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Modelling of Cranking Behaviour in Heavy Duty Truck Engines

In modern heavy duty trucks the battery is a central component. Its traditional role as an energy source for engine cranking has been extended to include powering a number of electrical components on the truck, both during driving and during standstill. As a consequence of this it is important to know how much a battery in use has aged and lost in terms of ca- pacity and power output. The difficulty in measuring these factors on a battery in use causes problem, since heavy duty truck batteries are often replaced too early or too late, leading to unnecessary high replacement costs or truck standstill respectively. The overall goal of the effort, of which this thesis is a part, is to use a model of the cranking behaviour of a heavy duty truck engine, which depends on the battery condition, to estimate the ageing and wear of a heavy duty truck battery. This thesis proposes a modelling approach to model the components involved in engine cranking. In the thesis work, system identification is made of the systems forming part of the cranking of a heavy duty truck engine. These components are the starter battery, the starter motor and its electrical circuit and the internal combustion engine. Measurement data has been provided by Scania AB for the evaluation of the models. The data has been collected from crankings of a heavy duty diesel engine at different temperatures and battery charge levels. For every cranking lapse the battery voltage and current have been measured as well as the engine rotational speed. A starter battery model is developed and evaluated. The resulting battery model is then incorporated into two different engine cranking models, Model 1 and Model 2, including a starter motor model and an internal combustion engine model apart form the battery model. The two cranking models differ in several aspects and their differences and resulting evaluations are discussed. The battery model is concluded to be sufficiently accurate during model verification, however the two cranking models are not. Model 2 is verified as more correct in in its output than Model 1, but neither is sufficiently accurate for their purpose. The conclusion is drawn that the modelling approach is sound but development of Model 2 is needed before the model can be used in model-based condition estimation.

Erik Andersson


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