To develop optimum vehicle designs, engineers must balance vehicle attributes to meet various product requirements, relating to specific market demands. It is commonplace to see trade-off relationships between noise and other important attributes. Balancing decisions are best made when detailed information is available through appropriate simulation and testing. This paper describes three case studies exemplifying such trade-off situations, and the technical approaches taken to decide the best attribute balance for each vehicle product.
Demand is increasing for outstanding fuel economy in gasoline engines especially in Hybrid Electric Vehicles (HEVs). An enabler for good efficiency is rapid combustion, but this can erode the refined sound quality that customers expect from gasoline engines. A simulation approach was used to improve gasoline combustion noise early in a project. Mitigation measures were optimized for drive-cycle fuel efficiency, combustion noise and engine mass.
Car makers must provide cruising comfort through low road noise (more prominent with quieter electric propulsion) and simultaneously deliver competitive driving dynamics. A vehicle structure’s dynamic response strongly influences both ride comfort and road noise, and is significantly impacted by the integration of a heavy battery pack. Using a multi attribute optimization approach, an optimum balance was found for a Battery Electric Vehicle (BEV) to control road noise whilst giving good vehicle structural response and reduced mass.
Electric cars deliver strong and responsive vehicle performance and good cruising comfort, but their dynamic driving experience and brand appeal is marred by the lack of informative acoustic feedback from the power unit. An augmented power unit sound system was calibrated to deliver an exciting and authentic acceleration feel during spirited driving, while retaining acoustic comfort during cruising and minimizing environmental noise pollution.
Attribute Balancing – Trading-Off Noise Versus Emissions, Ride and Handling and Driveability
Proceedings
24.01.2020
16 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Deutsch
Multi Attribute Optimization: Fuel Consumption, Emissions and Driveability
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