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Driving Range Estimation and Trajectory Planning

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Driving Range Estimation and Trajectory Planning ( driving-range-estimation-and-trajectory-planning )

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7 Implementation into the Delivered Application The initial application, from Porsche Engineering Services s.r.o., provides the necessary java-based code which already works well. The connection between the tablet and this app is done by reading CAN signals in the vehicle, and transmitting them to the tablet per Bluetooth communication protocol. The class diagram of the application, with developed code, is displayed in appendix C of this thesis. 7.1 Reading Signals For the purpose of this thesis, the app reads only two CAN signals and those are the ”Remaining driving range” and the ”Average Consumption”. The following example of code was used to read these values in the algorithm. SignalValue drivingRange = signalMgr . getSignalValue (”Energy ”, NiceNameStorage .CUSTOM REMAINING DRIVING RANGE) ; SignalValue avgConsumption = signalMgr . getSignalValue (”Energy ”, NiceNameStorage .CUSTOM AVERAGE CONSUMPTION) ; During the implementation, there was a problem with this value because it didn’t corre- spond to the reality (the driving range algorithm wasn’t calibrated) so, I was forced to edit this signal so that the average consumption of the vehicle was statically defined as 20 kWh/100 km, and the algorithm responsible for getting this signal value was corrected. 7.2 Example of Implementation to the App The developed application was implemented as Google Map Fragment with widget prop- erties. The controls are inside the fragment and it inherited the Graph object from the delivered app. This Graph object has built functions and these functions allow that the object can be resized and moved all over the screen. It means that the Google Map Frag- ment is not implemented as a static object. It is a more user-friendly solution. There are several layouts in the app, and there was created the new one named Google Map. An example is shown in Fig. 7.1 . 35

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