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This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel.

A growing number of electric vehicles (EV's) are being used in fleet applications, creating a need for accurate estimates of vehicle mass while the vehicles are in operation. In this work, on-road energy use data are compared with simulated energy use to identify vehicle mass. The testing was performed on an electric Ford Transit Connect light-duty delivery vehicle in service with the Massachusetts Institute of Technology's facilities department. Driving data was collected using specific protocols designed to yield optimal inputs for identification, as well under normal driving conditions for evaluating the algorithms ability to identify parameters in worst-case scenarios. In this work, the identified mass is used to optimize fleet performance by providing more insight into the in-service weight of the vehicles, as well as by providing better electric vehicle range estimates to improve fleet utilization.