The distance that an electrically powered bicycle can cover depends on factors such as the route’s elevation profile, the motor support selected, and the fitness of the cyclist. This fact requires the cyclist to estimate which motor support may be chosen to reach the goal with the battery’s current state of charge. For this reason, we propose a battery management control system based on a nonlinear model predictive controller (NMPC) for pedal-electric drive units (Pedelecs) that takes into account route information and cyclist fatigue. The goal is to guarantee a user-defined state of charge (SoC) at the end of the route while minimizing cyclist fatigue. The degree of support of the Pedelec is considered as the manipulated variable. In order to find an optimal level of assistance, the NMPC minimizes a quadratic cost function that is subject to three nonlinear distance-dependent models. The first two models describe the bicycle dynamics and the discharge behavior of the battery. To obtain an estimate of the maximum voluntary force that the cyclist can apply, the third model describes the cyclist’s fatigue. The identified models and the control strategy are validated with a trekking Pedelec on a 33 km test track. The proposed NMPC is able to guarantee a predefined target SoC at the end of the track while keeping the estimated cyclist’s fatigue low.

电动自行车可以覆盖的距离取决于各种因素，例如路线的海拔高度，所选的马达支撑以及骑车人的身体状况。这个事实要求骑自行车的人估计可以选择哪种电动机支持以达到电池当前充电状态的目标。因此，我们提出了一种基于非线性模型预测控制器（NMPC）的电池管理控制系统，该系统适用于踏板电动驱动单元（Pedelecs），其中考虑了路线信息和骑车人的疲劳。目标是在路线尽头确保用户定义的充电状态（SoC），同时最大程度地减少骑车人的疲劳感。Pedelec的支持程度被视为操纵变量。为了找到最佳的协助水平，NMPC最小化了服从三个非线性距离相关模型的二次成本函数。前两个模型描述了自行车动力学和电池的放电行为。为了获得对骑车人可以施加的最大自愿力量的估计，第三个模型描述了骑车人的疲劳程度。徒步旅行的Pedelec在33上验证了所识别的模型和控制策略 公里测试轨道。拟议的NMPC能够在赛道尽头确保预定义的目标SoC，同时保持较低的骑车人疲劳度。