A Deep Learning-Based Strategy to Initiate Diesel Particle Filter Regeneration

Deep learning (DL)━based approaches enable unprecedented control paradigms for propulsion systems, utilizing recent advances in high━performance computing infrastructure connected to modern vehicles. These approaches can be employed to optimize diesel aftertreatment control systems targeting the red...

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Veröffentlicht in:	SAE International journal of engines 2022-01, Vol.15 (5), p.601-612, Article 03-15-05-0032
Hauptverfasser:	Aslandere, Turgay, Fan, Ke, DeSmet, Frederik, Roettger, Daniel
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Control algorithms Control systems Deep Learning Design and construction Diesel fuels Diesel motor Dilution DPF Regeneration Driving conditions Energy efficiency Equipment and supplies Fluid filters Fuel consumption Fuel economy Fuel filters LSTM Machine learning Neural Networks Optimization Oxidation Propulsion system performance Regeneration Soot Traffic control Traffic speed Vehicles
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