Energy management strategies for plug-in hybrid electric vehicles

Along with the common goal of reducing fuel consumption for vehicles, the hybrid electric vehicle stands out as a mean for more fuel efficient driving. Besides the conventional combustion engine and fuel tank, the hybrid electric vehicle is also equipped with an electric motor and a battery for propulsion. Car manufacturers are presently working to provide the markets with the next step of this concept, the plug-in hybrid electric vehicle, allowing the on-board battery to be recharged from the power grid. The aim of this master thesis is to evaluate two different energy management strategies for plug-in hybrid electric vehicles. These energy management strategies consist of both control strategies as well as battery discharge strategies. This thesis evaluates an already existing control strategy based on rules for energy management. It also involves Matlab R ï¿¿ and Simulink R ï¿¿ implementation of a control strategy; the Equivalent Consumption Minimization Strategy (ECMS), which is based on the concept of optimal control. ECMS operates by continuous evaluation of the fuel consumption cost for different power splits as a basis for selecting the most fuel efficient operating point between the internal combustion engine and the electric motor. Two battery discharge strategies have been investigated. The first one is the Charge Depletion Charge Sustaining (CDCS) strategy, depleting the battery in an all-electric drive first and then operating in sustaining mode. The other method is to blend the use of the electric motor with the combustion engine at various points throughout the entire trip in a blended mode discharge strategy. A comparison has been made between a rule-based control strategy with CDCS and the ECMS control strategy for both blended and CDCS discharge. The comparison is done with respect to fuel consumption but side effects related to the battery power usage are observed as well. It is concluded that fuel consumption using ECMS with a blended discharge can be reduced by 4.2 % on average and by 1.0 % on average for CDCS discharge, compared to using the rule-based control strategy with CDCS. Battery power losses are reduced by 15.6 % under a blended discharge strategy and by 7.9 % for CDCS discharge.

[1]  A. G. Butkovskiy,et al.  Optimal control of systems , 1966 .

[2]  Jens Groot,et al.  State-of-Health Estimation of Li-ion Batteries: Cycle Life Test Methods , 2012 .

[3]  Aymeric Rousseau,et al.  Comparison of Powertrain Configuration for Plug-in HEVs from a Fuel Economy Perspective , 2008 .

[4]  Lino Guzzella,et al.  Optimal control of parallel hybrid electric vehicles , 2004, IEEE Transactions on Control Systems Technology.

[5]  Chen Zhang,et al.  Real-time optimal control of plug-in hybrid vehicles with trip preview , 2010, Proceedings of the 2010 American Control Conference.

[6]  Giorgio Rizzoni,et al.  Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles , 2011, IEEE Transactions on Vehicular Technology.

[7]  Viktor Larsson.  On Discharge Strategies for Plug-in Hybrid Electric Vehicles , 2011 .

[8]  Bo Egardt,et al.  Predictive energy management of a 4QT series-parallel hybrid electric bus , 2009 .

[9]  Heinz Wenzl,et al.  Life prediction of batteries for selecting the technically most suitable and cost effective battery , 2005 .

[10]  Giorgio Rizzoni,et al.  A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[11]  B. He,et al.  Optimisation-based energy management of series hybrid vehicles considering transient behaviour , 2006 .

[12]  Giorgio Rizzoni,et al.  Energy management for plug-in hybrid electric vehicles using equivalent consumption minimisation strategy , 2010 .

[13]  Lino Guzzella,et al.  Vehicle Propulsion Systems , 2013 .

[14]  M Maarten Steinbuch,et al.  Design, implementation, and experimental validation of optimal power split control for hybrid electric trucks , 2012 .