Battery Endurance Research Articles

Dynamic collaborative truck-drone delivery with en-route synchronization and random requests

Coordinated truck and drone delivery is gaining popularity in logistics as it can greatly reduce operation costs. However, existing studies on related operations management problems typically ignore the following important features: (i) the random appearance of requests, which require operators to dynamically respond to the requests; and (ii) the decisions of optimal launch and retrieval locations for trucks and drones instead of fixed to customer locations, which can significantly impact the overall time costs. To tackle these challenges, this study investigates the dynamic collaborative truck-drone routing problem with randomly arriving requests and synchronization on routes. We model the problem as a Markov Decision Process (MDP) and solve the MDP via a reinforcement learning (RL) approach. The proposed RL approach determines: (i) whether each request should be serviced upon arrival, (ii) which truck or drone should be assigned for the request, and (iii) the optimal en-route take-off and landing positions for paired trucks and drones. We further employ a framework of decentralized learning and centralized dispatching in RL to increase performance. Numerical experiments are conducted to assess the proposed solution approach on instances generated based on both the Solomon dataset and real-world operational data of a logistics operator in Singapore over several benchmark algorithms under various battery endurance levels of drones and distinct transportation scenarios including node-based dynamic collaborative truck-drone routing problem, dynamic non-collaborative truck and drone routing problem, and dynamic vehicle routing problem. The results show that our RL solution outperforms the benchmark algorithm in total profit by an average of 28.03 %, and our en-route takeoff and landing scenario outperforms the benchmark scenarios in total profit by an average of 8.43 % in multi-day instances. Additionally, compared to the traditional node-based landing scenario, employing our en-route takeoff and landing strategy can save 0.9 h/(drone*day) of waiting time on average.

Research on Automatic Recharging Technology for Automated Guided Vehicles Based on Multi-Sensor Fusion

Automated guided vehicles (AGVs) play a critical role in indoor environments, where battery endurance and reliable recharging are essential. This study proposes a multi-sensor fusion approach that integrates LiDAR, depth cameras, and infrared sensors to address challenges in autonomous navigation and automatic recharging. The proposed system overcomes the limitations of LiDAR’s blind spots in near-field detection and the restricted range of vision-based navigation. By combining LiDAR for precise long-distance measurements, depth cameras for enhanced close-range visual positioning, and infrared sensors for accurate docking, the AGV’s ability to locate and autonomously connect to charging stations is significantly improved. Experimental results show a 25% increase in docking success rate (from 70% with LiDAR-only to 95%) and a 70% decrease in docking error (from 10 cm to 3 cm). These improvements demonstrate the effectiveness of the proposed sensor fusion method, ensuring more reliable, efficient, and precise operations for AGVs in complex indoor environments.

Research on Sales of New Energy Vehicles Based on ARIMA Model

Firstly, this article identifies seven indicators based on electric vehicles: battery endurance, number of public charging facilities, government subsidies, gasoline prices, electricity prices, carbon emissions, and number of power battery companies. Spearman correlation analysis is conducted between indicator data and sales of new energy electric vehicles. The correlation coefficients of the first five indicators are all greater than 0.6, which is the main influencing factor. Then, the sales of new energy vehicles are predicted using a yearly forecasting method. Due to the close correlation between the sales of new energy vehicles and the five main indicators, the ARIMA time series model is first used to predict the annual quantity of the five indicators. Then, using these five indicators as inputs and sales as outputs, a BP neural network model is established to predict the annual sales of new energy vehicles in China to be 46051748 units in ten years. Finally, quantifying the score of ecological environment assessment using four evaluation indicators: sulfur dioxide, smoke emissions, ammonia nitrogen, and carbon dioxide. The entropy weight method was used to determine the weights of each indicator, and the results were 37.25%, 30.96%, 21.24%, and 10.55%, respectively. Then apply it to the TOPSIS model to obtain ecological environment scores for different years, and analyze the correlation between the degree of electrification and ecological environment scores. Finally, it can be concluded that the larger the market share of new energy vehicles, the better the ecological environment.

Development of wearable device and synchronized Mobile application to monitor vital signs in real time

AbstractSilent hypoxia is a complication that can lead to severe respiratory disorders at a time when people feel well, especially in Covid-19, and lung destruction if not intervened early. In this paper, a wearable device and a mobile application synchronized with the device were developed to measure people’s vitals and reduce the risk of silent hypoxia. The device consists of 2 parts: a wristband and a finger-clip, and includes a micro-controller, a bluetooth module, a Li-Po battery and various sensors to measure body temperature, pulse and SpO2 values. It can measure body temperature, pulse and SpO2 instantly or at certain programmable intervals like a holter device through the mobile app. These measured values can be recorded and various analyzes can be performed on them. Also the measurement results can be sent via an e-mail. When the vitals reach or exceed the predefined risk levels, a notification can be generated. The proposed device was evaluated in various performance tests and compared to a commercial pulse oximeter as well as alternatives in the literature. It was tested for connectivity and transmission, battery endurance and field testing. In the comparison test, error values of 1.8%, 3.5% and 0.6% were achieved in SpO2, pulse and temperature measurements, respectively. The device is both a measurement and holter instrument for body temperature, pulse and SpO2 values. With this feature, it can be actively used by both patients and their relatives in hospital or home environment.

Short-term calving front dynamics and mass loss at Sálajiegna glacier, northern Sweden, assessed by uncrewed surface and aerial vehicles

Abstract Uncrewed aerial vehicles (UAVs) are frequently used in glaciological applications, among other things, for photogrammetric assessments of calving dynamics at glacier termini. However, UAVs are often limited by battery endurance and weight constraints on the scientific payload that can be added. At Sálajiegna, the largest freshwater calving glacier in Sweden, we explored the combined use of a versatile maritime robot (uncrewed surface vehicle, USV) and a UAV to characterise Sálajiegna's short-term and seasonal calving front dynamics and mass loss. For this, a photogrammetric payload suite was integrated into the USV. Consecutive USV surveys of Sálajiegna's front, followed by point cloud based calving detection and surface-reconstruction based volume quantification, allowed for a detailed description of calving-induced terminus changes and is hence suggested as a viable alternative to the differencing of digital elevation models. By combining USV and UAV measurements, we identify sectors of high and low calving activity, a calving front retreat of up to 56 m and a thinning rate in the terminus region of 5.4 cm d−1 during the summer of 2022.

Supercapacitor Implementation To Prototype Energy Storage System Optimizer in PV System

This research aims to create a prototype of an energy storage optimizer in a PV system using a supercapacitor in combination with batteries. The purpose of adding supercapacitors in this prototype is to maintain battery usage when there is a change in irradiation with a loading source that refers to the amount of electrical current and voltage as a supply of energy. A sudden increase in load without an energy buffer component can affect battery endurance. This can result in a decrease in electrical energy supply. Based on experimental results, it has been found that the battery and supercapacitor complement each other. The battery works more statically while the supercapacitor works more dynamically. Thus, the power output to the load is more stable, and the battery can be more durable because when there are extreme changes in irradiation and load, they can be overcome by supercapacitors.

The Orienteering Problem with Drones

We extend the classical problem setting of the orienteering problem (OP) to incorporate multiple drones that cooperate with a truck to visit a subset of the input nodes. We call this problem the OP with multiple drones (OP-mD). Drones have a limited battery endurance, and thus, they can either move together with the truck at no energy cost for the battery or be launched by the truck onto short flights that must start and end at different customer locations. A drone serves exactly one customer per flight. Moreover, the truck and the drones must wait for each other at the landing locations. A customer prize can be collected at most once, either upon visiting it by the truck or upon serving it by a drone. Similarly to the OP, we maximize the total collected prize under the condition that the truck and the drones return to the depot within a given amount of time. We provide a mixed-integer linear programming formulation for the OP-mD and devise a tailored branch-and-cut algorithm based on a novel decomposition of the problem. We solve instances of the OP-mD with up to 50 nodes within one hour of CPU time with a standard computational setup. Finally, we adapt our framework to solve closely related problems in the literature and compare the resulting computational performance with that of previous studies.

Ubiquitous Unmanned Aerial Vehicles (UAVs): A Comprehensive Review

Unmanned aerial vehicles (UAVs) or drones have found applications in various fields including military operations, construction, parcel delivery, mapping, medical, search and rescue missions, exploration of hidden areas, monitoring power lines and oil rigs, precision farming, aerial surveillance, and wireless communication. Due to their modular flexibility and programmability, drone technologies have become a strategic industrial/commercial sector that has gained significant attention as it redefines and combines different emerging domains, such as: service and delivery convergence, lean manufacturing, environmental monitoring and security. The use of swarms of interconnected UAV systems has gained popularity in providing innovative approaches to tasks requiring wide-area multiple collaborative sensors. However, despite advances in maneuverability, user interface, and cost-effectiveness, drones still face limitations in flight autonomy due to restricted flight time for continuous missions. Battery endurance, drone weight, and payload are critical development vectors. This review highlights the importance of drones, their future development priorities and functionality. Specifically, it focuses on UAV classification, swarms, and charging. Moreover, it explores UAV applications, challenges, and security issues based on recent research and development. Additionally, the study delves into anti-drone technology that can assist countries in controlling and ensuring the safe utilization of drones.

Assessing benthic marine habitats colonized with posidonia oceanica using autonomous marine robots and deep learning: A Eurofleets campaign

This paper presents a methodology for observing and analyzing marine ecosystems using images gathered from autonomous marine vehicles. Visual data is composed in photo-mosaics and classified using machine learning algorithms. The approach expands existing solutions, enabling extended monitoring in time, space, and depth. Imagery was collected during a field campaign in the Spanish marine and terrestrial protected area of Cabrera, Balearic Islands, colonized by the endemic seagrass species Posidonia oceanica (Po). The operations were performed using three distinct platforms, an Autonomous Underwater Vehicle (AUV), an Autonomous Surface Vehicle (ASV) and a Lagrangian Drifter (LD).Results are compared to prior habitat maps to assess seagrass meadow distribution. The proposed solution can be scaled and adapted to other locations and species, considering limitations in data storage and battery endurance.

Impacts of the Form Design and Operational Factors on the Energy Consumption of a Solar-Powered Boat: A System Dynamics Approach

This research paper aims to design a solar-powered boat and to analyze the effects of environmental and form-related factors on power consumption and battery duration by utilizing a system dynamics approach-based simulation. The boat form is designed as the planing hull and its hull resistance analysis was ensured in Maxsurf package program. PV panels having 548 W power output, and two battery packs with 4660 Wh capacity were placed on the hull body to employ an electrical motor having a 10 kW nominal power output. Two MPPTs were implemented in the system to increase solar system efficiency. The relationships between all system components were modeled in Vensim software to observe battery endurance changes under different conditions. Results demonstrated that the ideal vessel speed is calculated to be around 7 knots with roughly 8 hours of battery duration for the designed boat. 40% state of charge is a critical level for sailing, because 1.63 hours of cruising duration can be provided at 40% level while the speed is kept at 5 m/s (9.72 knots). Indeed, boat’s rising trim angle shorten the battery discharge time, thus navigation by no trim angle is the most effective usage for the vessel.

Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends.

Recently, unmanned aerial vehicles (UAVs) or drones have emerged as a ubiquitous and integral part of our society. They appear in great diversity in a multiplicity of applications for economic, commercial, leisure, military and academic purposes. The drone industry has seen a sharp uptake in the last decade as a model to manufacture and deliver convergence, offering synergy by incorporating multiple technologies. It is due to technological trends and rapid advancements in control, miniaturization, and computerization, which culminate in secure, lightweight, robust, more-accessible and cost-efficient UAVs. UAVs support implicit particularities including access to disaster-stricken zones, swift mobility, airborne missions and payload features. Despite these appealing benefits, UAVs face limitations in operability due to several critical concerns in terms of flight autonomy, path planning, battery endurance, flight time and limited payload carrying capability, as intuitively it is not recommended to load heavy objects such as batteries. As a result, the primary goal of this research is to provide insights into the potentials of UAVs, as well as their characteristics and functionality issues. This study provides a comprehensive review of UAVs, types, swarms, classifications, charging methods and regulations. Moreover, application scenarios, potential challenges and security issues are also examined. Finally, future research directions are identified to further hone the research work. We believe these insights will serve as guidelines and motivations for relevant researchers.

Rancang Bangun Alat Pengukur Suhu Badan Tanpa Kontak Berbasis Arduino dengan Modul Inframerah dan Bluetooth

The measurement of body temperature is necessary to prevent the spread of Covid-19. However, it must be conducted without any physical contact. This study aims to help users measure body temperature more quickly and accurately to detect Covid-19 suspects at a safe distance. The monitoring processes use the HC-05 bluetooth module and an application made with the MIT App Inventor is installed on an Android smartphone. This experiment uses an Arduino Uno with MLX90614 infrared temperature sensor, 16x2 LCD for text display, a buzzer as a warning alarm if the temperature is over 38ºC, and a tripod slot. The test conducted for MLX90614 sensor involved temperature measurement for a heated tap water object and three participants. The results indicated that the instrument can measure temperature with an accuracy of 98.63% to 99.55% through an effective distance of 5 cm. The designed software ran well on several versions of Android devices. Bluetooth connection test results in a maximum distance of 20 m with an obstacle and 25 m without an obstacle. A one-time battery endurance test is also carried out, which shows that the device can run for 165 minutes, while the bluetooth module is able to transmit the data to smartphone for 210 minutes.

Implementasi Plts 50 Wp Untuk Penerangan Jalan Di Kelurahan Manggar Kota Balikpapan

Street lamp lighting (SLL) is a complementary element of the street that serves to illuminate the environment around the street, so that road users will feel safe and comfortable. Based on the PkM team survey, RT 100 Manggar Village does not yet have a SLL. Therefore, the PkM team has designed 1 unit of Solar Power Plant (PLTS) with a capacity of 50 wp in the RT with 5 point DC lamp loads on the main road and 1 point placed in a resident's house, as well as placing the panel boxes. The main components of PLTS 50 wp include 50 wp solar panels, 35 ampere controllers, 35 Ah VRLA batteries, fiber cables, 3 watt DC lights, and fittings. The results of the battery endurance test after the solar panel is charged for 5 hours (when the sun is hot), the PLTS is able to turn on 6 light points (3 watts) for 46 hours. This proves that the PLTS is very efficient and suitable to be implemented as a source of PJU electrical energy in Manggar Village, Balikpapan City

Simultaneous and rapid estimation of state of health and state of charge for lithium-ion battery based on response characteristics of load surges

The state of charge (SOC) and the state of health (SOH) are two crucial parameters for monitoring the battery status because SOH determines whether the battery can continue to operate safely and stably, and the SOC determines the battery's endurance. This paper proposes an online synthesis method based on the response characteristics of load surges and an improved fuzzy cerebellar model neural network (IFCMNN) to co-estimate SOH and SOC. Firstly, multiple features are extracted from the voltage response signals. Then, the grey relational analysis is utilized to verify the rationality of the extracted features and to fuse key features for SOH and SOC estimation. Secondly, IFCMNN models aiming at estimating SOH and SOC are proposed to estimate the SOH and SOC simultaneously and quickly. Finally, experimental results on ten batteries with different aging levels show that the proposed method can achieve fast estimation of SOH and SOC at 1.64 % and 2 % resolution accuracy respectively, regardless of the temperature and in rush currents variations. In addition, the proposed model has higher estimation accuracy compared with other traditional methods. Thus, the proposed method has a high generalization ability.

Joint path planning and scheduling for vehicle-assisted multiple Unmanned Aerial Systems plant protection operation

Unmanned Aircraft Systems (UAS) for plant protection have been playing increasingly important roles in agricultural field management. Nevertheless, constrained by pesticide loading capacity and battery endurance limit, the operation area of UAS or UAS swarm is quite limited, prohibiting them from serving large-scale applications. Previous works in the UAS or UAS swarm operation never considered vehicle scheduling, thus unable to cover multiple segmented agricultural fields distributed large-scale. Therefore, we propose a joint path planning and scheduling for vehicle-assisted multiple plant protection Unmanned Aerial Systems (UASs) operation. With the help of vehicle, UASs are able to spray pesticide on numerous fields distributed large-scale. Vehicle stop selection, the coordination between vehicle scheduling and UASs tasks assignment along with path planning could be determined and optimized. The test results show that the overall efficiency and the vehicle stop selection are greatly affected by the UAS number and loading capacity. Vehicle-assisted multi-UASs total operation time and non-spraying flight distance could be saved, with optimized vehicle stops utilizing a binary-coded genetic algorithm (GA). The total operation time difference could be minimal in certain cases when deploying different UAS swarms with the same UAS number but different loading capacity. A significant reduction in total operation time and UASs non-spraying flight distance could be achieved by using the proposed method. Compared with other methods, the total operation time could be reduced by 2 h when controlling 2 or 3 UASs; moreover, a sharp decrease (20–30%) in UAS swarm non-spraying flight distance wastage could be attained.

Experimental study on thermal management of lithium-ion battery with graphite powder based composite phase change materials covering the whole climatic range

In order to ensure the normal operation of lithium-ion battery in any climate environment, it is necessary to explore the temperature dependence of lithium-ion battery performance, and adopt an effective and low energy thermal management system to maintain the temperature of lithium-ion battery within the normal operating temperature range. In this paper, the temperature dependence of heat generation behavior and charge–discharge performance of lithium-ion battery was studied, and the critical temperature of heat preservation and preheating process was determined. On this basis, graphite powder/paraffin composite phase change material and graphite powder/paraffin/nickel foam ternary composite phase change material with optimized composition were prepared. The thermal management experiment of large-capacity rectangular lithium iron phosphate battery covering the whole climatic range was carried out. The performance of the following thermal management modes was compared: air natural convection, paraffin, graphite powder/paraffin composite, graphite powder/paraffin/nickel foam ternary composite. The experiment showed that the necessary heat dissipation of lithium-ion battery was needed at 20℃ and 40℃ to avoid exceeding the upper limit of normal working temperature. The charge–discharge performance of lithium-ion battery was very sensitive to low temperature environment, especially the battery endurance and charge–discharge capacity. Both graphite powder/paraffin and graphite powder/paraffin/nickel foam composites can effectively control the surface temperature rise of lithium-ion battery, and keep the surface temperature above 0℃ for at least 40 min in the extremely cold environment of −20℃. They also had similar heating efficiency. However, the ternary composite had better temperature homogeneity in both the high temperature environment of 40℃ and the extremely cold environment of −20℃. The maximum temperature difference of ternary composite in the preheating process was 21.43% lower than that of binary composite.

A Comprehensive Review of Micro UAV Charging Techniques.

The groundbreaking Unmanned Aerial Vehicles (UAVs) technology has gained significant attention from both academia and industrial experts due to several applications, such as military missions, power lines inspection, precision agriculture, remote sensing, delivery services, traffic monitoring and many more. UAVs are expected to become a mainstream delivery element by 2040 to address the ever-increasing demand for delivery services. Similarly, UAV-assisted monitoring approaches will automate the inspection process, lowering mission costs, increasing access to remote locations and saving time and energy. Despite the fact that unmanned aerial vehicles (UAVs) are gaining popularity in both military and civilian applications, they have a number of limitations and critical problems that must be addressed in order for missions to be effective. One of the most difficult and time-consuming tasks is charging UAVs. UAVs’ mission length and travel distance are constrained by their low battery endurance. There is a need to study multi-UAV charging systems to overcome battery capacity limitations, allowing UAVs to be used for a variety of services while saving time and human resources. Wired and Wireless Power Transfer (WPT) systems have emerged as viable options to successfully solve this difficulty. In the past, several research surveys have focused on crucial aspects of wireless UAV charging. In this review, we have also examined the most emerging charging techniques for UAVs such as laser power transfer (LPT), distributed laser charging (DLC), simultaneous wireless information and power transfer (SWIPT) and simultaneous light wave information and power transfer (SLIPT). The classification and types of UAVs, as well as various battery charging methods, are all discussed in this paper. We’ve also addressed a number of difficulties and solutions for safe operation. In the final section, we have briefly discussed future research directions.

Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review

Recently, unmanned aerial vehicles (UAVs), also known as drones, have come in a great diversity of several applications such as military, construction, image and video mapping, medical, search and rescue, parcel delivery, hidden area exploration, oil rigs and power line monitoring, precision farming, wireless communication and aerial surveillance. The drone industry has been getting significant attention as a model of manufacturing, service and delivery convergence, introducing synergy with the coexistence of different emerging domains. UAVs offer implicit peculiarities such as increased airborne time and payload capabilities, swift mobility, and access to remote and disaster areas. Despite these potential features, including extensive variety of usage, high maneuverability, and cost-efficiency, drones are still limited in terms of battery endurance, flight autonomy and constrained flight time to perform persistent missions. Other critical concerns are battery endurance and the weight of drones, which must be kept low. Intuitively it is not suggested to load them with heavy batteries. This study highlights the importance of drones, goals and functionality problems. In this review, a comprehensive study on UAVs, swarms, types, classification, charging, and standardization is presented. In particular, UAV applications, challenges, and security issues are explored in the light of recent research studies and development. Finally, this review identifies the research gap and presents future research directions regarding UAVs.

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

AbstractNowadays, with the rapid progress of Internet‐based and distributed systems such as cloud computing, peer‐to‐peer networking, and Internet of Things (IoT), significant improvements in almost every engineering and commercial field have been made. On the basis of IoT, smart cities are formed utilizing intelligent information processing, universal connectivity, ubiquitous sensing, and real‐time monitoring. Energy conservation is one of the significant issues in current IoT development due to the poor battery endurance of IoT objects. Over the last years, with smart cities' explosive growth, a huge range of studies regarding energy efficiency have been done. The diversity of sparse data and multi‐sourcing is utilized in developing IoT scenarios. In order to use efficiently of these data to improve the IoT services, data fusion plays an important role. It saves network resources, improves data transmission efficiency, and extracts useful information from raw data. To the best of our knowledge, there is still a lack of a comprehensive and systematic study about surveying and analyzing the available energy‐efficient data fusion techniques in the IoT. Thus, this article aims to address this gap using a systematic method.

Comparative evaluation for recycling waste power batteries with different collection modes based on Stackelberg game

The new energy vehicle industry is booming, but the subsequent problem of vehicle power batteries’ “scrap tide” is still severe. How to establish and improve the end-of-life power battery recycling system to avoid the “catastrophic” environmental consequences has become an urgent global problem needing a solution. This article constructs three recycling models for manufacturer recycling, retailer recycling, and mixed recycling. By using Stackelberg game and market real data, the influence of carbon trading policy outside the supply chain, power battery endurance capacity and advertising effects within the supply chain on the selection of recycling channels was studied. The results showed: (1) Different recycling channels did not affect the wholesale price, retail price, and market demand for raw material power batteries in the positive supply chain; (2) The total profit function of manufacturers and retailers had a “U-shaped” non-linear relationship with power battery endurance capacity and has a positive linear relationship with the advertising effect. Taking the R&D endurance capacity of 0.4 and the total endurance capacity of 62 kWh as the lowest dividing point, it will decrease first and then increase; (3) The increase in the recycling competition coefficient had a greater impact on the consumption of carbon emission rights in the mixed recycling model than on savings in carbon emission rights, and retailers were the indirect “victims” of rising carbon trading prices; (4) Endurance capacity, advertising effects, and carbon trading prices determined the economics of the recycling model and the carbon emission reduction potential. Manufacturers, retailers, and governments can refer to the value range of each variable to select the most appropriate recycling mode.