Computationally Efficient Reduced-Order Powertrain Model of a Multi-Mode Plug-In Hybrid Electric Vehicle for Connected and Automated Vehicles

摘要

This paper presents the development of a reduced-order powertrain model for energy and SOC estimation of a multi-mode plug-in hybrid electric vehicle using only vehicle speed profile and route elevation as inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon energy optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND), Eco Routing, and PHEV mode blending. The reduced-order powertrain model enables Connected and Automated Vehicles (CAVs) to utilize the onboard sensor and connected data to quickly react and plan their maneuvers to highly dynamic road conditions with minimal computational resources. Although overall estimation accuracy is less than neural network and high-fidelity models, emphasis on runtime minimization with reasonable estimation accuracy enables energy optimization of CAVs without a need for computationally expensive server-based models. Performance of the model is evaluated on a fleet of second-generation Chevrolet Volts in a variety of driving scenarios and drive cycle durations. On-road testing indicates that the model can estimate actual vehicle behavior and energy consumption with a median estimation accuracy of over 90% and a runtime less than 0.3 seconds. This makes the model highly advantageous for real-time energy optimization in CAVs.