STELLA — Efficient Mobility for Electric Velomobiles
2013–2017 Evolutionary Optimisation Human-Machine Systems Efficient Mobility
STELLA — Efficient Mobility
STELLA addressed intelligent transport questions for electrically assisted velomobiles — one of the most energy-efficient personal vehicles. The two core challenges were: computing near-optimal driving strategies from elevation profiles, and integrating the human rider into those strategies.
Research threads
Energy-optimal driving strategies
- Generating near-optimal velocity profiles from GPS/elevation data using evolutionary optimisation
- Realistic vehicle and environment models required for real-world transfer
- Precise localisation while in motion
Human-machine hybridisation
- Adaptive control strategies that exploit human-machine complementarity
- Optimised training plans derived from physiological models
- Maximising joint efficiency of rider and electric assist
Publications (selected)
- Hagg & Asteroth. How to Successfully Apply Genetic Algorithms in Practice: Representation and Parametrisation. INISTA 2015
- Spieker, Hagg et al. Multi-Stage Evolution of Single- and Multi-Objective MCLP. Soft Computing, Springer, 2017
- Gaier & Asteroth. Evolution of Optimal Control for Energy-Efficient Transport. IEEE IV 2014
Partners
Prof. Alexander Asteroth (lead) — H-BRS / TREE.