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![FIGURE 1. An example of an electric vehicle caught fire [6].](publication/365267685/figure/fig1/AS:11431281096508918@1668170689297/An-example-of-an-electric-vehicle-caught-fire-6_Q320.jpg)
According to the United States environmental protection agency (EPA), every burned gallon of gasoline generates 8.87 Kg of CO2. The pollution created by vehicles’ fuel consumption has been one of the primary sources of environmental contamination that can lead to more climate changes and global warming. Thus, science and technology have converged o...
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... EVs, the battery pack comprises several modules, each containing up to hundreds of batteries. Figure 3 illustrates the charging and discharging processes of each Li-ion battery. In general, each battery consists of two electrodes (positive and negative) with a separation region between them. ...
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... contrast, the anode electrode uses lithium-carbon compounds (graphite). For instance, as shown in Figure 3, the cathode electrode uses lithium-metal oxide (i.e., LCA), whereas the anode electrode uses LiCarbon. Generally, the electrons flow according to redox reactions, which can be divided into two categories. ...
Citations
... Thus, lithium-ion (Li-Ion) batteries are currently the best energy storage technology for EVs/HEVs and, as such, have been widely investigated in the literature [7,8]. The focus of previous studies ranges from determining the batteries' carbon footprint and energy consumption during production [9][10][11] to conducting numerous experiments designed to obtain electric equivalent circuit models based on charging/discharging characteristics with different types of current waveforms or impedance measurements. ...
... • Weak cell identification. Figure 5a shows the voltage ∆Vcell of two fully charged LFP cells, and Figure 5b shows the same ∆Vcell of two fully charged NCA cells during the internal impedance measurement according to Formulas (7) and (8). In Figure 5a, the slower OCV relaxation is seen in one of the LFP-type cells; therefore, it has a higher internal resistance. ...
This paper presents an experimental comparison of two types of Li-ion battery stacks for low-voltage energy storage in small urban Electric or Hybrid Electric Vehicles (EVs/HEVs). These systems are a combination of lithium battery cells, a battery management system (BMS), and a central control circuit—a lithium energy storage and management system (LESMS). Li-Ion cells are assembled with two different active cathode materials, nickel–cobalt–aluminum (NCA) and lithium iron phosphate (LFP), both with an integrated decentralized BMS. Based on experiments conducted on the two assembled LESMSs, this paper suggests that although LFP batteries have inferior characteristics in terms of energy and power density, they have great capacity for improvement.
... The physical models are highly accurate but demand a high computational burden due to the involvement of numerous partial differential equations. In contrast, the equivalent circuit models, while less accurate than the former, are considerably simpler as they are described by algebraic and ordinary differential equations [28]. Nonetheless, these methods necessitate the precise identification of the battery open-circuit voltage (OCV) curve as a function of the SOC. ...
Currently, the urgent needs of sustainable mobility and green energy generation are driving governments and researchers to explore innovative energy storage systems. Concurrently, lithium-ion batteries are one of the most extensively employed technologies. The challenges of battery modeling and parameter estimation are crucial for building reliable battery management systems that ensure optimal battery performance. State of charge (SOC) estimation is particularly critical for predicting the available capacity in the battery. Many methods for SOC estimation rely on the knowledge of the open-circuit voltage (OCV) curve. Another significant consideration is understanding how these curves evolve with battery degradation. In the literature, the effect of cycle aging on the OCV is primarily addressed through the look-up tables and correction factors applied to the OCV curve for fresh cells. However, the variation law of the OCV curve as a function of the battery cycling is not well-characterized. Building upon a simple analytical function with five parameters proposed in the prior research to model the OCV as a function of the absolute state of discharge, this study investigates the dependency of these parameters on the moved charge, serving as an indicator of the cycling level. Specifically, the analysis focuses on the impact of cycle aging in the low-, medium-, and high-SOC regions. Three different cycle aging tests were conducted in these SOC intervals, followed by the extensive experimental verification of the proposed model. The results were promising, with mean relative errors lower than 0.2% for the low- and high-SOC cycling regions and 0.34% for the medium-SOC cycling region. Finally, capacity estimation was enabled by the model, achieving relative error values lower than 1% for all the tests.
... The performance and safety of these batteries are of utmost importance to users. However, during usage, lithium-ion batteries may encounter various issues such as internal short circuits, electrolyte leakage, and failure of cathode and anode materials, which can lead to performance degradation, safety hazards, or even failure [5] (Fig. 3). ...
... A. Calculate the local mean of the image, LocalMean. B. Compute the local contrast of the image according to formula (5). (5) C. Sharpen the image according to formula (6). ...
Neutron imaging technology is a novel non-destructive testing technique that combines nuclear technology with digital imaging technology. Neutron radiation has significant advantages in detecting light elements and isotopes, making it complementary to X-ray imaging. This paper focuses on lithium-ion batteries and addresses the high level of speckle noise and the low brightness and clarity of neutron projection images. To improve the image quality of neutron projection images, this study proposes methods for noise suppression and image enhancement. Firstly, the median filtering algorithm is utilized to remove speckle noise in the image, and then the gradient operator is applied to sharpen the image and reduce the blurring effect caused by the filtering algorithm. In terms of image enhancement, the quality of the image is improved from two aspects: brightness adjustment and edge sharpening, aiming to enhance image details and improve image contrast. This study tests the algorithm using real neutron projection images and compares it with seven typical image processing algorithms, using peak signal-to-noise ratio, image feature similarity index, average gradient, and no-reference structural clarity as evaluation indicators for image quality. The experimental results show that the proposed method can effectively remove speckle noise in neutron projection images of lithium batteries, significantly improve image clarity and contrast. Compared with the comparative methods, the proposed method has the best edge-preserving ability, the highest signal-to-noise ratio, and clearer image details. In addition, testing with neutron projection images of three non-lithium battery samples demonstrates the good universality of the proposed method in enhancing neutron projection images.
... By monitoring these parameters, deviations from normal operating conditions can be detected, and the degradation process can be tracked. To estimate the RUL accurately, various data-driven techniques and algorithms are utilized (Chen et al., 2019;Elmahallawy et al., 2022). Machine learning algorithms, such as regression models, neural networks, decision trees, and support vector machines, are commonly employed to analyze historical data, extract patterns, and predict the remaining operational life (Rathore & Harsha, 2022;Zhou et al., 2020). ...
... Mechanism models possess fundamental physical concepts; yet, they encounter numerous challenges. One essential need for developing of a mechanism model is a deep comprehension of the degradation process [25,26]. Unfavorably, the present understanding of the degradation mechanism for lithium-ion batteries is insufficient. ...
... Developing semi-empirical models is relatively straightforward, and they possess inherent physical concepts. These models offer an intermediate between fully empirical and mechanism methods for enhancing lifetime prediction models [26,[34][35][36]. ...
The lifetime of a lithium-ion battery (LIB) is primarily influenced by several key factors including time (number of charge-discharge cycles and performance time), temperature, and current rate (C-rate). In this study, a semi-empirical model is employed to predict the lifetime of LIBs by incorporating these variables. Previous models mainly neglect the influence of performance time on LIB capacity loss. However, our analysis reveals that adding performance time parameter significantly improves the model's accuracy. Additionally, the present model investigates the relationship between the functions of the semi-empirical model and factors such as temperature and Crate , thereby improving the precision of the model's predictions. The experiments are conducted at several temperatures (25, 35, and 45 • C), each subjected to three current rates of 0.5C, 1 C, and 1.5C to develop the models. The proposed model for estimating LIB capacity loss consists of two terms that account for the impacts of cycling and performance time. The optimization findings indicate that both temperature and current rate have an impact on the cycling term. However, the term of performance time has temperature functions. Validation tests of the model are conducted at 25 • C-0.8C and 45 • C-1.7C. There is a perfect agreement between the model results 2 and the experimental data. Using this model, it is possible to predict the lifetime of LIB with a relative error of less than 2%. Furthermore, this error diminishes as the number of cycles increases. Moreover, the proposed model allows for a quantitative evaluation of the individual contributions of cycling or performance time to the total LIB capacity loss. The results reveal that when the LIB undergoes more cycles, the proportion of capacity loss ascribed to performance time increases, while the proportion assigned to cycling declines. This indicates that the electrochemical processes responsible for the gradual capacity loss, which have no relationship to cycling, happen more rapidly during the LIB's operation.
... Some methods related to ANN, gradient boosting, and SVM for the estimation of SOC and SOH are covered in [14] without HIs and their relation to BMS functionality. Model-based, data-driven, and ML-based estimation methods of SOH and RUL are covered in [15] with special emphasis on onboard technique. Critical reviews of BMS functionality are covered in [16] listing all major computation applications running in BMS. ...
... When Whittingham [45] developed intercalation materials in 1976, research and development of rechargeable LMBs became popular [46]. Rechargeable LMBs have low working potential and high specific capacity, making them candidates for electric vehicle (EV) propulsion despite the fact that safety concerns have impeded commercialization [15]. The merits of lithium nickel cobalt aluminium oxide (NCA) in terms of longevity, power density, energy storage, cost, and safety are listed in [15]. ...
... Rechargeable LMBs have low working potential and high specific capacity, making them candidates for electric vehicle (EV) propulsion despite the fact that safety concerns have impeded commercialization [15]. The merits of lithium nickel cobalt aluminium oxide (NCA) in terms of longevity, power density, energy storage, cost, and safety are listed in [15]. A comparison of the major lithium battery chemistries on the basis of cost, life span, performance, safety, power density, and energy density is provided in [16]. ...
With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics.
... Some methods related to ANN, gradient boosting, and SVM for the estimation of SOC and SOH are covered in [14] without HIs and their relation to BMS functionality. Model-based, data-driven, and ML-based estimation methods of SOH and RUL are covered in [15] with special emphasis on onboard technique. Critical reviews of BMS functionality are covered in [16] listing all major computation applications running in BMS. ...
... When Whittingham [45] developed intercalation materials in 1976, research and development of rechargeable LMBs became popular [46]. Rechargeable LMBs have low working potential and high specific capacity, making them candidates for electric vehicle (EV) propulsion despite the fact that safety concerns have impeded commercialization [15]. The merits of lithium nickel cobalt aluminium oxide (NCA) in terms of longevity, power density, energy storage, cost, and safety are listed in [15]. ...
... Rechargeable LMBs have low working potential and high specific capacity, making them candidates for electric vehicle (EV) propulsion despite the fact that safety concerns have impeded commercialization [15]. The merits of lithium nickel cobalt aluminium oxide (NCA) in terms of longevity, power density, energy storage, cost, and safety are listed in [15]. A comparison of the major lithium battery chemistries on the basis of cost, life span, performance, safety, power density, and energy density is provided in [16]. ...
... Generally, two main categories of SoH estimation can be distinguished: i) model-based and ii) data-driven methods. [15] In general, such methods aim to deeply predict the processes involving Li-ion battery cycle and calendar aging. [16] Specifically, in the view of the expected widespread of electric vehicles, several methodologies for LIB SoH estimation and remaining useful life (RUL) prediction have been widely investigated through suitable model-based approaches. ...
Transportation electrification is accelerating the clean energy transition. Due to high efficiencies and energy density, Li‐ion batteries (LIBs) are used as on‐board energy carrier for battery electric vehicles (BEVs). LIBs are subject to rapid degradation due to fast‐charging, mechanical, electrical and thermal factors. Thus, state‐of‐health (SoH) prediction is required to optimize LIBs exploitation over their lifespan. An online accurate and easy‐of‐implementation battery SoH prediction and monitoring method for BEV applications is here presented. The method implements discrete wavelet transform (DWT) analysis to voltage profiles, measured while driving. Specifically, an extensive cycle aging experimental campaign on NCR 18650 cells was performed, applying two typical US test drives (urban and extra‐urban drive cycle, respectively) to the cells at different SoH. Moreover, tests carried out on LIBs at different temperatures demonstrated that temperature effect on the implemented DWT‐based method can be distinguished and separated from cycle aging effect. The proposed method allows a real‐time SoH estimation showing a good accuracy (MAE, ME and RMSE respectively result in 0.917, 2.897 and 1.32) without requiring high computational efforts. This allows to assess battery SoH during the driving. The method can also be extended to other chemistries requiring a dedicated experimental activity for the parameters tuning.
... The current trends in ICTs for community development encompass a wide range of applications, including the use of communication technology in adolescent relationships and identity development, continuous-variable quantum secret sharing in wireless links, and the development of future specialists' communicative competence (Cyr et al., 2014;Liu et al., 2021;Ponomarenko et al., 2023). Furthermore, the comprehensive review of lithium-ion batteries modeling and state of health prediction reflects the ongoing advancements in battery technologies, which have implications for communication power supply and emerging businesses (Wu et al., 2023;Elmahallawy et al., 2022). ...
This paper conducts a comprehensive comparative review of technology-driven community development initiatives in the United States and various regions across Africa. The study aims to shed light on the diverse approaches and outcomes of projects leveraging technology to empower communities in different socio-cultural and economic contexts. The review begins by examining the landscape of community development in both the USA and Africa, emphasizing the challenges faced by urban and rural communities alike. It establishes a foundation for understanding the increasing role of technology in addressing these challenges, framing it as a dynamic and evolving field with vast potential for positive impact. Key findings from the comparative analysis are distilled, emphasizing the importance of context-specific approaches in technology-driven community development. Projects that demonstrate a nuanced understanding of local needs, cultural dynamics, and infrastructural realities exhibit greater success. The review underscores that successful implementation requires more than the transfer of technology; it demands a deep integration into the fabric of community life. The paper also touches upon overarching insights, highlighting the transformative power of technology when thoughtfully applied to community development. It acknowledges the significance of community engagement, adaptability, and a holistic understanding of local nuances as critical factors influencing project success. In concluding remarks, the paper encourages continued research and collaboration. It recognizes the ongoing journey towards harnessing technology's potential for global community empowerment and emphasizes the importance of cross-cultural partnerships, interdisciplinary collaboration, and knowledge sharing. Ultimately, the paper seeks to contribute to the ongoing discourse on technology in community development, offering insights that can inform future projects, policies, and academic pursuits in this dynamic field.
... In essence, electrical cars appeal as a transformative force rather than a mere fleeting trend, paving way for a transportation system that is both economically efficient and environmentally friendly. Electric vehicle health monitoring [4], range prediction, and route planning have the potential to drastically improve healthcare and alleviate worries over limited driving range. In addition to providing a means of transportation that is more advantageous and beneficial to one's health, electric vehicles also can provide more fitness benefits. ...
The automotive industry is experiencing a revolutionary wave due to the rapid spread of electric vehicles (EVs), which is paving the way for a fundamental and long-lasting revolution in the way we approach transportation. The global movement to reduce greenhouse gas emissions and lessen the environmental impact of traditional internal combustion engine vehicles has seen a significant boost in the popularity of electric vehicles as people come together to support environmentally conscious and sustainable mobility solutions. But the ecology surrounding electric vehicles must continue to flourish if the particular problems that EVs present are to be successfully addressed. Chief among these are the formidable foes of range anxiety and battery health management. Range anxiety is a real issue felt by many potential EV owners worry about becoming stuck because their battery has run out before reaching their destination. This psychological barrier is very noticeable and makes present and future EV owners doubtful. In addition, the longevity and health of EV batteries are essential to their continued effectiveness and affordability. The driving range and operating efficiency of the vehicle are directly affected by the gradual degradation of the battery due to several factors like aging, charging patterns, and temperature. This research presents an integrative and holistic approach to address these pressing issues, enhancing and elevating the whole EV ownership experience by combining Electric Vehicle Health Monitoring (EVHM) with Electric Vehicle Range Prediction (EVRP) and Route Planning (EVRP). Combining these three essential elements creates an all-encompassing plan created to not only lessen these enormous obstacles but also accelerate the switch to electric vehicles by giving consumers the knowledge and assurance they require for a smooth, eco-friendly, and sustainable mobility in the future.
