Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm
1
Computer and Automation Department, University of Salamanca, Plaza de la Merced s/n, 37002 Salamanca, Spain
2
Artificial Intelligence Department, Polytechnic University of Madrid, Campus Montegancedo s/n, Boadilla del Monte, 28660 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(11), 2501; https://doi.org/10.3390/s17112501
Received: 12 September 2017
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Revised: 26 October 2017
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Accepted: 27 October 2017
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Published: 31 October 2017
(This article belongs to the Special Issue Smart Vehicular Mobile Sensing)
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.
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Keywords:
intelligent transport systems; information fusion; vehicular sensor network; energy efficiency
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MDPI and ACS Style
De La Iglesia, D.H.; Villarrubia, G.; De Paz, J.F.; Bajo, J. Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm. Sensors 2017, 17, 2501. https://doi.org/10.3390/s17112501
AMA Style
De La Iglesia DH, Villarrubia G, De Paz JF, Bajo J. Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm. Sensors. 2017; 17(11):2501. https://doi.org/10.3390/s17112501
Chicago/Turabian StyleDe La Iglesia, Daniel H., Gabriel Villarrubia, Juan F. De Paz, and Javier Bajo. 2017. "Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm" Sensors 17, no. 11: 2501. https://doi.org/10.3390/s17112501
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