CAE Method for linking electrochemical Lithium-ion models into integrated system-level models of electrified vehicles

Historically, electrical-equivalent modeling of battery systems has been the preferred approach of engineers when modeling hybrid and electric vehicles at the system level. This approach has provided modeling engineers good boundary conditions for batteries, with accurate terminal voltage and state of charge (SOC) calculations; however, it fails to provide insight into the electrochemical processes taking place in their Lithium-ion cells, necessary to optimize control algorithms and predict aging mechanisms within the battery. In addition, the use of predictive battery models that simulate electrochemical mechanisms empowers engineers with the ability to predict the performance of a Lithium-ion cell without requiring cells to be manufactured. If hardware is already available and tested, the use of physics-based battery models allows the simulation of the cell to be done well beyond the conditions at which the battery has been tested. Thus battery testing and characterization effort is reduced significantly without compromising results accuracy. This paper proposes a method of linking electrochemical Lithium-ion models of battery systems with multi-domain (electrical, mechanical, thermal, and flow domains) system-level models of hybrid and battery electric vehicles. The resulting technology provides accurate battery state and performance prediction at minor additional computation cost and links cell design parameters with vehicle performance and energy management analysis.