Why should the automated guided vehicles’ batteries be used in the manufacturing plants as an energy storage?

Yesilyurt, Ozan; Bauer, Dennis; Emde, Alexander; Sauer, Alexander

doi:10.1051/e3sconf/202123101004

Cited by 4 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: State-of-the-artmentioning

confidence: 99%

“…Publication [50] explores the use of AGV batteries as portable energy storage devices to reduce peak loads at manufacturing enterprises. For this purpose, scenarios were created to determine the electricity costs of small and medium-sized enterprises.…”

Section: State-of-the-artmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Notable studies include Vitalii et al (2021), which found that replacing human-controlled vehicles with autonomous mobile robots could significantly reduce energy consumption, even without modifying the control system. Ozan et al (2021) explored reducing peak loads and lowering energy costs for companies implementing AGVs in their production processes. Additionally, several other relevant studies on the subject include Guilherme et al (2019), which focused on the sustainable implementation of AGVs in the Brazilian industry; Puneeth et al (2018), which examined the size of AGV fleets and their impact on operations; and Hee-Woon and Hwally (2018), which looked at the efficiency of AGVs for small businesses.…”

Section: Literature Reviewmentioning

confidence: 99%

FMEA Model in Risk Analysis for the Implementation of AGV/AMR Robotic Technologies into the Internal Supply System of Enterprises

Bekishev,

Pisarenko,

Arkadiev

2023

Risks

View full text Add to dashboard Cite

In the evolving economic landscape, Industry 4.0 emphasizes strategic planning and operational progress for large enterprises. This transformation relies on smart robotization technologies like AGVs (Automated Guided Vehicles) and AMRs (Autonomous Mobile Robots) for reducing transportation time, thereby reducing energy costs per unit of production, increasing energy efficiency, as well as replacing combustible-fuel-powered tools with electric ones. A number of concerns arise with their introduction into the production cycle. This research aims to provide a methodical basis for averting substantial mistakes when executing projects centered around the incorporation of AGVs/AMRs into in-house logistics systems. The FMEA method and empirical analysis were employed to achieve a more accurate risk assessment. APIS and MS Excel softwares were chosen. We investigated the potential hazards related to the incorporation of mobile robotic solutions and identified both external and internal threats. To streamline and improve project efficiency, a risk management algorithm for high-tech projects is presented in the paper. Integrating FMEA into projects implementing robotic technologies can lead to significant enhancements in risk reduction, and therefore cost savings, efficiency, safety, and quality, while fostering a culture of collaboration and problem solving. The research contributes to the literature by introducing an AMR planning and control framework to guide managers in the decision-making process, thereby supporting them to achieve optimal performance. Finally, we propose an agenda for future research within the field of interest.

show abstract

“…Peak shaving can be a lucrative way to save on electricity costs [7]. Using forecasts, peak shaving aims to reduce current load peaks and thus reduce grid charges.…”

Section: Introduction 1motivation and Problem Formulationmentioning

confidence: 99%

Effects of Load Forecast Deviation on the Specification of Energy Storage Systems

Emde,

Märkle,

Kratzer

et al. 2023

Designs

Self Cite

View full text Add to dashboard Cite

The liberalization of the German energy market has created opportunities for end-consumers, including industrial companies, to actively participate in the electricity market. By making their energy loads more flexible, consumers can generate additional income and thus save money. Energy storage systems can be utilized to achieve the required flexibility by temporarily storing excess electrical energy in the form of heat, cold, or electricity for later use. This publication focuses on how the dimensionality of energy storage is influenced by load forecasting. The results show that inaccuracies in load forecasting lead to a direct over-dimensioning and thus, a deterioration of the economics of energy storage technologies. Using two scenario cases, it shows on the one hand how important good forecasts are and on the other hand that buffers must be included in the conceptual design in order to be able to compensate for forecast errors.

show abstract

Why should the automated guided vehicles’ batteries be used in the manufacturing plants as an energy storage?

Cited by 4 publications

References 22 publications

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

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

FMEA Model in Risk Analysis for the Implementation of AGV/AMR Robotic Technologies into the Internal Supply System of Enterprises

Effects of Load Forecast Deviation on the Specification of Energy Storage Systems

Contact Info

Product

Resources

About