Browse Publications Technical Papers 2018-01-0417

System Design Model for Parallel Hybrid Powertrains using Design of Experiments 2018-01-0417

The paper focuses on an optimization methodology, which uses Design of Experiments (DoE) methods to define component parameters of parallel hybrid powertrains such as number of gears, transmission spread, gear ratios, progression factor, electric motor power, electric motor nominal speed, battery voltage and cell capacity. Target is to find the optimal configuration based on specific customer targets (e.g. fuel consumption, performance targets). In the method developed here, the hybrid drive train configuration and the combustion engine are considered as fixed components.
The introduced methodology is able to reduce development time and to increase output quality of the early system definition phase. The output parameters are used as a first hint for subsequently performed detailed component development. The methodology integrates existing software tools like AVL CRUISE [5] and AVL CAMEO [1].
The new approach of the present methodology includes specific integrated transmission and electric motor models in the vehicle simulation loop. This enables a calculation of appropriate efficiency maps, based on the defined parameters in every simulation loop. Characteristic parameters of the involved drive train components are delivered by integrated databases and in addition, a battery cell table of available products on the market is linked to the system.
Restrictions for design space are technically and physically based, as well as driven by benchmark data. Links between the parameters based on their dependencies to the targets, like maximum vehicle speed, improve the information quality output of the DoE model.
An integrated plausibility check selects the valid combinations of drivetrain components based on vehicle targets. In a multidisciplinary optimization process, this procedure avoids results, which include optimum values for one parameter (e.g. fuel consumption) but would lead to disadvantages in other criteria (e.g. performance).


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