Physics-Based and Data-Enhanced Model for Electric Drive Sizing during System Design of Electrified Powertrains

Abstract

In multi-drive electrified powertrains, the control strategy strongly influences the component load collectives. Due to this interdependency, the component sizing becomes a difficult task. This paper comprehensively analyses different electric drive system sizing methods for multi-drive systems in the literature. Based on this analysis, a new data-enhanced sizing approach is proposed. While the characteristic is depicted with a physics-based polynomial model, a data-enhanced limiting function ensures the parameter variation stays within a physically feasible range. Its beneficial value is demonstrated by applying the new model to a powertrain system optimization. The new approach enables a detailed investigation of the correlations between the characteristic of electric drive systems and the overall vehicle energy consumption for varying topologies. The application results demonstrate the accuracy and benefit of the proposed model.

Environmental Impact Assessment and Classification of 48 V Plug-in Hybrids with Real-Driving Use Case Simulations

“…𝑁−1 𝑡=0 (13) where 𝐽 is the total fuel consumption, 𝐿 is the fuel consumption matrix, and 𝑁 is the number of steps in the driving cycle. The costs vary with the state vector 𝑥(𝑡), the state of charge (SOC) in this case, and the control input 𝑢(𝑡).…”

“…The largest reduction potential was achieved with topologies, where the electric engine is placed between the transmission and wheels in combination with a belt-integrated starter generator. The authors of [13] focused on the machine design during the system-level optimization process and considered the changes of the machine characteristics during the sizing process. Similarly, most of the fuel reduction potential could be gained with 20-25 kW mechanical power of the electric machine.…”