Automated Deployment of Modelica Models in Excel via Functional |

Automated Deployment of Modelica Models in Excel via Functional

John Batteh, Jesse Gohl, Anand Pitchaikani (Modelon) Alexander Duggan, Nader Fateh (ESTECO)

CHALLENGE - To meet the demand for increased model-based engineering, the ability to efficiently develop and deploy models across an enterprise is a key enabler. Open standards such as the Modelica modeling language and Functional Mockup Interface (FMI) for model exchange and co-simulation can streamline the modeling and deployment process. Since modeFRONTIER includes a widely-used Excel interface, adding FMU simulation capability easily via FMI Add-in for Excel is a natural extension. In this paper, 

SOLUTION - Using existing interfaces, integration with modeFRONTIER is demonstrated and illustrated with several different example models in different physical domains to highlight the range of applications and types of analyses that can be covered with the automated toolchain. One example is the optimization of hybrid vehicle electric range. Critical battery parameters strongly affect vehicle range. Battery performance is affected by both battery temperature and battery age. A series hybrid truck model is implemented with the Modelon Vehicle Powertrain package. Here it is possible to run the simulations over the battery aging factor and then simply construct the distributions offline, thereby saving computational resources. The simulations are run starting with a battery SOC=1 until the SOC is depleted via repeated execution of the New European Driving Cycle (NEDC) cycles.










BENEFITS - In this paper, several different example models illustrate the integration and analysis capabilities of modeFRONTIER and Modelica models. In the highlighted example, results from modeFRONTIER over the entire age range show that any arbitrary vehicle age distribution can be assumed and the fleet range distribution calculated. Figure 25 above shows the calculated distribution for a normal BatteryAge distribution with a mean=0.5 and standard deviation=0.05. Similar calculations could be done over a range of different drive cycles, battery ages, and temperatures to estimate more complex fleet populations.