Proactive Maintenance Strategy Research Articles

Ship Maintenance Management Practices: Insights from Internships in Shipping Engineering

This research investigates ship maintenance management practices among senior students in shipping engineering during their internships at a vocational school in Jakarta. The study explores the challenges faced, strategies employed, and lessons learned in managing maintenance tasks onboard ships. Seven participants were interviewed, highlighting technical malfunctions, resource limitations, time constraints, environmental factors, and communication barriers as primary challenges. Participants utilized preventive maintenance, cross-training, task prioritization, collaborative problem-solving, and digital tools to enhance operational efficiency. Lessons underscored the importance of preparation, teamwork, adaptability, continuous learning, and communication skills in navigating maritime maintenance complexities. The findings emphasize the role of proactive maintenance strategies and technological integration in optimizing sustainability and operational reliability within the maritime industry. Integrating practical training into educational curricula and embracing digital innovations are recommended for preparing future professionals. Future research could explore larger sample sizes and quantitative methodologies to validate findings and quantify the impact of maintenance practices. This study contributes to enhancing vocational education in maritime sciences and informs industry practices for advancing ship maintenance management globally.

Evaluation of Slurry-Eroded Rubber Surface Using Gloss Measurement

Slurry erosion testing is essential for evaluating the durability of materials under erosive conditions. This study examines the slurry erosion behaviours of chloroprene rubber (CR) under varying impact conditions to assess its durability. Traditional mass loss methods and qualitative techniques, including microscopy, SEM, and AFM, were employed to analyse eroded CR samples. Results indicate that cumulative material loss in CR increases linearly with sand impingement after approximately 60 kg of sand and correlates with an impact energy of about 30 kJ. The highest erosion rate was found at an impact angle of 15°. Erosion mechanisms vary with impact angle, affecting surface topography from cutting and ploughing at lower angles to deformation and crater formation at higher angles. Despite their efficacy, these methods are time-intensive and costly. This paper presents a novel approach utilising gloss measurement for continuous, non-destructive monitoring of eroded rubber surfaces. Gloss measurements are 24 times faster than traditional mass loss methods. Correlating gloss values with cumulative material loss, steady-state erosion, and impact energy offers significant time savings and an enhanced understanding of the erosion process. Experimental results demonstrate the effectiveness of gloss measurement as a reliable tool in slurry erosion testing of rubbers. The quantitative output from gloss measurements could support proactive maintenance strategies to extend service life and optimise operational efficiency in industrial applications, particularly in the mining industry.

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems.

Workforce forecasting in the building maintenance and repair work: Evaluating machine learning and LSTM models

Effective workforce forecasting is critical to strategic management in construction projects, particularly ensuring staffing is optimized for efficient and timely project completion. This aspect holds particular importance in densely populated urban environments, where the complexity and scale of high-rise building construction exacerbate the need for proactive maintenance strategies. This research aims to compare the efficacy of various machine learning models, including support vector machine (SVM), extreme gradient boosting (XGBoost), gradient-boosted decision trees (GBDT), and multi-layer perceptron (MLP) models, in predicting the workforce demand for building maintenance and repair work. Accordingly, 128 sets of data composed of seven indicators collected from the Hong Kong building maintenance and repair contractors were curated to serve as input-dependent variables to forecast the demands for different labor categories in addition to the overall workforce demands. The performance metrics for the four machine learning models were evaluated using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and determination coefficient (R2), with GridSearch employed to optimize their hyperparameters for enhanced predictive accuracy. The evaluation using MAE, MSE, RMSE, and R2 revealed that the XGBoost model achieved the most favorable results for predicting workforce demand on the testing sets (0.1790, 0.0582, 0.2412, 0.9915). Compared to MLP (0.1828, 0.0775, 0.2783, 0.8821) and GBDT (0.1828, 0.0828, 0.2878, 0.8739), XGBoost demonstrated a stronger performance in this specific task. Furthermore, the study also explored the potential of a long short-term memory (LSTM) model to forecast the gross value of building maintenance and repair work undertaken by main contractors, which strongly correlates with workforce demand. This approach aimed to capture the consistent growth trend observed across various maintenance activities. Consequently, the models developed in this study serve as valuable tools for optimizing workforce allocation and financial planning, particularly in dynamic and ever-changing environments.

Butterfly valve erosion prediction based on LSTM network

When valves and pipelines are used, erosion wear is a major concern. Erosion wear can lead to equipment downtime, material replacement, and other issues, as well as seal surface failure. The research delves into the phenomenon of erosion in butterfly valves, crucial components utilized across diverse industrial sectors. Erosion primarily affects the valve's disc and seat regions, leading to premature wear and compromised performance. To address this issue, the research employs computational fluid dynamics (CFD) and machine learning techniques, including long short-term memory (LSTM) and BP neural networks, to predict erosion rates under various conditions (different valve openings and particle diameters). Results indicate that erosion escalates with time and larger valve openings, highlighting the need for proactive maintenance strategies. The LSTM model demonstrates superior predictive capabilities compared to the BP neural network, offering valuable insights for improving butterfly valve design and operational efficiency in industrial settings. Furthermore, to seek a more efficient network configuration, the intelligent search capabilities of the particle swarm optimization (PSO) algorithm have been utilized to systematically explore the optimal network structure parameters. The prediction results highlight the advantages of LSTM in handling time series data, particularly in predicting erosion rates with complex dynamic characteristics.

Exploring technological and operational challenges in high-pressure: High-temperature drilling techniques

This comprehensive review explores the complex landscape of HPHT drilling, examining the underlying principles, innovative technologies, and operational strategies used to overcome challenges such as wellbore stability, fluid management, equipment reliability, and downhole conditions. By understanding and addressing these challenges, the oil and gas industry can advance HPHT drilling capabilities and unlock new sources of energy to meet growing global demand. High-pressure, high-temperature (HPHT) drilling techniques have become indispensable for the oil and gas industry to tap into deep hydrocarbon reservoirs located in challenging environments. Despite their significance, HPHT drilling operations pose a myriad of technological and operational challenges that demand meticulous attention to safety, efficiency, and success. This comprehensive review embarks on an exploration of the intricate landscape of HPHT drilling, delving into the fundamental principles, innovative technologies, and operational strategies pivotal in surmounting obstacles such as wellbore stability, fluid management, equipment reliability, and downhole conditions. The review begins by delineating the distinctive characteristics of HPHT environments, highlighting the formidable challenges inherent in drilling operations conducted under extreme pressures and temperatures. Subsequently, it elucidates the critical importance of maintaining wellbore stability amidst such conditions, elucidating the risks of collapse and blowouts and offering insights into mitigation strategies ranging from meticulous drilling fluid selection to advanced wellbore monitoring techniques. Fluid management emerges as a focal point, as the review navigates through the complexities of selecting, formulating, and managing drilling fluids tailored for HPHT environments. Innovative technologies such as nanotechnology applications and synthetic-based mud systems are explored for their efficacy in addressing fluid-related challenges and enhancing drilling performance. The reliability and integrity of equipment constitute another critical aspect examined in the review, with emphasis on mechanical challenges, material selection, and proactive maintenance strategies essential for safeguarding equipment performance and longevity in HPHT operations. Furthermore, the review presents case studies and best practices gleaned from successful HPHT drilling projects, offering valuable insights and lessons learned to inform future endeavors.

Remaining Useful Life Estimation (RUL) For Critical Mechanical Components In Tractor

The proposed framework utilizes an evolutionary mathematical approach to optimize data-related parameters for estimating the Remaining Useful Life (RUL) of critical mechanical components. By integrating advanced time window techniques and considering factors like downtime and failure occurrences, it offers a comprehensive solution for RUL prediction across various industrial contexts. Continuous monitoring systems play a pivotal role by detecting component degradation early, allowing for proactive maintenance and minimizing catastrophic failures. Combining Mean Time to Repair (MTTR) and Mean Time Between Failures (MTBF) offers valuable insights into system performance, enabling proactive maintenance strategies. This review explores mathematical methodologies for predicting RUL in tractors, crucial for enhancing maintenance planning and remanufacturing engineering.

THE RELIABILITY EVALUATION OF THE DECK MACHINERY AND GALLEY EQUIPMENT OF A BULK CARRIER BY UTILIZING THE FAILURE RECORDS

Among various modes of transportation, maritime transportation holds critical importance since it provides substantial carrying capacity with low unit costs. To perform seamless and efficient operations in maritime transportation plays a pivotal role in achieving sustainable development goals and the International Maritime Organization (IMO) targets. The execution of uninterrupted operations can only be carried out with the existence of reliable systems. Creating reliable systems onboard is possible through the implementation of planned and proactive maintenance strategies and leveraging experiences gained from past failures. 10-year failure records of bulk carriers have been scrutinized within the scope of system reliability to determine critical equipment and units. The data has been categorized into subgroups under four fundamental headings, and subsequent reliability analyses have been conducted on each subgroup. Within the subgroups, the reliability of navigation equipment should be improved since it has the highest failure rate and its malfunction can cause very serious marine accidents. This equipment is followed by fire-fighting systems, cargo equipment, and GMDSS instruments which are essential for ship operations based on reliability results. Therefore, regular failure records, planned and proactive maintenance strategies, and also extra efforts should be performed on this equipment to ensure sustainable and seamless operations in the maritime sector.

Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability

Existing energy management systems are becoming increasingly insecure and inefficient due to the rapid adoption of smart grid technology. Current research indicates that effectively managing dynamic energy flows, adjusting to changing needs, and protecting against new cyber threats remain significant challenges for the smart grid system. An advanced and comprehensive plan for managing smart grids is therefore required, capable of addressing these delicate and multifaceted problems. The proposed framework addresses these challenges through unifying several key aspects, it includes an advanced data acquisition system that captures real-time data from various grid sources, enabling comprehensive energy monitoring and dynamic flow analysis. By integrating predictive algorithms, the framework provides precise energy demand forecasting, which is essential for adaptive grid management. A significant contribution is the incorporation of an AI-based module for diagnostics and prognostics, which leverages machine learning techniques to shift from reactive to proactive maintenance strategies. The optimal power flow (OPF) optimization module represents a central component of the framework. It employs advanced computational methods to ensure efficient and cost-effective power distribution, particularly in grids incorporating renewable energy sources. Additionally, the architectural framework is strengthened by a robust cybersecurity module designed to safeguard against a wide range of cyber threats, maintaining the integrity of both operational and consumer data. This paper also addresses practical implementation challenges such as compatibility with existing infrastructure, investment costs, and the need for specialized training. This solution represents a new benchmark for smart grid operations, ensuring more sustainable and efficient energy systems.

Corrosion fatigue analysis of NREL’s 15-MW offshore wind turbine with time-varying stress concentration factors

Over the last twenty years, significant development in wind turbine technologies has led to a dramatic increase in the scale of wind turbines with many now beginning to be installed in offshore locations. Consequently, modern multi-megawatt offshore wind turbines are exposed to increased cyclic loading in addition to an increased risk of corrosion attack. The combination of these two factors may result in wind turbine support structures becoming increasingly vulnerable to fatigue corrosion. The objective of this work is to investigate the impact of material thinning in fatigue-prone areas with respect to fatigue loading and ultimately to examine the potential repercussions on the lifespan of wind turbine support structures. To achieve this, a composite model is constructed coupling results from a multi-body structural dynamic model with time-varying Stress Concentration Factors (SCF) obtained from a finite element model (FEM) of NREL’s 15-MW monopile-based offshore wind turbine. The nonlinear aeroelastic multi-body dynamic model of the wind turbine is used to generate stress time histories for a set of environmental conditions based on the operational conditions of the wind turbine. The finite element model of the wind turbine is then used to identify fatigue-vulnerable regions in the wind turbine support structure and calculate SCFs for these specific areas. The integration of SCFs into the fatigue calculations reduced the lifespan of the turbine tower by a factor of 4, demonstrating the importance of precisely modelling such local stress concentrations for effective fatigue analysis. A novelty of this work arises in the ability of the finite element model to update the SCFs of the fatigue-prone areas over time as corrosion-induced wastage alters the substructure’s geometry, thereby inducing a global redistribution of stresses. A fatigue analysis is carried out availing of the SCFs which vary annually in addition to the stress-time histories produced by the multi-body dynamic model. The results illustrate that the phenomenon of corrosion thinning induced an 8.9% reduction in the fatigue life of the wind turbine tower, thus emphasising the significant importance of proactive maintenance strategies to mitigate the impact of corrosion.

Advancing maintenance, monitoring, and control techniques for the optimization of power electronic systems

This paper explores the innovative methodologies and technologies in predictive maintenance, real-time monitoring, fault detection, and advanced control strategies for power electronic devices. Initially, we delve into data acquisition and preprocessing techniques crucial for ensuring the quality and reliability of data used in predictive models. These models, leveraging machine learning and time-series analysis, predict the remaining useful life of devices, guiding proactive maintenance strategies. Furthermore, we discuss the significance of risk assessment and decision support systems in prioritizing maintenance tasks and allocating resources efficiently. The narrative then shifts to real-time monitoring and fault detection, emphasizing the role of sensor integration, data fusion, anomaly detection, and diagnostics in maintaining system integrity. Condition-based maintenance strategies, underscored by real-time data analytics, are presented as a means to optimize maintenance activities and enhance operational performance. The paper concludes with a detailed examination of advanced control strategies, including model predictive control, reinforcement learning, and distributed control and optimization techniques, highlighting their potential to improve system efficiency, reliability, and adaptability. Through comprehensive research and analysis, this work aims to provide valuable insights into the development of sophisticated maintenance and control mechanisms for power electronic systems, ultimately contributing to their longevity and operational efficacy.

Enhancing GNSS Deformation Monitoring Forecasting with a Combined VMD-CNN-LSTM Deep Learning Model

Hydraulic infrastructures are susceptible to deformation over time, necessitating reliable monitoring and prediction methods. In this study, we address this challenge by proposing a novel approach based on the combination of Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) methods for Global Navigation Satellite Systems (GNSS) deformation monitoring and prediction modeling. The VMD method is utilized to decompose the complex deformation signals into intrinsic mode functions, which are then fed into a CNN method for feature extraction. The extracted features are input into an LSTM method to capture temporal dependencies and make predictions. The experimental results demonstrate that the proposed VMD-CNN-LSTM method exhibits an improvement by about 75%. This research contributes to the advancement of deformation monitoring technologies in water conservancy engineering, offering a promising solution for proactive maintenance and risk mitigation strategies.

A REVIEW ON TOTAL PRODUCTIVE MAINTENANCE (TPM)

Total Productive Maintenance (TPM) is transforming manufacturing landscapes. This review investigates its impact, revealing how TPM fosters a culture of continuous improvement through employee involvement and proactive maintenance strategies. By analyzing 30 research papers, it unveils the potent link between TPM and increased productivity alongside reduced waste. Highlighting Overall Equipment Effectiveness (OEE) as a crucial metric, the review emphasizes the importance of 5S as a foundational step for successful TPM implementation. With global adoption surging, TPM is solidifying its position as a world-class strategy for achieving peak manufacturing performance." Keywords: Total Productive Maintenance (TPM), OEE, Improve Productivity, 5S.

Investigating the Effects of Ageing on Transmission System Dependability Through the Use of an Artificial Neural Network

As power transmission systems age, their dependability becomes increasingly critical for maintaining reliable electricity supply. However, accurately predicting the impact of ageing on system dependability remains a challenging task due to the complex interactions among various components and environmental factors. In this study, we propose the utilization of an artificial neural network (ANN) approach to investigate the effects of ageing on transmission system dependability. The ANN model is trained on historical data encompassing a wide range of parameters including equipment age, maintenance records, environmental conditions, and system performance metrics. By capturing the nonlinear relationships within the data, the ANN can effectively model the intricate dependencies between ageing and system dependability. Through comprehensive simulations and sensitivity analyses, we demonstrate the capability of the ANN to forecast the degradation of transmission system dependability over time. Moreover, the model enables the identification of key factors driving system reliability decline, thereby facilitating proactive maintenance strategies and resource allocation.

Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach

This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance.

AI-Powered Innovations in Electrical Engineering: Enhancing Efficiency, Reliability, and Sustainability

Artificial Intelligence (AI) has emerged as a transformative force in the field of Electrical Engineering, revolutionizing traditional practices and unlocking unprecedented possibilities. This research paper investigates the integration of AI-powered innovations to enhance efficiency, reliability, and sustainability within electrical engineering systems. Through a comprehensive review of existing literature, this study delves into key applications of AI, including predictive maintenance, optimal resource allocation, and fault detection, among others. Utilizing advanced machine learning algorithms and data analytics techniques, AI facilitates real-time decision-making processes, enabling proactive maintenance strategies and optimizing system performance. Moreover, AI-driven approaches contribute to the enhancement of reliability by predicting potential failures and implementing pre-emptive measures, consequently reducing downtime, and improving operational continuity. Furthermore, the implementation of AI in electrical engineering fosters sustainability by optimizing energy consumption, mitigating environmental impacts, and facilitating the integration of renewable energy sources into power grids. By leveraging AI technologies, electrical engineering systems can adapt to dynamic operational conditions, maximize resource utilization, and minimize environmental footprints, thereby paving the way for a more efficient, reliable, and sustainable future. This paper underscores the transformative potential of AI in shaping the landscape of electrical engineering and provides insights into future research directions to harness the benefits of AI-powered innovations further.

Impacts of climate change on environmental and economic sustainability of flexible pavements across China

Pavements are susceptible to accelerated deterioration due to changing climate conditions, leading to increased maintenance and excess fuel consumption through pavement-vehicle interaction. China's diverse climates raise concerns about the environmental and economic sustainability of flexible pavements amid climate change and the effectiveness of preventative maintenance strategies. This study examines climate change's potential impacts on long-term pavement performance, greenhouse gas (GHG) emissions, and costs, employing the Mechanistic-Empirical Pavement Design Guide method. The calibrated World Bank's Highway Development and Management Model 4 assesses the impacts of surface characteristics on vehicle fuel consumption. Life cycle assessment and life cycle cost analysis quantify GHG emissions and costs. Results reveal significant pavement deterioration in Southeast and Central China. Preventative maintenance strategies reduce fuel consumption, with GHG emissions and cost savings from smoother driving conditions outweighing those from maintenance. These insights stress the importance of proactive maintenance strategies for mitigating climate-induced deterioration and enhancing sustainability.

Implementation of Health Monitoring System of Induction Machine Using Computational Intelligence

The induction machine is a critical component in various industrial applications, and its reliable operation is paramount for uninterrupted processes. To ensure the continuous performance and prevent unexpected failures, the implementation of an effective health monitoring system is imperative. In this study, we propose a novel approach to monitor the health of induction machines using computational intelligence techniques. The proposed system integrates advanced computational intelligence algorithms, including machine learning and signal processing methods, to analyze the operational data and identify potential faults or anomalies in the induction machine. Specifically, we employ techniques such as artificial neural networks, support vector machines, and genetic algorithms to process the sensor data acquired from the induction machine. The health monitoring system operates in real-time, continuously analyzing various parameters such as current, voltage, temperature, and vibration patterns to detect deviations from normal operating conditions. By establishing baseline performance profiles and employing pattern recognition algorithms, the system can identify early signs of degradation or impending failures, allowing for proactive maintenance interventions. Moreover, the proposed system offers flexibility and scalability, enabling adaptation to different types of induction machines and operating environments. It facilitates remote monitoring capabilities, enabling maintenance personnel to access real-time health status and diagnostic information from anywhere, facilitating timely decision-making and proactive maintenance strategies.

Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance

In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the ensemble of FNN and LSTM models, capitalizing on their collective learning. This ensemble approach outperforms individual models, ensuring stable and accurate power output predictions. Additionally, machine learning techniques are applied to detect wind turbine performance deterioration, enabling proactive maintenance strategies and health assessment. Crucially, our analysis reveals the uniqueness of each wind turbine, necessitating tailored models for optimal predictions. These insight underscores the importance of providing automatized customization for different turbines to keep human modeling effort low. Importantly, the methodologies developed in this analysis are not limited to wind turbines; they can be extended to predict and optimize performance in various machinery, highlighting the versatility and applicability of our research across diverse industrial contexts.

A Framework for Managing and Monitoring the Battery Health Parameters of Rental Electric Vehicles

The rapid adoption of electric vehicles (EVs) has paved the way for a sustainable transportation future. However, ensuring the safety and reliability of rental EVs, particularly regarding their battery health, presents unique challenges. This paper proposes a comprehensive framework for monitoring and managing rental EV batteries, encompassing real-time data acquisition, advanced analytics, and proactive maintenance strategies. By implementing such a framework, rental EV providers can optimize battery performance, extend lifespan, and guarantee the safety and convenience of their customers. The surging popularity of rental electric vehicles (EVs) poses unique challenges in ensuring their safety and reliability, particularly concerning battery health. This paper presents a comprehensive framework for proactive monitoring and management of rental EV batteries. The framework encompasses real-time data acquisition of key battery parameters, advanced analytics for predicting degradation and identifying influencing factors, and data-driven maintenance strategies like cell balancing and optimized charging. Implementing this framework empowers rental EV providers to maximize battery lifespan, enhance customer experience, and contribute to a sustainable transportation future. This leads to improving the factors and the process to monitor the battery health parameters of the rental electric vehicle. By adopting the various monitoring algorithms to measure and monitor the battery health parameters such as State of Charge, State of Health, battery voltage, balancing current, and temperature are monitored by the lifespan of the battery is extended and the safety and reliability of the battery is ensured by monitoring the temperature.