Understanding EV Regen: Car Scanner Data Analysis for Optimal Efficiency

Electric vehicle (EV) owners are increasingly interested in maximizing efficiency and understanding their car’s performance. One key aspect of EV efficiency is regenerative braking, or “regen.” But how much of a difference does regen actually make? By leveraging Car Scanner Data, we can quantify the impact of regenerative braking on an EV’s energy consumption and miles per gallon equivalent (MPGe).

Recently, a 26-mile trip provided insightful data on regen. Using a car scanner app, parameters were recorded throughout a drive that included both freeway and city driving. The journey, lasting 50 minutes and 14 seconds and covering 26.013 miles, offered a real-world scenario to analyze. Starting with a 100% State of Charge (SOC), the battery concluded at 60%.

The car scanner data revealed that while the car initially drew 7.255261 kWh of battery capacity, regenerative braking contributed 1.61607 kWh back into the system. This resulted in a net energy usage of 5.639189 kWh. This translates to a remarkable ~22.3% regeneration rate, higher than anticipated and a testament to the effectiveness of regen systems. The impact on efficiency is significant: the trip MPGe was 155 with regen factored in, but dropped to 121 MPGe without considering the energy recovered through regenerative braking.

To achieve these figures, a straightforward process was followed:

  1. Data was recorded during the drive using a car scanner app.
  2. The recorded data was exported as a CSV file for further analysis.
  3. The total energy consumed (5.639189 KWh) was calculated – essentially the area under the battery power curve, considering negative values representing regen.
  4. The gross energy drawn (7.255261 KWh) was determined by calculating the area under the curve for only positive power values.
  5. Regenerated energy (1.61607 KWh) was found by calculating the area under the curve for negative power values, representing energy flowing back into the battery.

In conclusion, analyzing car scanner data offers valuable insights into EV performance. This experiment underscores that regenerative braking is not just a marginal feature but a substantial contributor to EV efficiency, potentially providing around 25% energy recovery in real-world driving conditions. This data-driven approach highlights the importance of understanding and optimizing driving habits to maximize the benefits of regenerative braking in electric vehicles.

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