Electric vehicle design, racing and distance to empty algorithms

Abstract

This research began with the goal of designing and building an electric motorcycle to compete in the Isle of Man TT Zero race. A set of parametric physics-based models was derived to size the batteries and motors, predict vehicle speeds and predict the time required to finish the race. In June 2011 the motorcycle design and simulations were tested in three races on the Isle of Man. Post-race analysis showed that the predictions had less than 10% error. The energy estimation methods that were developed for the motorcycle were subsequently modified and applied to non-racing electric vehicles. Instead of predicting the energy required to travel a known route, it is more useful for non-racing applications to consider the reverse scenario, which is the distance the vehicle can travel before charging is required. This is referred to as the Distance to Empty (DTE). Recent studies have shown that current DTE algorithms are inadequate and cause "range anxiety" among users. This is because conventional approaches only use past driving data to estimate DTE and thus are unable to accurately predict changes in driving conditions. However, the algorithm developed in this thesis uses measurements from the past along with knowledge of the future route. A multivariate linear regression model is used to adjust a historical average of energy consumption based on estimated changes in speed, traffic and temperature. Finally, the new DTE algorithm was compared to conventional methods by simulating a large number of full battery discharges under realistic driving conditions. A Markov-based stochastic speed profile generator was used as input to the models. Example simulations show that including future driving conditions in the DTE algorithm can significantly reduce error.