a response surface will often converge to the nearest local optimum. There does not exist any method to guarantee a global optima for non-convex multi-modal functions. For such problems, we propose an efficient algorithm to find as many distinct local optima as possible. The proposed method is specifically designed to work in large dimensions (about 100 ∼ 1000 design variables and similar number of constraints) and can identify most of the locally optimal solutions in a reasonable amount of time.

This investigation utilizes energy analysis and statistical methods to optimize step gear automatic transmissions gear selection for fuel consumption. A full factorial matrix of simulations using energy analysis was performed to determine the optimal number of gears and gear ratios that provide the best fuel consumption performance for a particular vehicle - engine application. The full factorial matrix setup as a design of experiment (DOE) was applied to five vehicle applications, each with two engines to examine the potential differences that variations in road load and engine characteristics might have on optimal transmission gearing selection. The transmission gearing options considered in the DOE were number of gears, launch gear ratio and top gear ratio. Final drive ratio was also included due to its global influence on vehicle performance and powertrain operating speeds and torque.

A new approach for modeling and analysis of a transmission and driveline system is proposed. By considering the stiffness, damping and inertias, model equations based on lumped parameters can be created through standard Lagrangian Mechanics techniques. A sensitivity analysis method has then been proposed on the eigenspace of the system characteristic equation to reveal the dynamic nature of a transmission and driveline system. The relative sensitivity calculated can clearly show the vibration modes of the system and the key contributing components. The usefulness of the method is demonstrated through the GM 6-speed RWD transmission by analyzing the dynamic nature of the driveline system. The results can provide a fundamental explanation of the vibration issue experienced and the solution adopted for the transmission.

Evaluation of one year of in-use operating data from first generation Chevrolet Volt Extended-Range Electric Vehicle (E-REV) retail customers determined trip initial Internal Combustion Engine (ICE) starts were reduced by 70% relative to conventional vehicles under the same driving conditions. These Volt drivers were able to travel 74% of their total miles in EV without requiring the ICE's support. Using this first generation Volt data, performance of the second generation Volt is projected. The Southern California Association of Governments (SCAG) Regional Travel Survey (RTS) data set was also processed to make comparisons between realistic PHEV constraints and E-REV configurations. A Volt characteristic E-REV was found to provide up to 40 times more all-electric trips than a PHEV over the same data set.

Spot weld separation in vehicle development stage is one of the critical phenomena in structural analyses regarding quasi-static test condition, like roof strength or seat/belt pull. It directly reduces structural performance by losing connected load path and occasionally introduces tearing on surrounding sheet metals. Traditionally many efforts have been attempted to capture parent metal ductile fracture, but not applied to spot weld separations in automotive FEA simulations. [1,2,3] This paper introduces how to develop FFLD failure criteria from a series of parametric study on ultra high strength sheet steel and deals with failure criteria around spot weld and parent metal. Once the fracture strains for sheet steels are determined, those developed values were applied to traditional spot weld coupon FEA simulations and tests. Full vehicle level roof strength FEA simulations on a typical automotive body structure were performed and verified to the physical tests.

Research Council for Automotive Repairs (RCAR) has developed a bumper test at 10 km/h to assess the damageability and repairing cost during a low speed collusion. For minimum damage and minimum repairing cost during low speed collusion it is necessary to design a bumper beam which provides structural stiffness and reduced deflection. Often it is challenging to design a front bumper beam to meet all safety requirements including, RCAR, high speed offset barrier and pedestrian protection, since these requirements are not necessarily compatible with each other. Design changes in rails and packaging constraints add to this challenge. In this study, design of six sigma (DFSS) and finite element analysis are used to study the parameters that affect the stiffness and deflection of the front bumper beam.

The present paper reports on a study of the HVAC energy usage for an EREV (extended range electric vehicle) implementation of a localized cooling/heating system. Components in the localized system use thermoelectric (TE) devices to target the occupant's chest, face, lap and foot areas. A novel contact TE seat was integrated into the system. Human subject comfort rides and a thermal manikin in the tunnel were used to establish equivalent comfort for the baseline and localized system. The tunnel test results indicate that, with the localized system, HVAC energy savings of 37% are achieved for cooling conditions (ambient conditions greater than 10 °C) and 38% for heating conditions (ambient conditions less than 10 °C), respectively based on an annualized ambient and vehicle occupancy weighted method. The driving range extension for an electric vehicle was also estimated based on the HVAC energy saving.

In this paper, we propose algorithms for cost minimization of physical wires that are used to connect electronic devices in the vehicle. The wiring cost is one of the most important drivers of electrical architecture selection. Our algorithms perform wire routing from a source device to a destination device through harnesses, by selecting the optimized wire size. In addition, we provide optimized splice allocation with limited constraints. Based on the algorithms, we develop a tool which is integrated into an off-the-shelf optimization and workflow system-level design tool. The algorithms and the tool provide an efficient, flexible, scalable, and maintainable approach for cost analysis and architecture selection.

The General Motors (GM) 1ET35 drive unit is designed for an optimum combination of efficiency, performance, reliability, and cost as part of the propulsion system for the 2014 Chevrolet Spark Electric Vehicle (EV) [1]. The 1ET35 drive unit is a coaxial transaxle arrangement which includes a permanent-magnet (PM) electric motor and a low loss single-planetary transmission and is the sole source of propulsion for the battery-only electric vehicle (BEV) Spark. The 1ET35 is designed with experience gained from the first modern production BEV, the 1996 GM EV1. This paper describes the design optimization and development of the 1ET35 and its electric motor that will be made in the United States by GM. The high torque density electric motor design is based on high-energy permanent magnets that were originally developed by GM in connection with the EV1 and GM bar-wound stator technology introduced in the 2Mode Hybrid electric transmission, used in the Chevrolet Volt and in GM eAssist systems.