Dynamic programming (DP) provides the optimal global solution to the energy management problem for hybrid electric vehicles (HEVs), but needs complete a-priori knowledge of the driving cycle and has high computational requirements. This article presents a possible methodology to extract rules from the dynamic programming solution to design an implementable rule-based strategy. The case study considered is a series/parallel HEV, in which a clutch allows to switch from one configuration to another. The strategy works according to a two layer policy: the supervisory controller, which decides the powertrain configuration (either series or parallel), and the energy management, which decides the power split. The process of deriving the rules from the optimal solution is described. Then, the performance of the resulting rule-based strategy is studied and compared with the solution given by the dynamic programming, which functions as a benchmark.

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Dynamic programming is known to provide the optimal solution to the energy management problem. However, it is not implementable online because it requires complete a-priori knowledge of the driving cycle and high computational requirements. This article presents a methodology to extract an implementable rule-based strategy from the dynamic programming results and thus build a near-optimal controller. The case study discussed in this paper focused on mode switching in a series/parallel hybrid vehicle, in which a clutch may be used to change the powertrain topology. Because of the complexity of the system, the controller is divided in two layers: the supervisory controller, which decides the powertrain configuration, and the energy management, which decides the power split. The process of deriving the rules from the optimal solution is described in detail. Then, the performance of the resulting rule-based strategy is studied and compared with the solution given by dynamic programming, which functions as a benchmark. Then another comparison is performed with respect to the equivalent consumption minimisation strategy (ECMS) which, if optimally tuned, can achieve optimal performance as close to DP as possible with the advantage of being implementable.

Nowadays, electric vehicles represent a promising solution for reducing the fuel consumption (and thus the carbon dioxide emission) and the pollutant emissions of road vehicles, especially in highly congested urban areas, although their driving range and usability still limit the customer acceptance even for a city car application. Extended-range electric vehicles may partly overcome these limitations, having an auxiliary power unit which can provide electrical energy to the powertrain once the battery has been depleted. On the other hand, the operations of such an auxiliary power unit should be almost completely unnoticeable in order to avoid any impacts on the electric driving experience, resulting in considerably reduced drive-related noise. Therefore the aim of this work is the design, through numerical simulation, of a powertrain controller capable of minimizing the carbon dioxide emission of a range-extended electric vehicle and, at the same time, avoiding any discomfort for the passenger related to the auxiliary power unit operations. Starting from the development of a powertrain controller focused on minimization of the carbon dioxide emission of the vehicle, the main noise targets will be defined and their effects on the energy management system will be analysed.

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