Route Optimized Energy Management of a Connected and Automated Multi-mode Hybrid Electric Vehicle using Dynamic Programming 2019-01-1209
This paper presents a methodology to optimize the blending of Charge Depleting (CD) and Charge Sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) that reduces overall energy consumption when the selected route cannot be drive purely electric. The PHEV used in this investigation is the second generation Chevrolet Volt and as many as four instrumented vehicles were utilized simultaneously on road to acquire validation data. The optimization method utilized is dynamic programming (DP) and is paired with a reduced fidelity propulsion system and vehicle dynamics model to enable compatibility with embedded controllers and be computationally efficient of the optimal blended operating scheme over an entire drive route. The objective of the optimizer is to enable future Connected and Automated Vehicles (CAVs) to best utilize onboard energy for minimum energy consumption based on velocity and elevation profile information from Intelligent Transportation Systems (ITS), Internet of Things (IoT), High-Definition Mapping, and onboard sensing technologies. Emphasis is placed on runtime minimization to quickly react and plan a truly optimal mode scheme to highly dynamic road conditions with minimal computational resources. On-road performance of the optimizer paired with automated CD and CS mode selection is evaluated on a fleet of four instrumented Chevrolet Volts in a variety of driving scenarios. Results indicate variable energy savings depending on drive route, with potential ranging from 5 to 12% with initial determination of the complete route optimization in less than 7 seconds.