Predictive Energy Management Strategy Including Traffic Flow Data for Hybrid Electric Vehicles

Predictive Energy Management Strategy Including Traffic Flow Data for Hybrid Electric Vehicles

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

2017

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.

2015 Fluid & Solid Mechanics
PT - Power Trains
TS - Technical Sciences
Traffic
Industrial Innovation
eHorizon
Hybrid electric vehicles
Predictive energy management
Traffic flow data

http://resolver.tudelft.nl/uuid:9f6d2666-57e3-47c0-8c71-6546cdec0768

https://doi.org/10.1016/j.ifacol.2017.08.1775

781888

2405-8963

IFAC-PapersOnLine, 50 (1), 10046-10051

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