Predictive energy management strategy including traffic flow data for hybrid electric vehicles

K.R. Bouwman, T.H. Pham, S. Wilkins, T. Hofman

Research output: Contribution to journalConference articlepeer-review

23 Citations (Scopus)
7 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)10046-10051
Number of pages6
Issue number1
Publication statusPublished - 1 Jul 2017
Event20th World Congress of the International Federation of Automatic Control (IFAC 2017 World Congress) - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20


  • eHorizon
  • hybrid electric vehicles
  • predictive energy management
  • traffic flow data


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