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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.

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.

Finite element dummy models have been more and more widely applied in virtual development of occupant protection systems across the automotive industry due to their predictive capabilities. H350 dyna dummy model [1] is a finite element representation of the Hybrid III male dummy [2], which is designed to represent the average of the United States adult male population. Lower extremity injuries continue to occur in front crash accidents despite increasing improvement of vehicle crashworthiness and occupant restraint system. It is therefore desirable to predict lower tibia injury numbers in front occupant simulations. Though lower tibia loading/index predictions are not studied as much as the FMVSS 208 regulated injury numbers, the tibia indices are injury criteria that need to be assessed during IIHS and Euro NCAP frontal offset occupant simulations. However during front crash simulations, it is very difficult to achieve good correlations or predictions of lower tibia loadings.

It is obvious at this point even to the most casual observer of the automotive industry that efforts to reduce mass throughout the vehicle are at a fervor. The industry is facing its most significant increase in fuel economy standards in its history, and light-weighting the vehicle is a major enabler. Despite the performance and quality of the brake system being intensely related to its mass, it too has not been spared scrutiny. However, like many modern automotive subsystems, it is very complex and mass reduction opportunities that do not sacrifice performance or quality are not always obvious. There are some interesting and sometimes even profound relationships between mass and other vehicle attributes built into brake system design, and making these more visible can enable a better balancing of brake system with the rest of the vehicle design objectives. Examples include - what is the cost, in terms of brake system mass, of added engine power? Of tire and wheel size?

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.

General Motors has introduced a new front wheel drive seven speed dry dual clutch automatic transmission in 2014. The 250 Nm input torque rated gear box was designed and engineered for a global market in both front wheel drive and all-wheel drive configurations. The transmission has integrated start/stop capability enabled by the use of an electric motor driven pump and a pressurized accumulator. The architecture selected was chosen for optimization of packaging, fuel economy, mass, shift pleasability, and NVH. High mileage durability and world class drivability were the cornerstone deliverables during the engineering and design process Fuel efficiency is estimated to be 3% - 10% improvement over a conventional six speed automatic transmission. FWD variant wet mass of 78.1 kg was achieved through the rigorous engineering processes used to optimize the transmission system.

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.

Aerodynamic vehicle design improvements require flow simulation driven iterative shape changes. The 3-D flow field simulations (CFD analysis) are not explicitly descriptive in providing the direction for aerodynamic shape changes (reducing drag force or increasing the down-force). In recent times, aerodynamic shape optimization using the adjoint method has been gaining more attention in the automotive industry. The traditional DOE (Design of Experiment) optimization method based on the shape parameters requires a large number of CFD flow simulations for obtaining design sensitivities of these shape parameters. The large number of CFD flow simulations can be significantly reduced if the adjoint method is applied. The main purpose of the present study is to demonstrate and validate the adjoint method for vehicle aerodynamic shape improvements.

The helical spring is one of fundamental mechanical elements used in various industrial applications such as valves, suspension mechanisms, shock and vibration absorbers, hand levers, etc. In high speed applications, for instance in the internal combustion engine or in reciprocating compressor valves, helical springs are subjected to dynamic and impact loading, which can result in a phenomenon called “surge”. Hence, proper design and selection of helical springs should consider modeling the dynamic and impact response. In order to correctly characterize the physics of a helical spring and its response to dynamic excitations, a comprehensive model of spring elasticity for various spring coil and wire geometries, spring inertial effects as well as contacts between the windings leading to a non-linear spring force behavior is required. In practical applications, such models are utilized in parametric design and optimization studies.

This paper presents a novel approach in real-time engine modeling. Unlike standard practices, which involve system level modeling, the presented methodology is a hybrid physical/system domain solution. Specifically, for each subsystem that the engine is divided into, a physical, map-based, or combination physical/map-based solution is chosen depending on the available computational power and the desired model detail. The resulting semi-physical engine models are suitable for real-time applications, such as Hardware-In-Loop (HiL) simulations, and, at the same time, re-usable to a large extent when model updates are required. In addition, since the proposed methodology allows for variable level of detail -from models as simple as pure map-based look-ups for torque, airflow, and exhaust temperature, all the way to models capable of predicting crank angle resolved cylinder pressure- it provides natural adjustability to the ongoing growth of computer power.

With the ever increasing pressure to improve the fuel economy of vehicles, there has been a corresponding interest in reducing the mass and size of vehicles. While mass is easily quantifiable, vehicle size, particularly the notion of “interior space” as perceived by the customer, is not. This paper explores different ways in which vehicle spaciousness can be quantified and explores new metrics based on customer verbatims. A novel ‘spaciousness calculator’ combines individual metrics to provide a singular holistic rating for spaciousness, useful during vehicle development. Beyond spaciousness, the paper discusses techniques to quantify the ‘packaging efficiency’ of a vehicle; this allows engineers to maximize the interior space for a given exterior size.

An area of brake system design that has remained continually resistant to objective, computer model based predictive design and has instead continued to rely on empirical methods and prior history, is that of sizing the brake pads to insure satisfactory service life of the friction material. Despite advances in CAE tools and methods, the ever-intensifying pressures of shortened vehicle development cycles, and the loss of prototype vehicle properties, there is still considerable effort devoted to vehicle-level testing on public roads using “customer-based” driving cycles to validate brake pad service life. Furthermore, there does not appear to be a firm, objective means of designing the required pad volume into the calipers early on - there is still much reliance on prior experience.

The goal of this paper is to discuss the critical aspects of damper tuning for production vehicles. These aspects include ride and handling performance attributes, damper basics, conflicts in achieving desirable results, tuning philosophies, and the influence of the damper design. The marketplace has become increasingly competitive. Customer preference, cost, mass and regulatory pressures often conflict. Yet each year more vehicles are required to do all these things well. Damper tuning can play a significant role in resolving these conflicts. Although many papers have been written on the theory behind damper design and capabilities, there has been very little written about the techniques of tuning dampers for production vehicles. This paper attempts to discuss the critical aspects of damper tuning for production vehicles in four sections. The first section discusses the performance attributes of ride and handling. The second section provides a basic understanding of dampers.

Diesel engine control has already become complex, and in order to meet future emissions standards (such as Euro 4) it is likely to be the control system that will provide the needed performance increment. Common rail fuel injection offers yet more degrees of freedom which will need to be exploited as new emissions standards emerge. Whatever the emissions standards, there is a need to reduce risk at the earliest stages in the development of the powertrain. This will involve early and extensive simulation of the powertrain including its control system, sensors and actuators. What is the best way to achieve this using current tools? The result lies in a combination of a phenomenological model of the engine and a flexible controls environment. To illustrate the principles of developing prototype control systems, we will use the example of the CPower environment, which is a combination of a detailed engine simulation code (GT-Power) and the Simulink simulation environment.

This article describes a system level 1D simulation tool that has been constructed on the Quasi-steady (QS) method. By assuming that spatial changes are much greater than the temporal ones, rigorous 1D governing equations can be considerably simplified thus becoming less computationally demanding to solve and therefore suitable for control oriented modeling purposes. With the proposed tool exhaust pipe wall temperature profiles, including multiple-wall-layer configurations, are solved through a finite difference scheme. Momentum equation is included for predicting pressure losses due to frictions and geometric irregularity. Exhaust fluid properties (transport and thermodynamic) are evaluated according to NASA or JANAF polynomial thermal data basis. The proposed tool allows the consideration of an arbitrary number of chemical species and reactions in the entire system. A novel semi-automatic approach was developed to handle catalytic reaction kinetics intuitively.

Genetic Algorithms have proved to be very useful as global search methods for multi-dimensional optimization problems. One drawback, however, is that they are inefficient from the point of view of the number of function evaluations. This paper presents a two phase approach to optimization, using Game Theory in an initial step which provides a family of designs which are close to the Pareto frontier. The starting population for the genetic algorithm is then selected from the non-dominated designs produced in the first phase. This ensures that the genetic algorithm starts with a population of points which are already optimized to a large degree.

Belt-drive systems are a commonly used for power transmission in automotive applications, notably in engine and vehicle auxiliary subsystem drives. In order to characterize the physics of a belt drive system and its response to speed and load excitations, a comprehensive model of belt elasticity and of belt-pulley contact and friction is required. In practical applications such models are utilized in parametric design and optimization studies, and computational efficiency is therefore also a key requirement. In this paper a belt drive dynamics model is presented, in which the belt is modeled by means of geometrically exact cables that can undergo large rigid body motions but whose strains remain small. The Finite Element approach is used in order to efficiently discretize these elastic components. In addition, a state-of-the-art dynamic friction model (LuGre) is used in order to model the friction loads between the belt and pulleys.

The energy storage system (ESS) is the key enabler to hybrid electric vehicles (HEVs) that offer improved fuel economy and reduced vehicle emissions. The power capability of a battery has significant impact on the fuel economy of HEVs. This paper presents the power capability testing of a lithium-ion battery with a conventional metal oxide cathode using the hardware in the loop (HIL) at a wide range of charge/discharge conditions and at different temperatures. The achieved test results provide critical data of battery power characteristics and effectively accelerate the development of battery power prediction algorithm.