Autonomous Vehicles Energy Based Operation Capacity Planning

Szpytko, Janusz; Hyla, P.; Duarte, Yorlandys Salgado

doi:10.36897/jme/130719

Cited by 1 publication

(2 citation statements)

References 14 publications

(8 reference statements)

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“…Current research [44][45][46][47][48][49][50] analyzes the charging voltage curves to study the battery characteristics. The DC charging voltage curve can be used to analyze the mechanisms of battery aging and degradation, as well as to assess the condition of a battery.…”

Section: State-of-the-artmentioning

confidence: 99%

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A Novel Methodology Based on a Deep Neural Network and Data Mining for Predicting the Segmental Voltage Drop in Automated Guided Vehicle Battery Cells

Pavliuk,

Cupek,

Steclik

et al. 2023

Electronics

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AGVs are important elements of the Industry 4.0 automation process. The optimization of logistics transport in production environments depends on the economical use of battery power. In this study, we propose a novel deep neural network-based method and data mining for predicting segmented AGV battery voltage drop. The experiments were performed using data from the Formica 1 AGV of AIUT Ltd., Gliwice, Poland. The data were converted to a one-second resolution according to the OPCUA open standard. Pre-processing involved using an analysis of variance to detect any missing data. To do this, the standard deviation, variance, minimum and maximum values, range, linear deviation, and standard deviation were calculated for all of the permitted sigma values in one percent increments. Data with a sigma exceeding 1.5 were considered missing and replaced with a smoothed moving average. The correlation dependencies between the predicted signals were determined using the Pearson, Spearman, and Kendall correlation coefficients. Training, validation, and test sets were prepared by calculating additional parameters for each segment, including the count number, duration, delta voltage, quality, and initial segment voltage, which were classified into static and dynamic categories. The experiments were performed on the hidden layer using different numbers of neurons in order to select the best architecture. The length of the “time window” was also determined experimentally and was 12. The MAPE of the short-term forecast of seven segments and the medium-term forecast of nine segments were 0.09% and 0.18%, respectively. Each study duration was up to 1.96 min.

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Section: State-of-the-artmentioning

confidence: 99%

“…Paper [48] describes a strategy for controlling an AGV device after optimizing its performance. The authors developed a model that simulates the energy approach to determine the predicted trajectory of an AGV over a three-hour period.…”

Section: State-of-the-artmentioning

confidence: 99%