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This research proposes the utilization of Peltier modules to convert electrical energy from thermal energy to show its potential as a renewable energy source for residential and commercial application. The study, whose results are presented in this manuscript, was conducted in the city of Monterrey, located in the northeast of Mexico. The energy source was tested and analyzed utilizing a set of statistical metrics and further comparison against experimental test results. The Distrito Tec area in Monterrey city is an academic complex and it was chosen for this study, which consisted of the indirect measurement of the average annual heat energy stored within the buildings' cement structure. The aim was to obtain the annual accumulated electrical power in Wh per year that Peltier modules could provide in Distrito Tec, which is located in a city with solar irradiation levels above the world average. The proposal in this paper could encourage further investigation regarding this energy that is currently waste heat. More specifically, the results of this research highlight the importance of thermoelectric modules and seek to motivate research to improve their properties and make them more efficient and more viable as well. Thermoelectric modules have the potential to be part of the solution to sustainable development as presented in the United Nations SDG-7—ensure access to affordable, reliable, sustainable and modern energy for all. [ABSTRACT FROM AUTHOR]

AbstractIn order to reach the fire protection standard for new energy vehicle battery packs, the incorporation of SiO2 aerogel particles as a functional filler in the nitrogen and phosphorus fire-retardant system is necessary due to the inadequate mechanical properties. The effects of SiO2 aerogel on the properties of the coatings for the electric car battery pack were investigated by fire protection test, thermogravimetric analysis, limited oxygen index, UL-94 vertical test, combustion test, mechanical properties test, and weathering resistance test. The backside temperature fireproof coatings containing 1.5% SiO2 aerogel was effectively reduced to 250.2 ± 0.1 °C and the residual weight of the coating reached 32.61 ± 0.01%. The composition and structure of the carbon layer were analyzed further. Especially, the weight loss percentage of the fireproof coating is only 5.87 ± 0.02% in the water resistance test. However, excessive SiO2 aerogel decreased the performances of fireproof coatings. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) play an important role in increasing the use of renewable energy sources. Owing to the temperature sensitivity of lithium-ion batteries (LIBs), battery thermal management systems (BTMSs) are crucial to ensuring the safe and efficient operation of BESSs. Previous works mainly focused on evaluating the performance of BTMS; however, little attention has been paid to the minimum cooling requirements of BESSs, which are important for optimizing the design and operation of BTMSs. To bridge the knowledge gap, this work investigated the performance of air cooling for a battery cabin under different charge/discharge (C) rates by using a computational fluid dynamics (CFD) model, which is coupled with a battery model. Simulation results show that the inlet airflow rate has the strongest influence. For the studied cases, when the battery operates at C-rates lower than 3, the inlet temperature should be controlled below 35 °C, and the gap between the batteries should be greater than 3 mm to meet the minimum heat dissipation requirement. At a C-rate of 0.5C, natural convection is sufficient to meet the cooling need, whereas at 1C or higher C-rates, forced convection has to be used. Increasing the number of batteries, for example, from 6 to 8, has negligible impact on the inlet flow required to assure the heat dissipation. [ABSTRACT FROM AUTHOR]

Nowadays, what captures consumers' primary attention is how to purchase electric vehicles with long range and desirable price. Lightweight construction stands as one of the most effective approaches for prolonging range and lowering costs. As a consequence, it is particularly imperative to undertake lightweight design optimization for the battery bracket of new energy vehicles by applying 3D printing technology. To actualize this goal, Rhino software was initially employed for 3D modeling to design the battery bracket system for a pure electric vehicle in China. Subsequently, topology optimization design of the battery bracket was carried out by adopting Altair Inspire software. Last but not least, manufacturing and assembly inspection were completed using a 3D printer. The results show that the maximum displacement of the battery lower tray bracket after topology optimization is 3.20 mm, which is slightly higher than before, but still relatively small. The maximum Mises equivalent stress rose to 240.7 MPa post-optimization, but brought about a uniform stress distribution at the bottom of the bracket. In comparison, the minimum factor of safety met design requirements at 1. The mass was lessened to 0.348 kg, representing a 49.2% decrease in comparison with pre-optimization levels. The 3D-printed bracket was fabricated by employing a 3D printer, thereby achieving the aforementioned mass abatement. The battery pack parts exhibited a bright surface with low roughness and no discernible warping or deformation defects. As revealed by the assembly results, the components of the battery pack bracket are tightly coordinated with each other, with no evident assembly conflicts, revealing that the dimensional accuracy and fit of the completed parts meet production requirements. These findings lay solid groundwork for the mass production of high-performance battery pack brackets. [ABSTRACT FROM AUTHOR]

Electric vehicles are the future of mobility. Electric vehicles have batteries to store energy and the most common type of batteries used in electric vehicle's battery packs are lithium-ion cells. These cells have very high energy density and dissipate heat during charging and discharging cycles. There is a need to have an efficient cooling system to dissipate this heat. Bigger-size batteries in four-wheelers use liquid cooling to ensure faster charging and longer battery life. Surface cooling and tab cooling are two popular types of liquid cooling systems for battery packs. Surface cooling is a preferred type of cooling system as it is less complex and cheaper, but it creates a temperature gradient inside the cell which is detrimental to cell life. This work proposes tab cooling as a solution to improve the life cycle of lithium-ion cells. Two sets of the battery pack, one with tab cooling and the other without a cooling system were tested under different conditions for multiple fast charging and discharging cycles until their initial capacity was reduced by 30%. The results show that with tab cooling the battery performed better and battery degradation was reduced. [ABSTRACT FROM AUTHOR]

Interest towards electric vehicle adoption is on the rise due to the lower running and maintenance cost it offers, along with zero tailpipe emissions. Range anxiety is one of the only concern that affects the adoption of electric vehicles. The state of charge of the traction battery pack has to be accurately determined and provided to the user to avoid range anxiety. Minute battery parameters has to be considered to improve the accuracy of the state of charge determination. In order to overcome the problem of range anxiety, an innovative strategy that takes into account how vibrations affect the performance of EV batteries is developed in this research. By doing this, the state of charge estimation precision is improved and thereby raises the driver's faith in electric vehicles. The impacts and vibrations felt on the traction battery pack during driving would lead to heat generation. The heat generated is found to be highest when the vibrations resonate at the natural frequencies of the traction battery pack. The natural frequency of the battery pack is considered when the battery is kept in the battery chamber of the two-wheeler electric vehicle. The vibrations at natural frequency produces heat which is accounted for when the state of charge is determined. To obtain accurate state of charge estimation, a Kalman filter-based approach is used. The Kalman filter-based estimation uses the conventional methods which are the open circuit voltage method and the Coulomb counting method to improve the estimation process along with the consideration of the heat component due to vibrations and impact. The vibration analysis is performed using MATLAB, while the state of charge determination is implemented in hardware and the Kalman estimation done using Python. The system is modelled on an electric two-wheeler platform and the testing is done to compare the state of charge accuracy of the open circuit voltage method, the Coulomb counting method and the Kalman filter-based estimation approach. The inclusion of the vibrational heat analysis for State of Charge estimation in the hardware testing of the electric two-wheeler provides an accurate state of charge value. [ABSTRACT FROM AUTHOR]

Lithium‐ion cell chemistries are favored in the automotive sector, as they enable electric vehicles (EVs) to compete with traditional gasoline‐powered vehicles in terms of performance, range, and cost. The life and performance of these packs depend upon the Battery management system which monitors and controls the pack. The modeling, simulation, and analysis of a lithium‐ion battery pack that closely resembles an actual automobile battery are the focus of this paper. Real cells that are commercially available and specifically developed for automotive applications are modeled in the simulation environment. The cell characterization and formation of pack topology with heat exchange are addressed. A commercial electric car's power train model and EV dynamics are used in the simulation environment to test the modeled battery pack over various drive cycles. The performance analysis is upscaled by considering different aspects like resistive forces while driving, ambient temperature, initial conditions while the vehicle starts, etc. The battery manufacturer can verify the battery's performance under different load conditions, with a high degree of similarity to real‐world scenarios. The simulations are conducted on MATLAB/Simulink 2021b software. [ABSTRACT FROM AUTHOR]

Batteries are essential to mobilization and electrification as they are used in a wide range of applications, from electric vehicles to small mobile devices. All these devices are powered with AC or DC inside their systems, so they require different battery systems depending on their technical requirements. Batteries show unique characteristics depending on their types, and their needs vary based on their performance, ambient conditions, and so forth. One of the main demands for them is thermal stability. For batteries, thermal stability is not just about safety; it's also about economics, the environment, performance, and system stability. This paper has evaluated over 200 papers and harvested their data to build a collective understanding of battery thermal management systems (BTMSs). These studies are specifically designed to solve different problems. This paper has been prepared to show what these systems are, how they work, what they have been designed for, and under what conditions they should be applied. The BTMSs have been evaluated based on their method, method tools, discharge rate, maximum temperature, temperature difference values, and ambient and inlet temperatures. After evaluating over 200 studies, the results indicate that the passive BTMSs are not useful the cases where the temperature reaches higher values suddenly, especially for system systems that require higher discharge rates. On the other hand, active cooling methods do not manage the temperature difference in the battery cells. However, hybrid cooling methods address both cases admirably by compensating for both of their weaknesses and bringing out their advantages. The general optimum temperature for lithium battery batteries is 55°C. Even though there are many other parameters that need to be considered before making a decision for a BTMS design, the best performance for an optimum system seems to be methods 34, 38, and 22 as they are able to provide lower maximum temperature and temperature difference in the cells. [ABSTRACT FROM AUTHOR]

The transportation sector is under increasing pressure to reduce greenhouse gas emissions by decarbonizing its operations. One prominent solution that has emerged is the adoption of electric vehicles (EVs). As the electric vehicles market experiences rapid growth, the utilization of lithium-ion batteries (LiB) has become the predominant choice for energy storage. However, it is important to note that lithium-ion battery technology is sensitive to factors, like excessive voltage and temperature. Therefore, the development of an accurate battery model and a reliable state of charge (SOC) estimator is crucial to safeguard against the overcharging and over-discharging of the battery. Numerous studies have been conducted to address lithium-ion battery cell modeling and SOC estimations. These studies have explored variations in the number of RC networks within the model and different estimation methods. However, it is worth mentioning that the capacity of a single lithium-ion battery cell is relatively low and cannot be directly employed in electric vehicles. To meet the total capacity and voltage requirements for electric vehicles, multiple cells are typically connected in series or parallel configurations to form a battery pack. Surprisingly, this aspect has often been overlooked in previous research. To tackle this overlooked challenge, our study introduces a comprehensive battery pack model and an advanced Battery Management System (BMS). We then integrate these components into an electric vehicle model. Subsequently, we simulate the integrated EV-BMS model under the conditions of four different urban driving scenarios to replicate real-world driving conditions. The BMS that we have developed includes an Extended Kalman Filter (EKF)-based SOC estimation system, a mechanism for controlling coolant flow, and a passive cell-balancing algorithm. These components work together to ensure the safe and efficient operation of the battery pack within the electric vehicles. [ABSTRACT FROM AUTHOR]

Electric powered vehicle popularity gives us better option for reducing the hydrocarbon emissions for the clean environment and will help to minimizing the troposphere of ozone degradation. Evs that are powered by batteries are becoming more commonplace worldwide. A strong Battery Management System (BMS) is essential for the reliable and safe operation of batteries, which have been widely used in many large power applications, including EVs and hybrid EVs. Their optimal plan and the management are significant for safe and beneficial tasks. This review paper examined with about the oldest kind of battery-latest powered battery (Pb-acid battery) and the most recent innovation of battery, lithium-ion battery. The present paper deals about the recent improvement in three important areas like Battery pack shape, different battery material and thermal behaviour which covered main challenges for EVs operation. [ABSTRACT FROM AUTHOR]

The use of a polymer composite material in electric vehicles (EVs) has been extensively investigated, especially as a substitute for steel. The key objective of this manuscript is to provide an overview of the existing and emerging technologies related to the application of such a composite, especially for battery pack applications, in which its high strength-to-weight ratio, corrosion resistance, design flexibility, and durability are advantageous compared to any metal in general. This study explores the key considerations in the design and fabrication of composites, including base material selection, structural design optimization, reinforcement material, manufacturing processes, and integration with battery systems. The paper also discusses the performance characteristics of composite battery pack structures, such as mechanical properties, thermal management, safety aspects, and environmental sustainability. This study aims to contribute to sharpening the direction of future research and innovations in the area of composite battery pack technology. [ABSTRACT FROM AUTHOR]

The fire hazard resulting from the thermal runaway of lithium-ion batteries constitutes an severe threat for electric vehicles, and discovering an effective and prompt method for suppressing battery fire is still challenging. In this paper, a finite volume model for simulating the process of extinguishing lithium-ion battery fire was established, and the effect of water mist on suppressing battery fire was studied. It was found that the larger the droplet size, the greater the initial spray velocity, or the higher the nozzle height, the worse the fire extinguishing effect. The larger number of nozzles, the greater the spray flow, or the earlier the spray start time, the better the fire extinguishing effect. As the spray duration increased, the suppression effect at the center module gradually increased. When the water mist nozzle faced the center point of the module, the temperature rise was much lower than at other angles, and the fire extinguishing performance was the most effective. [ABSTRACT FROM AUTHOR]

This manuscript proposes a hybrid method for optimum sizing and energy management (EM) of hybrid energy storage systems (HESSs) in Electric vehicle (EV). The proposed hybrid method is combined performance of Honey Badger Algorithm (HBA) and recalling-enhanced recurrent neural network (RERNN), commonly called HBA-RERNN method. The major objective of proposed system is reducing the vehicle life time cost. The HESSs are incorporated with battery and super capacitor (SC). The proposed method is utilized to solve combined energy management and optimization size. Based on the variables, such as size of battery pack and super capacitor pack, HESS size is reflected. Depend on various sensitivity factors, optimum hybrid energy storage systems size and financial costs are analyzed. At last, the performance of proposed system is implemented on MATLAB site and compared with several existing systems. From this simulation outcome, it concludes that the proposed system diminishes the overall cost and battery degradation cost as 66625 USD than the existing systems. The efficiency of the proposed system achieves 94.8763%. [ABSTRACT FROM AUTHOR]

Accurate prediction and monitoring of the state of charge (SOC) of each cell in a battery pack are of great significance for safe driving of electric vehicles. Subject to the factors of differences between cells, temperature, and aging, the accuracy of existing SOC prediction methods may be affected in applications. This article employs a data-driven method with a proposed novel transfer learning framework considering conditional probability distribution adaptation to solve the impact of the aforementioned influencing factors on SOC prediction and obtains a favorable SOC prediction result for each cell. As experiments of actual battery pack provided by China First Automobile Work based demonstrated, the proposed method can accurately predict SOC of each cell under different influencing factors by using only one cell modeling data. Moreover, the proposed method is robust against model parameter uncertainties, sensor noise, etc. [ABSTRACT FROM AUTHOR]

The heat dissipation around battery cells should be thoroughly examined to keep the battery pack running properly. This article mainly focuses on the 3D analysis of thermal distribution in lithium‐ion battery (LIB; 14650, 18650 and 26650) with varied geometry sizes and the thermal distribution of LIB packs (6s and 12s series) at different charge/discharge rate. It has been concluded that the capacity loss increases at higher SOC levels for the calendar aging as derived from the lumped model approach. Additionally, the rate of heat generation with the increase in battery size and its effect on the overall battery temperature has been observed. The proposed work has been simulated in COMSOL Multiphysics 5.6 for observing the temperature variations within the battery pack and the vital findings are stated from the conclusions. [ABSTRACT FROM AUTHOR]

This paper outlines a new simulation approach to study the effect of factors on the state of charge (SOC) consistency of Li‐ion battery packs. The proposed method is based on a system model and the Monte Carlo method. The system model includes 100 serial connected equivalent circuit models and their respective parameters. This paper divides the internal and external factors into two categories: independent factors such as internal resistance, capacity, Coulombic efficiency, leakage current, and comprehensive factors related to temperature. The Monte Carlo method simulates the differences and distribution of independent factors among cells. Temperature‐dependent factors are modeled with empirical formulas. Simulation results show that the main factors affecting SOC consistency are differences in battery temperature, leakage current, and Coulombic efficiency. Meanwhile, slight differences in capacity and internal resistance have little effect on SOC inconsistency. Furthermore, this paper demonstrates the effectiveness of the system model. [ABSTRACT FROM AUTHOR]

In this paper, the temperature response of a lithium‐ion type 18650 battery pack cooled by a thermoelectric air‐cooling module is presented. The effects of the airflow rate, on‐off cooling fan position, charging and discharging rate and cooling by the thermoelectric air‐cooling module are investigated. The charging and discharging procedures with different current rates of 2 A (0.06 C), 3 A (0.09 C), and 4 A (0.12 C) are investigated. The results showed that different test conditions of the thermoelectric air‐cooling module have a substantial effect on the temperature of the battery pack. It was demonstrated that a thermoelectric air‐cooling module has a decreased battery temperature of lower than 40°C. The operating conditions of the cooling fans have a substantial effect on battery temperature. The experiment in which all the cooling fans are turned on shows a higher cooling capacity than natural air cooling by approximately 16%–57%. However, when setting the on‐off position of the fans that are installed in different positions, the cooling capacity when the fans at the inlet and thermoelectric cooling heat sink are turned on and the fan at the outlet battery pack is turned off is slightly less than that when all the cooling fans are turned on by approximately 25%–57%. Therefore, it can be selected as an optimum for cooling the battery pack due to less power consumption and noise issues. The proposed research can be implemented as a guideline for the cooling system of energy storage, such as small electric vehicle platforms, ground batteries, and solar energy storage. [ABSTRACT FROM AUTHOR]

Academic research in the battery field frequently remains limited to small coin or pouch cells, especially for new materials that are still rather far from commercialization, which renders a meaningful evaluation at an early stage of development challenging. Here, the realization of large lab‐scale pouch cells comprising Sn0.9Mn0.1O2 (SMO), prepared via an easily scalable hydrothermal synthesis method, as an alternative active material for the negative electrode and LiNi0.6Mn0.2Co0.2O2 (NMC622) as a commercially available active material for the positive electrode is reported. Nine double‐layer pouch cells are connected in series and parallel, suitable for powering a remote‐controlled vehicle. Subsequently, these SMO‖NMC622 cells are critically evaluated by means of an early‐stage life cycle assessment and compared to graphite‖NMC622 cells, in order to get first insights into the potential advantages and challenges of such lithium‐ion chemistry. [ABSTRACT FROM AUTHOR]

This article presents an efficient and fast echelon battery equalization method based on wide voltage range bidirectional converter combined with DBSCAN (density-based spatial clustering of applications with noise) clustering control strategy. Thanks to the wide voltage range and bidirectional buck-boost characteristics of the four-switch bidirectional converter, the battery energy can be reasonably redistributed between the battery and the super capacitor. The converter operating in synchronous rectification mode can not only achieve bidirectional energy flow in a wide voltage range, but also has a high energy conversion efficiency. The DBSCAN clustering algorithm is used to divide all battery cells into groups, and each group may contain one or more adjacent and nonadjacent battery cells. First, the nonadjacent single cells are balanced and integrated into adjacent group with the closest voltage. Then, the groups with different voltages are balanced by group-to-group, thereby realizing rapid balance of the entire battery pack. In order to verify the effectiveness of the proposed method, simulations and experimental verification were carried out. The experimental results show that, comparing with traditional battery balancing methods, the proposed method achieves shorter balancing time and higher balancing efficiency. [ABSTRACT FROM AUTHOR]

The safety concerns associated with lithium-ion batteries (LIBs) have led to the development of a novel framework combining advanced machine learning (ML) techniques with multiphysics modeling. Herein, we report an ML framework aiming to predict the occurrence of thermal runaway (TR) in the LIB module by employing a multiphysics model that incorporates thermal, electrochemical, and degradation sub-models. The focus of this research lies in understanding the degradation phenomenon associated with the breakdown of the solid electrolyte interface (SEI) on the negative electrode, which can trigger TR. The developed multiphysics model enables the investigation of electrochemical and degradation processes within batteries under various conditions, including constant charge/discharge and driving cycles. To capture the spatio-temporal temperature change, a graph neural network (GNN) for spatial change is coupled with a Long Short-Term Memory (LSTM) network for temporal evolution to form an integrated framework. The results demonstrate the high accuracy of the ML model in predicting battery temperatures in a module based on spatial and temporal temperature data obtained from temperature sensors attached to the batteries, hence, offering a means to detect TR before it occurs by identifying potential thermal hotspots. • A multiphysics model of thermal runaway in a battery module is developed. • The decomposition of solid electrolyte interface is considered. • Machine learning was implemented to predict temperature during battery operation. • A modeling method was implemented to generate realistic data on EV driving behavior. [ABSTRACT FROM AUTHOR]

Safety risk assessment is essential for evaluating the health status and averting sudden battery failures in electric vehicles. This study introduces a novel safety risk assessment approach for battery systems, addressing both cell and pack levels with three key indexes. The core of the assessment lies in representing the relative deviation of cell voltages through scatter diagrams across various stages of service life. Specifically, the study quantifies voltage deviations and deviation angles within different state of charge intervals to gauge safety risks at the cell level. Leveraging clustering algorithms aids in identifying outlier values. Furthermore, the dispersion of scatter points is utilized to assess safety risks at the pack level. Validation of this proposed model is conducted through cycling tests on battery modules with a deformed cell, demonstrating its efficacy in capturing inconsistent features of mechanical deformation abuse across the three indexes, thereby triggering alarms during operation. Moreover, the method is applied to assess the safety risks of five hazardous and accident-prone vehicles, enabling comprehensive evaluation of potential faulty cells and safety risks at the pack level. This proposed approach offers a fresh perspective on the comprehensive safety risk assessment of battery systems. [ABSTRACT FROM AUTHOR]

To enhance the heat transfer, an innovative origami channel heat exchanger is proposed and its performance is studied experimentally and numerically. The experimental results show the maximum temperature on the origami channels' pipe wall is 2.8 °C lower than that on the flat channel with the same the heat flux of 9500 W/m2. Additionally, the temperature difference across the pipe wall of the origami channel shows a reduction of 5.7 °C compared with that across the flat channel. The simulation results show the mean heat transfer coefficient of the origami channel increases by up to 331% compared to the flat channel with the 0.012 kg/s mass flow rate. Nevertheless, the origami channel leads to an extra energy consumption penalty. To evaluate overall performances of the origami channel, this paper introduces three dimensionless efficiency factors to quantify relevant costs in terms of energy, volume and mass. It is found that the origami channel achieves much higher dimensionless mass efficiency and volume efficiency but less dimensionless energy efficiency than the flat channel. The comprehensive evaluation, which considers all the three factors, reveals that the combined efficiency factor of origami channels with folding angles ranging from 65° to 75° improves by 80% to 95% compared to the flat channel. [ABSTRACT FROM AUTHOR]

The safety of battery system is compromised by the abusive operation and aging, potentially resulting in the abnormal voltage levels. Rapid detection and accurate diagnosis of voltage fault are crucial for ensuring the safety of battery packs. A battery voltage fault diagnosis method is proposed by using the mutual information in this work, which can identify faulty cells timely. Specifically, the voltage of battery pack in an electric vehicle is collected, and the mutual information of voltages between each paired-cells is calculated. The presence of faulty cells disturbs the original distribution of mutual information. Therefore, the Ryan Joiner (RJ) test is used to judge whether the normal distribution of calculated mutual information or not. The interquartile test is used to detect the outliers. The faulty cell is diagnosed by matching the cell corresponding to the outliers. The defaults resulting from an internal short circuit and mechanical vibration are analyzed and diagnosed. The method can avoid misdiagnosis caused by the mutual influence between different faults. Furthermore, the fault diagnosis capability of presented method is discussed under various states of aged cells. The experimental results verify the effectiveness, robustness, model-free, and easiest-to-implement of the presented method. • A battery voltage fault diagnosis method using the mutual information. • Ryan Joiner test is used for the normality test. • The interquartile test is used for outlier detection. • The proposed scheme is verified under several faults. • The method exhibits robustness under different aging states. [ABSTRACT FROM AUTHOR]

• Battery thermal management is studied with phase-change materials and heat pipes; • Phase change materials improve the temperature uniformity of the battery pack; • Phase change-heat pipe cooling reduces by 22.3% battery temperature than using air; • Phase change material RT31 reduces by 11.8 % battery temperature at 5–50 W·m−2·K−1; • Short circuit of combination cooling reduces by 29.9% temperature than air cooling. Lithium-ion battery packs generate high-level heat under harsh and rigorous conditions. The inadequate heat dissipation results in high pack temperature above the safe operating range. This study investigates an efficient and cost-effective battery thermal management system based on phase change materials and a micro heat pipe array. The battery is treated as a single domain. A battery pack with six series stacked batteries is analyzed using the Newman-Tiedemann-Gu-Kim model. The thermal performance of a battery thermal management system is analyzed using phase change materials with various melting temperature values, ambient temperature, and convective heat transfer coefficients of micro heat pipe arrays. Results show that the maximum temperature of the cooling system with micro heat pipe arrays and phase change materials is 33.8 °C, which is reduced by 22.3 % and 7.8 % compared to the air-cooled and cooling system with micro heat pipe arrays. At forced convection of 50 W·m−2·K−1, the maximum battery temperature values for the phase change material RT31, RT35, and RT42 are reduced by 11.8 %, 8.9 %, and 5.4 % compared to those at forced convection of 5 W·m−2·K−1. Under short-circuit conditions, the cooling system with micro heat pipe arrays and cooling system with micro heat pipe arrays − phase change materials reduce the maximum battery temperature by 12.4 % and 29.9 % compared to the air-cooled system. Results show that the developed model for the battery thermal management system provides a reference for designing phase change materials with micro heat pipe arrays. [ABSTRACT FROM AUTHOR]

The electric vehicles are not completely emission-free. The vehicles consume power and the power generation processes involve carbon emissions, resulting in indirect vehicle usage carbon emissions. A use-phase carbon emissions assessment can help electric vehicle designers and manufacturers analyze the onboard carbon footprint and mitigate vehicle-related carbon emissions through energy consumption optimization. In this article, we focus on the use-phase carbon footprint based on the energy losses in electric vehicle battery packs. A battery pack energy loss model is established to examine the carbon footprint of four main subsystems of the battery pack, including the energy storage system, thermal management system, and battery junction box. We use the proposed method to evaluate the battery pack use-phase carbon emissions in four different cities from four main continents. The results show that the highest emissions appear in Tokyo (436.2 kgCO 2) due to the relatively high greenhouse gas factor on the grid, the London has the lowest 207.5 kgCO 2 emissions due to the massive adoption of sustainable energy during power generation. With a similar high greenhouse gas factor, but Sydney shows lower emissions (347.5 kgCO 2) compared to Tokyo, which illustrates the closer the annual average temperature to the battery suitable operation range, the lower energy loss happens inside the battery pack due to the low thermal management load and resistance energy loss in the cells. This electric vehicle has the highest energy loss rate (20.8%) in New York, 343.6 kgCO 2 emissions, and the highest thermal management system energy losses show the high seasonal temperature difference causing the high thermal management load to maintain the battery temperature. The proposed method can be used for different battery pack configurations and vehicle uses, providing researchers with a robust and versatile use-phase carbon emissions assessment strategy. • A battery pack use-phase carbon footprint assessment strategy is proposed. • The carbon emissions for four battery pack subsystems are calculated. • Temperature has the greatest impact on the battery pack carbon emissions. [ABSTRACT FROM AUTHOR]

The growing popularity of electric vehicles brings opportunities and challenges to the battery industry. Designers need to develop reliable battery packs to ensure the safety of consumers' property and passengers' lives. Due to the complex structure of the battery pack, the traditional finite element analysis design consumes a lot of computational resources. The utilization of multibody system dynamics (MSD) and machine learning (ML) methods can assist developers in the efficient design of reliable battery packs. In this work, an MSD model of a battery pack was constructed based on the recursive idea, which can characterize the state information of each cell, such as velocity, acceleration, deformation, etc., during extrusion. By utilizing ML techniques, it is possible to achieve both the forward and reverse design of the adhesive for the battery pack. This enables accurate prediction of battery deformation under various adhesive stiffness and damping coefficients, as well as different battery SOCs. Consequently, the design of the battery adhesive can be guided, resulting in minimal distortion of the battery pack during extrusion and reducing the risk of internal short circuits. This method enables efficient battery pack design and provides ideas for future reliable battery pack designs. • An MSD model of the battery pack was proposed. • Six ML models were utilized to predict the extrusion deformation. • The DNN model which performs best was used to optimize the adhesive. • This method provides ideas for reliable battery pack design. [ABSTRACT FROM AUTHOR]

This study contains experimental and numerical investigation of the shape effect of the pack on the performances of PCM (or GNP/PCM) based passive thermally protected battery packs. Also, the performances of the PCM and thermally enhanced GNP/PCM composite for battery thermal protection are compared from low to high discharge rates. The thermal response results show that the cylindrical pack performs worse than the rectangular pack in terms of the maximum temperature and maximum temperature difference performance criteria. For the maximum temperature restriction criteria, 7% GNP/RT44 composite, that has enhanced thermal conductivity, performs better at first, but then its performance deteriorates compared to RT44 depending on the increase of its viscosity. It was determined that 7% GNP/RT-44 extend the effective protection times by 95.8%, 73.3% and 28.0% at the discharge rates of 4.4 W, 6.6 W and 8.8 W, respectively. [ABSTRACT FROM AUTHOR]

Electric vehicles are a clean energy transportation option recently emerged as an alternative to the conventional engine powered vehicles. These vehicles are using Lithium ion battery as energy storage for their propulsion because of its energy density. Due to the chemical reaction of battery elements and internal resistance, the battery releases heat during charging and discharging. Unless this heat is controlled to be within the battery optimum operating temperature, the battery performance and life will be seriously affected. This paper is dedicated to conduct fundamental studies on the battery cooling. A three dimensional numerical analysis is made using PHOENICS version 2018. Cylindrical type battery cell is used in this study. The study gives insight to thermal response, flow field and temperature profiles in the battery module with air flow direction relative to battery cell orientation and cell arrangement. Temperature variation along the flow, and transverse direction of the battery pack is discussed. With a 5 mm inter cell spacing and symmetric arrangement, maximum cell to cell temperature variation in the axial flow type is observed to be 0.2 °C, while in the radial flow type about 3.5 °C for same arrangement. Quantitative and qualitative results obtained from the numerical test are presented. Furthermore, this work is believed to contribute to the improvement of battery air cooling system design in such a way the associated cost and efficiency is compromised. [ABSTRACT FROM AUTHOR]

The health and safety of the battery pack are directly influenced by the state of health of its cells. However, due to the aging inconsistency among cells and the limited measurability of physical quantities for cells within the battery pack, traditional approaches to state of health estimation of cell have significant limitations. This study introduces a machine learning approach for evaluating the state of health of cells within the battery pack. Firstly, a branch charging capacity estimator utilizing BiGRU is formulated, facilitating precise estimation of battery pack branch charging capacity across diverse charging conditions. Then, three categories of features, including aging features, inconsistency features, and operating condition features, are extracted based on aging experimental data at the battery pack level and battery pack branch charging capacity. These features are input into the support vector regression-based generic model, facilitating precise state of health estimation for all cells within the battery pack. The generalization of the model is validated under both five-stage constant current charging conditions and two-stage constant current charging conditions. Additionally, the discussion includes how the choice of model parameters affects the precision of cell state of health estimation. The method proposed enables precise monitoring of cell state of health within the battery pack, offering valuable potential for ensuring overall battery pack safety and issuing safety alerts for cells. • A high-precision branch charging capacity estimator has been developed. • The aging, inconsistency, and operating condition features are extracted. • High-precision estimation of SOH of all cells within battery pack can be achieved. • The model's generalization is validated under different operating conditions. [ABSTRACT FROM AUTHOR]

The complex operational conditions in real-world electric vehicles (EVs) contribute to the complexity of managing and maintaining battery packs. Adding to these challenges is the intricate task of modeling the inconsistent coupling among individual cells within these packs. This study addresses the ongoing challenges in modeling lithium-ion battery (LIB) cells within packs and estimating their state of health (SOH) for practical applications. This research proposed a PCA-CNN-Transformer method to model and predict the SOH model of real-world EV. Three main contributions are presented: a novel approach to defining an attenuation SOH model based on delivered energy, a methodology utilizing Principal Component Analysis (PCA) for cell modeling, and an SOH estimation model employing CNN-Transformer architecture. To address both pack and cell-level modeling, a hierarchical feature extraction approach is proposed. The health features extracted from both levels are assessed using grey relational analysis, showing a strong correlation with LIB SOH, exceeding 0.70. The proposed cell modeling method significantly reduces data size by 96%, enhancing computational efficiency. Furthermore, the integration of 1D-CNN in the SOH estimation model overcomes the limitations of the attention mechanism, achieving a MAE with 0.0406 and r-square of 0.9327, improved the original transformer network performance by 10.95%. This study also examines and discusses the performance of the informer and transformer models, elaborating why the informer model underperformed in this dataset. [Display omitted] • A SOH attenuation model based on energy delivered is proposed for EVs. • A hierarchical feature extraction method from both cell-level and pack-level data is proposed. • Principal Component Analysis (PCA) is implemented to model cell-level features. • A novel CNN-Transformer for the SOH estimation model and bench-marking with an informer network are introduced. [ABSTRACT FROM AUTHOR]

• The heat generation model for cylindrical Li-ion battery was built. • A liquid immersion cooling battery pack containing 60 batteries were established. • At 2C discharge rate, 0.5 L/min flow rate was recommended. • The battery pack can address localized high-rate discharge events (4.5C or 6.5C). • Liquid immersion cooling BTMSs have great heat dissipation performance. With the increasingly severe challenges of the thermal management of battery packs for electric vehicles, the liquid immersion cooling technology has gradually attracted more attention due to its superior characteristics such as high heat dissipation efficiency, well temperature uniformity and low risk of thermal runaway. To investigate the heat transfer characteristics of the liquid immersion cooling BTMSs, the 3D model of the 60-cell immersion cooling battery pack was established, and a well-established heat generation model that leveraged parameters derived from theoretical analysis and experiments was incorporated into the 3D simulation to analyze the thermal characteristics of battery pack. The simulation results showed that under the discharging rate of 2C, even with the lowest coolant flow rate in this work (0.2 L/min), no localized abnormal overheating within the battery pack was observed, and the highest temperature was 34.22 °C. The temperature uniformity within the battery pack improved with the increase of the coolant flow rate. It was recommended to maintain a flow rate above 0.5 L/min to ensure a temperature difference below 5 °C. The experimental apparatus of the immersion cooling battery pack was also developed to explore the heat dissipation and temperature uniformity at 2C discharge rate. The simulation results were in well agreement with the experimental results, with the deviation less than 0.43 °C when the flow rate exceeded 0.6 L/min. The effect of the localized high-rate discharge events (4.5C and 6.5C) at different battery pack locations on temperature distribution and uniformity was further discussed based on the verified 3D model. The results suggested that liquid immersion cooling systems offered promising potential for achieving both efficient heat dissipation and preventing thermal runaway in battery packs. [ABSTRACT FROM AUTHOR]

This comprehensive study delves deeply into the realm of electric vehicle (EV) battery temperature management, with a central focus on optimizing cooling systems using ethylene glycol solutions. The research rigorously examines the interplay between ethylene glycol concentrations and cooling methods on EV battery performance. ANSYS and MATLAB along with analytical methods were used for this project. Key findings reveal a consistent inverse relationship between ethylene glycol concentration and cooling efficiency, favoring lower concentrations. Indirect cooling, achieved with a 30% ethylene glycol solution, emerges as the most effective method, while direct cooling proves less efficient. Notably, mineral oil cooling at 0.04 m/s outperforms air cooling, albeit with higher energy consumption. Consequently, air cooling is recommended for smaller battery packs due to its lower power demand. The study also underscores the importance of selecting ethylene glycol concentrations based on environmental conditions, advocating lower concentrations for warmer climates and higher concentrations for colder regions. In summary, this research significantly advances EV technology by advocating for customized cooling system designs that consider environmental factors, battery specifications, and practical implementation. It provides a critical roadmap for enhancing EV thermal management systems' efficiency and sustainability. Further research is needed to explore the long-term effects of diverse cooling methodologies on battery longevity and performance within the practical constraints of EV integration. • Cooling effectiveness increases with lower concentration. • Indirect cooling outperforms direct methods. • Higher coolant flowrate enhances heat extraction but requires more energy. • A 50% ethylene glycol solution is suitable for most weather conditions. [ABSTRACT FROM AUTHOR]

• A space-efficient protective method for battery pack is proposed. • Resin material offers high bearing capacity while foam improves energy absorption. • The protecting mechanisms of various matrix materials are revealed. • Stiffness balance between foam and battery for better protecting effect is unlocked. Mechanical protection of battery packs is challenging due to the limited available space. While filling packing gaps with protective structures is practical and space-efficient, there is a lack of reference for selecting proper matrix materials for the structures. To address this issue, we conducted compression tests on cylindrical battery packs embedded into 3D-printed thin-walled resin, foam, and polyurethane structures. A comprehensive comparison was made among battery packs protected by various materials, as well as the non-protected one, to evaluate their protecting performance including deformation nonuniformity, failure rate, and failure thresholds under various loading conditions. Results showed that the resin- and foam-protected battery packs experienced less damage under similar load level and similar crushing energy, respectively. The underlying mechanisms were further investigated by combining tests and simulations. It is revealed that the stiffness of filling material greatly influences the protective effect, which results in the different distributions of both stress and strain energy in the battery pack. The stiffness balance between the foam and the batteries to achieve better protecting effect was also unlocked under varying crushing energies. Our findings shed light on the space-efficient protection of battery packs under crash. [ABSTRACT FROM AUTHOR]

The effectiveness of suppression of lithium ion battery, LIB, fires with water mist was investigated experimentally using a previously developed bench-scale wind tunnel. The experiments were conducted on lithium ion cell arrays constructed from twelve cylindrical (18,650 form factor), fully charged, lithium cobalt oxide cells, which were densely packed in a rectangular configuration without gaps. An electric heater was employed to initiate thermal runaway in one cell. Failure propagation was then tracked using thermocouples located at the bottom surfaces of the cells. Heat release due to flaming combustion was computed based on the oxygen consumption method. Experiments conducted at 640 l min−1 of air, 320 l min−1 of air, and 186 l min−1 of nitrogen tunnel purge flow were used as reference points. All reference point experiments underwent complete cascading failure. Addition of 1.0 and 1.6 g s−1 of water mist to 320 and 640 l min−1 of air produced agent concentrations of 14.1 and 11.1 wt%, respectively, which are slightly above the concentration of water mist recommended for suppression of traditional fires. Application of water mist at 1.0 and 1.6 g s−1 prevented cascading failure in 40% and 50% of the tests, respectively, and significantly reduced the rate of failure propagation in the other tests. At both water mist delivery rates, flaming combustion associated with burning of battery ejecta was inhibited, reducing the combustion efficiency below 50%. One of the key findings of this study is that suppression of flaming combustion is not sufficient to stop cascading failure. The array must be continually cooled with water mist until the temperature of the cells decreases below a certain threshold, which prevents chemical reactions between battery materials inside the cell casings. [ABSTRACT FROM AUTHOR]

This experimental study focused on the comparison of thermal protection performances of macro and/or nano enhanced organic PCM in a representative battery pack from low to high discharge rates. The macro/nano enhanced RT-44 provides the desired battery thermal protection requirements in terms of criteria such as maximum temperature and maximum temperature difference restriction and uniform temperature distribution throughout the battery pack. It increases the effective battery thermal protection time by 117%-32% depending on the discharge rates. While PCM thermal protection provides a more homogeneous temperature distribution throughout the battery pack, nano and/or macro enhanced one provides it throughout the cell. The macro enhancement essentially makes a major contribution to shortening the cooling time for the next use. [ABSTRACT FROM AUTHOR]

Large battery packs are used in electric vehicles. Heat is generated when the battery pack is being used. Therefore, it is necessary to predict battery heat generation. An enhanced electro-thermal model is developed to describe the temperature distribution inside a battery pack. It combines the dynamic resistance model and the current distribution model. The resistance model is affected by the thermal and electrical parameters, while the current distribution model considers the interaction between cell status and current variation in the parallel branch. The proposed model can accurately predict the temperature change of cells in the pack under static and dynamic current conditions. Experiments are conducted to validate the prediction accuracy. Most of the average absolute errors (AEave) between the predicted value and test value displayed on the experimental device do not exceed 0.4 °C under static current conditions, and all of them are below 0.1 °C under dynamic current conditions. The two existing models, namely the state-of-charge (SOC)-dependent resistance model [R(SOC)] and SOC-T-dependent resistance model [R(SOC, T)], have AEave values of 1.6 and 0.54 °C when the pack is discharged at 0.5 C. In contrast, the AEave value achieved by our proposed model is 0.4 °C. Under dynamic current conditions, the maximum AEaves are 0.42 °C for the R(SOC) model, 0.26 °C for the R(SOC, T) model, and 0.16 °C for the proposed model. These results demonstrate that the proposed model provides more accurate predictions of the temperature rise inside the pack than the popular existing models. [ABSTRACT FROM AUTHOR]

Summary: The performance of lithium‐ion (Li‐ion) batteries in a battery module is significantly affected by the operating temperature. Temperatures above 40°C can speed up the battery aging and shorten the lifespan and may even lead to thermal runaway. Besides, the maximum temperature deviation between the cells inside the battery module is normally expected to be less than 5°C. Among various cooling strategies used in thermal management of Li‐ion batteries, application of micro heat pipe arrays (MHPAs) has received little attention. Hence, in the present study, a feasibility study has been conducted to assess the thermal performance of such a thermal link in controlling both the maximum temperature and temperature dispersion within the desired range. The MHPA‐assisted cooling system was tested under various rates of heat generations (10‐25 W total) and degrees of inclination (−90° to +90°). Experimental results have revealed a significant influence of the MHPA angle of inclination on the temperature of the evaporating section of the heat pipe. The best thermal performance was found to be at an angle of inclination of −15° where the condenser is located above the evaporating section. Performance degradation was observed for all the MHPA orientations where the condenser is located below the evaporating section. The thermal performance of the MHPA‐assisted cooling system is also influenced by the rate of generated heat. The maximum effective thermal conductivity for the MHPA was measured to be about 75 000 W/m2 °C. A dimensional figure of merit is defined for the MHPA‐assisted cooling system. [ABSTRACT FROM AUTHOR]

Summary: As an energy storage component of an electric vehicle, the battery pack is subject to continuous vibration when working. It is very important to analyze the dynamic characteristics of the battery pack through finite element method (FEM) for safety concerns. However, because of the complication of structure and the number of parts in a battery pack, it is impractical to develop an accurate FEM model. Thus, simplified methods are always applied for modeling. Because battery modules are the most complicated parts and make up the majority of proportion in a battery pack, it is the key to simplification. Using multibody models, this research proposes a simplified method for soft‐connected battery modules, which can be applied to the battery pack FEM model, accurately calculate the dynamic characteristics of a battery pack, and use less computing resources compared with traditional simplified methods. A practical battery pack structure is modeled using this method. Compared with the traditional ones, this method reduces 90% nodes and 97% meshes, excluding the influence of battery pack enclosure, and consumes 40% less memory during calculation. This method also has higher computing efficiency for larger battery packs with more battery modules. The simulation results are well matched with experimental results, which indicate the universality of the simplified model. [ABSTRACT FROM AUTHOR]

Summary: The parallel air‐cooled system is commonly applied in electric vehicles to cool the battery pack, in which flow pattern significantly influences the system cooling performance. In this paper, the curved divergence and convergence plenums are used to design the flow pattern in the parallel air‐cooled system for battery thermal management. The computational fluid dynamics (CFD) method is used for performance evaluation of the cooling system. The height distribution of control points is used to describe the plenum shape. During the optimization process, flow resistance network model is employed to obtain the velocity distribution of the system. An effective strategy is developed to adjust the height distribution of the control points for flow pattern design. Experiments are conducted to verify the CFD method and calculation model. Then typical cases are adopted to evaluate the proposed strategy for flow pattern design. The numerical results show that the strategy can effectively control the system flow pattern by designing the plenum shape. The cell temperature difference of the optimized system achieves an 86% reduction with the pressure drop only increased by 26%. Furthermore, compared with the optimized system with linear plenums in the reference, the optimized system with curved plenums achieves 48% reduction for the cell temperature difference without the pressure drop increased. Finally, experiments are conducted for the optimized system to verify the effectiveness of the developed strategy. The present study can provide a guide for the design of efficient parallel air‐cooled BTMS. Highlights: Shape optimization of the curved plenums for air‐cooled BTMSs is conducted.Effective strategy is developed to optimize the shape of curved plenums.ΔTmax of the optimized BTMS is reduced by 86% with ΔP only increased by 26%.Optimized BTMS with curved plenums performs better than that with linear plenums.Experiments are done to verify the calculation models and optimization strategy. [ABSTRACT FROM AUTHOR]

With the increasing scale of battery systems, the impact of battery inconsistency due to aging on battery pack performance becomes increasingly significant. To achieve high-precision battery pack modeling, we propose an in-situ estimation method for battery inconsistency parameters. The proposed method utilizes current and voltage data recorded by the battery management system (BMS). The respective terminal voltage errors are used as the loss function, and the equivalent circuit model and the fully connected neural network are combined to realize the estimation of inconsistency parameters. Through optimization algorithm, the inconsistency parameters of all cells in the battery pack are simultaneously estimated. The estimation results of full capacity, high-end capacity, and internal resistance exhibit high accuracy with root-mean-square error (RMSE) values of 0.82% (0.393 Ah), 0.70% (0.336 Ah), and 3.34% (0.097 m Ω), respectively. The battery pack model constructed using the estimation results maintains high accuracy across various operating conditions, demonstrating the effectiveness of the proposed method for inconsistency estimation and battery pack modeling. • Estimate the battery inconsistency parameters based on the similarity of the battery SOC-OCV curves with the BMS data. • Combine the neural network and equivalent circuit model to simultaneously estimate the capacity, internal resistance and SOC parameters of each cell in the battery pack. • The accuracy of the proposed method was demonstrated by comparing measured parameters with estimated parameters, and the effectiveness was further demonstrated by battery pack model simulation results. [ABSTRACT FROM AUTHOR]

In order to develop the liquid cooling plate of the battery pack with high efficiency, low resistance and uniform temperature, a special-shaped pin fin was proposed, and the influence mechanism of its structure and arrangement parameters on the flow and heat transfer performance in the flow channel of the liquid cooling plate is investigated numerically, and the temperature uniformity coefficient was used to quantify the distribution of temperature to verify the comprehensive heat dissipation performance. The results show that the arrangement of the pin fin disturbs the flow in the channel and increases the flow velocity near the upper surface of the channel, which is beneficial to enhance heat dissipation and increases the flow resistance at the same time; When the parameters of structure and layout of the pin fin are in a certain range, the comprehensive performance coefficient TP of the flow in the channel is greater than 1, in other words, the combined performance of the flow in the channel is improved. When the height ratio is 0.7, the width-length ratio is 0.5, the width ratio is 0.3, and the spacing ratio is 1.0, the TP of flow heat transfer in the flow channel is the best, and when Re = 1000, the TP of flow heat transfer is improved by 58%; When this pin fin is applied to the serpentine channel of the cooling plate of an in-use engineering vehicle, the highest temperature of the cooling plate is reduced by 3.5 K, the temperature difference is reduced by 3.5 K. The temperature uniformity coefficient is reduced by 56%, that is the heat dissipation performance of the liquid cooling plate can be improved comprehensively after adding the pin fin. [ABSTRACT FROM AUTHOR]

While large scale deployment of plug-in electric vehicles (PEVs) offers promising advantages, such as environmental benefits, energy security, and economic stability, it also poses certain cybersecurity related challenges. Unlike power grid security, PEV cybersecurity is significantly underexplored. However, cyberattacks on PEVs may lead to disastrous situations such as out-of-service EVs via denial-of-charging (DoC) or battery pack damage via overcharging. In this article, we attempt to address this issue by exploring control-oriented approaches for PEV cybersecurity. Specifically, we focus on designing algorithms for detecting cyberattacks that can potentially affect PEV battery packs during charging. We discuss two algorithms: first, static detector that utilizes only measured variables, and, second, dynamic detector that utilizes the knowledge of system dynamics along with the measurements. Furthermore, we propose a filter-based design approach for the dynamic detector that considers a multiobjective criterion including stability, robustness, and attack sensitivity. We perform theoretical analysis and simulation studies to evaluate the effectiveness of the algorithms under DoC and overcharging attacks that indicate the superiority of dynamic detector in terms of attack detectability. [ABSTRACT FROM AUTHOR]

Hybrid energy storage systems using battery packs and super capacitor (SC) banks are gaining considerable attraction in electric vehicle (EV) applications. In this article, a new modular reconfigurable multisource inverter (MSI) is proposed for active control of energy storage systems in EV applications. Unlike the conventional approaches, which use massive high-power dc–dc converters with bulk magnetic elements for combining SC banks and battery packs, the new approach utilizing the MSI offers magnetic-less structures. This reduces the weight and volume of the power electronics interface and offers simple control. Along with the proposed MSI, a space vector modulation technique and a deterministic state of charge (SOC) controller are also introduced for the control of the switching actions and operation of the SC bank. Simulations using MATLAB/Simulink and experimental results on a scaled down lab prototype are studied to assess the concepts. [ABSTRACT FROM AUTHOR]

This article newly suggests two-step Hamming neural network (HNN)-based pattern recognition which combines the discrete wavelet transform (DWT)-based multi-resolution analysis (MRA) for providing an innovative and unique cell distinction. In contrast to the conventional methods, there are some remarkable features. First, the discharging/charging current signals (DCCSs) for reflecting load conditions with various dissimilar scenarios are used as representative patterns in the first-step HNN. Specifically, the usage of decomposed low- or high-frequency components (A5/D5) by the DWT-based MRA enables added characteristic parameters for providing the closest matches. Second, according to state-of-charge (SOC) range, especially in high SOC ranges, the allowable maximum charging currents in constant current/voltage (CC/CV) schemes are adjusted for avoiding over-charging. Third, the second-step HNN finally completes the recognition after closely matching several representative discharging/charging voltage signals applying the selected DCCS in the first step. Finally, simultaneous internal parameter extraction in the equivalent electrical circuit model during the second-step HNN provides useful information for verification of this article. Then, unnecessary experimental procedure and the time required for getting parameters of an arbitrary lithium-ion cell are abbreviated or reduced. [ABSTRACT FROM AUTHOR]

To solve the problems of ampere-hour integration method for battery packs with inaccurate state of charge (SOC) estimation due to sensor accuracy, battery aging, integration error and initial value, the parameters of SOC initial value, nominal capacity and integration period of the traditional ampere-hour integration were improved. The first-order RC equivalent circuit model was established based on the internal relationship between the SOC and the open circuit voltage (Uo). In the circuit model, the Uo was extracted and updated in real time by the recursive parameter-free least squares (PF-RLS) with forgetting factor. The temperature with crucial effect on Uo was considered, and a three-dimensional model of SOC-Uo-θ was established to provide an accurate initial value for the improved ampere-time integration method. Based on the inconsistency of battery pack, the maximum voltage and the minimum voltage fusion algorithm were proposed， and the federal urban driving schedule（FUDS) working condition detection and the real vehicle working condition verification were carried out. The results show that the accuracies of the PF-RLS online extraction of Uo, the single cell SOC and the battery pack SOC algorithm are respective 2.55%, 3.20% and 4.00%, which meets the technical requirements of QC/T 897—2011 battery management system for electric vehicles claim. [ABSTRACT FROM AUTHOR]

A new battery cooling system for thermal management is proposed that exploits the high heat transfer rates of boiling using the fuel of hybrid electric vehicles. The new design incorporates fuel boiling within tubes embedded in the aluminum block that house the batteries. One of the advantages of the proposed system is the prevention of direct contact between the fuel and the batteries. The proposed system uses tubes, distributed through the battery pack, which contain stationary boiling fuel. The aluminum conducts the heat away from the batteries to the coolant undergoing phase change in the tubes. The aim of using the aluminum block between the boiling coolant and the batteries is to reduce the temperature through the battery pack and the temperature difference across each battery compared to the direct contact, to lower the amount of vehicle fuel in the battery pack, and to eliminate direct contact between the batteries and the coolant. The performance of the proposed thermal management system is assessed through simulations for 600 s charging and discharging cycles of the rates of 6C and 4C. At a rate of 6C it is found that the proposed system achieves a 1 °C higher than the direct contact pool and propane based system in terms of the maximum battery temperature, however it had a similar variation pattern. However, the proposed system achieves a maximum temperature difference of 3.5 °C compared to a 10 °C difference in the pool and propane based system. [ABSTRACT FROM AUTHOR]

Most scholars use atomic force microscopy (AFM) to analyze battery pack materials. Because of the defects of low resolution and limited application of AFM, an atomic force microscopy system based on IPC is designed. The atomic force microscopy is constructed by the system, and the tunnel effect of quantum theory is used to construct AFM. The atomic linearity probe and battery material surface should be used as two electrodes. A certain voltage is applied between battery material and needle tip. When the distance between battery material and needle tip is very close, the tunnel current will be generated. The piezoelectric ceramic material is used to control the needle tip to scan the battery material surface. The probe is in the vertical direction of battery material. The variation of the current in the upper tunnel reflects the fluctuation of material characteristics. The characteristics of battery pack materials can be analyzed by atomic force microscopy based on IPC. The experimental results show that the average accuracy and analysis time of this method are 98.89% and 5.81ms, and the analysis effect is good. [ABSTRACT FROM AUTHOR]

• Heat Transfer Performance using Nanofluids in Liquid Cooling for Cylindrical Battery Pack. • Two types of nanofluids are used (i.e., CuO/EG, Al 2 O 3 /EG, TiO 2 /EG, and SiO 2 /EG) along with a base fluid of ethylene glycol–water (EG-water 50%). • Developed model uses a nonlinear system of PDEs that can be converted into an ODEs by using appropriate similarity variables. • Among the nanoparticles CuO-EG showed excellent results by reducing the battery temperature up to 70.37%. • Whereas, by using Al 2 O 3 -EG the maximum temperature drop achieved was only 30.09%. The study investigates the heat transfer performance of nanofluids in liquid cooling for cylindrical battery modules. Two types of NFs, CuO/EG-water and Al 2 O 3 /EG-water, were used along with a base fluid of ethylene glycol–water (EG-water 50 %). The energy equations consider the effects of viscous dissipation and heat generation. The model produced a nonlinear system of partial differential equations (PDEs) that can be converted into ordinary differential equations (ODEs) by using appropriate similarity variables. The ODEs are then solved numerically using the shooting process in combination with the Runge-Kutta-Fehlberg method. The results for several parameters related to thermal, velocity fields, drag force, and HT rate are shown in the 2D and 3D simulations. The study provides insights into thermal and velocity profiles, fractional drag force, and heat transfer rate in a nanofluid flow around a cell in a cylindrical LIB module. According to the findings, the increase in volume fraction enhances the temperature of the two nanoparticles by (36.52 %, 44.20 %), respectively. CuO:EG-water nanofluids achieve ideal temperatures 7.68 % more quickly than Al 2 O 3 /EG-water nanofluids due to their distinct thermophysical properties. CuO/EG-water nanofluids show a 56.47 % temperature increase due to heat variation, whereas Al 2 O 3 /EG nanofluids experience a 16.30 % temperature decrease at Q < 0. Moreover, the drag force grows by 45.36 % and 47.94 %, while heat transfer increases by 50.17 % and 58.38 % due to nanoparticles. CuO:EG-water nanofluids exhibit better heat transfer performance, surpassing Al 2 O 3 :EG-water nanofluids by 8.20 %. [ABSTRACT FROM AUTHOR]

Accurate power prediction for a battery pack is of great importance for efficient and safe operation of electric vehicles. However, a battery pack typically consists of dozens of cells connected in series, and how to accurately predict its peak power in practical applications has always been a challenging issue. This paper proposes a low-complexity peak power prediction method for a series-connected battery pack, where the peak power of battery pack is predicted depending only on representative cells. Firstly, considering state of charge and voltage limits for power prediction, the representative cells are selected by using two easily available variables, namely characteristic voltage and ohmic resistance. Secondly, the state of charge and voltage of the representative cell are estimated accurately by the dual adaptive extended Kalman filter algorithm. Finally, the multi-parameter limited method for battery pack is developed to predict the peak power under Urban Dynamometer Driving Schedule test at different temperatures. The experimental results verify the feasibility and robustness of the proposed selection method, and the peak power of battery pack is predicted with low complexity and high accuracy. [ABSTRACT FROM AUTHOR]

Energy policy of EU promotes electro-mobility, especially the use of bicycles and electric bicycles. PEDELEC is a form of an electric bicycle that does not require a registry plate, and with a battery power 250 W electric motor helps the driver to overcome the altitude differences and influence of the wind. Batteries for electric bicycles are based on Li-Ion technology. Battery packs are made of standard cells that have a lifecycle of 1000 cycles. In realistic exploration conditions, the lifecycle is four years and up to a third of the declared cycle of use. The reason for this is the climatic conditions which lead to increased temperatures in usage and charging temperatures, which significantly reduce their life cycle. Paper on a practical example shows the dynamics of PEDELEC usage, energy needs for specific route characteristics, as well as changes in the thermal pattern of the battery and its condition after 4 years of use. The analysis was performed with Non-Destructive Testing (NDT) using infrared thermography, which confirmed the inhomogeneity of thermal patterns and the moment of final battery failure. [ABSTRACT FROM AUTHOR]