Synthesis of Representative Driving Cycles with Respect to Time-Dependent Load Conditions

The design of hybrid electric vehicles based on real-world driving conditions becomes increasingly important due to strict legislation and complexity of future powertrain concepts. Therefore, component sizes should be optimized not only with respect to fuel consumption but also regarding real-world load conditions. Since e.g. a proper design of the cooling system of electrical components requires consideration of time-related power demand, this paper proposes a novel concept of incorporating time frame-based load analysis (TFBA) into driving cycle synthesis. The synthesis is carried out within a two-layer optimization framework where the first layer considers statistical features and the second considers quantities directly related to a vehicles' load implication. By taking into account Markov chain theory, a class frame is derived which then serves as design space for optimizing a sequence of micro-trips with respect to meeting target criteria based on the concepts of mean tractive force and TFBA. To prove practicality, an exemplary driving cycle is synthesized and the method is validated within a parameter study. Results show that the synthetic driving cycle is representative with respect to the considered target criteria and moreover statistical quantities used for validation are within an error tolerance of 5 %.