Search Results

Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

It is not uncommon for complex engineering products to undergo several design iterations due to changing market expectations or inadequate performance. In such circumstances, a prototype is generally available that could be used for performance analysis before a revision to the design is made. The availability of a prototype can be an invaluable tool for the analysis of the impact of potential design changes on the system performance. In this paper, a process is proposed for the derivation of a physical model that could be used for design analysis. The process uses model identification for determination of model complexity and numerical optimization for estimation of model parameters. This process is applied to a new pneumatic fan clutch system that has been developed to improve the efficiency of engine temperature regulation in heavy-duty commercial vehicles. This system is currently in a prototype phase and its detailed physical model is required for design trade-off analysis