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Energy storage devices (ESD) which are intended to power electronic devices, used in close contact of human skin, are desirable to be safe and non-toxic. In light of this requirement, Zn based energy storage devices seem to provide a viable pathway as they mostly employ aqueous based electrolytes which are safe and non-toxic in their functioning. Additionally, having a flexible ESD will play a crucial role as it will enable the ESD to conform to the varying shapes and sizes of wearable electronics which they energize. In this work, we have developed an inkjet-printed Zinc ion battery (IPZIB) with planar electrode configuration over bond paper substrate. Zn has been used as the negative electrode, MnO2 is used as the positive electrode with Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) as the active binder. Conducting tracks of reduced graphene oxide (rGO) are used to construct the current collector on the paper substrate. The fabricated IPZIB delivered a high discharge capacity of 300.14 mAh g−1 at a current density of 200 mA g−1. The energy density of the IPZIB is observed as 330.15 Wh kg−1 at a power density of 220 W kg−1 and retains an energy density of 94.36 Wh kg−1 at a high power density of 1650 W kg−1. Finally, we have demonstrated the capability of the IPZIB to power a LED at various bending and folding conditions which indicates its potential to be used in the next generation flexible and wearable electronic devices. [ABSTRACT FROM AUTHOR]

Wearable electronic energy storage devices usually require the integration of flexible mechanisms and uncompromising electrochemistry of lithium-ion batteries (LIBs). Most of the studies in this field have mainly focused on enhancing the electrochemical properties of the material, while the significance of its flexibility and mechanical properties has been relatively ignored. In this study, the 20 wt % carbon fibers (CFs) composite N-doped flexible carbon nanofiber paper (NFCFP) was prepared through a facile PFI refiner and Bauer sieve of pulping and papermaking machinery sieve-assisted methods combined with annealing technology. The NFCFP-700 with high pyridine N content shows good ion transport and storage performance, the specific capacity remained 308 mAh⸱g−1 after 100 cycles at 0.1 C, and even at 1 C, the specific capacity of the electrode material instead increases constantly at 142 mAh⸱g−1. According to density functional theory (DFT) calculations, the coexistence of pyridine N content and defects in carbon materials was found to enhance the adsorption energy (E ad) of lithium, and the N4V2 locally show the most negative electrostatic potential (ESP) distribution, which was more favorable for the adsorption of Li+ being positively charged. This insight from DFT studies combined with experimental data emphasizes the favorable impact of pyridine N content and defects on the lithium adsorption process in carbon materials, also providing valuable information for the design and optimization of materials for lithium storage applications. [ABSTRACT FROM AUTHOR]

Cellulose is now considered as an appealing environment friendly material for the development of sustainable separators for rechargeable batteries. This study thus aims to develop high performance paper based ceramic separators with superior electrolyte wettability (>170 %), high thermal stability (>200 °C) and excellent flame safety. While developing paper based ceramic separator, extensive studies are carried out to understand thenature and functionality of different nano-structured ceramic materials (Al 2 O 3 , BaTiO 3 and ZrO 2) impregnated in paper matrix. Nano-ZrO 2 facilitates an effective pathway for Li +-ion transport (t Li + = 0.51), whereas BaTiO 3 and Al 2 O 3 ceramics show moderate Li +-ion transport properties (t Li+ of 0.29 and 0.30 respectively). Electrochemical Impedance Spectroscopy (EIS) measurement clearly reveals that ZrO 2 exhibits more interfacial compatibility with the electrodes (MCMB and LiNi 1/3 Mn 1/3 Co 1/3 O 2) compared to the other two ceramic separators during full cell operation. The developed paper separators show excellent electrochemical properties in terms of cycling (tested 300 cycles), multi-electrode compatibility and rate capabilities at different current densities of 0.1–1.2 mAcm−2. The pre- and post-electrochemical EIS data reveal that ZrO 2 based impregnation offers significantly lower charge transfer resistance compared to the other two separators. The study thus demonstrates a real promise for its successful application in Li-ion battery towards achieving sustainability and safety. [Display omitted] • Sustainable, flexible and high safety ceramic based paper separators for LIBs. • Separators were developed using easy to scale up wet-coating process. • Ceramic impregnation affects physical and electrochemical properties of the separators. • Paper separators show superior electrolyte wettability and high thermal stability. • Better electrochemical performances and flame safety are other important advantages. [ABSTRACT FROM AUTHOR]

MoO 3 -based anodes for lithium storage have attracted more attentions due to the relatively high specific capacity of 1117 mAh g–1 and good chemical stability. Compound MoO 3 with other functional materials and construction of unique micromorphologies have been proved to be efficient strategies for improving the electrochemical performances of MoO 3 -based anodes. Herein, MoO 3 /TiO 2 composites were successfully biosynthesized with different micromorphologies. In the preparation processes, the hydrothermal and solvothermal treatments were carried out successively in the presence of cellulosic filter paper as the biotemplate, followed by calcination of the as-obtained composites in air, resulting in the final MoO 3 /TiO 2 composite materials. The two microstructures of the MoO 3 microfiber with entire TiO 2 nanoparticles coating and the MoO 3 microrod formed by stacking the microsheets with loosely TiO 2 nanoparticles covering were prepared under different pH values of the precursor. Compared with the bare MoO 3 materials, the corresponding MoO 3 /TiO 2 composites showed enhanced electrochemical performances as anode materials for lithium-ion batteries. Especially the MoO 3 /TiO 2 composite with core-shell microfibrous structure derived from cellulosic filter paper presented relatively better test results for lithium storage, delivering an initial discharge capacity of 1297.6 mAh g−1 with the first Coulombic efficiency of 65.8%, and a reversible specific capacity of 403.6 mAh g−1 after 200 cycles. It is demonstrated that a well-defined TiO 2 coating layer can effectively inhibit the volume variations of MoO 3 matter in the lithiation/delithiation processes. Combined with the porous microfibrous structure that alleviates the stress, provides more reaction sites and reduces diffusion paths of lithium-ion, a promising MoO 3 -based anode material was achieved. This work provides a facile biotemplate-based strategy for the design and fabrication of metal oxide-based composites with adjustable microstructures for energy storage. [Display omitted] • Cellulosic filter paper derived MoO 3 /TiO 2 composites with variable micromorphologies were biosynthesized. • Microfibrous MoO 3 was coated by TiO 2 nanoparticles uniformly using the precursor with original pH value. • It showed enhanced reversible capacity, cycling stability and rate capability as an anode material for lithium-ion batteries. • The microstructural feature of biotemplate and the entire TiO 2 coating contribute to the improved lithium storage property. [ABSTRACT FROM AUTHOR]

The paper describes the digital twin of a Li-ion battery cell based on the MATLAB/Simulink generic model. The digital twin is based on measured data for constant current/constant voltage charging and discharging cycles with State of Health (SoH) up to 79%, also including fast charging. Mathematical equations used for the digital twin are obtained by 3D data fitting of measured SoH, battery capacity, and battery cell current. The input to the proposed digital twin is only the measured battery cell current, and its output includes State of Charge (SoC), SoH, and battery cell voltage. The designed digital twin is tested and compared with MATLAB/Simulink generic model and battery cell measurements for constant discharging current and dynamically generated discharging current profile. The results show significant improvement in the generic MATLAB/Simulink model. [ABSTRACT FROM AUTHOR]

Until recently, lead-acid batteries were the go-to source for storing energy for UPS/Inverter applications. The most common types of batteries used in wind applications are Valve-Regulated Lead-Acid batteries (VLRAs). But, lead-acid batteries have drawbacks that make them risky and expensive to use in wind turbine applications. They are the element that is most likely to fail at the moment when they are most needed. It is hard enough to deploy and manage lead-acid batteries traditionally. But when VRLAs are used in remote facilities, there are some problems that increase the effort and the cost of using them. Currently, the Soweto Small Wind Turbine is incorporated with the Vertiv (Inverter) and VRLA battery type. TP 100 Vision lithium iron phosphate (LiFePO4) batteries offer substantial advantages. This battery system is ideal for both UPS/Inverters and energy storage systems, offering excellent compatibility and a secure, durable lifespan. Factory testing was carried out on the installation and testing of a TP 100 Vision battery to a Vertiv-type UPS at a South African company. A variable resistive load bank was added to the UPS output in order to test and evaluate the outcome. This paper presents the factory testing results and proposes the implementation of the TP100 Vision battery to a 500W Small Wind Turbine (SWT) in Soweto, Johannesburg, South Africa. [ABSTRACT FROM AUTHOR]

This paper defines terms such as "recycling rate" that enable the characterization of flows of battery materials and expands the terminology to accommodate the description of complex product recycling. It also estimates the maximum percentage of U.S. demand for critical elements that could be satisfied by recycling as demand continues to grow, and it defines and estimates the recycling rate for lithium-ion batteries. Finally, it clarifies the role of manufacturing scrap as the domestic U.S. supply chain is built up. It concludes that recycling will be important in the long term, but growth still requires that new material be extracted to supply additional material. [ABSTRACT FROM AUTHOR]

A user-friendly, low-cost detector able to read the RGB indexes of microfluidic paper-based analytical devices (µPADs) was developed. The RGB-detector was built with 3D printing using PLA+ and reused Li-ion batteries. It is Arduino-based, which provides an easy interface between the sensor TCS3200, which reads the quadratic wave of the times corresponding to the RGB numbers, the Arduino itself, whose software translates the times into RGB values, and the touchscreen display, NX3224T028, which shows the results. This detector permits multi-sample analysis since it has a sample holder that can keep up to six µPADs simultaneously and rotate after the display's request. This work shows how the readings of the RGB indexes by the proposed RGB-detector implement the measurements' reproducibility. As a proof-of-concept, the RGB-detector application to a green array of µPADs for pH measurement coupled with chemometric analysis allowed us to achieve good results in terms of precision and agreement with the pH values measured by a classical pH-meter. [ABSTRACT FROM AUTHOR]

Tin monosulfide (SnS) serves as a promising anode material in lithium-ion batteries (LIBs) due to its high theoretical capacity. However, its low rate capability and cycle stability due to poor electrical conductivity and large volume change remain urgent issues to be resolved. Here, we provide an efficient and facile strategy to achieve three-dimensional (3D) freestanding sulfur and nitrogen co-doped SnS/graphene nanocomposite (SnS/3DSNG). Benefiting from excellent electronic conductivity, improved volume flexibility, short transport length, and numerous active sites, the nanocomposite exhibits high initial discharge capacity (1030 mA h g−1 at 1 A g−1), excellent cycling stability (530 mA h g−1 at 1 A g−1 after 500 cycles) and great rate performance. The pseudo-capacitance can be improved (93.9 % at 1 mV s−1) by regulating the ratio of S and N co-doped graphene (3DSNG) and SnS. This study presents an efficient and simple method to improve the electrochemical performance of SnS-based lithium-ion batteries. [ABSTRACT FROM AUTHOR]

This paper is a product purity study of recycled Li-ion batteries with a focus on hydrometallurgical recycling processes. Firstly, a brief description of the current recycling status was presented based on the research data. Moreover, this work presented the influence of impurities such as Cu, Fe and Mg on recovered cathode materials performance. The impact of the impurities was described depending on their form (metallic or ionic) and concentration. This work also reviewed hydrometallurgical recycling processes depending on the recovered material, obtained purity and recovery methods. This purity data were obtained from both research and battery industry actors. Finally, the purity study was completed by collecting data regarding commercial battery-grade chemical compounds and active lithium cathode materials, including required purity levels and allowed impurity limitations. [ABSTRACT FROM AUTHOR]

The present paper uses scientometric indicators to examine the Li-ion battery research in India as reflected through Web of Science Core Collection (WoS) data of 32 years (1989-2020). India produced 2864 publications during the period but the publication activity increased manifold during the last decade. The output was scattered among 1346 institutions. However, 28 institutions produced 1% or more of the total output. One third (34.5%) of the output emerged out of international collaboration. About 71.5% of the output were in journals with impact factor >=2. The paper also identifies the aspects/direction of research through keyword analysis besides identifying preferred journals and prolific authors. [ABSTRACT FROM AUTHOR]

With the widespread application of energy storage stations, BMS has become an important subsystem in modern power systems, leading to an increasing demand for improving the accuracy of SOC prediction in lithium-ion battery energy storage systems. Currently, common methods for predicting battery SOC include the Ampere-hour integration method, open circuit voltage method, and model-based prediction techniques. However, these methods often have limitations such as single-variable research, complex model construction, and inability to capture real-time changes in SOC. In this paper, a novel prediction method based on the KF-SA-Transformer model is proposed by combining model-based prediction techniques with data-driven methods. By using temperature, voltage, and current as inputs, the limitations of single-variable studies in the Ampere-hour integration method and open circuit voltage method are overcome. The Transformer model can overcome the complex modeling process in model-based prediction techniques by implementing a non-linear mapping between inputs and SOC. The presence of the Kalman filter can eliminate noise and improve data accuracy. Additionally, a sparse autoencoder mechanism is integrated to optimize the position encoding embedding of input vectors, further improving the prediction process. To verify the effectiveness of the algorithm in predicting battery SOC, an open-source lithium- ion battery dataset was used as a case study in this paper. The results show that the proposed KF-SA-Transformer model has superiority in improving the accuracy and reliability of battery SOC prediction, playing an important role in the stability of the grid and efficient energy allocation. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel aramid nanofibers reinforced cellulose membrane was developed as an advanced LIB separator. • The large pores and insufficient thermomechanical properties of cellulose separator was improved. • The influence of aramid nanofibers on the comprehensive performance of cellulose composite separator was presented. • The composite separator achieved well balance between high safety and excellent electrochemical performance. Commercial polyolefin separators suffer from poor electrolyte wettability and low thermal stability, which give rise to unsatisfied electrochemical performance and severe safety hazards for lithium-ion batteries (LIBs). Herein, a novel paper-based composite separator composed of electrolyte-affinitive cellulose fibers (CFs) and thermally stable aramid nanofibers (ANFs) was successfully fabricated through the traditional papermaking method. The ANFs as a functional nanofiller played crucial roles in improving the defects of pure CF separator including large-sized pores, low mechanical strength and high flammability. Specifically, the CF/ANF composite separator with 20 wt% ANFs (CF/ANF-20) possessed narrow micropores, satisfied tensile strength (33 MPa), excellent thermal resistance (without dimensional shrinkage up to 200 °C) and flame retardancy, which greatly enhanced the safe operation of battery. Moreover, inheriting from the highly porous structure and exceptional electrolyte affinity of CF separator, the CF/ANF-20 composite separator exhibited appropriate porosity and superior electrolyte wettability, which brought about a high electrolyte uptake (157%), thus endowing it with better ionic conductivity (0.75 mS cm−1) and lower interfacial resistance compared to commercial polypropylene separator. Accordingly, the LiFePO4/Li half cells using CF/ANF-20 separator delivered outstanding rate capability and stable cycling performance. All results indicate that the CF/ANF-20 separator shown great balance between the electrochemical performance and safety is an intriguing candidate for advanced LIBs. [ABSTRACT FROM AUTHOR]

The state of health (SOH) of lithium-ion batteries is an important indicator for evaluating the degradation of battery performance, which is crucial in battery management systems. With the development of science and technology, data-driven models used to predict SOH are widely used, while data-driven models generally suffer from a narrow estimation range, long prediction time, and poor accuracy. Therefore, this paper introduces the extreme learning machine model with a single hidden layer that incorporates hybrid kernel functions, which overcomes the mapping defect problem of single kernel extreme learning machine and extreme learning machine by the introduction of hybrid kernel functions and improves the range and speed of data-driven models for SOH prediction. In addition, to automatically find the optimal parameters of the kernel extreme learning machine to improve the accuracy, the dung beetle algorithm with strong parameter finding ability is used to optimize the model in this paper. Meanwhile, based on the algorithm, we introduced the optimal Latin hypercube idea and weight factor for initializing the population and regulating the position update, respectively, which effectively improved the convergence and parameter-seeking ability of the algorithm to improve the accuracy of the model. The experiments verified the reasonableness and effectiveness of the proposed model, in which the optimal results of root mean square error, average absolute percentage error, and coefficient of determination of the proposed model on the battery dataset were 0.177%, 0.046%, and 99.76%, respectively, which reflected the high-precision prediction ability and strong robustness. [ABSTRACT FROM AUTHOR]

Though lithium-ion batteries are extensively applied in electric vehicles as a power source due to their excellent advantages in recent years, the security risk has inarguably always existed. The state of health (SOH) of lithium-ion batteries is one of the most important indicators related to security, the prediction of SOH is paid close attention spontaneously. To improve the prediction accuracy of SOH, this paper constructs an SOH prediction model based on a multi-kernel relevance vector machine and error compensation (EC-MKRVM). The provided model comprises a pre-estimation model and an error compensation model, both of which use the multi-kernel relevance vector machine (MKRVM) algorithm. The pre-estimation model takes the feature factors extracted in the charging segment as the input variable and the SOH pre-estimation value as the output. The error compensation model takes the pre-estimation error sequence as the input variable and the SOH prediction error as the output. Finally, the SOH prediction error is used to compensate for the SOH pre-estimation value of the pre-estimation model, and the final SOH prediction value is obtained. To verify the effectiveness and advancement of the model, the CACLE dataset is used for comparative experimental analysis. The results show that the proposed prediction model in this paper has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

The accurate estimation of lithium-ion battery state of charge (SOC) is the key to ensuring the safe operation of energy storage power plants, which can prevent overcharging or over-discharging of batteries, thus extending the overall service life of energy storage power plants. In this paper, we propose a robust and efficient combined SOC estimation method, GRU-ASG, which combines the gated recurrent unit (GRU) neural network and the adaptive Savitzky-Golay filter (ASG). Firstly, the "many-to-one" structure GRU is used to establish the mapping model between the battery-measured variables (voltage, current, temperature) and SOC, and achieve the SOC initial estimation. Then, the output SOC of the GRU network is smoothed online using the Spielman coefficient–based ASG filtering algorithm proposed in this paper to reduce the fluctuation of SOC. Finally, the accurate and stable estimated SOC is obtained. This paper uses six different operating condition datasets collected from an energy storage plant during the discharge process and uses four of them as training datasets and the remaining two as test datasets. The results show that the proposed method can select the optimal window length online adaptively to smooth the initial estimate of SOC. Moreover, the estimation accuracy of the proposed method is the highest compared to the single GRU network and the GRU network with a combination of other filtering algorithms. In particular, the mean square error (MSE) is less than 0.15% and the mean absolute error (MAE) is less than 3% for the two test sets. [ABSTRACT FROM AUTHOR]

Accurately assessing the state of health (SOH) of lithium batteries is of great significance for improving battery safety performance. However, the current assessment for SOH suffers from the difficulty of selecting health features and the lack of uncertainty using data-driven methods. To this end, this paper proposes a health state assessment method for lithium-ion batteries based on health feature extraction and an improved Informer model. First, multiple features that can reflect the SOH of lithium-ion batteries were extracted from the charging and discharging time, the peak value of incremental capacity curve (ICC), and the inflection point value of differential voltage curve, etc., and the correlation between multiple health features and the health state was evaluated by gray correlation analysis. Then, an improved Informer model is proposed to establish a health state estimation method for lithium-ion batteries. Finally, the proposed algorithm is tested and validated using publicly available battery charge/discharge datasets and compared with other algorithms. The results show that the method in this paper can realize high-precision SOH prediction with a root-mean-square error (RMSE) of 0.011, and the model fit reaches more than 98%. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in modern society as important energy storage devices due to their high energy density, rechargeable performance, and light weight. However, the capacity and performance of lithium-ion batteries gradually degrade with the number of charge or discharge cycles and environmental conditions, which can affect the reliability and lifetime of the batteries, so it is necessary to accurately evaluate their health. The belief rule base (BRB) model is an evaluation model constructed based on rules that can handle uncertainties in the operation of lithium-ion batteries. However, lithium-ion batteries may be affected by disturbances from internal or external sources during operation, which may affect the evaluation results. To prevent this problem, this paper proposes a disturbance-considering BRB modeling approach that considers the possible effects of disturbances on the battery in the operating environment and quantifies the disturbance-considering capability of the assessment model in combination with expert knowledge. Second, robustness and interpretability constraints are added in this paper, and an improved optimization algorithm is constructed that maintains or possibly improves the resistance of the model to disturbance. Finally, using the lithium-ion batteries provided by the National Aeronautics and Space Administration (NASA) Prediction Centre of Excellence and the University of Maryland as a case study, this paper verifies that the proposed modeling approach is capable of constructing robust models and demonstrates the effectiveness of the improved optimization algorithm. [ABSTRACT FROM AUTHOR]

Although possessing well‐defined nanostructures and excellent multi‐electron redox properties, polyoxometalate clusters have poor intrinsic electrical conductivity and are prone to aggregation due to large surface energy, which makes them difficult to be fully utilized when applying as electrode materials for lithium‐ion batteries. In this paper, monodisperse K7MnV13O38 (MnV13) clusters are achieved by rationally utilizing nano‐sized high conductive carbon dots (CDs) as stabilizers. Benefiting from the fully exposed redox sites of MnV13 clusters (high utilization rate) and sufficient interfaces with carbon dots (extra interfacial energy storage), the optimized MnV13/10CDs anode delivers a high discharge capacity up to 1348 mAh g−1 at a current density of 0.1 A g−1 and exhibits superb rate/cycling capabilities. Density functional theory (DFT) calculations verify that ionic archway channels are formed between MnV13 and CDs, eliminating the bandgap and greatly improving the electron/ion conductivity of MnV13 and CDs. This paper paves a brand‐new way for synthesis of monodisperse clusters and maximization of extra interfacial energy storage. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for rapidly balancing lithium-ion batteries using a single DC–DC converter, enabling direct energy transfer between high- and low-voltage cells. Utilizing relays for cell pair selection ensures cost-effectiveness in the switch network. The control system integrates a battery-monitoring IC and an MCU to oversee cell voltage and ensure battery protection. A prototype circuit with twelve lithium-ion batteries demonstrates the method's efficacy, achieving a remarkable balancing time of 48 min during charging with a maximum efficiency of 89.85%. Comparative analysis with other methods underscores the superior performance of the proposed balancing circuit in terms of balancing time and implementation cost. Furthermore, this paper delves into hardware aspects of battery management systems (BMSs) for electric vehicles and stationary applications. It offers an overview of prevailing concepts in state-of-the-art systems, aiding readers in assessing considerations essential for BMS design in various applications. The discussion includes examples of battery packs sourced from commercially available electric vehicles. Subsequently, the manuscript addresses implementation aspects concerning the measurement of critical physical variables such as voltage, current, and temperature, alongside balancing strategies. [ABSTRACT FROM AUTHOR]

Batteries play a crucial role in the domain of energy storage systems and electric vehicles by enabling energy resilience, promoting renewable integration, and driving the advancement of eco-friendly mobility. However, the degradation of batteries over time remains a significant challenge. This paper presents a comprehensive review aimed at investigating the intricate phenomenon of battery degradation within the realm of sustainable energy storage systems and electric vehicles (EVs). This review consolidates current knowledge on the diverse array of factors influencing battery degradation mechanisms, encompassing thermal stresses, cycling patterns, chemical reactions, and environmental conditions. The key degradation factors of lithium-ion batteries such as electrolyte breakdown, cycling, temperature, calendar aging, and depth of discharge are thoroughly discussed. Along with the key degradation factor, the impacts of these factors on lithium-ion batteries including capacity fade, reduction in energy density, increase in internal resistance, and reduction in overall efficiency have also been highlighted throughout the paper. Additionally, the data-driven approaches of battery degradation estimation have taken into consideration. Furthermore, this paper delves into the multifaceted impacts of battery degradation on the performance, longevity, and overall sustainability of energy storage systems and EVs. Finally, the main drawbacks, issues and challenges related to the lifespan of batteries are addressed. Recommendations, best practices, and future directions are also provided to overcome the battery degradation issues towards sustainable energy storage system. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries, as the main energy storage component of electric vehicles (EVs), play a crucial role in ensuring the safe and reliable operation of the battery systems through monitoring their state of health (SOH). However, temperature variations and battery aging have significant impacts on the internal parameters of lithium-ion batteries, and these changes directly correlate with the accuracy of battery SOH estimation. To address these issues, this paper proposes an estimation method for lithium-ion battery SOH that considers the impact of temperature. The method begins with reconstructing a second-order hybrid equivalent circuit model for lithium-ion batteries, through which an adaptive update rate for battery model parameters is designed. On this basis, a nonlinear observer for battery states is introduced by integrating filters to estimate SOH. The proposed method considers the impact of capacity in the design of the parameter adaptive update rate, enabling the capacity to be dynamically adjusted based on the actual state of the batteries. This reduces the cumulative error in the SOC observer and improves the modeling accuracy of battery models. Experimental results demonstrate that the method proposed in this paper exhibits exceptional performance in SOH estimation under different temperature conditions. The mean absolute error for SOH estimation does not exceed 0.5%, and the root mean square error does not exceed 0.2%. This method can significantly improve the estimation accuracy of SOH, providing a more efficient and accurate solution for battery management systems in EVs. [ABSTRACT FROM AUTHOR]

Accurate prediction of remaining useful life (RUL) plays an important role in maintaining the safe and stable operation of Lithium-ion battery management systems. Aiming at the problem of poor prediction stability of a single model, this paper combines the advantages of data-driven and model-based methods and proposes a RUL prediction method combining convolutional neural network (CNN), bi-directional long and short-term memory neural network (Bi-LSTM), SE attention mechanism (AM) and adaptive unscented Kalman filter (AUKF). First, three types of indirect features that are highly correlated with RUL decay are selected as inputs to the model to improve the accuracy of RUL prediction. Second, a CNN-BLSTM-AM network is used to further extract, select and fuse the indirect features to form predictive measurements of the identified degradation metrics. In addition, we introduce the AUKF model to increase the uncertainty representation of the RUL prediction. Finally, the method is validated on the NASA dataset and the CALCE dataset and compared with other methods. The experimental results show that the method is able to achieve an accurate estimation of RUL, a minimum RMSE of up to 0.0030, and a minimum MAE of up to 0.0024, which has high estimation accuracy and robustness. [ABSTRACT FROM AUTHOR]

Environmental issues have driven the booming development of lithium-ion battery technology, and improving the accuracy of state-of-charge (SOC) measurements will play an important role in prolonging battery cycle life and improving safety. In this paper, an improved genetic-back propagation network construction strategy is built to stably and accurately estimate the SOC at different temperatures. The design of the established SOC neural network estimation model is optimized by using a genetic algorithm to initialize the weights and thresholds of the backpropagation (BP) neural network. Then, the above algorithms are validated using Dynamic Stress Test (DST) and Beijing Bus Dynamic Stress Test (BBDST) datasets. The experimental results show that the MAE and RMSE of the SOC estimation results based on the BBDST dataset are 0.0133, 0.0143 and 0.0058, 0.0082 under the conditions of − 5 °C and 35 °C. Therefore, the improved genetic-back propagation network construction algorithm has a more stable iterative process and higher estimation accuracy, which provides a new method for SOC performance estimation of lithium-ion batteries. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze how global research cooperation patterns in the secondary battery industry have changed over recent years and to identify the evolution in the focus of research. To this end, network analysis was performed using the nationality information of the authors of a 10-year multinational joint research paper related to lithium-ion batteries. Furthermore, keyword analysis and topic modeling were performed using the abstract data from the study. The results of this study confirm that some countries that are not well-known in the field, such as Australia, Spain, and France, showed high centrality, compared with the level of cooperation scale. Additionally, six research topics were identified. According to a comparison over the first half of the decade, no difference was observed in the appearance of keywords indicating high energy density and conductivity with lithium, a key mineral. Keyword distribution was high for topics like battery charging and discharging in the first half of the decade, and for next-generation battery materials, such as solid electrolytes, lithium metal anodes, and lithium–sulfur batteries in the second. These results provide insights into the establishment of research and development (R&D) cooperation strategies by countries and pre-planning by companies in the battery industry. [ABSTRACT FROM AUTHOR]

This paper describes the design of a control unit for efficient battery charge management in battery electric vehicles (BEVs). The system design aims at controlling the performance of the charging process of dual lithium-ion battery blocks in electric vehicles, with a main battery that powers the vehicle and an auxiliary one for servicing the ancillary equipment. In this paper, we design and analyze the protocol of a control unit that operates and regulates the battery charge in electric vehicles to obtain optimum performance. The so-designed system enhances the battery charge process and protects the main battery from capacity reduction, thus enlarging the driving range of the electric vehicle. We design a specific protocol for an electric circuit that reproduces the structure of the battery charge system of an electric vehicle. The control system improves the efficiency of the auxiliary battery charge by 4.5%. The theoretical simulation matches experimental values in a simulation test by 98.4%. [ABSTRACT FROM AUTHOR]

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise. [ABSTRACT FROM AUTHOR]

The open-circuit voltage (OCV) curve has a significant influence on the accuracy of the state of charge (SOC) estimation based on equivalent circuit models (ECMs). However, OCV curves are tested through offline experiments and are hard to be very accurate because they constantly change with the test method's ambient temperature and aging status. Recently, researchers have attempted to improve the accuracy of OCV curves by increasing the volume of sample data or updating/reconstructing the curve combined with practical operation data. Still, prior offline tests are essential, and experimental errors inevitably exist. Consequently, a SOC estimation method without any offline OCV tests might be an efficient route to improve the accuracy of SOC. According to this idea, this paper presents a novel method for SOC estimation, which is based on online OCV curve construction. Meanwhile, a stepwise multi-timescale parameter identification algorithm is designed to improve the interpretability and precision of the estimated ECM parameters. The results demonstrate that the maximum SOC estimation error is only 0.05% at 25 °C, indicating good robustness under various ambient temperatures and operational conditions. [ABSTRACT FROM AUTHOR]

This paper investigates modeling techniques for the mathematical representation of HV (high-voltage) Li-ion batteries to be used in conjunction with battery emulators for the test cell environment. To enable the impact of the battery response to be assessed in conjunction with other electrified systems, battery emulators are used with advanced mathematical models describing the expected voltage output with respect to current load. This paper conducted research into different modeling types: electrochemical, thermal, and electronic equivalent circuit models (EECMs). EECMs were identified as the most suitable to be used in conjunction with emulation techniques. A foundation EECM was created in conjunction with a thermal part to simulate thermal dependency. Hybrid Pulse Power Characterization (HPPC) tests were conducted on an NMC Li-ion cell across a range of temperatures from −20 °C to 25 °C. Using parameter optimization techniques, the HPPC test data were used to identify the resistance, capacitance, and the open-circuit voltage of the cell across a range of state of charge bounds and across a temperature range of 0 °C to 25 °C. The foundation model was assessed using identified parameters on two current profiles derived from drive cycles across a temperature range of 0 °C to 10 °C. The FMU (Functional Mockup Unit) model format was determined as the required interface for an AVL battery emulator. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in electric vehicles and renewable energy storage systems due to their superior performance in most aspects. Battery parameter identification, as one of the core technologies to achieve an efficient battery management system (BMS), is the key to predicting and managing the performance of Li-ion batteries. However, due to the complex chemical reactions and thermodynamic processes inside lithium-ion batteries, coupled with the influence of the external environment, accurate identification of lithium-ion battery parameters has become an urgent problem to be solved. In addition, data-driven parameter identification can enable battery models to better understand battery behavior, which is one of the focuses of future research. For this reason, this paper comprehensively reviews the application of data-driven parameter identification methods in different scenarios. Firstly, the research briefly explains the working principle of lithium-ion batteries and the key parameters affecting their performance. Secondly, this paper deeply discusses data-driven methods for parameter identification, which are widely used nowadays, and provides improvement ideas to address the shortcomings of traditional methods. Finally, the paper discusses the challenges faced by parameter identification technology for lithium-ion batteries and envisages future prospects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into the battery systems. Among other critical functionalities, BMSs provide information about the key states of the batteries under usage, including state of charge (SOC) and state of health (SOH). This paper proposes a data-driven approach for the joint online estimation of SOC and SOH utilizing multi-task learning (MTL) approaches, particularly highlighting cross-stitch units and cross-stitch networks. The proposed model is able to achieve an accurate estimation of SOC and SOH in online applications with optimized information sharing and multi-scale implementation. Comprehensive results on training and testing of the model are presented. Possible improvements for future work are also discussed in the paper. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive analysis of global patent trends in battery recycling, focusing on secondary batteries and related technologies across Korea, China, and the United States. The methodology involved collecting data from various patent databases, followed by quantitative analysis to identify technology trends and guide future development. The research employed statistical tools to analyze patent activities, including the frequency and scope of patent filings, and comparative analysis to highlight differences between countries. This study reveals distinct emphases on technologies such as lithium-ion and waste battery recycling, highlighting notable differences in patent activities among key companies and countries. China's large number of patents in battery manufacturing processes contrasts with the USA's focus on electrochemical cell construction and storage systems, while Korea shows significant activity in waste battery technology. The novelty of this paper lies in its detailed comparative analysis of patent trends across these three major economies, providing insights into the technological focuses and priorities of each country. The study also identifies key challenges, such as the need for consistent innovation and broader geographic coverage in Korea, enhancing patent influence and international presence in China, and ensuring high patent quality and fostering innovation in lagging sectors in the United States. Addressing these challenges through enhanced collaboration, increased R&D investments, and supportive policies is crucial for strengthening the global position and driving further innovation in the battery recycling sector. [ABSTRACT FROM AUTHOR]

This paper proposes a high-efficiency and compact fuel cell–battery hybrid power system without DC/DC converters. Generally, fuel cells supply power to charge lithium batteries or loads using DC/DC converters. The disadvantages of a DC/DC converter are its complex design, poor efficiency, and large volume. Therefore, improvements in the volume, weight, and efficiency are the main objectives of the proposed topology, which is suitable for stable operation in power equipment. This paper proposes a novel topology without DC/DC converters for a fuel cell–battery hybrid forklift system and analyzes, discusses, and verifies it with experimental measurements. Additionally, the proposed topology uses an average charging method to charge the Li-ion battery. The dynamic response of fuel cells is slower than that of Li-ion batteries. By properly configuring the voltages of a fuel cell and a lithium battery, we propose a hybrid system that can maintain a stable output and high efficiency in different operating modes without DC/DC converters. Detailed efficiency calculations and comparisons reveal that the method proposed in this paper achieves an efficiency increase of 5.36% compared with traditional approaches, while maintaining a set charging current. The proposed topology and charging method are verified with experiments on a 10 kW fuel cell–battery system, and the results indicate that the proposed method without DC/DC converters is more suitable for hybrid applications than traditional methods. The proposed system achieves optimal efficiency of 98.27%, surpassing the performance of a traditional hybrid system employing regulated DC/DC converters. Additionally, the system incorporates a mechanism to achieve constant current control, ensuring precise control over the desired charging current. The error in the desired charging current, determined through the average charging method, is 5%. [ABSTRACT FROM AUTHOR]

With the massive use of lithium-ion batteries in electric vehicles and energy storage, the environmental and resource problems faced by used lithium-ion batteries are becoming more and more prominent. In order to better resource utilization and environmental protection, this paper employs bibliometric and data analysis methods to explore publications related to waste lithium-ion battery recycling from 1984 to 2021. The Web of Science core set from the SCIE online database was used for this article. These findings demonstrate a considerable increase trend in the number of publications published in the subject of recycling used lithium-ion batteries, with a natural-sciences-centric focus. Argonne National Lab, Chinese Academy of Sciences, and China Academic and Scientific Research Center are the top three institutions in terms of quantity of papers published. The affiliated journals corresponding to these three institutions also have high impact factors, which are 106.47, 44.85, and 58.69, respectively. In comparison to comparable institutes in other nations, the American Argonne National Laboratory supports 223 research articles in this area. China and the US make up the majority of the research's funding. The two key aspects of current lithium-ion battery recycling research are material structure research and environmentally friendly recycling. Nevertheless, high-capacity lithium-ion batteries, waste lithium-ion integrated structures, and gentle recycling of spent lithium-ion batteries will be the major aspects of study in the future. It is hoped that the above analysis can bring new ideas and methods to the field of waste lithium-ion battery recycling and provide a basis for the subsequent research and application of waste lithium-ion battery recycling. [ABSTRACT FROM AUTHOR]

In order to accurately identify the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries, this paper proposes an SOH estimation algorithm for lithium-ion batteries based on stream learning and LightGBM. To address the problem of inconsistent data length, which makes it difficult to establish the state mapping relationship between degraded data and health state, the health factors in this paper are extracted from capacity degradation features, entropy features, and correlation coefficient features. Then, the landmark isometric mapping (L-ISOMAP) manifold learning algorithm is used to dimensionally reduce the input feature set and map the high-dimensional features to the low-dimensional space to solve the dimensional explosion problem. Finally, a LightGBM prediction model is developed to perform SOH prediction on different datasets, and the superiority of the multidimensional model is evaluated. The experimental results show that the goodness-of-fit is 0.98 and above, and the MSE values are below 4 × 10−4. Comparing several prediction models, the LightGBM model has the best performance and better results in several indexes, such as MSE and RMSE. Under different working conditions, the proposed model in this paper has a goodness-of-fit of more than 0.98 in dataset B, which proves that the proposed model has a strong generalization ability. [ABSTRACT FROM AUTHOR]

As a core component of new energy vehicles, accurate estimation of the State of Health (SOH) of lithium-ion power batteries is essential. Correctly predicting battery SOH plays a crucial role in extending the lifespan of new energy vehicles, ensuring their safety, and promoting their sustainable development. Traditional physical or electrochemical models have low accuracy in measuring the SOH of lithium batteries and are not suitable for the complex driving conditions of real-world vehicles. This study utilized the black-box characteristics of deep learning models to explore the intrinsic correlations in the historical cycling data of lithium batteries, thereby eliminating the need to consider the internal chemical reactions of lithium batteries. Through Pearson correlation analysis, this study selects health indicators (HIs) from lithium battery cycling data that significantly impact SOH as input features. In the field of lithium batteries, this paper applies ABC-BiGRU for the first time to SOH prediction. Compared with other recursive neural network models, ABC-BiGRU demonstrates superior predictive performance, with maximum root mean square error and mean absolute error of only 0.016799317 and 0.012626847, respectively. [ABSTRACT FROM AUTHOR]

Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles. [ABSTRACT FROM AUTHOR]

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to ensure safe and stable battery operation. This paper proposes a method for estimating SOH and predicting RUL of lithium-ion batteries by charging feature extraction and ridge regression. First, three sets of health feature parameters are extracted from the charging voltage curve. The relationship between these health features and maximum battery capacity is quantitatively evaluated using the correlation analysis method. Then, the ridge regression method is employed to establish the battery aging model and estimate SOH. Meanwhile, a multiscale prediction model is developed to predict changes in health features as the number of charge-discharge cycles increases, combining with the battery aging model to perform multistep SOH estimation for predicting RUL. Finally, the accuracy and adaptability of the proposed method are confirmed by two battery datasets obtained from varying operating conditions. Experimental results demonstrate that the prediction curves can approximate the real values closely, the mean absolute error (MAE) and root mean square error (RMSE) calculations of SOH remain below 0.02, and the maximum absolute error (AE) of RUL is no more than two cycles. [ABSTRACT FROM AUTHOR]

Accurate and stable estimation of the state of health (SOH), which is one of the critical indicators to characterize the ability of lithium-ion (Li-ion) batteries to store and release energy, is critical in the stable driving of electric vehicles. In this paper, a novel SOH estimation method based on the aging factors of battery, which combines convolutional neural network (CNN), wavelet neural network (WNN), and wavelet long short-term memory (WLSTM) named CNN–WNN–WLSTM, is designed. The proposed CNN–WNN–WLSTM estimation scheme inherits both the fast convergence and robust stability of the WNN, as well as the ability of long short-term memory neural network (LSTM) to extract the time series features of the data; moreover, using CNN can make the proposed algorithm extract the data features from the original battery data automatically, and the WNN–WLSTM is then adopted to produce the final SOH estimation by exploiting the features from the CNN. To further speed and achieve global optimization, the RMSprop optimizer, instead of the usually used Adagrad optimizer, is chosen as the solver of the CNN–WNN–WLSTM network. Experimental results on data set from the NASA Ames Prognostics Center of Excellence show that the proposed algorithm can be commendably used for Li-ion battery health management by quantitative comparison with other commonly used machine learning methods, such as back-propagation neural network, WNN, LSTM, WLSTM, convolutional neural network–long short-term memory neural network (CNN–LSTM), and Gaussian process regression. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for enhancing their reliability and safety. Addressing the issue of inaccurate RUL predictions caused by the nonlinear decay resulting from capacity regeneration, this paper proposes an indirect lithium-ion battery RUL prediction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional neural network (CNN)–bidirectional gated recurrent unit (BiGRU). The method extracts Health Indicators (HI) from the battery-charging stage and employs CEEMDAN to decompose HI into several components. These components are then input into a component prediction model for forecasting. Finally, the predicted component results are fused and input into a capacity prediction model to achieve indirect RUL prediction. Validation is conducted using the lithium-ion battery dataset provided by NASA. The results indicate that, under prediction starting points (STs) of 80 and 100, the maximum average absolute errors do not exceed 0.0096 and 0.0081, and the maximum root mean square errors do not exceed 0.0196 and 0.0115, demonstrating high precision and reliability. [ABSTRACT FROM AUTHOR]

Highlights: Research progress of advanced optical fiber sensors in traction batteries and energy storage batteries is summarized. The embedded application mechanisms of different optical fiber sensors in batteries are discussed. Advanced optical fiber sensors adapting to batteries with diverse materials are reviewed. Advanced optical fiber sensors driving the development of future smart batteries are prospected. The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist. Now the battery is still a "black box", thus requiring a deep understanding of its internal state. The battery should "sense its internal physical/chemical conditions", which puts strict requirements on embedded sensing parts. This paper summarizes the application of advanced optical fiber sensors in lithium-ion batteries and energy storage technologies that may be mass deployed, focuses on the insights of advanced optical fiber sensors into the processes of one-dimensional nano–micro-level battery material structural phase transition, electrolyte degradation, electrode–electrolyte interface dynamics to three-dimensional macro-safety evolution. The paper contributes to understanding how to use optical fiber sensors to achieve "real" and "embedded" monitoring. Through the inherent advantages of the advanced optical fiber sensor, it helps clarify the battery internal state and reaction mechanism, aiding in the establishment of more detailed models. These advancements can promote the development of smart batteries, with significant importance lying in essentially promoting the improvement of system consistency. Furthermore, with the help of smart batteries in the future, the importance of consistency can be weakened or even eliminated. The application of advanced optical fiber sensors helps comprehensively improve the battery quality, reliability, and life. [ABSTRACT FROM AUTHOR]

The battery pack is at the heart of electric vehicles, and lithium-ion cells are preferred because of their high power density, long life, high energy density, and viability for usage in relatively high and low temperatures. Lithium-ion batteries are negatively affected by overvoltage, undervoltage, thermal runaway, and cell voltage imbalance. The minimisation of cell imbalance is particularly important because it causes uneven power dissipation by each cell and, hence, temperature distribution that adversely impacts the battery lifetime. Several papers in the literature proposed advanced cell-balancing techniques to increase the effectiveness of basic cell-balancing approaches, reduce power losses, and reduce the number of components in balancing circuits. The new developments and optimisations over the last few years have been particularly intense due to the increased interest in battery technologies for several end-use applications. This paper reviews and discusses recent cell-balancing techniques or methods, covering their operating principles and the optimised utilisation of electrical components. [ABSTRACT FROM AUTHOR]

Effective thermal management is essential for ensuring the safety, performance, and longevity of lithium-ion batteries across diverse applications, from electric vehicles to energy storage systems. This paper presents a thorough review of thermal management strategies, emphasizing recent advancements and future prospects. The analysis begins with an evaluation of industry-standard practices and their limitations, followed by a detailed examination of single-phase and multi-phase cooling approaches. Successful implementations and challenges are discussed through relevant examples. The exploration extends to innovative materials and structures that augment thermal efficiency, along with advanced sensors and thermal control systems for real-time monitoring. The paper addresses strategies for mitigating the risks of overheating and propagation. Furthermore, it highlights the significance of advanced models and numerical simulations in comprehending long-term thermal degradation. The integration of machine learning algorithms is explored to enhance precision in detecting and predicting thermal issues. The review concludes with an analysis of challenges and solutions in thermal management under extreme conditions, including ultra-fast charging and low temperatures. In summary, this comprehensive review offers insights into current and future strategies for lithium-ion battery thermal management, with a dedicated focus on improving the safety, performance, and durability of these vital energy sources. [ABSTRACT FROM AUTHOR]

In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities. [ABSTRACT FROM AUTHOR]

In the energy crisis and post-epidemic era, the new energy industry is thriving, encompassing new energy vehicles exclusively powered by lithium-ion batteries. Within the battery management system of these new energy vehicles, the state of charge (SOC) estimation plays a pivotal role. The SOC represents the current state of charge of the lithium-ion battery. This paper proposes a joint estimation algorithm based on genetic algorithm (GA) simulating biogenetic properties and support vector regression (SVR) to improve the prediction accuracy of lithium-ion battery SOC. Genetic algorithm support vector regression (GASVR) is proposed to address the limitations of traditional SVR, which lacks guidance on parameter selection. The model attains notable accuracy. GASVR constructs a set of solution spaces, generating initial populations that adhere to a normal distribution using a stochastic approach. A fitness function calculates the fitness value for each individual. Based on their fitness, the roulette wheel method is employed to generate the next-generation population through selection, crossover, and mutation. After several iterations, individuals with the highest fitness values are identified. These top individuals acquire parameter information, culminating in the training of the final SVR model. The model leverages advanced mathematical techniques to address SOC prediction challenges in the Hilbert space, providing theoretical justification for handling intricate nonlinear problems. Rigorous testing of the model at temperatures ranging from −20 ∘ C to 25 ∘ C under three different working conditions demonstrates its superior accuracy and robustness compared to extreme gradient boosting (XGBoost), random forest regression (RFR), linear kernel function SVR, and the original radial basis kernel function SVR. The model proposed in this paper lays the groundwork and offers a scheme for predicting the SOC within the battery management system of new energy vehicles. [ABSTRACT FROM AUTHOR]

This paper presents an approach to a go-kart chassis design, vehicle dynamics calculation, Li-ion battery capacity analysis, and electric motor choice for optimized vehicle performance. The chassis analysis shown in this paper was performed using a CAD/FEA software package, SolidWorks Student Edition. Three highlights can be found in this paper: (1) An original design was implemented; the basic analysis was composed of chassis optimization using beam elements and modeling such an optimized chassis "locally" with solid elements for sub-modeling purposes. (2) The most stressed tube joint was sub-modeled to calculate the risk of tube wall stability. (3) Vehicle dynamics were calculated for the case of braking on a curved path and the case of a collision with the front tire due to road imperfection. The authors intend to install a data acquisition system in the future to analyze the stress of local chassis tubes. The results of the SolidWorks analysis indicate a safety design for the chassis concept. The results for the sub-model stability (buckling) analysis show that the chosen tube wall thickness-to-diameter ratio gives safety factor values ranging from 1.6 to 5. Based on the stress distribution, some improvement in the middle part of the chassis can be made by using a half-a-millimeter-thicker wall tube or a larger tube diameter of a few millimeters to lower the stress. The latter will be described in this paper. [ABSTRACT FROM AUTHOR]