Case study
STELLA — Efficient Mobility for Electric Velomobiles
Energy-optimal driving strategies and human-machine hybridisation for electrically assisted velomobiles.
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.