Characterizing Regenerative Braking of Tesla Model 3 Using Dynamometer Testing

Regenerative braking has a strong influence on the energy efficiency and drivability of battery-electric vehicles. This study establishes an empirical baseline analysis under controlled conditions of the regenerative braking behavior of the 2020 Tesla Model 3 to support the interpretation of on-road performance and serve as a reference for subsequent testing and analysis. The tests were performed on a four-wheel-drive chassis dynamometer at Argonne National Laboratory, combining Multi Cycle Testing (MCT) to simulate real world driving patterns (city, highway) with coast-down tests to isolate periods where the motor is operating in regen mode and compare the behavior across different parameters. Vehicle data was collected from the vehicle using taps in the Controller Area Network (CAN) bus as well as a high-resolution power analyzer. The vehicle displayed the highest efficiency during simulated city driving conditions (3.62 miles/kWh followed by highway (3.40 miles/kWh) and aggressive (2.53 miles/kWh) conditions, though aggressive driving showed the highest energy recovery. Regen energy recovery was most efficient in the 10 – 30 mph range, with the rear motor regenerating all the energy while the front motor used a small amount of power. Standard regen mode achieved 57% greater deceleration during coast down compared to Low Regen mode, and showed a much lower variability during different simulated uphill and downhill conditions. Standard mode collected more energy than Low mode in all cases apart from simulated downhill tests where Low mode performed better. These results provide an overview of the Tesla Model 3 regenerative braking behavior and delineate operating regimes that maximize efficiency and quantify trade-offs between deceleration stability and energy recovery across driver-selectable modes. The results provide a rigorous, reproducible baseline and measurement protocol that can enable cross-vehicle benchmarking, validate vehicle/software-in-the-loop models, and inform future controller calibration and the design of on-road and track experiments