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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.

This paper describes a new method to synthesize driving cycles, where not only the velocity is considered but the road slope information of the real-world measured driving cycle as well. Driven by strict emission regulations and tight fuel targets, hybrid or electric vehicle manufacturers aim to develop new and more energy-and cost-efficient powertrains. To enable and facilitate this development, short, yet realistic, driving cycles need to be synthesized. The developed driving cycle should give a good representation of measured driving cycles in terms of velocity, slope, acceleration, and so on. Current methods use only velocity and acceleration and assume a zero road slope. The heavier the vehicle is, the more important the road slope becomes in powertrain prototyping (as with component sizing or control design); hence, neglecting it leads to unrealistic or limited designs. To include the slope, we extend existing methods and propose an approach based on multidimensional Markov chains. The validation of the synthesized driving cycle is based on a statistical analysis (as the average acceleration or maximum velocity) and a frequency analysis. This new method demonstrates the ability of capturing the measured road slope information in the synthesized driving cycle. Furthermore, results show that the proposed method outperforms current methods in terms of accuracy and speed.Index Terms-Driving cycle compression, hybrid electric vehicle (HEV) design, multidimensional Markov chains, slope and velocity synthesis.

Powertrain system design optimization is an unexplored territory for battery electric trucks, which only recently have been seen as a feasible solution for sustainable road transport. To investigate the potential of these vehicles, in this paper, a variety of new battery electric powertrain topologies for heavy-duty trucks is studied. Thereby, topological design considerations are analyzed related to having: (a) a central or distributed drive system (individually-driven wheels); (b) a single or a multi-speed gearbox; and finally, (c) a single or multiple electric machines. For reasons of comparison, each concurrent powertrain topology is optimized using a bilevel optimization framework, incorporating both powertrain components and control design. The results show that the combined choice of powertrain topology and number of gears in the gearbox can result in a 5.6% total-cost-of-ownership variation of the vehicle and can, significantly, influence the optimal sizing of the electric machine(s). The lowest total-cost-of-ownership is achieved by a distributed topology with two electric machines and two two-speed gearboxes. Furthermore, results show that the largest average reduction in total-cost-of-ownership is achieved by choosing a distributed drive over a central drive topology (−1.0%); followed by using a two-speed gearbox over a single speed (−0.6%); and lastly, by using two electric machines over using one for the central drive topologies (−0.3%).

Abstract-The energy efficiency of a hybrid electric vehicle is dictated by the topology (coupling option of power sources/sinks), choice (technology) and control of components. The first design area among these, the topology, has the biggest flexibility of them all, yet, so far in literature, the topology design is limited investigated due to its high complexity. In practice, a predefined small set of topologies is used to optimize their energy efficiency by varying the power specifications of the main components (sizing). By doing so, the complete design of the vehicle is, inherently and to a certain extend, sub-optimal. Moreover, various complex topologies appear on the automotive market and no tool exists to optimally choose or evaluate them. To overcome this design limitation, in this work, a novel framework is presented that deals with the automatic generation of possible topologies given a set of components (e.g., engine, electric machine, batteries or transmission elements). This framework uses a platform (library of components) and a hybrid knowledge base (functional and cost-based principles) to set-up a constraint logic programming problem and outputs a set of feasible topologies for hybrid electric vehicles. These are all possible topologies that could be built considering a fixed, yet large, set of components. Then, by using these results, insights are given on what construction principles are mostly critical for simulations times and what topologies could be selected as candidate topologies for sizing and control studies. Such a framework can be used for any powertrain application, it can offer the topologies to be investigated in the design phase and can provide insightful results for optimal design analyses.Index Terms-automatic topology generator, platform based design methodology, hybrid electric vehicles, constraint logic programming over finite domains.