Vehicle Velocity Prediction and Energy Management Strategy Part 2: Integration of Machine Learning Vehicle Velocity Prediction with Optimal Energy Management to Improve Fuel Economy

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction. These various velocity predictions are used as an input into an Optimal EMS derivation algorithm to derive an engine torque and engine speed control strategy that improves FE compared to current vehicle operation. Dynamic programming is used as the Optimal EMS because it provides a globally optimal control which is preferable for this investigatory study. The vehicle model used is real world validated and represents a 2017 Toyota Prius Prime operating in charge sustaining mode. The results show that actual vehicle velocity prediction from the prediction model achieves 85% of the FE improvement from an Optimal EMS derived using perfect 10 second prediction.