A Novel Development Method for a Control Model of TWC in Hybrid Electric Vehicles Based on Mechanism Simplification and Temperature Update

Under increasingly stringent emission regulations, hybrid electric vehicles face heightened requirements for tailpipe emission control. Their complex operational profiles and frequent transitions between power modes pose significant challenges to experimental efforts in emission measurement and aftertreatment control, leading to substantially increased economic and temporal costs that severely constrain emission optimization. To address these challenges, this study employs the development and calibration of a high-accuracy virtual model to conduct simulation experiments that accurately replicate real-world driving conditions, significantly reducing development costs while maintaining predictive accuracy. Focusing on hybrid electric vehicles, we developed an aftertreatment model integrating catalytic reaction mechanisms and energy conservation equations, incorporating coupled temperature and concentration calculations. This model enables real-time monitoring of dynamic oxygen storage capacity in the TWC, establishing a foundation for control strategies based on oxygen storage level. Parameter calibration was performed using bench test data across multiple operating conditions, with model accuracy validated via real vehicle tests under transient conditions. Results demonstrate that the absolute errors between simulated and actual aftertreatment outputs for total THC, CO, and NOx emissions remain consistently below 5.30%, confirming the model's accuracy in representing real-world emission characteristics of hybrid electric vehicles.