Abstract
This paper proposes a predictive driver coaching (PDC) system for fuel economy driving for hybrid electric trucks using upcoming static map and dynamic traffic data. Unlike traditional methods that optimize over engine torque and brake to obtain a speed profile, we propose to optimize over driving modes of trucks to achieve a trade-off between fuel consumption and trip time. The optimal driving mode is provided to the driver as a coaching recommendation. To obtain the optimal solution, the truck dynamics are firstly modeled as a hybrid controlled switching dynamical system with autonomous subsystems and then a hybrid optimal control problem (HOCP) is formulated. The problem is solved using an algorithm based on discrete hybrid minimum principle. A warm-start strategy to reduce algorithmic iterations is used by employing a shrinking horizon strategy. In addition, an extensive analysis of the proposed algorithm is provided. We prove that the the coasting mode is never optimal given the truck configuration and and we provide a guideline for tuning parameters to maintain the optimal mode sequence. Finally, the algorithm is validated using real-world data from baseline driving tests using a DAF hybrid truck. Significant reduction in fuel consumption is achieved when the data is perfectly available.
Highlights
IntroductionDue to a relatively low cost and flexibility in road selection and delivery locations, freight transportation will remain one of the dominant logistic solutions for the several decades
Due to a relatively low cost and flexibility in road selection and delivery locations, freight transportation will remain one of the dominant logistic solutions for the several decades.according to the European Commission reports, road transportation contributes to 23% of CO2 emission in Europe [1]
eco-driving assistance systems (EDAS) consists of a coulple of components including predictive cruise controller (PCC), adaptive cruise controller (ACC) and energy management system (EMS)
Summary
Due to a relatively low cost and flexibility in road selection and delivery locations, freight transportation will remain one of the dominant logistic solutions for the several decades. The research in this study is conducted in collaboration with DAF Trucks N.V. and focuses on speed profile and driving mode optimization using road and traffic preview data as part of the powertrain smart control solution. Acitve EDAS provide optimal speed profiles for drivers to track that minimize fuel consumption on a road segment. This can be formulated as an optimal control problem (OCP), where control inputs including the engine torque and gear positions are optimized. Some studies derived analytic solutions to the optimal velocity profile problem using Karush–Kuhn–Tucker (KKT) optimality conditions [17,18] These methods employed strong assumptions on the operating conditions and a simplified vehicle model.
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