Machine Learning based Battery Failure Prediction Using Random Forest Algorithm

This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of the nursing care delivery system. Historical and current healthcare challenges, such as nurse shortages and increasing patient acuity, necessitate innovative approaches to nursing care delivery. For instance, the COVID-19 pandemic highlighted the need for flexible and scalable staffing models to manage surges in patient volume and acuity. A descriptive study was conducted in 39 inpatient wards across a university hospital and three state hospitals, involving 117 ward-level observations. Data were collected using the Rush Medicus Patient Classification Scale and analysed using k-Nearest Neighbour, Support Vector Machine, Random Forest, and Logistic Regression algorithms. Effectiveness was measured by the accuracy of machine learning predictions regarding nurse staffing adequacy, while suitability was determined by the congruence between observed nursing care models and patient needs. STROBE checklist. The Random Forest algorithm demonstrated the highest accuracy in predicting both nurse staffing adequacy and the appropriateness of nursing care delivery systems. The study found that 68.4% of wards had sufficient nurse staffing and 26.5% of wards used appropriate care delivery models, with functional nursing and total patient care models being the most commonly used. The study highlights functional nursing and total patient care models, emphasising the need to consider nurse qualifications and patient needs in selecting care systems. Machine learning, particularly the Random Forest algorithm, proved effective in aligning staffing with patient requirements. Machine learning, particularly the Random Forest algorithm, proves effective in optimising nursing care delivery models, suggesting significant potential for enhancing patient care and nurse satisfaction. The research underscores machine learning's role in improving nursing care delivery, aligning nurse staffing with patient needs, and advancing healthcare outcomes. The findings advocate for integrating machine learning in the planning of nursing care delivery models. This study sets a precedent for using data-driven approaches to improve nurse staffing and care delivery, potentially enhancing global clinical outcomes and operational efficiencies. The global clinical community can learn from this study the value of employing machine learning techniques to make informed, evidence-based decisions in healthcare management. While the study lacked direct patient involvement, its goal was to enhance patient care and healthcare efficiency. Future research will aim to incorporate patient and public insights more directly.

Efforts to decarbonize the world have shown a quick increase in electric vehicles (EVs), limiting increasing pollution. During this electric transportation revolution, lithium-ion batteries (LIBs) play a vital role in storing energy. To determine the range of an electric vehicle (EV), the state of charge and the state of health (SOH) of the battery pack is essential. Access to high-quality data on battery parameters is a crucial challenge for researchers working in the energy storage domain due primarily to confidentiality constraints on manufacturers of batteries and EVs. This paper proposes a hybrid framework for predicting the state of a lithium-ion battery for electric vehicles (EV). Electric vehicles are growing worldwide because of their environmental and sustainability advantages. Batteries are replacing fossil fuels in electric vehicles. In order to prevent failure, Li-ion batteries in electric vehicles should be operated and controlled in a controlled and progressive manner to ensure increased efficiency and safety. An extreme gradient boosting (XGBoost) algorithm is used in this paper to estimate the state of health (SOH) of lithium-ion batteries used in electric vehicles. The model is subjected to error analysis to optimize the battery’s performance parameter. The model undergoes an error analysis to optimize its performance parameters. Furthermore, a state of health (SOH) estimation method based on the extreme gradient boosting algorithm with accuracy correction is proposed here to improve the accuracy of state of health (SOH) estimation for lithium-ion batteries. To describe the aging process of batteries, we extract several features such as average voltages, voltage differences, current differences, and temperature differences. The extreme gradient boosting (XGBoost) model for estimating the state of health (SOH) of lithium-ion batteries is based on the ensemble learning algorithm’s higher prediction accuracy and generalization ability. Experimental results suggest that the boundary gradient lifting algorithm model is capable of more accurate prediction.

Lithium-ion batteries are a key component of modern energy storage systems and electric transportation. Accurate prediction of their state of health is critically important for ensuring reliability, safety, and optimizing lifespan. This article presents an improved approach to state of health prediction based on machine learning. The implemented approach demonstrates high accuracy on synthetic data that mimics real degradation processes, with a coefficient of determination R² " 0.95. An analysis of feature importance was conducted, identifying key parameters influencing degradation. The proposed model serves as an effective tool for non-invasive diagnostics and predicting the remaining life of battery cells. This work proposes a method for predicting SOH that combines the identification of useful features from multidimensional time sequences of operating parameters with static characteristics of the battery cell. The regression model used is the random forest algorithm, known for its resistance to overfitting and ability to assess feature importance. The methodology includes extracting informative features from multidimensional time series of operational parameters (voltage, current, temperature, internal resistance, capacity) combined with static characteristics of the cell. An ensemble algorithm, random forest, is used to build the predictive model. The implemented approach demonstrates high accuracy on synthetic data that mimics real degradation processes, with a coefficient of determination R² " 0.95. An analysis of feature importance identified key parameters affecting degradation. The proposed model is an effective tool for non-invasive diagnostics and predicting the remaining life of battery cells. The comparison chart of predicted characteristics and real data shows a tight clustering of points around the bisector, visually confirming the high accuracy of the model. The histogram of error distribution resembles a normal distribution with a zero mean, indicating the unbiased nature of the predictions. The proposed method is non-invasive and could potentially be implemented in battery quality control systems for real-time assessment of battery condition.

Battery management systems (BMSs) play a crucial role in electric vehicles (EVs), relying significantly on two essential parameters: the state of charge (SOC) and state of health (SOH). However, accurately estimating the SOC and SOH in lithium-ion (Li-ion) batteries remains challenging. Many researchers have turned to machine learning (ML) techniques to tackle this issue. In this review, we provide a comprehensive study of popular ML methods for estimating the SOC and SOH. Beyond traditional models such as equivalent circuit models (ECMs) and electrochemical battery models, the review emphasizes the importance of supervised and unsupervised learning models, deep learning architectures, hybrid systems, and their respective performance metrics, in addition to data collection and feature engineering. Additionally, the review discusses current challenges and future opportunities for research.

Lithium-ion (Li-ion) batteries have gained considerable attention in the Electric Vehicle (EV) industry due to their high energy density, better lifespan, and higher nominal voltage.However, accurately estimating the State of Charge (SOC) and State of Health (SOH) for Li-ion batteries remains challenging due to its aging and nonlinear behaviour.This paper explores Battery Management System (BMS) models potential incorporating Artificial Intelligence (AI) estimation techniques, particularly Deep Learning (DL), to improve SOC and SOH model estimations.This research paper summarized and analyzed current BMS approaches by identify the potential gaps in existing research focus and propose another technique for further exploration in the EV Li-ion battery.Currently, there is a research gap in the existing studies, especially in the application of DL for SOC and SOH estimation.and underscores the need for more comprehensive exploration and refinement of DL methods.Future research should address these gaps to advance the integration of DL into BMS to ensure robust and reliable SOC and SOH estimations.Because of its features and capacity to improve SOC and SOH estimating health models accurately, deep learning has a lot of potential for studying SOC & SOH in BMS.As a result, there is opportunity to investigate the DL technique further in order to thoroughly and clearly examine the correctness of SOC & SOH model estimations in BMS.Index Terms-Battery management system (BMS), lithium-ion, artificial intelligence, state of charge (SOC), state of health (SOH) I. INTRODUCTIONDue to rising concerns about environmental pollution and global warming, modern society has made sustainability one of its top priorities.To ease these worries and move forward to a sustainable future, global energy strategies emphasise the switch from fossil fuels to renewable energy (RE) sources.The ability of RE to be efficiently used to manage energy consumption and cut carbon emissions makes it essential in the commercial, industrial, and residential sectors [1].Major contributors to carbon emissions include electricity and heat production, transport, manufacturing and construction, as well as agriculture [2].The transportation sector is among the leading causes of environmental pollution, contributing over one-third carbon dioxide emissions, of which vehicle transportation accounts for over 70% [3].Technological disruptions in the transportation sector facilitate decarbonisation because of rising environmental pollution and concerns about global warming.This transformation includes a move toward adopting electric vehicles (EVs), which have several benefits, such as shorter payback period and longer lifespan, helping to reduce CO2 emissions and change the way transportation will be in the future [4].With the promise of lower emissions and less reliance on oil, EVs have drawn much interest as a leading the way for environmental sustainability and emissions-free mobility [4], [5].Artificial intelligence (AI) is the most fascinating and discussed technology in the current decade for its nature to mimic human intelligence.The field of AI has shown an upward trend of growth in the 21 st century (from 2000 to 2015) [6].Artificial Intelligence (AI) is the study of creating machines that can perceive, analyze, comprehend, and react like humans.Simply said, the ultimate goal of artificial intelligence (AI) is to extend and argue humanity's capacity and efficiency in the work of changing nature and regulating society through intelligent machines.Simultaneously, artificial intelligence technologies that enhance the system's overall capabilities have significant importance.The legal standing of AI technology is examined in this study.In the current societal development stage, AI is rapidly developing its capabilities as a future technology.This research study aims to provide an overview of AI and explain how it relates to the concepts of SOC and SOH.A difficulty at the junction of AI is data integration.AI finds extensive application in the automotive, logistics, healthcare, stock trading, robotics, finance, transportation, and educational domains.AI techniques provide promise for the vehicle, the infrastructure, the driver, or the transport user-and especially for the interactions among them.Transportation is changing as a result of the EV industry's adoption of AI, particularly deep learning (DL) and machine learning (ML), which enhances user experience, efficiency, sustainability, and safety.

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

The use of Lithium-ion batteries in the automobile sector has expanded drastically in the recent years. The foreseen increment of lithium to power electric and hybrid electric vehicles has provoked specialists to analyze the long term credibility of lithium as a transportation asset. To give a better picture of future accessibility, this paper exhibits a life cycle model for the key procedures and materials associated with the electric vehicle lithium-ion battery life cycle, on a worldwide scale. This model tracks the flow of lithium and energy sources from extraction, to generation, to on road utilization, and the role of reusing and scrapping. This life cycle evaluation model is the initial phase in building up an examination model for the lithium ion battery production that would enable the policymakers to survey the future importance of lithium battery recycling, and when in time setting up a reusing foundation be made necessary.

Evapotranspiration (ET) is a vital variable for land‐atmosphere interactions that links surface energy balance, water, and carbon cycles. The in situ techniques can measure ET accurately but the observations have limited spatial and temporal coverage. Modeling approaches have been used to estimate ET at broad spatial and temporal scales, while accurately simulating ET at regional scales remains a major challenge. In this study, we upscale ET from eddy covariance flux tower sites to the regional scale with machine learning algorithms. Five machine learning algorithms are employed for ET upscaling including artificial neural network, Cubist, deep belief network, random forest, and support vector machine. The machine learning methods are trained and tested at 36 flux towers sites (65 site years) across the Heihe River Basin and are then applied to estimate ET for each grid cell (1 km × 1 km) within the watershed and for each day over the period 2012–2016. The artificial neural network, Cubist, random forest, and support vector machine algorithms have almost identical performance in estimating ET and have slightly lower root‐mean‐square error than deep belief network at the site scale. The random forest algorithm has slightly lower relative uncertainty at the regional scale than other methods based on three‐cornered hat method. Additionally, the machine learning methods perform better over densely vegetated conditions than barren land or sparsely vegetated conditions. The regional ET generated from the machine learning approaches captured the spatial and temporal patterns of ET at the regional scale.

Developing cars is a major factor that has determined the increasing of the civilization degree and the continuous stimulation of the society progress. Currently, in Europe, one in five active people and in the US, one in four, directly work in the automotive industry (research, design, manufacture, maintenance) or in related domains (fuel, trade, traffic safety, roads, environmental protection). On our planet the number of the cars increases continuously and he nearly doubled in the last 10 years. With increasing number of cars entered in circulation every year, is held and increasing fuel consumption, increased environmental pollution due to emissions from internal combustion engines (ICE), used to their propulsion. Reducing oil consumption takes into account the limited availability of petroleum reserves and reducing emissions that affect the health of population in large urban agglomerations. The car needs a propulsion source to develop a maximum torque at zero speed. This can not be achieved with the classic ICE. For ICE power conversion efficiency is weak at low speeds and it has the highest values close to the rated speed. Pollution reduction can be achieved by using electric vehicles (EV), whose number is still significant. The idea of an electrical powered vehicle (EV) has been around for almost 200 years. The first electric vehicle was built by Thomas Davenport in 1834 [Westbrook, 2005]] But over time, the batteries used for energy storage could provide the amount of electricity needed to fully electric propulsion vehicles. Electric vehicles are powered by electric batteries which are charged at stations from sources supplied by electrical network with electricity produced in power plants. Currently, a lot of researches are focused on the possibility of using fuel cells for producing energy from hydrogen. EV with fuel cell can be a competitive alternative to the standard ICE that is used in today’s cars. If performance is assessed overall thrust of the effort wheel and crude oil consumed for the two solutions: classic car with ICE and car with electric motor powered by electric batteries, the difference between their yields is not spectacular. In terms of exhaust emissions is the net advantage for electric vehicles. Pollutant emissions due to energy that is produced in power plants (plant property, located) are much easier to control than those produced by internal combustion engines of vehicles that are individual and scattered. Power plants are usually located outside urban areas, their emissions affects fewer people living in these cities. By using electric motors and controllers efficient, electric vehicles provide the means to achieve a clean and efficient urban transport system and a friendly environment. Electric vehicles are zero emission vehicles, called ZEV type vehicles (Zero-Emissions Vehicles).

Abstract. Eastern China is one of the most economically developed and densely populated areas in the world. Due to its special geographical location and climate, eastern China is affected by different weather systems, such as monsoons, shear lines, typhoons, and extratropical cyclones. In the near future, the rainfall rate becomes difficult to predict precisely due to these systems. Traditional physics-based methods such as numerical weather prediction (NWP) tend to perform poorly on nowcasting problems due to the spin-up issue. Moreover, various meteorological stations are distributed in this region, generating a large amount of observation data every day, which have great potential for application to data-driven methods. Thus, it is important to train a data-driven model from scratch that is suitable for the specific weather situation of eastern China. However, due to the high degrees of freedom and nonlinearity of machine learning algorithms, it is difficult to add physical constraints. Therefore, with the intention of using various kinds of data as a proxy for physical constraints, we collected three kinds of data (radar, satellite, and precipitation data) in the flood season from 2017 to 2018 in this area and preprocessed them into tensors (256×256) that cover eastern China with a domain of 12.8×12.8∘. The developed multisource data model (MSDM) combines the optical flow, random forest, and convolutional neural network (CNN) algorithms. It treats the precipitation nowcasting task as an image-to-image problem, which takes radar and satellite data with an interval of 30 min as inputs and predicts radar echo intensity with a lead time of 30 min. To reduce the smoothing caused by convolutions, we use the optical flow algorithm to predict satellite data in the following 120 min. The predicted radar echoes from the MSDM together with satellite data from the optical flow algorithm are recursively implemented in the MSDM to achieve a 120 min lead time. The MSDM predictions are comparable to those of other baseline models with a high temporal resolution of 6 min. To solve blurry image problems, we applied a modified structural similarity (SSIM) index as a loss function. Furthermore, we use the random forest algorithm with predicted radar and satellite data to estimate the rainfall rate, and the results outperform those of the traditional, nonlinear radar reflectivity factor and rainfall rate (Z–R) relationships that use logarithmic functions. The experiments confirm that machine learning with multisource data provides more reasonable predictions and reveals a better nonlinear relationship between radar echo and precipitation rate. Apart from developing complicated machine learning algorithms, exploiting the potential of multisource data will yield more improvements.

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and PSO-GRNN algorithm

Due to the high energy density, lithium ion battery (LIB) plays an important role in electric vehicle (EV). However, the requirement to the energy density keeps increasing. More and more materials are selected to improve the energy density to provide more distance for EV. Silicon, as a famous anode material, provides much higher specific capacity (3579-4200 mAh/g) than graphite (372 mAh/g) does. This research focused on thermal stability and safety of a 30Ah battery with silicon carbon anode. Aging battery would also be compared with the fresh battery after several cycles to different state of health (SOH). The batteries would be analyzed by accelerating rate colorimeter (ARC) to determine the safety. The differential scanning colorimeter (DSC) evaluated the thermal stability of the batteries. The thermal stability of ARC is decided by T1, T2 and T3 which are the self-heating temperature, thermal runaway triggered temperature and the highest temperature, respectively. The scanning electron microscope (SEM) could observe the surface differences with different SOH and various silicon proportion. The research revealed an overall safety assessment of the lithium ion battery with different content of silicon anode under different SOH. Keywords: Lithium ion battery, silicon carbon, thermal runaway, thermal stability Figure 1

Currently in series produced Lithium-Ion batteries – so called Generation 1 Lithium- Ion batteries – are used in high voltage battery applications in Hybrid, Plug-In-Hybrid and Electric vehicles. These Generation 1 battery systems proved already in series that Lithium-Ion batteries fulfill the high requirements regarding safety and electrical power in automotive applications. The real proof of the requested minimum 10 years life time requirement is not done yet. However, based on results from accelerated aging tests in laboratories one can expect that the Lithium-Ion batteries will fulfill the requested life time requirements of automotive field usage, too. Main targets of the development of the second generation of Lithium-Ion battery systems are decreased specific cost in EUR per kWh as well as increased volumetric and gravimetric energy density. Especially cost down will be the barrier to overcome in order to enable a wide use of electric drives in vehicles. Starting with a short overview of the actual status of Lithium-Ion battery technology this paper gives prognoses how the requirements to and the Key Performance Indicators of Generation 2 batteries will develop. Besides the already established high voltage systems used in Hybrid, Plug-In-Hybrid and Electric vehicles applications also new applications with a voltage level lower than 60 V are described. Such systems will be relevant for Boost-Recuperation-Systems (BRS) or Recuperation-Systems (RS). These systems are in direct competition to optimizing measures of conventional combustion engine powertrains. Hence an increased usage of such systems will only be possible when the demanding cost targets will be met. Focus of the paper will be batteries for the applications in Plug-In-Hybrids (PHEV), Electric vehicles (EV) and Boost-Recuperation-Systems (BRS).

Online state of health estimation of lithium-ion batteries through subspace system identification methods

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and efficient operation of electric vehicles (EVs). Conventional approaches, including Coulomb counting, electrochemical impedance spectroscopy, and equivalent circuit models, provide useful insights but face practical limitations such as error accumulation, high equipment requirements, and limited applicability across different conditions. These challenges have encouraged the use of machine learning (ML) methods, which can model nonlinear relationships and temporal degradation patterns directly from cycling data. This paper reviews four machine learning algorithms that are widely applied in SOH estimation: support vector regression (SVR), random forest (RF), convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). Their methodologies, advantages, limitations, and recent extensions are discussed with reference to the existing literature. To complement the review, MATLAB-based simulations were carried out using the NASA Prognostics Center of Excellence (PCoE) dataset. Training was performed on three cells (B0006, B0007, B0018), and testing was conducted on an unseen cell (B0005) to evaluate cross-battery generalisation. The results show that the LSTM model achieved the highest accuracy (RMSE = 0.0146, MAE = 0.0118, R2 = 0.980), followed by CNN and RF, both of which provided acceptable accuracy with errors below 2% SOH. SVR performed less effectively (RMSE = 0.0457, MAPE = 4.80%), reflecting its difficulty in capturing sequential dependencies. These outcomes are consistent with findings in the literature, indicating that deep learning models are better suited for modelling long-term battery degradation, while ensemble approaches such as RF remain competitive when supported by carefully engineered features. This review also identifies ongoing and future research directions, including the use of optimisation algorithms for hyperparameter tuning, transfer learning for adaptation across battery chemistries, and explainable AI to improve interpretability. Overall, LSTM and hybrid models that combine complementary methods (e.g., CNN-LSTM) show strong potential for deployment in battery management systems, where reliable SOH prediction is important for safety, cost reduction, and extending battery lifetime.