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Addressing Drivability in an Extended Range Electric Vehicle Running an Equivalent Consumption Minimization Strategy (ECMS)
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 12, 2011 by SAE International in United States
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The EcoCAR Challenge team at The Ohio State University has designed an extended-range electric vehicle capable of 50 miles all-electric range via a 22 kWh lithium-ion battery pack, with range extension and limited parallel operation supplied by a 1.8 L dedicated E85 engine. This vehicle is designed to drastically reduce fuel consumption, while meeting Tier II Bin 5 emissions standards. This vehicle design is implemented in a GM crossover utility vehicle as part of the EcoCAR Challenge. This paper explains the implementation of the vehicle's control strategy in order to maintain high efficiency and improve drivability. The vehicle control strategy employs both distinct operating modes and an Equivalent Consumption Minimization Strategy (ECMS) to find the most efficient operating point. The ECMS strategy does an online search for the most efficient torque split in order to meet the driver's command. The paper will explain how drivability is addressed inside and outside the ECMS algorithm in order to provide the best vehicle performance and efficiency.
CitationSchacht, E., Bezaire, B., Cooley, B., Bayar, K. et al., "Addressing Drivability in an Extended Range Electric Vehicle Running an Equivalent Consumption Minimization Strategy (ECMS)," SAE Technical Paper 2011-01-0911, 2011, https://doi.org/10.4271/2011-01-0911.
- Paganelli, G., Ercole, G., Brahma, A., Guezennec, Y., and Rizzoni, G., “General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles,” JSAE Review, vol. 22, no. 4, pp. 511-518, 2001.
- Musardo, C., Rizzoni, G., Guezennec, Y., and Staccia, B., “A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management,” European Journal of Control, vol. 11, no. 4-5, pp. 509-524, 2005.
- Gu, B. and Rizzoni, G., “An adaptive algorithm for hybrid electric vehicle energy management based on driving pattern recognition,” Proceedings of the 2006 ASME International Mechanical Engineering Congress and Exposition, 2006
- Wei, X. and Rizzoni, G., “Objective Metrics of Fuel Economy, Performance and Driveability - A Review,” SAE Technical Paper 2004-01-1338, 2004, doi: 10.4271/2004-01-1338.