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An Engine Stop Start System with Driver Behavior Learning and Adaption for Improving the User Experience
Technical Paper
2018-01-0609
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
Engine Stop/Start System (ESS) promises to reduce greenhouse emissions and improve fuel economy of vehicles. Previous work of the Authors was concentrated on bridging the gap of improvement in fuel economy promised by ESS under standard laboratory conditions and actual driving conditions. Findings from the practical studies lead to a conclusion that ESS is not so popular among the customers, due to the complexities of the system operation and poor integration of the system design with the driver behavior. In addition, due to various functional safety requirements, and traffic conditions, actual benefits of ESS are reduced. A modified control algorithm was proposed and proven for the local driving conditions in India.
The ways in which a given driver behaves on the controls of the vehicles like Clutch and Brake Pedals, Gear Shift Lever were not uniform across the demography of study and varied significantly. In addition, Authors also discovered that some drivers also deployed the parking brake during an idle stop. Thus, a concept of autonomous learning algorithm was envisaged, which would learn the driver behavior on the controls which influence the functions of ESS and then adapt the same conditions to trigger the auto engine stop and restart. This was aimed at improving the user experience and yet ensure the benefits of the ESS.
In this paper, the findings from previous works are analyzed to make grounds for the new submission and to identify the need for User Experience of ESS. The solution implemented to detect the driver behavior from the set of possible ways is discussed in detail and simulation case studies are discussed to ascertain the functions and benefits of the new algorithm.
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Athani, G., Gavarraju, S., Jain, P., Addala, S. et al., "An Engine Stop Start System with Driver Behavior Learning and Adaption for Improving the User Experience," SAE Technical Paper 2018-01-0609, 2018, https://doi.org/10.4271/2018-01-0609.Data Sets - Support Documents
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