MATEC Web Conf.
Volume 290, 20199th International Conference on Manufacturing Science and Education – MSE 2019 “Trends in New Industrial Revolution”
|Number of page(s)||7|
|Section||Management, Modelling and Monitoring of Manufacturing Processes|
|Published online||21 August 2019|
Remanent battery capacity estimation for autonomous ground industrial vehicles
University POLITEHNICA of Bucharest, Manufacturing Engineering Dept. (TCM), 313 Spl. Independentei, 060042, sector 6, Bucharest, Romania
* Corresponding author: firstname.lastname@example.org
When travelling in an industrial system for completing their assigned tasks, autonomous ground vehicles must estimate the remanent capacity of their batteries and decide if they are able to assume the next task and afterward travel to the charging or replacement station. The amount of energy needed for moving on a certain distance depends on a set of parameters belonging to the vehicle, to the runway and to the vehicle’s trajectory. The paper proposes a model for estimating the remaining capacity of the batteries after a certain distance would be covered by a vehicle. Parameters values were obtained by simulation, capacity loss was computed using the proposed model and then a neural network was taught to perform the estimation. The neural network was further used to simulate the situation when a vehicle is estimating the needed capacity before accepting a task to be performed. The results proved that the model and the network, even developed using low data volume and processing time, are able to provide accurate enough estimations and are able to allow future developments.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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