Powertrain modelling for engine stop–start dynamics and control of micro/mild hybrid construction machines. (September 2017)

• Record Type: Journal Article
• Authors: Dinh, TQ; Marco, J; Greenwood, D; Harper, L; Corrochano, D
• Other Names: Ebrahimi Kambiz guest-editor.; Mohammadpour Mahdi guest-editor.
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 231:Number 3(2017:Sep.)
• Page Start: 439
• Page End: 456
• Publication Date: 2017-09
• Subjects: Construction machine -- powertrain -- dynamic -- modelling -- optimisation -- start control; Machinery, Dynamics of -- Periodicals; Machine design -- Periodicals; 621.815
• DOI: 10.1177/1464419317709894
• Languages: English
• ISSNs: 1464-4193

Abstract

Engine stop–start control is considered as the key technology for micro/mild hybridisation of vehicles and machines. To utilise this concept, especially for construction machines, the engine is desired to be started in such a way that the operator discomfort can be minimised. To address this issue, this paper aims to develop a simple powertrain modelling approach for engine stop–start dynamic analysis and an advanced engine start control scheme newly applicable for micro/mild hybrid construction machines. First, a powertrain model of a generic construction machine is mathematically developed in a general form, which allows to investigate the transient responses of the system during the engine cranking process. Second, a simple parameterisation procedure with a minimum set of data required to characterise the dynamic model is presented. Third, a model-based adaptive controller is designed for the starter to crank the engine quickly and smoothly without the need of fuel injection while the critical problems of machine noise, vibration and harshness can be eliminated. Finally, the advantages and effectiveness of the proposed modelling and control approaches have been validated through numerical simulations. The results imply that with the limited data set for training, the developed model works better than a high fidelity model built in AMESim while the adaptive controller can guarantee the desired cranking performance.