Have you ever considered the potential of your Android Auto Car Scanner beyond just reading error codes? We’re diving into a fun and insightful experiment inspired by Prof. Robert Chung, using data from an OBD car scanner to estimate the elevation profile of a drive. This exploration utilizes readily available data from apps like CarScanner, turning your daily commute into a fascinating diagnostic exercise.
The concept revolves around leveraging the power, speed, and distance data that your android auto car scanner diligently records. By applying fundamental physics principles, specifically the equations of motion, we can calculate an estimated elevation profile. Think of it as a virtual ascent and descent map generated from your car’s performance data.
The underlying principle is energy balance accounting. For each short time interval during your drive, we can estimate the power required to overcome various forces:
- Kinetic Energy Change: The power needed to accelerate or decelerate the vehicle between the start and end of each interval.
- Air Resistance: The power consumed to push through the air, dependent on speed and aerodynamic properties.
- Rolling Resistance: The energy lost due to tire friction and other rolling components.
- Drivetrain Losses: Inefficiencies in the car’s powertrain that consume energy.
By summing these power requirements and comparing them to the actual power data reported by the android auto car scanner, the difference can be attributed to changes in gravitational potential energy – essentially, elevation changes.
Initial results, as seen in the example, indicate that the calculated elevation changes are exaggerated. This suggests potential inaccuracies in the initial data processing from the CarScanner app or the assumed values for parameters like aerodynamic drag and rolling resistance. The raw data format from some android auto car scanner apps can be challenging to work with, and massaging it into a usable format is crucial but prone to error.
However, this experiment highlights the potential of an android auto car scanner as a visual diagnostic tool. By driving a loop circuit multiple times, we can refine our estimations. The “virtual elevation” profile should ideally return to the same level at the loop’s starting point on each lap. Discrepancies would then guide adjustments to the estimated coefficients of aerodynamic drag (CdA) and rolling resistance (Crr) until consistency is achieved.
In this initial attempt, assumptions included:
- Drivetrain Efficiency: 85%
- Air density: 1.205 kg/m³
- Mass: 1,770 kg
- Crr: 0.017
- CdA: 0.80 m²
Future steps involve identifying a suitable loop circuit for testing and refining the data processing from the android auto car scanner app. Correcting data massaging and iterating on coefficient estimations should lead to a more accurate elevation profile. This methodology can evolve into a valuable technique for estimating vehicle aerodynamics using readily available data from your android auto car scanner, turning everyday drives into opportunities for automotive diagnostics and analysis.
