Personalised controller strategies for next generation intelligent adaptive electric bicycles
journal contributionposted on 16.07.2020, 10:37 authored by Lorenzo Stilo, Heinz Lugo, Diana Segura-VelandiaDiana Segura-Velandia, Paul ConwayPaul Conway, Andrew WestAndrew West
Air pollution and increasing traffic congestion means the current way of navigating through a city needs to be rethought. One of the possible solutions is to move away from internal combustion engines and embrace electric and hybrid vehicles. Electric Bicycles can offer an alternative to traditional modes of transport and support an environmentally friendly way to navigate an urban environment, with the benefit of encouraging physical exercise. There are still several issues that constrain a large-scale acceptance of Electric Bicycles, including the need for personalised controller strategies and the energy efficiencies. Current strategies do not include any analysis of rider’s capabilities, physiological factors or pedalling techniques. The research outlined in this paper involved 30 participants that volunteered to take part in an Incremental Sub-Maximal Ramp Test with the aim of understanding and quantifying pedalling characteristics and demonstrating that a better motor controller strategy tailored toward individual requirements is possible. Gender and Cycling Experience were the most prominent factors that differentiate the capabilities of the population. Three novel controller techniques (i.e. Fixed Percentage, Torque Filling and Real-Time Power mapping) are analysed and presented as innovative methods for next generation personalised controller strategies for Electric Bicycles.
Adaptive Informatics for Intelligent Manufacturing (AI2M) : EP/K014137/1
- Mechanical, Electrical and Manufacturing Engineering