Print Email Facebook Twitter Predictive Energy Management Strategy Including Traffic Flow Data for Hybrid Electric Vehicles Title Predictive Energy Management Strategy Including Traffic Flow Data for Hybrid Electric Vehicles Author Bouwman, K.R. Pham, T.H. Wilkins, S. Hofman, T. Publication year 2017 Abstract Within hybrid electric vehicles (HEVs) predictive energy management strategies (EMSs) have the potential to reduce the fuel consumption compared to conventional EMSs, where the drive cycle is unknown. Typically, predictive EMSs require a future vehicle speed profile prediction. However, when prediction is inaccurate, the systems fuel reduction performance and robustness may be compromised. Among many influential factors, inaccurate prediction is mainly caused by uncertain dynamic traffic conditions, e.g. traffic and traffic lights. This paper develops a predictive EMS, which enhances the equivalent fuel consumption minimization strategy (ECMS) with real-time traffic flow data and traffic light position to maximize fuel reduction performance of HEVs. Moreover, a Monte Carlo approach is exploited to handle the traffic light uncertainty. Simulation results demonstrate the benefits of Monte Carlo approach in predictive EMS to enhance the robustness and fuel reduction performance up to 2-11[%] compared to conventional strategies for various battery capacities. Subject 2015 Fluid & Solid MechanicsPT - Power TrainsTS - Technical SciencesTrafficIndustrial InnovationeHorizonHybrid electric vehiclesPredictive energy managementTraffic flow data To reference this document use: http://resolver.tudelft.nl/uuid:9f6d2666-57e3-47c0-8c71-6546cdec0768 DOI https://doi.org/10.1016/j.ifacol.2017.08.1775 TNO identifier 781888 ISSN 2405-8963 Source IFAC-PapersOnLine, 50 (1), 10046-10051 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.