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The hybrid vehicle control problem at the highest level is termed the Energy Management Strategy (EMS). This paper presents a new, and simple RuleBased (RB) EMS, whereby maximum power level of the electric machine during pure electric driving is the control design variable. This maximum power level determines the overall power -, and efficiency specifications of the electric machine. A RB EMS consist of a selection of driving modes. The RB EMS is compared with the strategy based on Dynamic Programming (DP), which is inherently optimal for a given cycle. The RB method proposed in this paper requires 3000 times less computation time with the same accuracy (±1%) as DP. The RB strategy in this paper is a semi-empirical EMS with which the generic component specifications for the secondary power source (battery, power electronics, electric machine), primary power source (engine) and transmission technology can be obtained. Provided these generic specifications, a technology designer can quickly specify the hybrid technologies. In this way, control, optimization and component design are merged in a single framework.

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a b s t r a c tA benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting-charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagin's minimum principlederived optimization tool HOT.

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