电动自行车辅助系统实现骑行者自适应支持的方法

Yannick Rauch, Reiner Kriesten
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引用次数: 0

摘要

在骑电动自行车时,骑行者会面临这样一个问题:电池中是否还有足够的能量,以达到所需的支持水平到达目的地。因此,电动自行车骑行者会产生续航焦虑。具体来说,这种焦虑是指担心在有机会充电之前,电池电量就会耗尽,从而无法再使用电动助力。然而,迄今为止,电动自行车骑行者必须自行决定可用的电池电量是否足以骑行计划的路线,或者是否可以到达理想的目的地。在这种情况下,面临的挑战是如何决定可以使用多少电力推进支持,以便在整个骑行过程中达到适当的强度。为了帮助电动自行车骑行者解决这个问题,本文的目的是提出一种方法,使系统能够在规定路线的整个骑行过程中为骑行者提供自适应支持。这包括以骑手需要适当努力的方式使用推进支持。骑行者自适应支持将通过电动自行车推进系统的自动模式来实现,该模式可自动设置相应的支持强度。辅助系统的设计目的是确保利用骑行者自适应辅助系统可以到达计划的目的地。为此,将根据可用能量和骑行路线优化并自动调整推进支持的使用。该系统将作为预测性能源管理系统来实施。该系统根据对骑行路线的能量需求预测和电动自行车电池的可用能量,计算出优化的支持策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approach for an Assistance System for E-Bikes to Implement Rider-Adaptive Support
When riding an e-bike, riders are faced with the question of whether there is enough energy left in the battery to reach the destination with the desired level of support. Therefore, e-bike riders have range anxiety. Specifically, this describes the fear that the battery charge will be exhausted before there is an opportunity to recharge it and that it will no longer be possible to use the electric support. However, e-bike riders have so far had to decide for themselves whether the available battery charge is sufficient for riding the planned route or whether the desired destination can be reached. In this context, the challenge is to decide how much electric propulsion support can be used so that an appropriate amount of effort can be achieved for the entire ride. In order to assist e-bike riders with this problem, the objective of this paper is to present an approach towards a system that provides rider-adaptive support over the entire ride of a defined route. This involves using the propulsion support in such a way that the rider requires an appropriate level of effort. The rider-adaptive support is to be implemented via an automatic mode of the e-bike propulsion system, which automatically sets the corresponding support intensity. The assistance system is designed to ensure that a planned destination can be reached using the rider-adaptive support. To achieve this, the use of the propulsion support is optimized and automatically adjusted according to the available energy and the route to be cycled. The implementation will be carried out as a predictive energy management system. This calculates an optimized support strategy based on an energy demand prediction for the route to be cycled and the available energy of the e-bike battery.
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