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Context 1
... Fig. 1 shows the most common current and voltage range at which the Li-ion battery operates. The x axis represents the current based on battery nominal capacity (C-rate) and the y axis shows the voltage ( v ). The discharge process is exhibited by positive current values, while negative current values can be equated to charging or regenerating processes. If the voltage of battery rises above the maximum de fi ned charging voltage, overcharge will take place; if it goes lower than the de fi ned cut-off discharge voltage, over discharge will occur. Therefore, there are two critical thresholds (gray zones), which are de fi ned based on the type of Li-ion battery (for example, the maximum voltage for LiCoO 2 is 4.35v, and the maximum voltage for LiFePO 4 is 3.7v) [40]. Batteries have been designed to work in the acceptable range so any over charge/ discharge can accelerate battery degradation and shorten life. However, the degradation rate of the battery in the acceptable range is not constant and depends on the rate of charge or discharge (stress factors). Typically, the discharge rate is very dynamic and directly depends on the operating condition. In fact, the discharge rate depends on the slope of the route, the weight of the car, and the speed and acceleration of the vehicle. In most cases, EV designers set a threshold to limit the maximum discharge current rate. During the charging process, the rate of charge remains fairly constant. A higher charging rate can charge the battery faster, on the other hand it can also reduce battery life [41]. Therefore, designers attempt to strike a balance between the charging rate and the impact that this rate can have on the life of the battery; this balancing process in fl uences the rate of charge available in charging stations (like level 1 and level 2) [42]. The battery management system (BMS) limits this rate during charge process. Moreover, the battery charging process is very sensitive to the environmental temperature. Fig. 2 shows the best range of the temperature for charging Lithium based batteries. In Ref. [39] the authors show that the effect of ambient temperature on the battery cycle life can be re fl ected by fi lm growth on the electrode. Low temperature reduces battery life due to a resulting increase in internal resistance, however, in higher temperatures, not only does the life of battery decrease, but also the risk of catastrophic failure is greater, which is a critical safety issue. For example, Dubarry et al. [43] did an experiment on two LiFePO 4 cells at 25 C and 60 C. The experiment shows that LiFePO 4 resistance for the battery at 60 C is fi ve times higher than a battery tested at 25 C. Since the complicated electrochemical processes in batteries are physically hard observe by any direct measurement, most battery monitoring solutions are based on recognizing the critical variables which are observable during operation, and providing accurate and practical information about internal battery chemical reactions. These monitored variables can contain information that is potentially related to battery issues, either measured at cell or package level, on a continual basis [46]. Some researches establish prognostic modeling based on variables continuously collected or measured from a designated experiment cycle in which the battery is subjected to a de fi ned testing procedures under stable conditions. These variables include voltage ( V ), current ( I ), internal battery resistance ( R ), battery temperature ( T b ), ambient temperature ( T a ), and operation time ( t ). This approach can help to avoid noise in the collected data, as well as uncertainty in the measurements from unexpected battery behavior which cannot be measured during full charge and discharge stages This approach can provide much more accurate and functional estimation of battery health status. The major drawback of this method is that it cannot be done online during normal battery operation. In most real in- fi eld applications, the most effective and simple method of monitoring battery behavior is based on observing battery voltage, current, temperature, and in some cases pressure. Some of these variables can be measured during battery operation without interruption of the main functionality of the device; this is referred to as online measurement [47 e 50]. These online measurements in reality suffer from signal noise, disturbances and poor quality, which can be a result of degraded sensors from harsh working environments. Therefore, the prognostic effort of estimating the battery health from online monitoring can lead to inaccurate results. Another issue with this approach is that these variables are dependent on the precision and accuracy of the sensors being used, which can have an impact on any monitoring or prediction results. For example, when a voltage sensor has a low accuracy such as Æ 0.1 V it cannot capture any changes within 0.01 V, which can affect the impedance calculation [51]. In addition, depending on the functionality of the battery, the battery pack might be required to respond to a power outage within milliseconds or seconds to evade loss of signi fi cant data. In these cases, data acquisition systems should be able to record raw data at a high frequency. Otherwise small errors will accumulate over time, negatively impacting the accuracy of results d particularly in determining the state of charge. The raw data available from a battery will usually contain speci fi c behavioral patterns that can be seen in features. From the raw data, many features can be extracted. Not all features will be degradation-related features, which are speci fi c features in the raw data that will change with battery internal chemical element density variation. The most useful features should represent noticeable trends during battery performance, and can be linked with the physical causes of degradation based on electrochemical reactions inside the battery. Their life and the associated failure mechanisms are strongly dependent on the architecture, load pro fi le, and control. Prognostics and health management (PHM) is an emerging science consisting of tools and techniques to evaluate the reliability of a component or system in its real life cycle conditions so as to determine the initiation of failure and mitigate system risk. It gives attention to predicting the future condition of a product or a system [52]. The phrase “ battery PHM ” has a wide variety of meaning, ranging from irregular manual measurements of voltage and electrolyte speci fi c characteristics to fully automated online observa- tion of various measured and estimated battery parameters. In the electric vehicle application domain, researchers have looked at the various failure modes of the battery subsystems. Different diagnostic methods have been evaluated, like discharge to a fi xed cut- off voltage, open circuit voltage and electrochemical impedance spectrometry (EIS) [53]. Therefore in this case, the crucial questions reduce to whether the battery will provide the required power during the driving operation, what kind of degradation processes are at operation, and how many more years this battery can support. These concepts are summarized in the terms state of charge (SoC) and state of health (SoH) [54]. This section elaborates these concepts and similar phrases for electric vehicle batteries. There are different methods and techniques to estimate battery SoC and SoH [55,56]. In this paper, these methods have been classi fi ed into three main groups: data-driven, physical-model based, and fusion model approaches [57]. All of these approaches will be explained in detail in Section 4. Battery state of charge (SoC) estimates the amount of energy remaining in a cell compared with the energy it had when it was fully charged, and gives the user an indication of how much longer a battery will last before it needs recharging. In electric vehicle applications, SoC works like a fuel gauge in a car. Therefore, the accurate estimate of SoC is the signi fi cant factor and it can have an in fl uence on battery health and safety over time. However, the SoC estimation is not an easy task and it depends on the battery ’ s chemistry and condition. Typically, SoC estimation is classi fi ed into two main categories: direct measurement and indirect estimation [58]. In fact, there are few methods to measure SoC directly from the chemical and physical properties of the battery, such as electrolyte PH, density measurements and cathodic galvanostatic pul- ses [22,59]. However, measuring these variables requires accurate measurement devices, which can be expensive, and have limited applicability in practice because it is dif fi cult, and sometimes impossible, to have access to materials inside the battery. To avoid these dif fi culties, indirect SoC estimation has been developed. In this category the variables which can be measured directly from the battery such as current, voltage, and temperature are going to be used to provide an accurate estimation of SoC. Fig. 3 shows the summery of SoC techniques classi fi cation with some preliminary examples [60,61]. However, SoC has a non-linear relationship with these parameters. Generally, SoC can be estimated from direct measurement variables in two ways: of fl ine and online. In of fl ine techniques, the battery needs to be charged and discharged in a speci fi c way to be able to extract features from the acquired data. Most often, of fl ine methods give accurate estimation of battery SoC, however they are time consuming, expensive, and interrupt main battery performance. These are the main reasons that researchers are conducting research on methods and techniques for online estimation of SoC [62 e 66]. The most common method of SoC calculation is Coulomb- counting. Equations (1) and (2) show this relation in charge and discharge ...
Context 2
... the impact that this rate can have on the life of the battery; this balancing process influences the rate of charge available in charging stations (like level 1 and level 2) [42]. The battery management system (BMS) limits this rate during charge process. Moreover, the battery charging process is very sensitive to the environmental temperature. Fig. 2 shows the best range of the temperature for charging Lithium based batteries. In ...

Citations

... Another group of scientists have studied to improve the materials inside the cell and synthesize new materials with superior features [15] [16]. On the other hand, the statistical quantification of the influence of extrinsic and intrinsic factors on the cell performance is another field in battery research [17][18][19][20][21]. Operating conditions affecting battery dynamics have been extensively reported in the literature. ...
... It was found that, collectively, these definitions state or imply, among others, that prognostics is, or should be, performed at the component or sub-component level. In Rezvanizaniani et al., 19 the system-level prognostics was defined in contrast with the cellular-level one in relation to battery health state problem. In contrast, the system level refers, in this case, to a single component, which is the battery made up of several identical cells. ...
This paper reviews methods and practices for addressing the concepts of system-level prognostics (SLP) and system remaining useful life (SRUL) estimation applied to multicomponent systems. A precise definition of SLP is provided, emphasizing the advantages of its use in terms of identifying the scope of SLP applications. In addition, a comprehensive review of the literature is provided to properly classify and compare the findings of previously published studies in the field of SLP and evaluate the effectiveness of the available methodologies within the different stages of prognostic development. Finally, and considering that SLP is still a relatively recent research field, we also provide a thorough discussion on the main challenges that remain to be solved before achieving complete technology transfer, as well as future research directions.
... Running these tests all the way to the end without an accurate degradation prediction not only costs a lot of time, especially for all test cycles that are very similar to the application with many pause times, but also a lot of money. As the degradation of the LIBs is a complex nonlinear process coupled with different physical and chemical mechanisms, e.g., SEI growth [4], lithium plating [5], particle cracking [6], which are affected by multiple stress factors, e.g., temperature, current rate, depth of discharge [7], an early-life stage prediction of the future degradation trajectories of LIBs is challenging. The intrinsic variability caused by minor inconsistencies throughout manufacturing processes will also cause significant degradation differences in the late-life stage under the same real-world operation conditions, which further increases the prediction difficulties [8,9]. ...
Article
Lithium-ion batteries degrade due to usage and exposure to environmental conditions, which affects their capability to store energy and supply power. Accurately predicting the capacity and power fade of lithium-ion battery cells is challenging due to intrinsic manufacturing variances and coupled nonlinear ageing mechanisms. In this paper, we propose a data-driven prognostics framework to predict both capacity and power fade simultaneously with multi-task learning. The model is able to predict the degradation trajectory of both capacity and internal resistance together with knee-points and end-of-life points accurately at early-life stage. The validation shows an average percentage error of 2.37% and 1.24% for the prediction of capacity fade and resistance rise, respectively. The model's ability to accurately predict the degradation, facing capacity and resistance estimation errors, further demonstrates the model's robustness and generalizability. Compared with single-task learning models for forecasting capacity and power degradation, the model shows a significant prediction accuracy improvement and computational cost reduction. This work presents the highlights of multi-task learning in the degradation prognostics for lithium-ion batteries.
... A few representative review papers on failure prognostics and SHM are (1) Heng et al. (2009);; Lei et al. (2018) for machinery health monitoring, with the first focusing on comparing traditional reliability engineering and prognostics, the second providing a balanced review on methodologies and industrial applications, and the third focusing on datasets and methodologies for health assessment and RUL prediction, and (2) Rezvanizaniani et al. (2014); Waag et al. (2014); Hu et al. (2020) for battery health management, with the first focusing on SOC and state of health monitoring as well as lifetime prognostics, the second on SOC and state of health monitoring, and the third on battery lifetime prognostics. ...
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As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.
... Though BEVs outsold PHEVs in several countries until 2014, PHEV sales have increased dramatically in the last 2 years, and they are now equal to BEV sales. Lead-acid batteries, NiMH batteries, and LIBs are the three types of batteries commonly used in the Indian market [52]. In April 2019, the government reintroduced FAME II for 5 years with a budgeted allocation of Rs. 10 000 crores. ...
Article
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A viable remedy for lowering hazardous greenhouse gas emissions and carbon footprint is through the adoption of electric vehicles (EVs). Electric vehicles minimize fossil fuel reliance and ozone-damaging compounds by supporting large-scale renewable deployment. However, EV modeling and manufacturing are continuing to change despite extensive study on the qualities and characteristics that are evaluated from time to time. This is due to restrictions on EV adoption and their charging infrastructure. The present study addresses the numerous modeling approaches and optimization strategies used in studies of EV, hybrid, plug-in hybrid, battery, and fuel cell EV penetration and adoption rates in the market. The study is unique for a developing country like India in that it addresses crucial challenges in adoption and the lack of charging facilities for EV consumers. In addition, when renewable energy sources are unavailable, the development and deployment of the vehicle-to-grid concept is an innovative strategy to provide auxiliary supply to the grid. It is concluded that considering the unique features of EVs is vital to their adoption and mobility.
... To quantify the battery health, the most common indicator or concept, used in the literature, is State-of-Health (SOH). The SOH indicates the specific performance and health status of a battery, at a certain point, compared to the pristine state of the same battery [15]. Although there is no clear definition of the SOH, different parameters of a battery can be used to describe the SOH, such as capacity and impedance, corresponding to the battery's energy and power, respectively [16]. ...
Article
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Lithium-ion batteries have good performance and environmentally friendly characteristics, so they have great potential. However, lithium-ion batteries will age to varying degrees during use, and the process is irreversible. There are many aging mechanisms of lithium batteries. In order to better verify the internal changes of lithium batteries when they are aging, post-mortem analysis has been greatly developed. In this article, we summarized the electrical properties analysis and post-mortem analysis of lithium batteries developed in recent years and compared the advantages of varieties of both destructive and non-destructive methods, for example, open-circuit-voltage curve-based analysis, scanning electron microscopy, transmission electron microscopy, atomic force microscopy, X-ray photoelectron spectroscopy and X-ray diffraction. On this basis, new ideas could be proposed for predicting and diagnosing the aging degree of lithium batteries, at the same time, further implementation of these technologies will support battery life control strategies and battery design.
... J o u r n a l P r e -p r o o f increases the number of battery charge/discharge cycles and could accelerate EV battery degradation (Dubarry et al., 2017). Indeed, the battery lifetime may depend on several factors such as temperature, time, charge/discharge cycles number and power rates, depth of discharge, battery SOC charging/discharging interval, capacity, C-rate, and previous degradation rate (Bishop et al., 2013;Rezvanizaniani et al., 2014;Pelletier et al., 2017 andThompson, 2018). However, in the literature, studies on the impact of V2G on batteries have shown variable results and there are conflicting opinions regarding this aspect (Lunz et al., 2012;Uddin et al., 2018). ...
Article
Electric car-sharing systems have attracted large attention in recent years as a new business model for achieving both economic and environmental benefits in urban areas. Among different types, the one considered in this paper is the so-called one-way car-sharing system whereby a user can begin and end a trip at any station of the system. At the same time, the Vehicle-to-Grid (V2G) concept is emerging as a possible innovative solution for smart power grid control. A management system that combines car-sharing system operations and V2G technology is a recent challenge for academia and industry. In this work, a mixed integer linear programming formulation is proposed to find the optimal management of electric vehicles in a one-way car-sharing system integrated with V2G technology. The proposed mathematical model allows finding the optimal start-of-day electric vehicles distribution that maximizes the total revenue obtained from system users and V2G profits through daily electric vehicles charging/discharging schedules. These schedules are based on mean daily users' electric vehicles requests and electricity prices. The model can be applied to evaluate the possible average daily profitability of V2G operations. In order to test the model performance, we applied it to a small-size test network and a real-size test network (the Delft network in the Netherlands). Under the model assumptions, the adoption of V2G technology allows to fully cover the daily charging costs due to users’ trips and to obtain V2G profits by taking advantage of electric vehicles unused time without significantly reducing the satisfied car-sharing system demand. Most of the energy purchased to charge the electric vehicles batteries is provided back to the grid during energy peak load demand, creating benefits also for energy providers.
... Among different HIs, capacity fade is more intuitive than the others. In addition, capacity fade is significantly affected by temperature, current rate, aging, and historical aging path [15]. Accurate estimation of capacity can adequately reflect the aging state of LIBs, and it is mainly applied in commercial EVs. ...
Article
State of health (SOH) estimation is critical to the safety of battery systems in real-world electric vehicles. Accurate battery health status is difficult to be measured during dynamic and robust vehicular operation conditions. This paper proposes a novel SOH estimation model based on Catboost and interval capacity during the charging process. A year-long operation dataset of an electric taxi is derived with all charging segments separated to construct the research dataset. The charging patterns are analyzed, and the segments with rich aging information are extracted, then a general aging feature of interval capacity is extracted by incremental capacity analysis. Furthermore, comparison with the other six machine learning methods is conducted, and five inputs are determined through Pearson correlation analysis, including start charging state of charge (SOC), end charging SOC, mileage, temperature of probe, and current. The results show the Catboost-based model achieves the best accuracy, with the mean absolute percentage error and root mean squared error limited within 2.74% and 1.12%, respectively. More importantly, a battery aging evaluation strategy and its further research plan is proposed for the application in real-world electric vehicles.
... In the calculation methods, the battery parameters (SOC-state of charge, SOH-state of health) are estimated using different algorithms [25][26][27]. Some of these are highlighted: coulomb counting [28], modified coulomb counting [29], statistical approaches [30,31], hybrid methods [28], machine learning [32][33][34], and degradation pattern recognition with transfer learning [35]. ...
... In the calculation methods, the battery parameters (SOC-state of charge, SOH-state of health) are estimated using different algorithms [25][26][27]. Some of these are highlighted: coulomb counting [28], modified coulomb counting [29], statistical approaches [30,31], hybrid methods [28], machine learning [32][33][34], and degradation pattern recognition with transfer learning [35]. ...
Article
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Hybrid vehicles are gaining increasing global prominence, especially in the military, where unexpected breakdowns or even power deficits are not only associated with greater expense but can also cost the lives of military personnel. In some cases, it is extremely important that all battery cells and modules deliver the specified amount of capacity. Therefore, it is recommended to introduce a new measurement line of rapid diagnostics before deployment, in addition to the usual procedures. Using the results of rapid testing, we recommend the introduction of a hierarchical three-step diagnostics and assessment procedure. In this procedure, the key factor is the building up of a hierarchical tree-structured fuzzy signature that expresses the partial interdependence or redundancy of the uncertain descriptors obtained from the rapid tests. The fuzzy signature structure has two main important components: the tree structure itself, and the aggregations assigned to the internal nodes. The fuzzy signatures that are thus determined synthesize the results from the regular maintenance data, as well as the effects of the previous operating conditions and the actual state of the battery under examination; a signature that is established this way can be evaluated by “executing the instructions” coded into the aggregations. Based on the single fuzzy membership degree calculated for the root of the signature, an overall decision can be made concerning the general condition of the batteries.
... Regarding battery RUL prediction, extensive researches have proved the effectiveness on accurate prediction. Generally, it mainly consists of three following classes: physics-based methods, data-driven methods, and hybrid methods that combine the previous two methods [2][3][4][5]. Building a universal physics-based model for all formulas or constructing specific models for each formula are both impossible and extremely complex. On the contrary, datadriven methods seem more flexible to the formula variation. ...
Article
Full-text available
Cycle life is a key performance indicator in the design and development of lithium-ion power batteries. In order to obtain an appropriate formula, developers need to conduct a large number of cycle life tests (CLTs). However, the high test cost and unbearable time overhead of CLT have seriously hindered the upgrade and development of lithium-ion power batteries. In this paper, a prediction-based CLT optimization method for cross-formula batteries is proposed, which can shorten the number of test cycles by predicting the remaining cycle life of batteries. Specifically, we design an AED-based instance transferability measurement method to select reference battery from the historical database according to curves distance and trend consistent. Then, a highly robust deep learning method named variable-length-input stacked denoising autoencoder (VLI-SDA) is proposed to achieve remaining useful life prediction. The VLI-SDA model adopts a variable-length input strategy to expand the receptive field, fully learn the degradation trend, and ensure an appropriate number of training samples. Combined with the inherent noise reduction capability of the SDA model, the VLI-SDA model can effectively solve the problem of cycle life prediction under high-temperature stress test and small sample conditions. The actual CLT data at three temperatures from a battery company verify the effectiveness of the proposed method. The test temperature, curve shape and other influencing factors are analyzed to help determine optimization strategies.