Assessment of the running resistance of a diesel passenger train using evolutionary bilevel algorithms and operational data. (October 2021)

• Record Type: Journal Article
• Authors: Sánchez, Luciano; Luque, Pablo; Álvarez, Daniel
• Is Part Of: Engineering applications of artificial intelligence. Volume 105(2021)
• Publication Date: 2021-10
• Subjects: Railways -- Passenger trains -- Evolutionary algorithms; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104405
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Evolutionary bilevel algorithms are used for approximating the running resistance on the basis of the long-term fuel consumption data of a diesel passenger train in different routes. The input data comprises the geometry of these routes, speed and acceleration limits and certain engine properties. A running resistance is found for which the consumptions predicted by the model are equal to the logged consumptions of the vehicle for each of the routes in the training set. The model has been validated with simulated data with known properties and also with a diesel-hydraulic railcar operating on a 94 km route in northern Spain. The error in the running resistance estimation using evolutionary algorithms with respect to the measurement with a coasting test was less than 4%. Highlights: The running resistance of a train is calculated from the average fuel consumption. Neither dedicated sensors nor access to the vehicle control unit are required. A quadratic expression of the resistance is obtained from a reduced set of routes. Bilevel evolutionary optimization results are consistent with the coasting tests.