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Electronics components are estimated to be between 9 to 15 % of a total vehicle cost, and this trend will remain strong for the next years. The amount of electronics content in a vehicle has grown steadily since 1970's, and as a result, development challenges such as testing and validation are a key aspect of its overall development costs. Testing costs can amount easily to US$ 500 k in medium complex automotive parts of a vehicle (e.g. instrument cluster) depending on a specific OEM customer demand, and this on top of limited regional laboratory capacity can also lead to increased testing time. The goal of this paper is to outline key aspects of electronics in vehicle components testing, including overall costs and timing, and propose a lean approach to optimize such costs & timing. The key aspects of such optimization include not only resources, but also laboratories and upfront OEM customer planning.

A test load specification is required to validate an automotive product to meet the durability and design life requirements. Traditionally in the automotive industry, load specifications for design validation tests are directly given by OEMs, which are generally developed from an envelop of generic customer usage profiles and are, in most cases, over-specified. In recent years, however, there are many occasions that a proposed load specification for a particular product is requested. The particular test load specification for a particular product is generated based on the measured load data at its mounting location on the given type of vehicles, which contains more realistic time domain load levels and associated frequency contents. The measured time domain load is then processed to frequency domain test load data by using the fast Fourier transform and damage equivalent techniques.

Visteon has developed a CAE procedure to qualify instrument panel (IP) products under the vehicle key life test environments, by employing a set of CAE simulation and durability techniques. The virtual key life test method simulates the same structural configuration and the proving ground road loads as in the physical test. A representative dynamic road load profile model is constructed based on the vehicle proving ground field data. The dynamic stress simulation is realized by employing the finite element transient analysis. The durability evaluation is based on the dynamic stress results and the material fatigue properties of each component. The procedure has helped the IP engineering team to identify and correct potential durability problems at earlier design stage without a prototype. It has shown that the CAE virtual key life test procedure provides a way to speed up IP product development, to minimize prototypes and costs.

Generalized predictive control (GPC) is a discrete time control strategy proposed by Clark et al [1]. The controller tries to predict the future output of a system or plant and then takes control action at present time based on future output error. Such a predictive control algorithm is presented in this paper for yaw stability management of an automobile. Most of the existing literature on the yaw stability management systems lacks the insight into the yaw rate error growth when the automobile is in a understeer or oversteer condition on a low friction coefficient surface in a handling maneuver. Simulation results show that the predictive feature of the proposed controller provides an effective way to control the yaw stability of a vehicle.

Based on our error budget analysis, the urea SCR aftertreatment system is uncontrollable under EPA 2007-emission level without an effective closed-loop control strategy. The objective of the closed-loop control is to improve transient response as well as reduce the steady state control error. But the inherent large dead time in the urea SCR aftertreatment system makes the closed-loop control a challenge. In this paper, an innovative closed-loop control architecture is introduced, which combines model-based feedforward control with variable gain-scheduling feedback control. Transient response is improved with the inverse-dynamic feedforward control and the variable-gain closed-loop control. The steady-state response is improved with the closed-loop control. Based on this new strategy, a controller is designed and validated under the simulation and test cell environment. Comparison with the baseline open-loop controller is also conducted. Finally, some conclusions are presented.

This paper introduces the Driver Stability System (DSS) at Visteon. DSS is a new active comfort / safety system for automobiles which controls the seat bolsters independently in real time to enhance the lateral support of the occupants. Under turning maneuvers, DSS reacts to the vehicle dynamics to provide an increased contact area between the occupants and their seats, allowing optimal occupant location with respect to such variables as steering wheel angle, lateral acceleration, yaw rate, and vehicle velocity. The lateral force compensation is directly coupled to the dynamic movement of vehicle chassis and the change of road profile. The system consists of the seat bolster assembly including DC motors, wheel speed sensors, steering wheel sensor, lateral accelerometer, yaw rate sensor, and electronic control unit (ECU). This paper also discusses the control concept of DSS and its realistic controller structure.