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Connected and automated vehicle (CAV) technologies promise a substantial decrease in traffic accidents and traffic jams, and bring new opportunities for improving vehicle’s fuel economy. However, testing autonomous vehicles in a real world traffic environment is costly, and covering all corner cases is nearly impossible. Furthermore, it is very challenging to create a controlled real traffic environment that vehicle tests can be conducted repeatedly and compared fairly. With the capability of allowing testing more scenarios than those that would be possible with real world testing, simulations are deemed safer, more efficient, and more cost-effective. In this work, a full-scale simulation platform was developed to simulate the infrastructure, traffic, vehicle, powertrain, and their interactions. It is used as an effective tool to facilitate control algorithm development for improving CAV’s fuel economy in real world driving scenarios.

This paper describes the new Honda R&D Americas Scale Model Wind Tunnel (SWT). To help address Honda's ongoing need to improve fuel economy, reduce the driving force of a vehicle, and decrease product development time, the wind tunnel was developed and implemented to achieve high accuracy aerodynamic predictions for product development and a significantly improved capability for vehicle aerodynamics research. The SWT can accommodate model scales up to 50%. The ¾-open jet test section has a top speed of 250 km/h, a 5-belt moving ground plane with a long center belt for proper wake simulation, a test section designed specifically for very low static pressure gradient, three separate dynamic pressure measurement systems for state-of-the-art blockage corrections, and an overhead traverse for specialized measurement activities. This paper describes the decision process that led to the SWT, key commissioning results, and performance validation results with models installed.

Although advanced driver assistance systems (ADAS) have been widely introduced in automotive industry to enhance driving safety and comfort, and to reduce drivers’ driving burden, they do not in general reflect different drivers’ driving styles or customized with individual personalities. This can be important to comfort and enjoyable driving experience, and to improved market acceptance. However, it is challenging to understand and further identify drivers’ driving styles due to large number and great variations of driving population. Previous research has mainly adopted physical approaches in modeling drivers’ driving behavior, which however are often very much limited, if not impossible, in capturing human drivers’ driving characteristics. This paper proposes a reinforcement learning based approach, in which the driving styles are formulated through drivers’ learning processes from interaction with surrounding environment.

In recent years, there has been renewed attention focused on open jet correction methods, in particular on the two-measurement method of E. Mercker, K. Cooper, and co-workers. This method accounts for blockage and static pressure gradient effects in automotive wind tunnels and has been shown by both computations and experiments to appropriately adjust drag coefficients towards an on-road condition, thus allowing results from different wind tunnels to be compared on a more equitable basis. However, most wind tunnels have yet to adopt the method as standard practice due to difficulties in practical application. In particular, it is necessary to measure the aerodynamic forces on every vehicle configuration in two different static pressure gradients to capture that portion of the correction. Building on earlier proof-of-concept work, this paper demonstrates a practical method for implementing the two-measurement procedure and demonstrates how it can be used for production testing.

With the emphasis of electrification in automotive industry, tremendous efforts are made to develop electric motors with high efficiency and power density, and reduce noise, vibration and harshness (NVH). A multiphysics simulation workflow is used to predict the eccentricity-induced noise for GM’s Bolt EV motor. Both static and dynamic eccentricities are investigated along with axial tilt. Analysis results show that these eccentricities play a critical role in the NVH behavior of the motor assembly. Transient electromagnetic (EM) analysis is performed first by extruding 2D stator and rotor sections to form 3D EM models. Sector model is duplicated to form full 360-degree model. Stator is split into three rotated sections to characterize stator skew, and the skew between two sections of rotor and magnets are also modelled. Sinusoidal current is applied and lumped-sum forces on each stator tooth are computed.

Vehicle electrification is driven globally due to the increased concerns on carbon emissions. But the challenges in customer acceptance remains esp. in relation to vehicle costs. Virtual simulations can help in cutting down product development cost and enable faster launch of new vehicles. An early stage system model based design iterations can help in cutting down the product development costs and building more robust products. In the current paper, we develop and analyze a battery pack system model for early phase design. We extend a previously developed system model to include critical physics like sub-component level multiphysics for electrical joint integrity. Also, we demonstrate an integration of 3D FEM & system model for improving the accuracy of joint temperature predictions during charging and/or discharging. A typical High Voltage (HV) battery system comprises of battery modules (Li-ion cells, cooling channels, structural frames, interconnect boards) and HV bus bars.

In the course of this paper, a model suitable for studying the performance - in terms of response time, current draw, and peak pressure capacity - of an electric booster-based brake system is introduced. Some discussion about the need the model is attempting to fulfill and how it fits into the vehicle development process is offered, before explaining the model in full. The equations describing the physics of the model are presented, and an explanation of how the elements of the model are integrated together into an easy to use, fast-running spreadsheet environment is given. Case study examples, validating the model against physical test (hardware in the loop) test results are shown, followed by sensitivity studies testing how changing parameters such as caliper Pressure-Volume curves, hydraulic system flow characteristics, voltage supply, and temperature conditions affect performance.

This paper presents development of a multi-scale material model for a 980 MPa grade transformation induced plasticity (TRIP) steel, subject to a two-step quenching and partitioning heat treatment (QP980), based on integrated computational materials engineering principles (ICME Model). The model combines micro-scale material properties defined by the crystal plasticity theory with the macro-scale mechanical properties, such as flow curves under different loading paths. For an initial microstructure the flow curves of each of the constituent phases (ferrite, austenite, martensite) are computed based on the crystal plasticity theory and the crystal orientation distribution function. Phase properties are then used as an input to a state variable model that computes macro-scale flow curves while accounting for hardening caused by austenite transformation into martensite under different straining paths.

The General Motors Reduced Scale Wind Tunnel Facility, which came into operation in the fall of 2015, is a new state-of-the-art scale model aerodynamic test facility that expands GM’s test capabilities. The new facility also increases GM’s aerodynamic testing through-put and provides the resources needed to achieve the growing demand for higher fuel economy requirements for next generation of vehicles. The wind tunnel was designed for a nominal model scale of 40%. The nozzle and test section were sized to keep wind tunnel interference effects to a minimum. Flow quality and other wind tunnel performance parameters are on par with or better than the latest industry standards. A 5-belt system with a long center belt and boundary layer suction and blowing system are used to model underbody flow conditions. An overhead probe traverse system is installed in the test section along with a model positioning robot used to move the model in an out of the test section.

This article covers an application of third-generation advanced high-strength steel (3GAHSS) grades to vehicle lightweight body structure development. Design optimization of a vehicle body structure using a multi-scale material model is discussed. The steps in the design optimization and results are presented. Results show a 30% mass reduction potential over a baseline mid-size sedan body side structure with the use of 3GAHSS.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.