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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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

• 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]

Battery-powered intelligent transportation systems (BPITS) are growing at an alarming rate, and a problem that cannot be ignored is that the safety of battery packs is currently the weak link in the security of BPITS. However, developing a fast and reliable diagnostic method is not easy due to the large number of battery cells and the difficulty to guarantee consistency among the individual cells. To better resolve this problem, a new fault diagnosis scheme based on voltage correlation coefficient (VCC) and independent component analysis (ICA) model is proposed in this paper. Where VCC is used to extract the fault signature of the battery pack independent of the cell inconsistency; and ICA is used to model the non-Gaussian distributed high-dimensional VCC signals in parallel and derive a statistical-based fault detection index. The kernel density estimation-based algorithm is used to calculate the theoretical threshold and to determine the optimal window width for VCC. In particular, a variable reconstruction-based fault isolation algorithm is proposed for accurate and reliable determination of the location of the faulty cell to prevent the smearing effect. Experimental results conducted on a real battery testbed under different conditions and configurations show that the new method provides significant improvements in both detection latency and positioning reliability in diagnosing internal short-circuit (ISC) faults with different failure grades. • A VCC-ICA-based ISC fault diagnosis method is designed for the battery pack of BPITS. • A KDE-based algorithm is used to set the threshold and window width for VCC. • A VCC reconstruction-based algorithm is applied for fault isolation and location. [ABSTRACT FROM AUTHOR]

Green transportation with net-zero CO 2 emissions can only be possible with electric cars that possess reliable long-life batteries. Current hybrid/electric vehicles come with Li-ion batteries that must be kept within certain temperature limits for their safe and economic operation. At higher ambient temperatures, thermal run-aways lead to battery fire risks. Operation temperatures can only be attained by incorporating a cost-effective battery thermal management (BTM) system. This work studies a new, versatile, and high-capacity phase change material (PCM) package for BTM. LiFePO 4 battery packs have been tested for different ambient temperatures in typical urban battery charge/discharge cycles. Our findings indicate that such packs with flexible PCM packages operate efficiently. We succeeded in keeping the maximum operation temperatures about 10–15 °C below that of reference battery packs without PCMs. Our economic and environmental assessments conclude that they will become an indispensable smart and clean pathway for real passive BTMs. Flexible PCM packages present a promising alternative to current BTM applications. The energy savings that are achieved with 30% prolonged battery life can decrease CO 2 emissions by 1215–1755 kg CO 2 depending on the reference fuel for one medium sized vehicle. Green transportation is no longer a myth. • Flexible package was developed for battery thermal management (BTM). • Passive BTM was achieved with nano Fe 3 O 4 -phase change material (PCM) composite. • The developed novel PCMs and packages can be used safely for green transportation. [ABSTRACT FROM AUTHOR]

• Proposed a Fuzzy FMEA for risk assessment of an immersion-cooled battery pack in EV. • Provide a detailed assessment of the probable causes and effects of failure to the design engineers. • An analysis of battery pack functions, failure modes, causes, and effects concerning their severity, occurrences, and detection ranks. • The most important causes of failure are sealing, BMS, structure design and assembly of mechanical components. • Using fuzzy inference engine, the RPN values are modified to improve the FMEA. Vehicle electrification is one of the changes in the modern-day car enterprise trend. The battery pack is the most vital and precarious part of a battery-powered electric vehicle, which necessitates accurate and reliable designs to ensure acceptable safety. To this end, one of the tried and tested methods that help identify problems and make products more reliable is Failure Modes and Effect Analysis (FMEA). This paper presents a Fuzzy FMEA for risk assessment of an immersion-cooled battery pack (ICBP) in electric vehicles. As a new technology, immersion cooling can facilitate high-rate fast charging and a longer battery life cycle for lithium-ion batteries. Different failure modes and relevant causes and effects are investigated in this vein. Each failure's severity, occurrence, and detection rank are extracted and displayed as a standard FMEA table. The monitoring results of all stages of design, manufacture, and initial tests indicate that the most important causes of failure in an ICBP are the sealing method, BMS function, cell temperature control, structure, and assembly of mechanical components. To some extent, it was attempted to reduce the occurrence of failures by recommending actions to reduce the occurrence of those failures. Consequently, a significant improvement was achieved, and the production and installation of problematic parts were managed efficiently and effectively with the help of an FMEA control plan table. [ABSTRACT FROM AUTHOR]

• The predicted results agree with the experimental results an average error of 1.28%. • Coolant-improved flow direction and thermophysical properties significantly affect the decreasing maximum operating temperature and temperature gradient across a cell. • The highest temperatures of the battery module are 30.06 °C, 30.00 °C, 29.91 °C, 29.89 °C, and 29.49 °C for models II, IV, III, I, and V. • The maximum temperature gradient across a cell, models I, II, and III give the highest value [0.42 °C], and consequently, models [0.40 °C] and model v [0.15 °C]. The operating battery temperature significantly affects electric vehicle performance, reliability, and safety. Therefore, batteries need to keep within the operating temperature design. The 3D Eulerian model is applied to determine battery thermal behavior with five different flow directions of coolant throughout the battery pack jacket. The computational domain consists of sixty cylindrical Li-ion cells inserted into the cooling module socket with constant power input conditions. The predicted results are consistent with the experimental results, with an average error of 1.28%. Coolant-improved flow direction and thermophysical properties significantly affect the decreasing maximum operating temperature and temperature gradient across a cell. The highest temperatures of the battery module are 30.06 °C, 30.00 °C, 29.91 °C, 29.89 °C, and 29.49 °C for models II, IV, III, I, and V, respectively. In addition, for the maximum temperature gradient across a cell, models I, II, and III yield the highest value [0.42 °C], followed by models IV [0.40 °C] and model V [0.15 °C], respectively. The proposed battery nanofluid cooling pack can therefore optimize the thermal management system of the EV pack. [ABSTRACT FROM AUTHOR]

Serially connected batteries present a cell-to-cell variation in their electrochemical behavior that tends to increase over the lifetime. The decision for the best circular economy option for aged or defective battery packs – i.e., repair, remanufacturing, or recycling – requires comprehensive testing, analysis, and an according data basis. Remanufacturing of battery packs refers to the replacement of individual (sub-)modules or cells which are defective or show a diverging aging behavior to the rest of the battery units. A detection algorithm is required to decide if a significant inhomogeneity in a serial connection of batteries is present. For this reason, virtual battery packs are built based on the measurement of 12 individual batteries of the same type. Cell-to-cell variations are determined at the begin of life and a novel representation for the quantitative and qualitative analysis is given. This work extracts impedance-based features of different serial battery pack configurations through a novel comparative analysis approach. The features are extracted from Bode and Nyquist plots and the real and imaginary parts of the impedance itself. The detectability is analyzed depending on the number of cells and the underlying effects are discussed. A detailed sensitivity analysis is carried out for the most promising features, in which the influence of the cell-to-cell variations, the aging condition, and the aging mechanism are analyzed. The feature that shows the highest sensitivity, the so called low-frequency minimum, is able to detect single outliers within a high number of serially connected cells. [Display omitted] • Detection of inhomogeneities in serially connected lithium-ion batteries. • Novel representation of cell-to-cell variations for analysis of Nyquist plots. • Comprehensive analysis of multiple features for the detection of inhomogeneities. • Sensitivity analysis of the studied features concerning the detection accuracy. [ABSTRACT FROM AUTHOR]

• Diverging channel design cooling plate with oblique fins is newly proposed. • Thermal unbalance of the battery module is improved using the proposed design. • Cell-to-cell temperature difference in the pack cooling is decreased by 19.07%. • The pumping power to satisfy the temperature conditions is decreased by 67.8%. In this study, the thermal and hydraulic performances of a battery thermal management system are investigated in terms of the design of the cooling plate. A diverging channel with oblique fins (DCOF) cooling plate is newly proposed to overcome the limitations of the conventional straight channel cooling plate. The diverging design is applied to control the coolant velocity and heat transfer area, with oblique fins inserted to enhance the heat transfer efficiency. A simulation model of the battery and cooling system is developed and experimentally validated. When the DCOF design is applied to the battery module, the temperature uniformity is improved in terms of the temperature difference and standard deviation. As the investigation is extended from the module to the battery pack, the temperature difference and temperature standard deviation in the battery module are improved by 19.07% and 27.14%, respectively. Considering the power consumption of the cooling system, as the thermal performance of the DCOF design and baseline design is maintained, the power consumption of the coolant pump of the DCOF design is significantly reduced by 67.8%. [ABSTRACT FROM AUTHOR]

Due to the importance of examining the temperature of battery packs (BYP), a study on the temperature of a lithium-ion BYP with 9 cylindrical cells is performed in this article. The BYP is filled with phase change material (PCM) to better temperature uniformity on the battery cells. The batteries and the PCM pack are placed in an air channel with two inlets. At the inlets, 4 arc blades are supposed to guide the airflow to enhance the uniformity of cooling in the BYP. By changing the horizontal and vertical distance (VD) of the batteries inside the pack, this study is carried out in 2500 s for the charging and discharging times of the batteries (1.5C rate). The thermal analysis is done on the batteries using COMSOL software. The results show that an increment in the horizontal distance (HD) of batteries has less effect on battery temperature (T-BT) and PCM phase change compared to their VD. By enhancing the horizontal and VDs of the batteries, the value of the PCM phase change during the maximum T-BT is higher. The heat transfer coefficient (HTCT) between the PCM and the air is also decreased by increasing the distance between the batteries. The T-BTs is not significantly impacted by changing the distance between them. • Study thermal management of a lithium-ion BYP with 9 cylindrical cells is performed. • BYP is filled with PCM to better temperature uniformity on the battery cells. • T-BTs is not significantly impacted by changing the distance between them. • HTCT between the PCM and air is also decreased by increasing the distance. [ABSTRACT FROM AUTHOR]

In this paper, a 3D battery pack (B-PK) containing 16 battery cells (BCL) with aligned and non-aligned arrangements is simulated. The batteries are 18,650 lithium-ion cylindrical ones. The B-PK is placed in a square air duct. Air enters from the top of the B-PK and exits from the bottom of the batteries. This study is performed by changing the inlet air velocity (V-AR) to the B-PK from 0.03 to 0.09 m/s for three charging and discharging cycles within 2000 s. The findings revealed that extending the duration and charging and discharging of the B-PK up to the third cycle increased the temperature of B-PK. The effect of V-AR changes is more pronounced in the second and especially the third cycles of battery charging process. The maximum (T-MX) and average (T-AV) temperatures of the B-PK as well as the temperature of air leaving the B-PK are all reduced when the V-AR is increased. The central batteries of the B-PK have a high temperature, whereas the batteries at the B-PK corners have a low temperature, according to an analysis of BCLs. Using an aligned arrangement of batteries in the pack reduces the T-AV of B-PK compared to the non-aligned arrangement of batteries. • A 3D battery pack containing 16 lithium-ion cylindrical battery cells is simulated. • Effect of inlet air velocity for three charging and discharging cycles are assessed. • Temperature of B-PK rises by extending its charging/discharging up to third cycle. • The maximum temperature of battery pack reduces by boosting the inlet air velocity. • Aligned batteries entails a lower battery temperature than non-aligned arrangement. [ABSTRACT FROM AUTHOR]

The numerical and transient behavior of a battery pack (BA-PK) consisting of three battery cells positioned within an air duct is investigated in this work. The BA-PK was connected to a solar system. The inside of the BA-PK is filled with phase change material. The air within the duct moved at a speed of 0.001–0.003 m/s around the BA-PK. The investigation was carried out by altering the dimensions of the BA-PK using COMSOL software. The finite element approach is used to solve the equations. The results of this research indicated that the battery temperature (Tbatt) drops gradually until the surrounding area is filled with molten PCM, but that once the PCM around the battery is entirely frozen, the Tbatt drops abruptly. The use of a larger BA-PK increases the temperature of the exhaust air out of the duct, but reduces the percentage of solid PCM. The Tbatt, as well as the maximum Tbatt and average TPCM within the compartment, are reduced by using a smaller BA-PK. In addition to increasing the quantity of solid PCM, increasing the speed lowers the temperatures of the batteries and PCM, as well as the temperature of the exhaust air. • Numerical and transient behavior of a battery pack positioned within an air. • Battery pack is filled with phase change material and was connected to solar system. • The use of a larger battery pack increases the temperature of the exhaust air. • Increasing the speed lowers the temperatures of batteries, PCM and exhaust air. [ABSTRACT FROM AUTHOR]

To address the problem of DC bus voltage surge caused by load demand fluctuation in an off-grid microgrid, here, an adaptive energy optimization method based on a hybrid energy-storage system to maintain the stability of DC bus voltage is presented. The adaptive energy optimization method consists of three parts: the average filtering algorithm, extracting fluctuating power in demand load; the supercapacitor terminal voltage control, keeping the terminal voltage of the supercapacitor near reference; and the battery pack balance control, adjusting the charge/discharge to balance the state of charge for battery packs. In this proposed method, after extracting the fluctuating power by the low-pass filter when the demand load fluctuates, the battery packs release the power to offset the low-frequency fluctuation load and the supercapacitor to instantaneously compensate the high-frequency fluctuation power, to prolong the service life of batteries and maintain the stability of DC bus voltage. The effectiveness of the proposed adaptive energy optimization method is validated and is confirmed to maintain the stable operation of the off-grid microgrid, extend the cycle life of batteries in off-grid microgrid simulations and experiments. [ABSTRACT FROM AUTHOR]

Depending on the level of electrification ([PH]EV, [M]HEV) and cell format (pouch, prismatic, cylindrical), the number of individual cells in a vehicle's battery pack can span several orders of magnitude (from tens to thousands). In this paper, we develop a novel analytical framework to investigate the impact of cell-level manufacturing variability on pack performance. Statistical distributions of pack energy and pack power are derived for any NsMp pack configurations and any level of cell-to-cell variability. These distributions are used to develop vehicle-dependent cell-level manufacturing requirements. The degree to which the series direction negatively affects pack statistics, and the degree to which the parallel direction improves pack statistics, is first quantified under a random sampling scenario. PHEV packs (96s1p) are found to have the highest pack-level statistical penalty, while EV packs made of small-format cylindrical cells (96s74p) incur virtually no statistical penalty. Pack-level statistics for Ns1p packs are shown to greatly benefit from the clustering of low-performing cells. Cell binning strategies for NsMp packs present both advantages and disadvantages. The impacts of cell aging and pack-level voltage limits are further discussed. The results of this study apply even in the presence of cell balancing and under partial SOC usage. [Display omitted] • A novel analytical model is developed to relate cell variability and pack statistics. • The model is valid for any level of cell variability and any pack configuration. • Pack distribution of energy and power is derived for different vehicle segments. • Cell binning strategies to optimize pack-level statistics are presented. • The effect of cell balancing, cell aging and pack-level voltage limits are discussed. [ABSTRACT FROM AUTHOR]

State-of-health (SOH) monitoring of lithium-ion batteries plays a key role in the reliable and safe operation of battery systems. Influenced by multiple factors, SOH is an aging path-dependent parameter, which challenges its accurate estimation and prediction. Numerous methods have been proposed for lithium-ion batteries SOH diagnostics and prognostics, but there is little discussion on how to characterize SOH. In this paper, we first review the existing characteristic parameters in defining battery SOH at cell-level and pack-level, and then propose some suggestions for SOH definitions. The impact of external influencing factors on battery degradation is introduced to lay a foundation for SOH estimation. SOH estimation methods are categorized by the parameters used to define SOH, i.e. capacity, impedance and aging-mechanism parameters. We discuss the goals of SOH monitoring and summarize its applications from short- and long-term perspectives accordingly. Finally, the challenges and future trends of battery SOH diagnostics and prognostics are thoroughly analyzed and discussed, in order to provide a comprehensive understanding for researchers and technologists. • Discuss how to comprehensively characterize battery SOH at cell-level and pack-level. • Analyze the strengths and weaknesses of the SOH estimation methods from the perspective of data acquisition sources. • Present the gaps between existing research on SOH estimations and SOH applications. • Summarize the challenges and prospects for SOH characterizations, estimations and applications. [ABSTRACT FROM AUTHOR]

The cooling structure of a battery pack and coupled liquid cooling and phase change material (PCM) were designed in a thermal management system to enhance the cooling performance and extend the service life of lithium-ion battery packs. Numerical simulations were conducted based on the finite volume method. This study focuses on factors such as the layout of the terminal, flow rate of the coolant, different sections of the cooling pipe, position of the cooling pipe, and coupled liquid cooling, and investigates their influences on the operating temperature. The results show that a reasonable terminal layout can reduce heat generation inside the batteries. The appropriate flow rate and position of the cooling pipe effectively reduced the maximum temperature and minimized energy consumption. Then, the PCM was placed between the adjacent batteries near the outlet to enhance the uniformity of the battery pack. The temperature difference was reduced to near 5 K. This study provides a clear direction for improving the cooling performance and extending the service life of battery packs. [ABSTRACT FROM AUTHOR]

A serious issue relative to the construction of electronic devices is proper power source selection. This problem is of particular importance when we are dealing with portable devices operating in varying environmental conditions, such as military equipment. A serious problem in the construction of electronic devices is the correct selection of the power source. In these types of devices, lithium-ion batteries are commonly used nowadays, and in particular their variety—lithium iron phosphate battery—LiFePO4. Apart from the many advantages of this type of battery offers, such as high power and energy density, a high number of charge and discharge cycles, and low self-discharge. They also have a major drawback—a risk of damage due to excessive discharge or overcharge. This article studies the process of charging and discharging a battery pack composed of cells with different initial charge levels. An attempt was made to determine the risk of damage to the cells relative to the differences in the initial charge level of the battery pack cells. It was verified, whether the successive charging and discharging cycles reduce or increase the differences in the amount of energy stored in individual cells of the pack. [ABSTRACT FROM AUTHOR]