In-field piecewise regression based prognosis of the IPC in electrically powered agricultural machinery

Juan Villacrés, Fernando Auat Cheein*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The energy consumption in electrically powered machinery (EPM) depends on the manoeuvres, the mass of the vehicle, its load, the characteristics of the terrain, the deformation of the wheel, the slippage, the ambient and batteries temperature, among other issues, changing the instantaneous power consumption (IPC) behaviour. An accurate estimate of energy consumption (and therefore, of the IPC) will lead to an efficient battery recharging strategy. To overcome the IPC unmodelled issues previously mentioned, this work presents a procedure for predicting the energy consumed by EPMs through IPC prognosis, tested and validated on three different terrain types: gravel, clay and pavement. To this end, a fixed polynomial model of the IPC with respect to the terrain type is obtained as a priori knowledge. Then, through new readings of the IPC, the model is updated by segments and later used for IPC prognosis given a previously defined route. The experimental results show an improvement in the estimation of energy consumption (and therefore, of the energy still available for traversing) of 56.22% with respect to the data provided by the manufacturer and of 7.14% compared to theoretical and empirical approaches previously published. Although tested in agricultural scenarios, the methodology presented here encourages to be applied in other contexts of electro-mobility since it offers a suitable technique for better managing operational costs.

Original languageEnglish
Article number107324
JournalComputers and Electronics in Agriculture
Volume202
Early online date14 Sept 2022
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Agricultural machinery
  • Energy consumption
  • Piecewise regression

ASJC Scopus subject areas

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture

Fingerprint

Dive into the research topics of 'In-field piecewise regression based prognosis of the IPC in electrically powered agricultural machinery'. Together they form a unique fingerprint.

Cite this