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Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming
Technical Paper
2019-01-1209
ISSN: 0148-7191,
e-ISSN: 2688-3627
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English
Abstract
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 completely driven in all-electric mode. 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 used is dynamic programming (DP) paired with a reduced-order powertrain model to enable onboard embedded controller compatibility and computational efficiency in optimally blending CD, CS modes over the entire drive route. The objective of the optimizer is to enable future Connected and Automated Vehicles (CAVs) to best utilize onboard energy for minimum overall energy consumption based on speed 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 an optimal mode scheme in 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 the drive route and initial battery SOC with potential ranging between 2 to 12% and less than 7 seconds initial optimization for a 24-mile drive cycle.
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Citation
Rama, N., Wang, H., Orlando, J., Robinette, D. et al., "Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming," SAE Technical Paper 2019-01-1209, 2019, https://doi.org/10.4271/2019-01-1209.Data Sets - Support Documents
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