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Optimal power management of an electric bicycle based on terrain preview and considering human fatigue dynamics

This paper proposes an optimal control approach to power management and pacing in an electric bicycle ride with the objective of minimizing travel time. We assume prior knowledge of upcoming terrain. Furthermore human pedaling force constraints are estimated by using a phenomenological fatigue dynamics model. Using upcoming terrain information, estimated rider's state of fatigue (SOF), measured velocity, cadence, and state of charge (SOC) of the battery, the optimal solution to the problem is obtained via a three-state dynamic programming (DP) approach. The proposed solution guides the rider by suggesting an optimal reference velocity and also optimally adjusts the electric-human power split. The optimal solution is compared to aggressive and conservative rule-based strategies that we have devised. Simulation results show that travel time can be significantly reduced with the optimal control approach.