Design and simulation of control systems for electric-assist bikes

Abstract

Control design plays a key role in electric-assist bikes. This paper has established dynamic models of electrically assisted biking systems, and then, designed proportion-assisted power controllers (PAPC) in accordance with Taiwan government rules. In the PAPC controllers, current bike speed and pedal torque are used as inputs to determine the assisted torque generated from a motor. On the other hand, a fuzzy logic controller (FLC) has been also designed, using the current bike speed and pedal frequency as inputs to determine the assisted torque. In order to compare these two controllers, simulations of riding bikes on a flat ground and different slopes have been performed. Simulation results show that the FLC outperforms PAPC in a more stable bike speed.

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

“…Some articles addressed the fundamental aspects of an electric bike (see Figure 2), such as motor control (i.e., field-oriented [16,17], fuzzy logic [18][19][20], firefly algorithm [21], particle swarm optimization [22], model predictive [23], reinforcement learning [24], and others [25][26][27][28][29][30][31]) using different inputs, such as torque (human and machine), power and speed to control the bike's motor. Studies on battery management system (BMS) and energy recovery [18,23,[31][32][33][34], explored methods of supervising and charging the batteries used by the bikes (State of Health and State of Charges) and also explored the possibility of recovering energy by braking and in downhill situations.…”

Design and implementation of a low cost electric bike for domestic purposes

“…Proportion assisted power controller and fuzzy logic based controllers are designed and compared which refers current speed and pedal torque to determine motor torque. Fuzzy logic controller found to be provided stable speed (4) . 8 Bit microcontroller based electronic system is designed to control the BLDC motor for e-bike (5) .…”

Enhanced fuzzy‐logic‐based power‐assisted control with user‐adaptive systems for human‐electric bikes

“…In a similar way, Cardone et al [11] also presented a model‐based optimal torque control considering the slope, the human torque, and the rolling resistance for power‐assisted electric bikes. In the literature, the fuzzy‐logic theory also has been employed to energetically adjust the assist power [12–17]. For example, Guarisco et al [15] developed a fuzzy‐logic controller (FLC) to ensure the distribution of the total power control by taking the human–bike coupling into consideration for an electric bike with all‐wheel drive.…”

“…For example, Guarisco et al [15] developed a fuzzy‐logic controller (FLC) to ensure the distribution of the total power control by taking the human–bike coupling into consideration for an electric bike with all‐wheel drive. In [16], Lee et al presented an FLC without a torque sensor and adopted the bike speed and cadence as fuzzy inputs to generate the appropriate assist power. Moreover, Tal et al [17] proposed a fuzzy‐logic‐based speed adaptation policy with wind awareness to improve the cycling experience.…”