Evaluation of Optimal State of Charge Planning for a Plug-in Hybrid Vehicle
2022-01-0895
03/29/2022
- Event
- Content
- There is and continues to be a widespread adoption of Plug-in Hybrid electric vehicles. Seen by many as a staging technology for fully electric vehicles, hybrid technologies enable the reduction of noxious tailpipe emissions and conformance with ever-decreasing allowable homologation limits. The complexity of the hybrid powertrain technology can lead to an energy management problem with multiple energy sinks and sources comprising the system resulting in a high-dimensional time dependent problem for which many solutions have been proposed [1]. Methods that rely on accurate predictions of potential vehicle operations are demonstrably more optimal when compared to rule-based methodology. In this paper, a previously proposed energy management strategy based on an offline optimisation using dynamic programming is investigated. This work explores the effects of drive cycle segmentation on the optimality of the results and is then coupled with an online model predictive control strategy to derive and follow an optimal battery state of charge trajectory. This approach reduces the size of the dynamic program without significant loss in the optimality of the results. The test vehicle modelled in Simulink is a P2 parallel hybrid configuration based on experimental powertrain data. As the dynamic program relies on future predictions of speed and load, potentially provided from navigation data, the actual drive cycle will vary from the prediction used to perform the offline optimisation. The results of the analysis are then compared to a rule-based charge deplete and sustain strategy which is employed in many current production vehicles using key performance criteria like fuel and energy consumption. Our investigation shows that fuel consumption improves over a Charge deplete and sustain strategy provided certain features of the driveline input can be predicted with a certain degree of accuracy.
- Citation
- Jegede, T., Knowles, J., Steffen, T., D'Amato, M. et al., "Evaluation of Optimal State of Charge Planning for a Plug-in Hybrid Vehicle," SAE Technical Paper 2022-01-0895, 2022, .