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Enhancing the efficiency of charging & parking processes for Autonomous Mobile Robot fleets: A simulative evaluation
Abstract
The allocation of tasks to Autonomous Mobile Robots in a production setting in combination with the most efficient parking and charging processes are the focus of this paper. This study presents a simulative evaluation of the theoretical allocation methods developed in Selmair and Maurer (2020) combined with either hard or dynamic availability rules to ascertain the most efficient parameters of an Autonomous Mobile Robot System. In order to quantify this efficiency, the following Key Performance Indicator (KPI) were considered: number of delayed orders, driven fleet metres and the percentage of available Autonomous Mobile Robot as determined by their state of charge. Additionally, as an alternative energy source, a fast-charging battery developed by Battery Streak Inc. was included in this study. The results show that, in comparison to a conventional and commonly used trivial strategy, our developed strategies provide superior results in terms of the relevant KPI.
... These strategies can be classified into two categories. First, the Rule-Based approaches [10] [11] where the vehicles visit the charging station at a predefined low State of Charge (SoC) and finish charging when reaching a predefined upper battery limit. However, these approaches do not consider the robot's available tasks. ...
... W i and b i are respectively the weights and bias terms, and X = x t h t−1 T . The sigmoid function σ and tanh are defined respectively by (9), (10). ...
... Search Space Input sequence length [5,120] LSTM units [16,128] Number of neurons in the dense layer [5,10] Estimator (TPE) [28]. The Bayesian optimization algorithm improves the searching process by keeping track of previous evaluation results. ...
The availability of electrical autonomous mobile robots is a main topic addressed by many researchers. Notably, the battery-powered Forklifts, recognized as battery consumers, have disadvantages of short autonomy and long charging time, which would require an accurate prediction of the charging queue waiting time to optimize the charging strategy. This paper proposes a machine learning approach to predict the charging queue waiting time for the electrical autonomous Forklifts (EAFLs) fleet. Results show accurate predictions with a mean absolute error of 1.52 min in comparison to the naive mean and median methods, which achieved a mean absolute error of, respectively, 5.54 min and 5.46 min.
... These operations include long-distance material transportation, automated charging, and loading/unloading materials onto processing equipment [4][5][6]. Consequently, there is a need for efficient trajectory planning and accurate, frequent parking control to ensure the reliability of the aforementioned operations [7,8]. However, in typical industrial scenarios, numerous dynamic and uncertain factors exist, including debris on the ground, the presence of mobile material boxes or workers, vehicle model errors, and wheel slippage. ...
Autonomous Ground Vehicles (AGVs) can transfer or load and unload material in industrial scenarios due to their flexibility and operability, freeing people from tedious and repetitive labour. However, the dynamic scene and the system model uncertainty reduce the parking accuracy of AGVs in industrial scenes, which seriously affects the autonomous operation robustness and accuracy. Using sliding mode parking control with trajectory planning based on iterative error compensation, this paper proposes a data-driven parking control-planning integration solution for AGVs in complex industrial scenes, which allows AGVs to park accurately and converge to the target parking site quickly. First of all, a data-driven discrete sliding mode controller has been developed to iteratively enhance parking accuracy and effectively rectify the target parking position. This controller showcases insensitivity towards disturbances encountered during the erratic iterative error compensation process, thereby ensuring rapid and asymptotic convergence of the parking error in industrial scenarios. Then, to achieve efficient and smooth planning with the target parking site constantly being corrected, an improved Bi-RRT based trajectory planning scheme is proposed by considering operational constraints and node expanding region division, which provides the trajectory that contributes to parking convergence for the proposed controller promptly. Finally, the efficiency of the proposed method is verified by real-world experiments with self-developed AGV in industrial scenes, and experimental results show that the proposed method achieves accurate parking control with efficient trajectory planning and rapid parking error convergence.
... In the domains of agricultural robots, warehouse mobile robots, mining robots, cruiser drones, and construction robots, only a few studies have explored fleet charging scheduling. A warehouse mobile robots operational scheduling strategy is proposed in [29], utilizing allocation methods and availability rules to ensure operational efficiency and avoid order delays. In [30], a genetic algorithm-based approach is designed to optimally schedule the operation and charging time of agricultural robots, considering the limited number of charging chargers. ...
... In the domains of agricultural robots, warehouse mobile robots, mining robots, cruiser drones, and construction robots, only a few studies have explored fleet charging scheduling. A warehouse mobile robots operational scheduling strategy is proposed in Ref. [29], utilizing allocation methods and availability rules to ensure operational efficiency and avoid order delays. In Ref. [30], a genetic algorithm-based approach is designed to optimally schedule the operation and charging time of agricultural robots, considering the limited number of charging chargers. ...
... AMR maintenance is mostly referred to a battery management issues [9]. Besides this, task allocation problems, including parking and charging processes [10] can be found. In the context of AMR operation, the digital twin approach was also considered [11]. ...
... The lack of energy-efficient solutions is currently the most pressing challenge facing the intelligence industry. To address this issue, several studies (3)(4)(5) have been performed. One such study, conducted by Zou et al., (6) investigated battery charging and swapping strategies. ...
The battery charger time is a major issue for mobile robots. The study of the power usage of each component is important for optimizing the overall power consumption. Additionally, knowing the total energy consumption before commanding a robot to execute a task is essential for effective queue management and determining which robots are ready to execute tasks or move to the charging station. In this paper, we propose an energy modeling system consisting of an energy sensing technique, logging, and a recurrent neural network prediction model. The model is configured to recognize the dynamic system of the drive unit with the support of the robot operating system. The proposed model has a prediction error of only 3.58%. The simulation and experimental results demonstrate the effectiveness of the proposed system.
Extreme fast charge (10 min to reach 80% state of charge) is one of the key limiting parameters preventing the widespread adoption of battery-based electric vehicles into the transportation sector. Many recent simulations and experimental-based studies have been recently published in this area with a specific focus on why extreme fast charge is challenging. These studies identified that cathode particle cracking and electrolyte transport limitation are the key barriers that have caused the well-known safety and stability problems. Interestingly, there have been very few studies that have demonstrated significant improvements toward a 10-min charge under high energy density conditions. Several strategies pertaining to electrolyte modifications (concentrated electrolyte and low viscosity additives) along with adaptive fast charging are highlighted and suggested.
The paper describes an approach for analyzing the use of a Laser-Guided Vehicle (LGV) in the context of the small and medium-sized enterprise. The use of LGVs is an efficient solution to provide more flexibility in the context of Just-In-Time production; however, the investment cost can limit this application. A methodology has been proposed in this work to analyze the technical feasibility of using an LGV in the manufacturing industry of customized products. The test case focuses on the study of a laser-guided system to optimize the handling of molds for customized production. In this scenario, an LGV is proposed to substitute manual carts used for moving molds from the warehouse to the injection machines. The traditional path included an intermediate station for pre-heating the molds in hot-air ovens. The proposed solution includes the study of an induction heating system on the LGV to optimize time and energy consumption.
Fast-charging is a key requirement for customer acceptance of battery electric vehicles. In this work, various methods for lithium-plating detection are applied to prismatic hard-case lithium-ion cells to demonstrate their applicability for large-format automotive cells despite possible thermal and electrochemical inhomogeneities. Different fast-charging profiles with a charging time of less than 30 min from 10% to 80% state-of-charge are examined with methods based on coulombic efficiency, cell voltage analysis and swelling force. To validate these results, a lifetime cycling test with a subsequent cell opening is performed, followed by a discussion on lithium-plating detectability and the restrictions of each method.
In order to further illustrate the applicability of the lithium-plating detection methods, the fast-charging ability of two cells with the same format but different cathode active materials, namely NMC622 and NMC811, are compared. Furthermore, a coupled electrochemical-thermal simulation is performed to study the effect of the cell design on the fast-charging ability and to elucidate why a higher cell energy density does not necessarily lead to a worse fast-charging ability.
Omni-directional mobile AGV can operate flexibly in a narrow space, which can replace heavy and dangerous labour to a greater extent and improve labour productivity. In this paper, the steering wheel integrated with walking and steering functions is taken as the driving unit, and the main research contents include: through digital structure design, the chassis structure and electrical control system of omnidirectional AGV are designed. Experiments show that the design realizes flexible operation of zero turning radius and 360-degree omnidirectional movement in narrow space, and meets the requirements of robot handling operation in complex environment.
The efficient distribution of scarce resources has been a challenge in many different fields of research. This paper focuses on the area of operations research, more specifically, Automated Guided Vehicles intended for pick-up and delivery tasks. In time delivery in general and flexibility in particular are important KPIs for such systems. In order to meet in time requirements and maximising flexibility, three prioritisation methods embedded in a task allocation system for autonomous transport vehicles are introduced. A case study within the BMW Group aims to evaluate all three methods by means of simulation. The simulation results have revealed differences between the three methods regarding the quality of their solutions as well as their calculation performance. Here, the Flexible Prioritisation Window was found to be superior.
Zusammenfassung
Der stetige Zuwachs von Ladeinfrastruktur für die Elektromobilität stellt die Verteilnetzbetreiber vor neue Herausforderungen. Vor allem im Zusammenspiel mit leistungsstarken Verbrauchern und Erzeugern (Wärmepumpen, Photovoltaik etc.) kann es zu erheblichen Mehrbelastungen im Netz kommen, welche dieses an dessen Betriebsgrenzen führen kann.
Aus dieser Motivation heraus soll diese Arbeit das grundlegende Verständnis für die Problematik der Integration von Ladeinfrastruktur in das elektrische Energiesystem erörtern. Dabei wird das prinzipielle Verhalten von Elektrofahrzeugen beim Ladevorgang aus Sicht des Netzes erarbeitet und eine mathematische Modellierung für die Analysen in zeitreihenbasierten Lastflussprogrammen vorgestellt. Zusätzlich werden die beeinflussenden Parameter des Ladevorganges diskutiert. Neben der Standortwahl ist die zu erwartende maximale Ladeleistung der Elektrofahrzeuge das entscheidende Kriterium für die Netzplanung. Eine Aufarbeitung dieser beiden Punkte zeigt, welche Erkenntnisse daraus für die Netzplanung abgeleitet werden können.
Abschließend wird ein Ausblick über weitergehende Herausforderungen zur allgemeinen Thematik der Integration von Elektrofahrzeugen im Verteilnetz für die nächsten Jahre gegeben. Einige Handlungsempfehlungen für eine erfolgreiche hochskalierte Integration von Ladeinfrastruktur schließen letztlich die Abhandlung.
Lithium-ion (Li-ion) batteries have been extensively used in electric vehicles, portable electronics, cell phones, and laptops. The charging protocol, as one of the most critical technologies for Li-ion battery systems, has a significant impact on battery performance. Charging current affects battery degradation and charging time, and therefore, it needs to be carefully optimized. To this end, a novel charging protocol using a series of constant charging currents has been developed, which considers the charging time and the battery capacity fade simultaneously. These two conflicting charging objectives are traded off by solving a multi-objective optimization problem based on battery electro-thermal-aging behavior. Particle swarm optimization (PSO) has been applied to obtain the optimal charging current profile. Three optimal charging strategies for minimum charging time, minimum battery aging, and balanced charging performance are obtained by changing the weight factor. The proposed balanced charging is capable of reducing the charging time significantly with a negligible increase in capacity degradation compared to the 0.5C CC-CV strategy recommended by the manufacturer.
Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for traction, especially nickel-manganese-cobalt (NMC) and nickel-cobalt-aluminum (NCA). An appropriate model of these batteries is fundamental for the simulation of several processes inside an EV, such as the state of charge (SoC) estimation, capacity and power fade analysis, lifetime calculus, or for developing control and optimization strategies. There are different models in the current literature, among which the electric equivalent circuits stand out, being the most appropriate model when performing real-time simulations. However, impedance models for battery diagnosis are considered very attractive. In this context, this paper compares and contrasts the different electrical equivalent circuit models, impedance models, and runtime models for battery-based EV applications, addressing their characteristics, advantages, disadvantages, and usual applications in the field of electromobility. In this sense, this paper serves as a reference for the scientific community focused on the development of control and optimization strategies in the field of electric vehicles, since it facilitates the choice of the model that best suits the needs required.
The battery charging strategy of automation guided vehicles (AGVs) is playing an important role in improving the utilization of AGVs. This paper proposes two dual-stage battery charging strategies of AGVs, which equip with lithium-ion battery for improving the utilization. In stage 1, two routing decisions based on heuristic algorithm are developed, namely the nearest charging station (NCS) and the minimum delay charging station (MDCS); while in stage 2, the duration of each charging occurrence is reduced considering the charging characteristic of lithium-ion battery. A real case is adopted to illustrate the applicability and effectiveness of the proposed approach.
Fast charging is seen as a means to facilitate long-distance driving for electric vehicles (EVs). As a result, roll-out planning generally takes a corridor approach. However, with higher penetration of electric vehicles in urban areas, cities contemplate whether inner-city fast chargers can be an alternative for the growing amount of slow public chargers. For this purpose, more knowledge is required in motives and preferences of users and actual usage patterns of fast chargers. Similarly, with increasing charging speeds of fast chargers and different modes (taxi, car sharing) also switching to electric vehicles, the effect of charging speed should be evaluated as well as preferences amongst different user groups. This research investigates the different intentions and motivations of EV drivers at fast charging stations to see how charging behaviour at such stations differs using both data analysis from charging stations as a survey among EV drivers. Additionally, it estimates the willingness of EV drivers to use fast charging as a substitute for on-street home charging given higher charging speeds. The paper concludes that limited charging speeds imply that EV drivers prefer parking and charging over fast charging but this could change if battery developments allow higher charging speeds.
The efficient allocation of tasks to vehicles in a fleet of self-driving vehicles (SDV) becomes challenging for large-scale systems (e. g. more than hundred vehicles). Operations research provides different methods that can be applied to solve such assignment problems. Integer Linear Programming (ILP), the Hungarian Method (HM) or Vogel's Approximation Method (VAM) are frequently used in related literature (Paul 2018; Dinagar and Keerthivasan 2018; Nahar et al. 2018; Ahmed et al. 2016; Koruko˘ glu and Ballı 2011; Balakrishnan 1990). The underlying paper proposes an adapted version of VAM which reaches better solutions for non-quadratic matrices, namely Vogel's Approximation Method for non-quadratic Matrices (VAM-nq). Subsequently, VAM-nq is compared with ILP, HM and VAM by solving matrices of different sizes in computational experiments in order to determine the proximity to the optimal solution and the computation time. The experimental results demonstrated that both VAM and VAM-nq are five to ten times faster in computing results than HM and ILP across all tested matrix sizes. However, we proved that VAM is not able to generate optimal solutions in large quadratic matrices constantly (starting at approx. 15 × 15) or small non-quadratic matrices (starting at approx. 5 × 6). In fact, we show that VAM produces insufficient results especially for non-quadratic matrices. The result deviate further from the optimum if the matrix size increases. Our proposed VAM-nq is able to provide similar results as the original VAM for quadratic matrices, but delivers much better results in non-quadratic instances often reaching an optimum solution. This is especially important for practical use cases since quadratic matrices are rather rare.
Material handling and logistics management that involve transportation of work pieces on production floor are important aspects to manufacturing that affect productivity and efficiency. Tow vehicles that are manually driven are currently used for this purpose. These processes can be better performed through automation. Automated guided vehicle (AGV) is an apt solution. AGVs are unmanned autonomous vehicles that can be programmed to perform versatile tasks. AGVs available in market are imported and hence have high capital cost and increased lead time for spare parts. Proposed AGV is built with a capital cost that is less than half of the existing AGVs. Its design is made indigenously, with most of its parts locally sourced. It can achieve a speed of 0.83 m/s, with a pulling capacity of 1,300 kg. Its rechargeable batteries sustain four hours of continuous operation for one complete discharge. It has been tested and found to effectively replace tow vehicles.
The growing demand for lithium-ion battery in electric vehicles has expedited the need for new optimal charging approaches to improve speed and reliability of the charging process without deteriorating battery performances. Many efforts have been deployed to develop optimal charging strategies for commercial lithium-ion batteries over the last decade. The active optimal charging strategies have great potential to meet the requirement. The present paper is a review of the studies on the constructing of optimal charging algorithms for Li-ion batteries. The battery models on which these protocols rest are stated, the generalized structures are examined, the advantages and the drawbacks of the mathematical controller algorithms are discussed and their applications are presented. Suggestions for overcoming the shortcomings of the proposed strategies are proposed. Challenges and future directions in the development of optimal charging strategies for commercial Li-ion batteries are also discussed.
A logistic control system is needed for a fully automated cargo transportation system using Automatic Guided Vehicles, as is currently being developed around Schiphol Airport, Amsterdam. This control system should cover a variety of logistics decisions, such as order consolidation, order scheduling, resource capacity management and traffic control. We address the problem of control flexibility and software adaptability versus stable, predictable and optimised overall system behaviour. We design a distributed logistic control framework in which local agents use heuristics to solve their control problems. We use simulation to evaluate our object library. The flexibility of the logistic control structure is illustrated by some examples of decision integration and model extensions. We conclude that modification flexibility, related to changes in the system structure and processes, is easier to achieve than integration flexibility, the integration of control tasks, because the latter is strongly influenced by the underlying information structure.
The goal of this report is to provide a basic overview of electricity storage technologies and their potential applications, especially with regards to the transition to an electricity system with high shares of renewable energies.
Motivated by the threat of global warming and other environmental impacts of energy usage, security of energy supply and economic reasons, there is an international effort to increase the share of renewable power generation in the electricity supply. However, the two fastest growing renewable energy sources, wind and solar power, are naturally fluctuating due to weather conditions as well as diurnal and seasonal patterns. Furthermore, the best harvesting potential does often not coincide spatially with the centers of power consumption.
Therefore, the transition to a power system with high shares of renewable power generation requires a differently structured electricity grid with higher transmission capacity in order to bridge spatial distance between supply and demand. Additionally, the power system requires capabilities in order to bridge distance in time between supply and demand, which we may call flexibility. Flexibility is the ability of the power system to match fluctuating generation with - also fluctuating - demand. Sources of flexibility can be dispatchable generation (fossil, hydro or biomass), demand response, the curtailment of renewable generation and/or electricity storage.
In general, different sources or combinations thereof can provide flexibility. The choice will depend on economic factors, social acceptance, ecological considerations and other factors. This report focuses on storage technologies and specifically those that are able to absorb electricity from the system and reconvert it into electricity at a later time.
With increasing shares of renewable generation, the electricity system is in a transition from currently demand driven centralized and fossil-based generation, where flexibility is predominantly provided by dispatchable generation, towards more supply driven regenerative and distributed energy production where additional sources of flexibility will be required.
This also affects the operating principles and the system stability. Traditionally, the major conventional power plants supply energy and balancing power to the grid and the power flow is always directed from the higher voltage levels (location of power plants) to the lower voltage levels (location of consumers). Due to an increase in renewable electricity generation on the mid and low-voltage level (residential PV systems, onshore wind power) the power flow direction can be inversed. Furthermore, during times with high renewable energy feed-in the electricity demand can be covered completely by renewable generators. This leads to situations where new ways of guaranteeing system stability have to be found: either conventional power plants have to be run at minimal load with high specific emissions, or electricity storage systems, demand side response and the renewable energy generators themselves have to supply the necessary system services.
Besides the integration of renewable electricity generation the most active field of development for electricity storage systems is the electrification of the transport sector. The demand for especially lithium-ion battery systems rises rapidly due to the introduction of plug-in hybrid and full electric vehicles. This transition is also linked to the electricity sector for two reasons: Firstly, the increased demand fosters mass-production of batteries which causes decreasing battery prices which also helps to introduce storage systems in the electricity grid. Secondly, the battery storage systems of the traffic sector can also be used as grid storage during times when the vehicles are plugged-in for charging. The link between these two sectors causes major transitions in the traditional world of utilities as automobile manufacturers start to produce their one “green” electricity for their electric vehicle fleet. On the other hand utility companies start getting involved in the mobility sector as they deploy the charging infrastructure and develop business models for electricity supply for e-mobility. Both trends result in a closer link of these two important branches of industry.
In order to keep this report reasonably compact it was limited to the discussion of electricity storage systems. However, this is not to be mistaken as a statement of preference for electricity storage as a source of flexibility. In some cases electricity storage is unavoidable, however, for the time being all flexibility sources are to be considered as at the current state of development there is no single solution that serves all flexibility needs. One alternative to electricity storage is heat storage where heat is the final form of energy used. Especially low temperature heat in buildings and process heat in the industry have the potential to provide significant amounts flexibility. A short discussion of potentials and limitations is given in chapter 2.
All applications of electricity storage make it necessary to understand the technology options and their alternatives. As the cost of electricity storage strongly depends on the specifics of the application, this report cannot give general recommendations for the use of certain technologies for given applications. It is a first introduction into electricity storage systems and their possible applications. The report is structured as follows:
Chapter 2 gives an overview on selected applications for energy storage systems. This covers mainly electricity grid services but also other important systems like electromobility .
Chapter 3 gives an introduction into important parameters and terminologies. A systematic classification of storage technologies helps to better understand the different main characteristics.
Chapter 4 is the main part of this report and contains the description of important (technically and operationally proven) electricity storage technologies with their technical parameters and their deployment potential. Mechanical, electrical, chemical, thermal and electrochemical (batteries) storage systems are covered.
Chapter 0 discusses the potential role of the different storage technologies.
A comprehensive discussion of demand and cost of storage for the integration of renewable energy exceeds the scope of this report. The feasibility of different storage options, the amount of storage required at different shares of renewable energy and the related costs are being discussed among experts and in public. The main complications in the search for answers to these questions is the fact that demands for generation, transmission and flexibility are interlinked and can replace each other to some extent.
An estimate of the storage demand for the coming years therefore depends on the share of renewable energy, the fraction of the different renewable sources, the flexibility of the conventional generation portfolio and the transmission capacities, the cost structure of each component above and their development throughout Europe.
1 Einleitung Unter dem Rubrum "Internet der Dinge" wird in der Logistik seit der Jahrtau-sendwende die Einführung cyberphysischer Technologien diskutiert. Die Logistik und das Internet der Dinge gelten als herausragende Anwendungsdomäne der vierten industriellen Revolution. In keiner anderen Branche wird in naher Zukunft ein so grundsätzlicher Wandel erwartet. Dies ist einerseits auf die rasante techno-logische Entwicklung zurückzuführen, andererseits sind viele der wesentlichen technischen und gesellschaftlichen Herausforderungen direkt oder indirekt mit der Logistik und einem effizienten Supply Chain Management verbunden. Dieser Beitrag geht der Frage nach, warum die vierte industrielle Revolution in der Logistik stattfindet und welchen Wandel dies mit sich bringen wird. 2 Die Vision vom Internet der Dinge oder: Der ideale logisti-sche Raum ist leer! 2008 saßen einige Ingenieure im Fraunhofer IML beisammen und überlegten, wie ein ideales intralogistisches System aussähe. Vor ihnen lag ein leeres Blatt Papier. Nach einer Stunde intensiver Diskussion befand sich lediglich die Zeichnung eines Regals am unteren Rand des Papiers. Man war sich einig, dass das Regal auch in Zukunft eine unabdingbare, raumsparende Form des Lagerns darstellt. Der restli-che Raum war leer und seine Form unbestimmt. Es entstand die Vision einer radikalen Neugestaltung innerbetrieblicher Logistik, die mit der vierten industriel-len Revolution Wirklichkeit werden könnte. Es beginnt damit, dass sich der Ort, an dem das System stehen soll, im Rahmen eines immer volatileren Produktions-und Handelsumfeldes nicht mehr dauerhaft bestimmen lässt. Der ideale Standort gilt nicht mehr für viele Jahre. Das logistische Netzwerk und seine Knoten müssen sich kontinuierlich den Gegebenheiten anpas-sen. Daher sollten logistische Knoten in Zukunft umzugsfähig sein. Dies verbietet viele Formen klassischer, technischer Infrastruktur.
This paper presents the overview of charging algorithms for lithium-ion batteries, which include constant current-constant voltage (CC/CV), variants of the CC/CV, multistage constant current, pulse current and pulse voltage. The CC/CV charging algorithm is well developed and widely adopted in charging lithium-ion batteries. It is used as a benchmark to compare with other charging algorithms in terms of the charging time, the charging efficiency, the influences on battery life and other aspects, which can serve as a convenient reference for future work in developing a charger for lithium-ion battery.
A customer-driven E-commerce economy leads to a higher expectation on delivery of products. The lead time in the supply chain becomes shorter and shorter and a higher efficiency on picking and packing activities for the warehouse is required. Compared with the traditional warehouse, robotic mobile fulfilment system can increase the efficiency of picking products to fulfil the orders with the characteristics of high volume and high variety consumption pattern. A mobile robot with a mobile storage rack follows the positioning identifier for moving. In the context of E-commerce implementation, data-driven optimization is one of the objectives for the robotic mobile fulfilment system. Data are collected from different sources such as sensor, tracking data and demand traffic. The objective of this paper is to simulate the charging activities for mobile robot under different designed instances. The results of the model show the charging time of different battery capacity of RC time and charged percentage.
This paper proposes an advanced decentralized method where an Automated Guided Vehicle (AGV) can optimally insert charging stations into an already assigned optimal tour of task locations. In today's industrial AGV systems, advanced algorithms and techniques are used to control the whole fleet of AGVs robustly and efficiently. While in academia, much research is conducted towards every aspect of AGV control. However, resource management or battery management is still one aspect which is usually omitted in research. In current industrial AGV systems, AGVs operate until their resource level drops below a certain threshold. Subsequently, they head to a charging station to charge fully. This programmed behaviour may have a negative impact on the manufacturing systems performance. AGVs lose time charging at inconvenient moments while this time loss could be avoided. Using the approach, an AGV can choose independently when it will visit a charging station and how long it will charge there. A general constrained optimization algorithm will be used to solve the problem and the current industrial resource management will be used as a benchmark. We use a simple extension of the Traveling Salesman Problem (TSP) representation to model our approach. The paper follows a decentral approach which is in the interest of the authors. The result of the proposal is a compact and practical method which can be used in today's operative central or decentral controlled AGV systems.
The Electrical Energy Storage (EES) is recognized, among all the possible options, as one of the most promising solutions to support the challenges posed by the decarbonization of the economy and integration into the distribution network of the electricity produced from intermittent and non-programmable renewable sources. The high-power request to the electricity network during charging process of electric busses, especially for fast, ultra-fast or flash charges, requires EES systems in order to level such impulsive requests. The research activity illustrated in this work investigates the most promising technologies for a stationary application for electric buses charging. We investigate different types of storage systems, whose peculiarities satisfy specific types of applications rather than others. The solutions under study for fast and flash charge are mechanical storage systems such as flywheels (FES), electrical and electrochemical systems such as supercapacitors (SC) and batteries, in particular, the high specific power Lithium Titanate (LTO). The results show the potential economic advantage of flywheels and supercapacitors as stationary storage systems over electrochemical storage.
Nowadays, although the lithium-ion batteries have been widely applied in the context of electric vehicles for passengers, lead-acid batteries are still prevalent in motive-power applications, such as electric pallet jacks and laser guided vehicles. The battery cost is the main disadvantage that limits the employment of lithium-ion solutions in such applications. Several strategies for reducing the battery life cycle cost have been discussed in the scientific literature. The opportunity charging is one of them, even though it is suitable only for batteries having high lifecycles and high charging/discharging rates, such as the Lithium Titanium Oxide ones. This paper aims at assessing a feasible solution to reduce the life cycle cost of the energy storage units for laser guided vehicles. A tool has been proposed to analyze the Total Cost of Ownership of batteries, under the adoption of an opportunity charging strategy. Simulations of energy consumption have also been included, to predict the battery cycles and the operation costs. The life cycle analysis has investigated the use of a compacted Lithium Titanium Oxide battery in comparison with a traditional lead-acid battery. The results have shown the feasibility of the Lithium Titanium Oxide solution and its economic advantage in an industrial context.
Extreme fast charging, with a goal of 15 minutes recharge time, is poised to accelerate mass market adoption of electric vehicles, curb greenhouse gas emissions and, in turn, provide nations with greater energy security. However, the realization of such a goal requires research and development across multiple levels, with battery technology being a key technical barrier. The present-day high-energy lithium-ion batteries with graphite anodes and transition metal oxide cathodes in liquid electrolytes are unable to achieve the fast-charging goal without negatively affecting electrochemical performance and safety. Here we discuss the challenges and future research directions towards fast charging at the level of battery materials from mass transport, charge transfer and thermal management perspectives. Moreover, we highlight advanced characterization techniques to fundamentally understand the failure mechanisms of batteries during fast charging, which in turn would inform more rational battery designs. Along with high energy density, fast-charging ability would enable battery-powered electric vehicles. Here Yi Cui and colleagues review battery materials requirements for fast charging and discuss future design strategies.
This paper proposes a modular on-road wireless power transfer (WPT) system with interoperable power adjustment mechanism. The study is anticipated to enhance the capability of on-road charging during the movement of automated guided vehicles (AGV), by which the traveling mileage is increased while the battery volume is decreased. The system design includes an interoperable power adjustment technique based on the detected impedance, and the moving position of AGV can be hence comprehended so as to facilitate the adjustment of output power from each transmission module in a more flexible way. Through the investigation of on-road charging efficiency, all of coil magnetic analysis, misalignment charging evaluation, system stress simulation, and resonant characteristics investigation are performed. Experimental results gained from software simulation and hardware realization are beneficial for AGV charging applications.
Industrie 4.0 soll zahlreiche Potenziale für die industrielle Wertschöpfung bieten, die vor allem kurz- bis mittelfristig jedoch durch Risiken in der Umsetzung begleitet werden. Dabei finden sich derzeit in der Forschung nur wenige Untersuchungen zu den Risikodimensionen von Industrie 4.0, denen allesamt ein ganzheitlicher Überblick fehlt. Auf Basis dieser Forschungslücke nutzt dieser Beitrag die Triple Bottom Line der Nachhaltigkeit, um Risiken in deren Dimensionen Ökonomie, Ökologie und Soziales zu analysieren. Dabei werden insbesondere kleine und mittlere Unternehmen (KMU) untersucht, da diese bislang vergleichsweise wenig Aufmerksamkeit im Rahmen von Industrie 4.0 erfahren haben. Basierend auf sechs Experteninterviews werden ökonomische, ökologische, soziale sowie weitere Risiken im Kontext von Industrie 4.0 für KMU analysiert. Aus den Ergebnissen werden Implikationen für die Praxis abgeleitet sowie Ansätze für weitere Untersuchungen aufgezeigt.
This paper explores how different routing techniques for the battery management of automated guided vehicles (AGVs) can affect the performance of a system. Four heuristics available in the literature were the basis of this study. Simulation models were developed to investigate how the routing of an AGV towards a battery station can affect the productivity of a manufacturing facility. Results show that the best productivity can be achieved when a routing heuristic tries to jointly minimise the total travel distance and waiting time at a battery station. The gain in productivity, when compared with the highest possible gain theoretically achievable, is quite substantial. It was also found that higher frequency of decision-making (i.e. decisions with smaller time interval) about battery swapping helps to increase the productivity of a system.
Battery charging and discharging regimes mostly attempt to maximize potential profit by following price signals. Combining a technical understanding of batteries with financial theory, researchers now present a framework that allows optimization of economic benefits considering both potential revenues and battery degradation.
Purpose of this paper is to explore the possibility of increasing manufacturing capacities in the short run through battery management of automated guided vehicles (AGVs). The paper investigates how the duration of battery charging for AGVs can be varied to increase flexibility of a manufacturing system. The key concept is that a lead acid battery, the most widely used battery type for AGVs, receives most of its charge during the initial phase (time) of charging as opposed to the later phase. Consequently, more productive hours can be obtained from the AGVs by reducing the duration of each charging occurrence (i.e., by recharging the batteries to less than full capacity). In this approach, an AGV needs to be recharged more frequently, but the total productive hours available from the AGVs can increase. Simulation models were developed to investigate the effect of this approach. Results show that productivity of a manufacturing system increases significantly through this approach. This approach can be quite helpful for a firm if AGVs are the bottleneck in its manufacturing plant and the firm needs to have a significant improvement of its productivity in the short-run. Also, this approach does not require a firm to make a large investment to add more AGVs to its facility.
Robotic mobile fulfillment systems (RMFS) have seen many implementations in recent years, due to their high flexibility and low operational cost. Such a system stores goods in movable shelves and uses movable robots to transport the shelves. The robot is battery powered and the battery depletes during operations, which can seriously affect the performance of the system. This study focuses on battery management problem in an RMFS, considering a battery swapping and a battery charging strategy with plug-in or inductive charging. We build a semi-open queueing network (SOQN) to estimate system performance, modeling the battery charging process as a single queue and the battery swapping process as a nested SOQN. We develop a decomposition method to solve the analytical models and validate them through simulation. Our models can be used to optimize battery recovery strategies and compare their cost and throughput time performance. The results show that throughput time performance can be significantly affected by the battery recovery policy, that inductive charging performs best, and that battery swapping outperforms plug-in charging by as large as 4.88%, in terms of retrieval transaction throughput time. However, the annual cost of the RMFS using the battery swapping strategy is generally higher than that of the RMFS using the plug-in charging strategy. In the RMFS that uses the inductive charging strategy, a critical price of a robot can be found, for a lower robot price and a small required retrieval transaction throughput time, inductive charging outperforms both plug-in charging and battery swapping strategies in terms of annual cost. We also find that ignoring the battery recovery will underestimate the number of robots required and the system cost for more than 15%.
The ability to charge battery electric vehicles (BEVs) on a time scale that is on par with the time to fuel an internal combustion engine vehicle (ICEV) would remove a significant barrier to the adoption of BEVs. However, for viability, fast charging at this time scale needs to also occur at a price that is acceptable to consumers. Therefore, the cost drivers for both BEV owners and charging station providers are analyzed. In addition, key infrastructure considerations are examined, including grid stability and delivery of power, the design of fast charging stations and the design and use of electric vehicle service equipment. Each of these aspects have technical barriers that need to be addressed, and are directly linked to economic impacts to use and implementation. This discussion focuses on both the economic and infrastructure issues which exist and need to be addressed for the effective implementation of fast charging at 400 kW and above. In so doing, it has been found that there is a distinct need to effectively manage the intermittent, high power demand of fast charging, strategically plan infrastructure corridors, and to further understand the cost of operation of charging infrastructure and BEVs.
In this paper, insight of thermal behavior of lithium-ion batteries under fast-charging profiles is investigated.
Although battery thermal behavior has been studied by published models, the reported modeling addresses a specific application: fast-charging applications. Most papers in literature present the fast-charging application from an electrical point-of-view. There, lacks a comprehensive electro-thermal model which can capture both the heat generation, voltage and current variation during the whole fast-charging process. In this study, two charging profiles that are commonly used for fast-charging applications are applied on two lithium-ion chemistries: lithium nickel manganese cobalt oxide (NMC) and lithium titanate (LTO). The first one is designed for high-energy density and the other made for high-power applications. To enlarge the study scope, the batteries have been tested at three environmental temperatures: 25 �C, 10 �C and 45 �C. In addition, a three-dimensional thermal model has been developed within the frame of open source computational fluid dynamics (CFD) to analyze the thermal behavior of lithium-ion batteries (LiBs). Thermal evolutions of the cells during the profile are recorded to witness the temperature distribution and the validation of the model. The model has indeed well captured the evolution process of the cells from electrical and thermal point-of-views and achieved reasonably good agreement with the measurements. For example, LTO-based cells have shown an interesting behavior for which the battery was able to undergo a fast-charge at any tested temperature and the temperature still remained stable (a temperature rise of less than a 3 �C). These parametric studies demonstrate that the model methodology can be used to predict LiB temperature distribution under fast-charging profiles.
Battery management for automatic guided vehicle (AGV) systems is important to reduce costs and increase the efficiency of the AGV systems. Valve-regulated lead-acid (VRLA) batteries, which are generally used in AGVs, should be charged at appropriate time intervals, to avoid the deterioration of batteries and to extend their lives. On the other hand, frequent charging affects the efficiency of AGVs. The cost of chargers, which determines the charging time of the batteries, should also be taken into account. It is not easy to select a proper battery management strategy that considers all these factors. To solve these problems, a battery management simulation is developed for evaluating battery related costs under various AGV operation modes and for designing battery management strategies. To verify its effectiveness, the simulation is applied to a body in white at the finishing line of an automobile manufacturing plant.
This paper presents an overview on charging strategies for lithium-ion batteries. Moreover, a detailed assessment of charging strategies is performed, based on an extensive experimental study with three different cell types.The experimental results reveal that the impact of charging currents and charging voltages on cycle life can vary markedly among different lithium-ion batteries. In general, the cycle life is influenced more by high charging currents than by high discharging currents. Different boost charging protocols have disclosed that high charging currents can deteriorate cycle life not only at high state of charge (SoC), but also at very low SoC. Our investigations on pulse charging show that lithium-ion cells withstand charging pulses of high current or high voltage without any deterioration in cycle life, when the duration of the pulses remains short and the mean current and voltage values are considerably lower. For pulses of less than 1 s, cycle life has been similar for pulsed and continuous charging with the same mean charging currents and identical cycle depths. This paper also presents the impact of charging currents and charging voltages on capacity utilization, charging time, and efficiency to support the development process of optimized charging protocols for practical applications.
Die vierte industrielle Revolution hat weitreichende Konsequenzen für die Logistik und ihr Selbstverständnis. Nicht zuletzt das Konsumverhalten der Gesellschaft führt zu neuen logistischen Anforderungen, für die sich das Konzept eines ,,Internet der Dinge und Dienste“ als wahrscheinliche Lösung abzeichnet. Dessen konsequente Umsetzung führt jedoch unweigerlich zur Notwendigkeit, einige grundlegende Konzepte der Logistik zu überdenken. In einem Versuch, ein Bild der Vision ,,Logistik 4.0“ zu skizzieren, geht der Beitrag der Frage nach, welche Paradigmenwechsel die vierte industrielle Revolution nach sich ziehen wird und wie diese proaktiv adressiert werden können.
An effective optimum charging technique for lithium ion batteries using a universal voltage protocol (UVP) that can accommodate cell aging is presented here. This charging method demands less learning to varying state-of-health (SOH) conditions with potential to improve charging efficiency and cycle life. The simplicity of UVP makes the implementation easier than the conventional constant current-constant voltage (CC-CV)-based methods. Here, the mathematical formulation, optimization targets (e.g. minimal time) and constraints (terminal voltages and other instrumental and cell electrochemistry-limited ones) are explained from the protocol design considerations. An equivalent circuit model was used and its parameters derived from the analysis of test data, which could yield a nonlinear varying current profile (VCP) by simulation and a genetic algorithm-based optimization. Both UVP and VCP were used in the validation to illustrate better charging efficiency and capacity retention, which showed a much improved cycle life.
Isothermal calorimetry was performed on Li\LiPF6 in ethylene carbonate: dimethyl carbonate\LiAl0.2Mn1.8O4-deltaF0.2 cells. The measured rate of heat generation varied substantially with time. To understand why, we investigated the entropy, irreversible resistance, and heats of mixing. Two methods for computing the heat of mixing, one computational and one analytic, are derived. We demonstrate how the energy balance of Rao and Newman accounts for heat of mixing across electrodes, but neglects heat of mixing within particles and in the electrolyte, which may be of equal magnitude. In general, the magnitude of the heat of mixing, which is the amount of heat released during relaxation after interruption of the current, will be small in materials with transport properties sufficiently high to provide acceptable battery performance, with the possible exception of heat of mixing within the insertion particles if the particle radius is large. Comparing simulations of heat generation to calorimetry measurements reveals that the entropic heat is significant and accounts for much of the variation of the rate of heat generation. The rate of irreversible heat generation is larger when the open-circuit potential varies steeply with lithium concentration, because of diffusion limitations within the solid. (C) 2003 The Electrochemical Society.
Die Individualisierung als postmodernes Lebensprinzip hat mit dem Aufkommen der Internetgesellschaft eine neue Dimension erreicht.
Individuelle Lebensentwürfe sind heute ebenso selbstverständlich wie die individuelle Gestaltung der Lebensräume und Produkte
oder die zeitlich und räumlich individuelle Versorgung mit Waren und Informationen. Ubiquitäres Computing sorgt für permanente
Erreichbarkeit, und virtuelle Habitate überlagern zunehmend die reale Welt.
Assuming that numerical scores are available for the performance of each of n persons on each of n jobs, the "assignment problem" is the quest for an assignment of persons to jobs so that the sum of the n scores so obtained is as large as possible. It is shown that ideas latent in the work of two Hungarian mathematicians may be exploited to yield a new method of solving this problem. 1.
Memory effects are well known to users of nickel-cadmium and nickel-metal-hydride batteries. If these batteries are recharged repeatedly after being only partially discharged, they gradually lose usable capacity owing to a reduced working voltage. Lithium-ion batteries, in contrast, are considered to have no memory effect. Here we report a memory effect in LiFePO4-one of the materials used for the positive electrode in Li-ion batteries-that appears already after only one cycle of partial charge and discharge. We characterize this memory effect of LiFePO4 and explain its connection to the particle-by-particle charge/discharge model. This effect is important for most battery uses, as the slight voltage change it causes can lead to substantial miscalculations in estimating the state of charge of batteries.
Growing global energy demands coupled with environmental concerns have increased the need for renewable energy sources. For intermittent renewable sources like solar and wind to become available on demand will require the use of energy storage devices. Batteries and supercapacitors, also known as electrochemical capacitors (ECs), represent the most widely used energy storage devices. Supercapacitors are frequently overlooked as an energy storage technology, however, despite the fact that these devices provide greater power, much faster response times, and longer cycle life than batteries. Their limitation is that the energy density of ECs is significantly lower than that of batteries, and this has limited their potential applications.
The coulomb counting method is expedient for state-of-charge (SOC) estimation of lithium-ion batteries with high charging and discharging efficiencies. The charging and discharging characteristics are investigated and reveal that the coulomb counting method is convenient and accurate for estimating the SOC of lithium-ion batteries. A smart estimation method based on coulomb counting is proposed to improve the estimation accuracy. The corrections are made by considering the charging and operating efficiencies. Furthermore, the state-of-health (SOH) is evaluated by the maximum releasable capacity. Through the experiments that emulate practical operations, the SOC estimation method is verified to demonstrate the effectiveness and accuracy.
Bees algorithm (BA) is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. In this paper a brief review of BA is first given, afterwards development of a BA for solving generalized assignment problems (GAP) with an ejection chain neighborhood mechanism is presented. GAP is a NP-hard problem. Many meta-heuristic algorithms were proposed for its solution. So far BA is generally applied to continuous optimization. In order to investigate the performance of BA on a complex integer optimization problem, an attempt is made in this paper. An extensive computational study is carried out and the results are compared with several algorithms from the literature.
This paper has been presented with the Best Paper Award. It will appear in print in Volume 52, No. 1, February 2005.
The effect of the charging protocol on the cycle life of a commercial 18650 Li-ion cell was studied using three methods: (1) constant current
(CC) charging, (2) constant power (CP) charging, and (3) multistage constant current (MCC) charging. The MCC-charging consists of two CC
steps, which starts with a low current to charge the initial 10% capacity followed by a high current charging until the cell voltage reaches 4.2V.
Using these methods, respectively, the cell was charged to 4.2V followed by a constant voltage (CV) charging until the current declined to 0.05 C.
Results showed that the cycle life of the cell strongly depended on the charging protocol even if the same charging rate was used. Among these
three methods, the CC-method was found to be more suitable for slow charging (0.5 C) while the CP-method was better for fast charging (1 C).
Impedance analyses indicated that the capacity loss during cycling was mainly attributed to the increase of charge-transfer resistance as a result of
the progressive growth of surface layers on the surface of two electrodes. Fast charging resulted in an accelerated capacity fading due to the loss
of Li+ ions and the related growth of a surface layer, which was associated with metallic lithium plating onto the anode and a high polarization at
the electrolyte–electrode interface. Analyses of the cell electrochemistry showed that use of a reduced current to charge the initial 10% capacity
and near the end of charge, respectively, was favorable for long cycle life.
Internet Der Dinge in Der Intralogistik
M. ten Hompel, Individualisierung als logistisch-technisches prinzip, in: W.
Günthner, M. ten Hompel (Eds.), Internet Der Dinge in Der Intralogistik, in: VDI-Buch, Springer, Berlin, Heidelberg, 2010, pp. 3-7, http://dx.doi.org/10.1007/
978-3-642-04896-8_1.
A neural network-based algorithm with genetic training for a combined job and energy management for AGVs
- P Pagani
- D Colling
- K Furmans
P. Pagani, D. Colling, K. Furmans, A neural network-based algorithm with genetic
training for a combined job and energy management for AGVs, Logist. J. (2018).
Enhancing charging & parking processes of AGV systems
- M Selmair
- T Maurer
M. Selmair, T. Maurer, Enhancing charging & parking processes of AGV systems:
Progressive theoretical considerations, in: The Twelfth International Conference
on Advances in System Simulation, 2020.