{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:19:21Z","timestamp":1770909561120,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T00:00:00Z","timestamp":1703894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of National Education of the Republic of Turkiye"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a special recurrent neural network (RNN) called Long Short-Term Memory (LSTM) is used to design a virtual load sensor that estimates the mass of heavy vehicles. The estimation algorithm consists of a two-layer LSTM network. The network estimates vehicle mass based on vehicle speed, longitudinal acceleration, engine speed, engine torque, and accelerator pedal position. The network is trained and tested with a data set collected in a high-fidelity simulation environment called Truckmaker. The training data are generated in acceleration maneuvers across a range of speeds, while the test data are obtained by simulating the vehicle in the Worldwide harmonized Light vehicles Test Cycle (WLTC). Preliminary results show that, with the proposed approach, heavy-vehicle mass can be estimated as accurately as commercial load sensors across a range of load mass as wide as four tons.<\/jats:p>","DOI":"10.3390\/s24010226","type":"journal-article","created":{"date-parts":[[2023,12,31]],"date-time":"2023-12-31T06:00:21Z","timestamp":1704002421000},"page":"226","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["LSTM-Based Virtual Load Sensor for Heavy-Duty Vehicles"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2394-8312","authenticated-orcid":false,"given":"Abdurrahman","family":"\u0130\u015fbitirici","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Information Engineering, University of Bologna, 40126 Bologna, Italy"},{"name":"Department of Engineering \u201cEnzo Ferrari\u201d, University of Modena and Reggio Emilia, 41125 Modena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2304-4394","authenticated-orcid":false,"given":"Laura","family":"Giarr\u00e9","sequence":"additional","affiliation":[{"name":"Department of Engineering \u201cEnzo Ferrari\u201d, University of Modena and Reggio Emilia, 41125 Modena, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Xu","sequence":"additional","affiliation":[{"name":"Volvo Trucks, 40508 Gothenburg, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9587-9924","authenticated-orcid":false,"given":"Paolo","family":"Falcone","sequence":"additional","affiliation":[{"name":"Department of Engineering \u201cEnzo Ferrari\u201d, University of Modena and Reggio Emilia, 41125 Modena, Italy"},{"name":"Mechatronics Group, Department of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7498","DOI":"10.1109\/TVT.2019.2921702","article-title":"Formulation and comparison of two real-time predictive gear shift algorithms for connected\/automated heavy-duty vehicles","volume":"68","author":"Xu","year":"2019","journal-title":"IEEE Trans. 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