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Original Articles

Disturbance observer approach for fuel-efficient heavy-duty vehicle platooning

, , , , & ORCID Icon
Pages 748-767 | Received 28 Mar 2019, Accepted 04 Dec 2019, Published online: 29 Dec 2019
 

ABSTRACT

Heavy-duty vehicle platooning has received much attention as a method to reduce fuel consumption by keeping inter-vehicle distance short. When a platoon follows a fuel-optimal velocity profile calculated using preview road slope information, significant improvement in the fuel economy occurs. To calculate the optimal velocity in the existing method, however, platoon should acquire expensive road slope data in advance. As an alternative, we propose a road slope estimation method, which enables platoon to calculate the optimal velocity profile without the usage of actual road slope data. Other major challenges in platoon operation include overcoming the effect of the vehicle model uncertainties and external disturbances for ensuring the control performance. The most significant part of the disturbances arises from slopes along a route. Existing method for reducing the effect of the slope employs a feed-forward type compensation in the control loop by combining the vehicle position acquired from GPS and the slope database. However, this method exhibits limitations: the mass of the vehicles in the platoon is uncertain which lowers the accuracy of the feed-forward compensation, and the platoon requires the pre-acquired slope database. To overcome these limitations, we propose an alternative method employing disturbance observer. Simulations of various scenarios are conducted to show the efficacy of the proposed method using the actual road slope data of a Swedish highway.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was partly supported by Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2014-3-00065, Resilient Cyber-Physical Systems Research), partially supported by Global Research Laboratory Program through the National Research Foundation of Korea (NRF-2013K1A1A2A02078326) and also supported in part by R&D Program of the Korea Railroad Research Institute (PK1901B1-1).

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