[EN]The use of electric bikes (e-bikes) has grown in popularity, especially in large cities
where overcrowding and traffic congestion are common. This paper proposes an intelligent engine
management system for e-bikes which uses the information collected from sensors to optimize battery
energy and time. The intelligent engine management system consists of a built-in network of sensors
in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused
and on the basis of this information the system can provide the user with optimal and personalized
assistance. The user is given recommendations related to battery consumption, sensors, and other
parameters associated with the route travelled, such as duration, speed, or variation in altitude. To
provide a user with these recommendations, artificial neural networks are used to estimate speed and
consumption for each of the segments of a route. These estimates are incorporated into evolutionary
algorithms in order to make the optimizations. A comparative analysis of the results obtained has
been conducted for when routes were travelled with and without the optimization system. From
the experiments, it is evident that the use of an engine management system results in significant
energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to
user behavior and the characteristics of the route
