Analysis and Comparison of Power Distribution System Topologies for Low-Voltage DC–DC Automated Guided Vehicle Applications

Hanschek, Andreas; Bouvier, Y.; Jesacher, Erwin; Grbović, Petar J.

doi:10.3390/en15062012

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Publication [41] is devoted to increasing the operating time of AGVs by implementing a kinetic energy recovery system to store braking energy. In this paper, the authors analyze and compare different energy distribution systems using batteries and supercapacitorcoupled batteries.…”

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

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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%

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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show abstract

“…FCVs have gradually become the mainstream development direction of NEVs, attracting the attention of governments and research institutes around the world [7,8]. Since FCVs typically contain two or more energy storage systems (ESSs) [9], such as a power battery pack, a fuel cell, and a supercapacitor [10], suitable and efficient energy management methods (EMMs) are critical for FCVs [11].…”

Section: Introduction 1motivationsmentioning

confidence: 99%

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

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Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.

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“…Moreover, there is the need for external systems, such as injectors or compressors, and internal combustion motors require monitoring of fuel and oil parameters, e.g., level, quality, and contamination [ 4 , 5 , 6 , 7 , 8 ]. Thanks to the improvement of DC and AC motor performance, innovative and more efficient power management and storage techniques, and increase in power and energy density of battery packs [ 9 , 10 , 11 , 12 ], many different classes of ground, aerial and underwater vehicles are being designed with a fully electrical powertrain, such as Unmanned Aerial Vehicles (UAV)s [ 13 , 14 , 15 , 16 ], cars [ 17 , 18 , 19 , 20 ], mobile robots in diverse industrial applications [ 21 , 22 , 23 , 24 ] and even short-range aircraft [ 25 ]. Moreover, the extensive use of distributed sensors allows for unprecedented levels of autonomy in mission management and totally novel (and sometimes disruptive) human–machine interfaces.…”

Section: Introductionmentioning

confidence: 99%

Battery Testing and Discharge Model Validation for Electric Unmanned Aerial Vehicles (UAV)

Nisio

Avanzini

Lotano

et al. 2023

Sensors

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Electrical engines are becoming more common than thermal ones. Therefore, there is an increasing interest in the characterization of batteries and in measuring their state of charge, as an overestimation would cause the vehicle to run out of energy and an underestimation means that the vehicle is running in suboptimal conditions. This is of paramount importance for flying vehicles, as their endurance decreases with the increase in weight. This work aims at finding a novel empirical model for the discharge curve of an arbitrary number of battery pack cells, that uses as few tunable parameters as possible and hence is easy to adapt for every single battery pack needed by the operator. A suitable measurement setup for battery tests, which includes voltage and current sensors, has been developed and described. Tests are performed on both constant and variable power loads to investigate different real-world scenarios that are easy to reproduce. The main achievement of this novel model is indeed the ability to predict discharges at variable power based on a preliminary characterization performed at constant power. This leads to the possibility of rapidly tuning the model for each battery with promising accuracy. The results will show that the predicted discharged capacities of the model have a normalized error below 0.7%.

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Analysis and Comparison of Power Distribution System Topologies for Low-Voltage DC–DC Automated Guided Vehicle Applications

Cited by 9 publications

References 26 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

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Battery Testing and Discharge Model Validation for Electric Unmanned Aerial Vehicles (UAV)

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