Modern agriculture urgently requires sustainable alternatives to broadcast herbicide use, which drives environmental harm and weed resistance. Precision weed management offers a solution but depends on accurate, real-time field perception. This research develops and validates an efficient artificial intelligence (AI) system for automated weed detection and species classification, utilizing the YOLOv8 (You Only Look Once, version 8) computer vision model to provide the necessary perceptual foundation for smart agricultural machinery. The core of our methodology is the implementation of the single-stage YOLOv8 deep learning architecture, chosen for its optimal balance of high accuracy and speed for real-time processing. To train and evaluate this model, a substantial dataset of over 20,000 high-resolution field images was curated. Images featured a primary row crop alongside multiple weed species and were captured under varied lighting and growth conditions to ensure robustness. Each image was annotated with bounding boxes and class labels (for specific weeds and crop) using fundamental Python-based tools. A critical component for model generalization was an extensive data augmentation pipeline executed using standard Python imaging libraries. This pipeline applied random geometric transformations (rotation, scaling, flipping) and photometric adjustments (brightness, contrast) to the training data, artificially expanding dataset diversity and teaching the model invariance to field variability. Advanced techniques like mosaic augmentation were also employed to enhance detection of small objects and improve contextual learning. The YOLOv8m (medium) model was trained using the PyTorch framework, initialized with pre-trained weights to leverage prior feature knowledge. Performance was rigorously evaluated on a held-out test set. The system demonstrated high efficacy, achieving a mean Average Precision (mAP) of 94.2% at a standard detection threshold. This metric confirms the model's excellent capability in both locating weeds and correctly identifying their species. Crucially, the system maintained an inference speed exceeding 120 frames per second, confirming its suitability for real-time deployment on field equipment. The success of this YOLOv8-based system carries significant implications. By providing instantaneous, species-specific weed maps, it enables a fundamental shift from blanket chemical application to targeted control. This capability directly facilitates mechanical weeding, micro-dose spraying, or other site-specific interventions. The potential reductions in herbicide volume are substantial, promising lower production costs, diminished environmental pollution, and a deceleration in the evolution of herbicide-resistant weeds. In conclusion, this research presents a practical and high-performance AI solution for a central challenge in precision agriculture. The streamlined YOLOv8 pipeline successfully translates complex visual field data into actionable intelligence for weed management. This work validates a scalable pathway to integrate robust computer vision into farming practices, directly contributing to the development of more productive, cost-effective, and ecologically sustainable agricultural systems.
Abstract Agricultural mechanization is essential for enhancing productivity and efficiency in farming; however, there is no comprehensive study conducted on the availability and utilization of agricultural machinery in the East Wallaga Zone of Oromia Regional State. This study sought to assess the availability and use of agricultural machinery, identify factors affecting its adoption, and evaluate its impact on productivity. Data were gathered from 380 participants, including 360 farmers and 20 agricultural specialists, through structured questionnaires, interviews, and focus group discussions. Statistical analysis was performed using SPSS version 22 to uncover trends and relationships regarding machinery availability, utilization, and influencing factors. The findings indicated that 68% of farmers continued to depend on traditional farming practices. Access to agricultural machinery was limited to only 11% of farmers, which perpetuated reliance on outdated methods and restricted productivity. Additionally, several challenges hindered machinery use, including fragmented land holdings (with an average size of 2.1 hectares), financial constraints (75% of respondents), high costs of machinery (60%), and a scarcity of local suppliers (72%). Other obstacles included difficult terrain (56%), lack of maintenance services, and unavailability of replacement parts. Farmers who utilized machinery experienced a 35% increase in crop yields compared to those who relied on traditional methods, with mechanization significantly alleviating labor demands during peak periods. The study underscores the urgent need for policy interventions aimed at improving access to machinery, enhancing affordability, and establishing support systems to empower smallholder farmers in boosting productivity and achieving sustainable agricultural development.
The digital twin of a technological machine can significantly improve the efficiency of operation and service of farm machinery. The authors carried out theoretical studies to identify the effectiveness of using digital twins in terms of the technical condition of agricultural machinery. The article considers a possibility of using digital twins of technological machines during their operation and maintenance to automate repair inspections, digitize engineering information on maintenance and repair, minimize equipment downtime, and ensure quality control and the safety of operation. The authors present a functional dependency formula of the efficiency of using digital twins in operation and maintenance. It is established that reasonable decisions on maintenance and repair of equipment can be made only in case of digital twin modeling of various full and partial failures, taking into account operation modes, environmental impact and the wear intensity of parts. Therefore, the authors proposed to install RFID tags and monitoring terminals based on GPS, ISOBUS and modern software. They can analyze in real time information on the compliance of the process indicators to the specified parameters, and give instructions and control actions to the machine operator to correct the operation of tractors, working tools of implements, and units. Studies have shown the effectiveness of using digital twins of agricultural machinery as they can reduce costs for maintenance, elimination of failures, and unplanned downtime, as well as increase productivity.
This thesis presents an extensive review of recent developments in geotechnical engineering, a field essential for ensuring the stability and safety of infrastructure. It concentrates on three key areas: the adoption of intelligent digitalization algorithms, advancements in geotechnical equipment, and the implementation of sustainable, zero-carbon strategies. These elements reflect the evolving nature of engineering, which now emphasizes efficiency, performance, and environmental responsibility. The first section explores the role of intelligent digitalization algorithms in geotechnical engineering. These advanced algorithms are increasingly being utilized to improve data processing and decision-making. By leveraging big data and machine learning, engineers can now analyze large volumes of information to predict ground behavior and assess potential risks more precisely. This capability enhances design accuracy and optimizes construction methodologies, ultimately leading to more resilient and secure structures. The second area of focus is the emergence of innovative geotechnical equipment that is transforming the industry. Technological progress has led to the creation of sophisticated tools and machinery that enhance both the accuracy and efficiency of geotechnical investigations. Examples of such innovations include automated drilling systems, high-resolution geophysical imaging techniques, and state-of-the-art monitoring devices that provide real-time data on soil conditions. These advancements not only improve the reliability of geotechnical data but also significantly cut down the time and costs associated with site investigations. The third key topic addressed in this thesis is the importance of integrating green, zero-carbon strategies into geotechnical engineering. With increasing awareness of climate change, the engineering sector faces growing pressure to develop environmentally sustainable solutions. This section discusses various approaches, such as the use of eco-friendly materials, the optimization of construction methods to minimize waste, and the incorporation of renewable energy into engineering designs. The objective is to ensure that engineering practices are not only technically robust but also aligned with global sustainability goals. This research draws upon contributions from leading academic and research institutions worldwide, showcasing a wealth of expertise in geotechnical engineering. By analyzing a range of studies and case examples, the paper highlights key technological advancements and anticipates future industry trends. The thesis underscores the necessity of continuous research and collaboration to address emerging challenges and ensure ongoing progress in the field. A particularly notable contributor to geotechnical engineering research is Taiyuan University of Technology. This institution has played a significant role in pioneering innovative research that integrates cutting-edge technologies and sustainable practices. The thesis details the university’s ongoing projects, collaborations with industry leaders, and contributions to global research initiatives, illustrating its dynamic role in advancing the discipline. Additionally, the thesis suggests several potential research directions for the future of geotechnical engineering. These include further exploration of artificial intelligence in predictive modeling, the development of smart materials that adapt to environmental conditions, and the enhancement of infrastructure resilience against climate change. By identifying these areas for further study, the paper provides a foundation for future research initiatives that could bring about transformative advancements in geotechnical engineering. In conclusion, this thesis not only provides a comprehensive review of significant progress in geotechnical engineering but also emphasizes the necessity of continued innovation to tackle global challenges. By focusing on digitalization, technological advancements in equipment, and sustainability strategies, the research contributes to the ongoing discourse on the future of engineering practices. The insights presented aim to inspire academia, industry professionals, and policymakers to engage in collaborative efforts that will drive the next generation of geotechnical engineering solutions.
This study aimed to improve cadets’ learning activity and outcomes in basic mathematical concepts within the Applied Mathematics course through the implementation of the Electronic Mind Mapping (E-Mapping) method. The research was conducted in the Ship Machinery Study Program and involved 26 cadets who had previously taken the course. A classroom action research approach was employed, consisting of three cycles. Each cycle included a pre-test to assess initial abilities, delivery of instructional material, and task implementation using the E-Mapping method. A post-test was then conducted to evaluate cadets’ understanding and learning outcomes. In this study, E-Mapping was facilitated through mobile devices—primarily smartphones—using the SimpleMind and Canva applications. These tools enabled cadets to create, edit, and visualize mind maps in a flexible and interactive manner. The mobile-supported nature of these platforms allowed cadets to access their mind maps anytime and anywhere, enhancing the flexibility, accessibility, and autonomy of their learning. This approach empowered cadets to engage with mathematical concepts beyond the constraints of classroom time, thereby deepening their understanding. The findings revealed that the use of E-Mapping significantly enhanced cadet learning outcomes, as evidenced by increased levels of learning activity and higher average post-test scores. Positive learning activity improved progressively across the cycles: 30.77% in Cycle I, 66.66% in Cycle II, and 82.05% in Cycle III. Similarly, the average post-test score increased from 64.35 in Cycle I to 73.46 in Cycle II, and 82.12 in Cycle III. The study concluded that the E-Mapping method significantly improved both cadet learning activity and outcomes in the Applied Mathematics course. This improvement indicated that E-Mapping made a meaningful contribution to strengthening cadets’ understanding of fundamental mathematical concepts—such as algebra, trigonometry, and number theory—which are essential for applying mathematics in the field of ship machinery engineering. By enabling the visualization of interconnections among concepts, E-Mapping helped cadets systematically organize and integrate knowledge, thereby facilitating the comprehension of abstract and complex material.
The article considers the theoretical and methodological principles of implementing ERP systems at machinebuilding enterprises of the agro-industrial complex (AIC) of Ukraine. In the context of digital transformation of the economy and increasing requirements for production efficiency, ERP solutions are becoming a strategic tool that ensures the integration of all business processes into a single information space. ERP systems contribute to the automation of production, logistics, finance, personnel and service management, increasing productivity, reducing costs and ensuring transparency of management decisions. The main principles of ERP implementation are described – integration, systematicity, adaptability, scalability and continuous monitoring. The stages of system implementation are presented, in particular, process diagnostics, modeling, selection of a software solution and assessment of effectiveness. It is noted that ERP systems contribute to increasing the efficiency of production, logistics, financial management, personnel, service and interaction with customers. The stages of implementation are outlined: diagnostics of the current state, process modeling, software selection, personnel training, phased implementation and evaluation of results. A functional model of an ERP system is presented, which covers PLM, CRM, HRM, SCM, finance, production management and service. The economic feasibility of ERP implementation is determined: under the conditions of the model, the system pays off within one year, and the return on investment is 100%. The ERP system is considered as a strategic tool for the digital transformation of an enterprise, which ensures flexible response to market changes, cost reduction, productivity growth and long-term competitiveness within the digital economy. The ERP system is positioned as a fundamental component of digital transformation, which forms the basis for sustainable development and competitiveness of machine-building enterprises in the agricultural sector. Modern business conditions require agro-industrial enterprises to constantly improve their approaches to management, in particular through the active implementation of digital technologies. This issue is especially relevant for machine-building enterprises specializing in the manufacture of machinery and equipment for agriculture, since the technological level and productivity of agricultural production largely depend on the efficiency of their activities. The digitalization of management processes opens up new opportunities for increasing the competitiveness of machine-building enterprises, optimizing production and logistics operations, operational analysis of market trends and making informed management decisions. The use of modern information and communication technologies allows not only to automate accounting and planning and economic functions, but also to ensure the integration of enterprises into digital agro-industrial ecosystems.
Alberto Patti, Stefano Barberis, Alberto Traverso
et al.
Electrification of thermal users through heat pumps can be a promising way to enhance the exploitation of increasing renewable electrical capacity, offering significant opportunities for decarbonizing the industrial sector. For this purpose, since commercial vapor compression cycles are not readily viable to displace fossil fuel boilers employed in industrial thermal processes, interest is growing towards high temperature heat pumps (supply temperature > 160 °C) and, among them, reverse Brayton cycles. This work proposes an innovative Brayton-based open heat pump cycle applied to a relevant industrial case study, with the aim of upgrading the available waste heat to the required process temperature levels. The on-design performance analysis of the reverse Brayton cycle is conducted using the modular in-house tool WTEMP-EVO. Subsequently, a sensitivity analysis is performed on temperature levels, heat sink, and compressor isentropic efficiency. Finally, an off-design model integrating existing machinery with their characteristic curves is developed to evaluate different system operating conditions, as well as possible solutions to improve system rangeability, establishing the groundwork for the implementation of an experimental prototype. Results show that the analyzed cycle can provide heat at temperatures above 200 °C with a coefficient of performance higher than 1.5 and a temperature lift of more than 100 °C, demonstrating its potential in the industrial sector.
Virtual metrology enables semiconductor manufacturers to continuously monitor and predict the quality and performance of semiconductor production during the manufacturing process. This kind of real-time monitoring tool is essentially important for maintaining process stability and ensuring consistent product quality. In this paper, a new rolling-window and production-maintenance-based approach is proposed on a weekly basis to establish the virtual metrology system. The proposed thickening and dewatering framework serves as the central core of the virtual metrology model, enabling real-time information and feedback to aid in making timely adjustments to the processing tool within machinery limitations. A meticulous approach to addressing each component within this innovative framework has been instrumental in achieving exceptional VM performance. The training dataset is routinely updated as the production maintenance is taking place to facilitate model retraining. A practical challenge to the domain engineer under certain circumstances where the process parameter considered in the virtual metrology model is realistically run out of specification is also rigorously investigated in this research. For the evaluation purposes, a real-world industrial case of the chemical vapor deposition process in semiconductor manufacturing is presented to illustrate the proposed approach. The proposed virtual metrology approach shows a substantial improvement in mean absolute error with 39.99%, 26.26%, and 30.75% for three different recipes, respectively, as compared to the base model currently implemented on the operation site. Note to Practitioners—Virtual Metrology relies on advanced data analytics and machine learning algorithms to establish predictive models for production measurements. This kind of models leverage historical raw trace data, sensor readings, and other relevant information to estimate critical quality characteristics, such as film thickness, wafer properties, and critical dimensions. To respond to dynamic changes of processing specification and production conditions, the dataset is collected online on a weekly basis to train the predictive model. Practically, an occurrence of production maintenance to trigger the model re-training and feature selection between rolling windows are particularly designed in the proposed approach as well.
The present review has identified the urgency and multi-dimensionality of the issue of integrating ethical principles into AI systems in enterprises. With the further integration of AI into machinery in business and commerce in general, the threats of algorithmic discrimination, information privacy, and non-transparency should no longer be considered secondary issues. They are core to organizational success and social responsibility. A conceptual model was also suggested with ethical core elements like fairness, privacy, and transparency being correlated to the enterprise goals like innovation, trust, and ROI. The review understands empirical evidence indicating that ethical AI practices do not just improve the levels of stakeholder trust, but also match long-term strategic value. Still, even being extremely progressive, the sphere is fragmented and in demand of normalization. The majority of organizations still have a problem with translating abstract ideas into practical implementation plans. Evaluations have shown that the path forward is to build strong frameworks, scalable tools, and inclusive governance systems that would enable to operationalise of ethics throughout the AI lifecycle. In this way, enterprises are able to create AI systems that can be smart, along with being fair, reliable, and sustainable.
B. Pamplin, P. Edwards, G. Martínez-Arellano
et al.
Demand for automation of the repetitive drilling operations in aerospace assembly is growing. With millions of holes per airframe, critical skills shortages, and complicated products, the case for automation is strong. However, industrial robots are not equipped to detect the wide range of possible anomalies such as tool damage or poor process conditions during drilling. The need for trustworthy monitoring is a serious barrier to adoption – a key enabler is that anomalies are detected early, preventing damage to components, machinery, or operators. Existing research shows that machine learning can be highly capable in anomaly detection in industrial processes, and this work focuses on best applying it to the specific anomaly conditions of a robotic drilling cell. This paper investigates a method using clustering and proposes a second classification stage, on motor current data from a novel robotic drilling cell. Time series data is collected on 109 example cycles and 18 artificial anomalies including tool breakage, incorrect parameters, and workpiece defects. A comparison of methods is performed to select the best foundation for a solution. All implementations show parity or improvement over the current standard in drilling, with accuracies over 90% attainable with all methods given sufficient data. Comparison shows that the clustering methods performed the best, with an achievable accuracy of 100% on the tested anomalies and no false alarms. The limitations of the system are identified and possible methods to improve accuracy are discussed. The impact of training data size on performance indicators is investigated and compared.
Feasibility checking is a step in manufacturing system engineering for verifying the normalization and effectiveness of a manufacturing system associated with a specific configuration of resources and processes. It enables factory operators to predict problems before operational time, thus preventing equipment and machinery accidents and reducing labor waste in physically organizing the shop floor. In Industry 4.0, feasibility checking becomes even more critical since emerging challenges, such as mass personalization, require reconfiguring work cells quickly and flexibly on demand. Regarding this need, digital twin technologies have emerged as an ideal candidate for practicing feasibility checking. Indeed, they are tools used to implement digital representations of manufacturing entities that can constitute a digital environment and context. Factory operators can test a manufacturing process within a digital environment in different contexts before the execution with physical resources. This approach currently receives significant attention from the manufacturing community; however, there is still a lack of sharing experiences to implement it. Thus, this paper contributes a methodology to engineer a digital environment and context for a manufacturing work cell using AAS digital twins and physics-based 3D digital twins technologies. Technically, this methodology is a specific case of N-DTs, a general methodology for engineering heterogeneous digital twins. The product assembly line case study, also presented in this paper, is a successful experiment applying the above contributions. The two methodologies and the case study can be helpful references for both public and private sectors to deploy their feasibility-checking frameworks and deal with heterogeneous digital twins in general.
Pumping water in water networks is generally the top energy demand for water systems. This study seeks to develop a large language model (LLM)-assisted framework for pump operation. Herein, ChatGPT was used to suggest pump control settings over 24 h that minimize energy use while maintaining pressure levels. In the proposed prompts, hourly information about the planned operation, i.e., pump control settings, minimum pressure levels, tank storage levels, and pump energy use, was provided. As the LLM suggests improved scenarios, EPANET results for these scenarios are fed back to it. This allows the LLM to learn and adjust future suggestions. The framework was validated on the example EPANET Net 3. Through iterative data exchange between the LLM and EPANET, the framework led to more energy-efficient pump scheduling. The LLM-assisted framework was compared with a genetic algorithm optimization. The results demonstrated that the proposed method outperformed the GA, achieving an energy reduction of 66.98%.
Kuo-Chien Liao, Jian-Liang Liou, Muhamad Hidayat
et al.
Pre-flight inspection and maintenance are essential prerequisites for aviation safety. This study focused on developing a real-time monitoring system designed to assess the condition of composite material structures on the exterior of aircraft. Implementing such a system can reduce operational costs, enhance flight safety, and increase aircraft availability. This study aims to detect defects in aircraft fuselages manufactured from composite materials by applying image visual recognition technology. This study integrated a drone and an infrared camera for real-time image transmission to ground stations. MATLAB image analysis software (MATLAB 2020b) was used to analyze infrared (IR) images and detect structural defects in the aircraft’s appearance. This methodology was based on the inspection of damaged engine cowlings. The developed approach compares composite material conditions with known defects before and after repair, considering mechanical performance, defect size, and strength. Simultaneously, tests were conducted on various composite material panels with unknown defects, yielding favorable results. This study underscores an integrated system offering rapid detection, real-time feedback, and analysis, effectively reducing time, and potential hazards associated with high-altitude operations. Furthermore, it addresses blind spots in aircraft inspections, contributing to effective flight safety maintenance.
Engineering machinery, tools, and implements, Technological innovations. Automation
During the COVID-19 epidemic, countries enacted autonomous measures to suspend long-distance travel. As a result, people used online platforms to share perspectives and disseminate their knowledge and skills. Internet learning content thus emerged as a primary solution. This study was conducted to assess the reactions of users to virtual tours. Participants were introduced to the 360-degree panoramic photography system of cultural monuments of the Taipei City Government and participated in an online cultural tour. A closed-ended questionnaire was distributed for their response. After compiling data from 31 participants, we analyzed the link between users’ demographic characteristics and their satisfaction levels with the online panoramic tour system. We discovered higher satisfaction rates of people with incomes exceeding that of the average participant. 83% of participants stated a willingness to explore scenic attractions virtually instead of physically traveling when unable to do so. The results of this study contribute to understanding the context of users’ post-visit satisfaction. The information gathered can be used to improve cultural heritage websites in terms of design, navigation, and cultural education, enabling virtual access to cultural sites and enriching users’ knowledge from home.
The nudge theory has been applied to improve safety behavior in various industries. We implemented the nudge theory in the construction industry to improve worker safety. Nudges for construction safety were grouped into three categories and used in a project. The application of nudges improved workers’ safety behavior, particularly in highlighting control measures. The background analysis results reveal that the implementation of nudges did not vary by gender, nationality, or age, demonstrating its high universality. The results of this study offer a reference for stakeholders in the construction industry who are aiming to bolster worker safety.
Unquestionably, hospital patient rooms require a proper lighting design. Dissimilar to cultural and artistic settings, where artistic discourse on light has significant importance, in medical settings, the most crucial conversation refers to standards. Research indicates that light in hospital settings has an impact on a patient’s physical and mental health. Effective lighting in medical settings can enhance the hospital’s positive experience and the speed at which patients recover from their diseases. It can also increase staff attentiveness and productivity. It is also critical to consider reducing electricity consumption in hospital settings that require lighting 24/7. Due to the high cost of lighting, access to natural light in combination with time-of-day controls minimizes energy consumption when daylight is available and impacts the hospital’s bottom line. The effect of light on hospital users was investigated in this article; therefore, it is important to understand both natural and artificial light sources in this regard. Natural light has many benefits for humans, and when it comes to electricity consumption, it is the best method because it is a free source; but, since natural light is not always available and cannot be used throughout the day, there is a need to have an artificial light source that gives the best lighting effect in terms of visual comfort and visual performance for users. Secondly, proper artificial light sources can reduce electricity consumption; hence, these two critical aspects were underlined in this study.
. Technological aspects of digitalization of machine-building production at the stage of pre-production engineering process (PEP) are viewed. Research methods: an exhaustive study of the basic concepts of the PEP defined by standardized documentation - national standards forming the basis of a unified system of technological documentation. It is shown that the formal transition to digital production chains the phenomenon of transformation of basic concepts - the main emphasis is on the modeling technologies at the price of production technologies. As a result of the analysis of EP stages in accordance with standardized documents, the EP digitalization peculiarities are shown. Research results: for the main design tasks of the technological content, the problems of PEP caused by production digitalization are presented: rational choice problems for processing machinery selection; problems of rational choice of processing methods; rational determination of process specification of production work. It is shown that in the conditions of production, equipped with programm support and hardware facilities for technological equipment control, it is necessary to improve feedback systems for the implementation of operational diagnostics and active control of process system elements for guaranteed product quality assurance. It is shown that the process, methods and means of PEP should be adapted to the conditions of a particular production. It is shown that the tasks of PEP related to the rational choice and assignment of processing conditions should be implemented directly at the operating step. It will allow taking into account in a timely manner the material properties variability for the workpiece and the cutting tool being in the intraprocess. Conclusions: the rational application of the principles of digitalization will allow treating the hardware/ software complex for technological design and control of processing machinery as technological artificial intelligence – the accumulated experience and knowledge of specialists of technological services adapted to the conditions of a particular enterprise.
Rahel Krause, Justin Kühn, Carsten Schiffer
et al.
Firefighters are exposed to high risks and hazards, such as flames and smoke, in their daily lives. To be protected against these risks, firefighters wear protective clothing. As an employer, it is the duty to provide firefighters with good protection according to DIN EN 469. To do so, it is necessary to select, procure and maintain suitable firefighters' protective clothing. In order to identify weaknesses in the above steps and to develop and present proposals for remedying the identified weak points, an empirical study was conducted. In preparation for this study, interviews were first conducted with members of fire brigades. Based on the interviews, the relevant standards and regulations for fire fighters´ clothing were classified and areas of tension between the standards and regulations as well as their design in everyday fire brigade life were identified. Based on this, a standardized quantitative survey was conducted and the answers of the respondents were empirically evaluated. The evaluation examined both the respondents' answering behavior and the dependency between the answering behavior for different questions due to demographic differences. A key finding is that women firefighters are less satisfied with their firefighting clothing compared to men firefighters. The firefighters' clothing fits them worse. They do not feel as safe and comfortable. Moreover, woman have less confidence in the protective clothing. There is a correlation between the fit of the clothing and the satisfaction, confidence and feeling of protection
Textile bleaching, dyeing, printing, etc., Engineering machinery, tools, and implements
Experimental analysis of the effect of a lithium-ion battery thermal management system using natural convection and phase change material (PCM) at 3 C discharge rate. The cells are placed in a 2 × 2 square configuration in an acrylic housing with the capability to contain PCM. The experimental setup simulates lithium-ion batteries using ceramic heaters, producing the heat energy per unit volume as produced in a lithium-ion cell. The system placed in natural convection instantly heats up to 45 °C in 305 s. On the other hand, the system, when placed in phase change material, shows that a temperature of 45 °C is achieved in 1490 s.