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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]

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]

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]

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]

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]

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]

In this paper, an active equalization method based on redundant battery is proposed. A redundant battery is added to a battery pack consisting of several series batteries. According to the capacity and SOC (state-of-charge) of series batteries in the battery pack, one of these batteries is in parallel with the redundant battery in a certain time period. On the one hand, the energy is transferred between the redundant battery and the equalized batteries. On the other hand, during the charging or discharging process, the redundant battery acts as a power source, which reduces the current of the equalized battery. By adjusting the length of time that redundant battery and series batteries are connected in parallel, the purpose of battery equalization will be achieved. The balance current is limited only by the maximum charge–discharge ratio of the batteries. As long as the sum of the difference of series batteries capacity is less than the redundant battery capacity, the balance of all batteries can be achieved. Compared with other active equalization methods, the energy storage element (redundant battery) is used to be the main component of the equalization circuit in this method. It not only has the advantages of simple equalization circuit and large equalization current, but also has higher energy density of battery pack. In this paper, the equalization circuit is given, and the equalization strategy with accurate battery parameters and uncertain battery parameters is proposed. The experimental results also verify the correctness of the theoretical analysis and modeling. [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]

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]

A pack of batteries is one of the most important and expensive assemblies for an electric vehicle. A pack of batteries is comprised of several batteries connected in series. The number of the batteries connected depends on the operating voltage of the vehicle's on-board system as well as on the individual characteristics of the batteries used, e.g. the operating voltage of a single cell. One or several cells of a pack of batteries could be damaged if improperly exploiting an electric vehicle- excessively discharging the batteries or overloading the electric vehicle. If a self-converted vehicle does not use an intellectual BMS (battery management system) that can identify and register voltage drop for any individual cell in the high-load regime, e.g. when accelerating, it is difficult to identify and change the cells damaged. In case a cell does not demonstrate a complete failure, it is almost impossible to identify a defect in any regime other than the load regime. The research developed and compared three different methods for identifying defective battery cells. The methods were approbated on a converted Renault Clio. The experiment involved making voltage measurements in road tests, running the electric vehicle on a roll test bench and making voltage measurements of maximally discharged batteries in the no-load regime. A comparison of the measurement results revealed that the measurements made in the road tests were the most accurate and useful. After the experiment, the defective battery cells were replaced, thereby restoring the performance of the battery pack. [ABSTRACT FROM AUTHOR]

The studies on an integrated approach for the battery (cell level), battery pack (system level) and battery pack enclosure (system level) are nascent till date. The present work proposes a combined approach for studying the coupled electrochemical-mechanical performance of the battery (cell level), battery pack and enclosure (system level). In this context, five research problems are undertaken involving the design parameters for the performance evaluation of batteries. The first issue undertaken is battery performance under stress conditions and for that varying initial stress and real-time stresses were applied, and variation in the capacity of the battery packs was studied. Initial stress was found to be in negative relation with the battery capacity. For understanding the mechanical damage (leakage, deformation, etc.), characterization of the battery cell under impact loading was done by subjecting battery cells to different impact loads and impact analysis was done using six statistical features extracted from the impact force processed signal of each test, namely: peak value, mean value, variance, skewness, kurtosis and pulse width. The third issue regards the manufacturing defects in the cell, which can potentially lead to inconsistency of cells performance in a pack and thus lower its total cycle life. Support vector classification clustering methods were applied to form a battery pack with similar performing cells and performance of the pack was compared with industrial packs in which earlier one was found to be better. The fourth research challenge involved the mechanical performance parameters such as strength, deformation, and natural frequency, which, in addition to temperature, are also critical to battery pack safety in the event of impact and vibrations under driving conditions. ANSIS and NSGA-II based approaches were used to optimize the battery design and resultant device shown maximum deformation reduced by 22.2%, the value of minimum inherent frequency improved by 3.2% and the value of mass decreased by 11.6%. The fifth research problem highlighted how, due to the enormous production of electric vehicles, by 2020, there will be 250,000 tons of batteries that need to be recycled. A combined experimental-numerical framework has been thereby suggested to mitigate it. The study resulted in the formation of an online monitoring system and safety design of batteries at both cell and system level (pack and enclosure). [ABSTRACT FROM AUTHOR]

The application of lithium‐ion batteries especially for electric vehicles has been limited by the factors of safety, lifetime, charging time, and cost. One of the principal limitations is that the performance of Li‐ion batteries drops intensely in a cold environment. Cold environment dramatically reduces the available capacity of the batteries and increases its internal impedance at the same time. Therefore, the estimation of state‐of‐health is of great importance in battery performance evaluation and lifetime prediction. Furthermore, the heating methods need to be developed to ensure that batteries work in abnormal temperature conditions. This paper conducts a comprehensive review specifically on the poor performance of lithium‐ion cells under severe conditions. The content contains three sections. First, a comprehensive study on the aging mechanisms of lithium‐ion batteries at cold temperatures is undertaken. Second, the estimation methods of the health state of the batteries are conducted, which is vital to understand the fundamentals and quantify the performance and aging effects for lithium‐ion batteries. Third, the heating methods are classified and studied in detail to reduce the degradation mechanism and promote the performance of lithium‐ion batteries under sub‐zero conditions. [ABSTRACT FROM AUTHOR]

Abstract The reliability assessment of battery packs is an important topic in the reliability design of electric vehicles. To improve the accuracy of the reliability analysis, a modified reliability model for lithium-ion battery packs is developed that couples multiphysics models, degradation models, and a multistate system reliability model. For the degradation models, a stochastic capacity degradation model and a dynamic response impedance (DRI) model are established based on experimental data: a) the stochastic capacity degradation characteristics of cells are investigated, and b) the dynamic response characteristics of the impedance with degradation and changing temperature are studied. It is found that Weibull distribution is slightly more appropriate than normal distribution when the temperature is 25 °C, then reversed with increasing temperature. The algorithm for distribution selection is proposed to describing this concept drift phenomenon. Then, the influence of DRI on the thermal performance is analyzed. Finally, reliability assessment of battery pack and several calculation examples are implemented for comparative analysis. The results show that the fading rate of cells in battery pack are accelerated when DRI is incorporated. Thus, multiphysics-based system reliability model with considering the stochastic capacity degradation and DRI enables more scientific and accurate reliability evaluation of battery pack. Highlights • A modified reliability model for lithium-ion battery packs is developed. • A stochastic degradation model with normal and Weibull distribution is established. • A temperature and capacity dependent dynamic response impedance model is established. • The effect of dynamic response impedance on reliability of battery pack is analyzed. • The simulation results show the fading rate of cells in battery pack are accelerated. [ABSTRACT FROM AUTHOR]

Abstract Cell variations always undermine the efficiency of energy utilization of battery pack. How does the temperature non-uniformity affect the status of cell variations remains unclear. This paper investigates the underlying mechanisms of the status change of cell variations caused by temperature non-uniformity in a battery pack. First, the status of cell variations has been defined using a graphical model, in which each cell has a coordinate of (capacity, electric quantity). Second, the status transformation caused by temperature non-uniformity has been clarified. Dynamics that describes the thermal-electrochemical effect on the movement of the coordinate for individual cells are firstly established by transformation functions. The movement of the cell locations in the graphical model can vividly interpret the evolution of cell variations caused by temperature non-uniformity with the help of the built transformation functions. Modelling analysis points out that 5 °C increase in the maximum temperature difference among the battery pack can lead to 1.5%–2% capacity loss of the battery pack, therefore the capacity loss caused by cell variations due to temperature non-uniformity should be considered in battery system design. The methodology can be further used to analyze the status change of cell variations in a battery pack caused by all kinds of temperature non-uniformities, benefiting the design of battery management system with high efficiency of energy utilization. Graphical abstract The evolution of cell variations within a lithium-ion battery pack caused by temperature non-uniformity. Image Highlights • The cell variations in battery pack caused by temperature non-uniformity is graphically shown. • The cell variations are defined by a graphical model named the E-Q Diagram. • Thermal-electro coupled dynamic functions are built to interpret the cell variation evolution. • 5 °C increase in temperature difference can lead to 1.5%–2% capacity loss of the battery pack. • The methodology can guide cleaner production of battery system with higher energy efficiency. [ABSTRACT FROM AUTHOR]

Micro-short circuit (MSC) of a lithium-ion battery cell is a potential safety hazard for battery packs. How to identify the cell with MSC in the latent phase before a thermal runaway becomes a difficult problem to solve. We propose a diagnosis method to detect the MSC according to the remaining charging capacity (RCC) variations between cells. When the charging cell voltage curve (CCVC) of the first fully charged cell is regarded as the benchmark, the RCC of each cell can be obtained by the CCVC transformation based on the uniform CCVC hypothesis. The accuracy of the RCC estimation method is validated under constant current and constant power charging experiments. The leakage current of the MSC cell can be obtained by the increase of the RCC after each charge, and it can be converted into the MSC resistance. We set up the battery pack system model with the MSC fault in Simulink ® to verify the effectiveness of the method when the battery pack is charged up to different charge cutoff voltages. Through a series of experiments with external resistors, the adaptability of the MSC diagnosis method is further validated for the aged cell, multi-stage charging, and constant power charging. [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]

With the rapid development of lithium-ion battery technology in the electric vehicle (EV) industry, the lifetime of the battery cell increases substantially; however, the reliability of the battery pack is still inadequate. Because of the complexity of the battery pack, a reliability design method for a lithium-ion battery pack considering the thermal disequilibrium is proposed in this paper based on cell redundancy. Based on this method, a three-dimensional electric-thermal-flow-coupled model, a stochastic degradation model of cells under field dynamic conditions and a multi-state system reliability model of a battery pack are established. The relationships between the multi-physics coupling model, the degradation model and the system reliability model are first constructed to analyze the reliability of the battery pack and followed by analysis examples with different redundancy strategies. By comparing the reliability of battery packs of different redundant cell numbers and configurations, several conclusions for the redundancy strategy are obtained. More notably, the reliability does not monotonically increase with the number of redundant cells for the thermal disequilibrium effects. In this work, the reliability of a 6 × 5 parallel-series configuration is the optimal system structure. In addition, the effect of the cell arrangement and cooling conditions are investigated. [ABSTRACT FROM AUTHOR]

State-of-charge (SOC) inconsistency impacts the power, durability and safety of the battery pack. Therefore, it is necessary to measure the SOC inconsistency of the battery pack with good accuracy. We explore a novel method for modeling and estimating the SOC inconsistency of lithium-ion (Li-ion) battery pack with low computation effort. In this method, a second-order RC model is selected as the cell mean model (CMM) to represent the overall performance of the battery pack. A hypothetical Rint model is employed as the cell difference model (CDM) to evaluate the SOC difference. The parameters of mean-difference model (MDM) are identified with particle swarm optimization (PSO). Subsequently, the mean SOC and the cell SOC differences are estimated by using extended Kalman filter (EKF). Finally, we conduct an experiment on a small Li-ion battery pack with twelve cells connected in series. The results show that the evaluated SOC difference is capable of tracking the changing of actual value after a quick convergence. [ABSTRACT FROM AUTHOR]

The state-of-charge (SOC) estimation approaches based on the pack model can hardly provide precise estimation due to cell difference, while the approaches based on each cell cost high computation resource, which are not suitable for real-time application. The estimation error can be further enlarged without considering balancing current. The SOC estimation approach of the battery pack considering balancing current is proposed, which dynamically searches for the cell with maximum or minimum voltage, and it only needs to calculate the selected cell in every estimation cycle. Compared to the approaches based on the pack model or each single cell, this approach can achieve precise pack SOC and cost less calculation time and resource. It has been verified with ten series-connected 200 Ah Li(NiCoMn)O2 batteries. The SOC estimation error is limited to 0.3% during the charging process, and a reduction of 2.5% is achieved compared to an error of 2.8% based on the pack model. A reduction of 1% is achieved compared to an error of 1.5% based on the pack model during the discharging process. Compared to an error of 1.7% without considering balancing current, a reduction of 1.4% is achieved during the charging process. [ABSTRACT FROM PUBLISHER]

Series battery equalisation can improve battery charge and discharge reliability and extend battery life. Inductor-based battery equalisation schemes have the advantages of simple topologies and control strategies. According to the energy transfer pathway, inductor-based battery equalisation schemes can be divided into cell-to-cell and cell-to-pack equalisation schemes. The control strategies of the cell-to-cell schemes are simple; the inductor can only transfer energy between the neighbouring cells, so the equalisation speed is low. The cell-to-pack schemes are able to accomplish energy transfer between the cells and pack by charging and discharging the inductors. The equalisation speed is high, but the control strategies may be complex. In this paper, different equalisation topologies are reviewed, then a unified control strategy which is applicable to all of the inductor-based equalisation topologies is proposed. The equalisation speeds and efficiencies of these different schemes, including the newly-proposed unified control strategy, are analysed and compared. Based on the theoretical analysis, simulations, and experimental verifications, it is concluded that this unified control strategy can perform the battery equalisation process quickly and efficiently. [ABSTRACT FROM AUTHOR]

In this paper, the development of a three-dimensional (3D) lithium titanium oxide (LTO) pouch cell is presented to first better comprehend its thermal behavior within electrified vehicle applications, but also to propose a strong modeling base for future thermal management system. Current 3D-thermal models are based on electrochemical reactions which are in need for elaborated meshing effort and long computational time. There lacks a fast electro-thermal model which can capture voltage, current and thermal distribution variation during the whole process. The proposed thermal model is a reduce-effort temperature simulation approach involving a 0D-electrical model accommodating a 3D-thermal model to exclude electrochemical processes. The thermal model is based on heat-transfer theory and its temperature distribution prediction incorporates internal conduction and heat generation effect as well as convection. In addition, experimental tests are conducted to validate the model. Results show that both the heat dissipation rate and surface temperature uniformity data are in agreement with simulation results, which satisfies the application requirements for electrified vehicles. Additionally, a LTO battery pack sizing and modeling is also designed, applied and displays a non-uniformity of the cells under driving operation. Ultimately, the model will serve as a basis for the future development of a thermal strategy for LTO cells that operate in a large temperature range, which is a strong contribution to the existing body of scientific literature. [ABSTRACT FROM AUTHOR]

As one single cell cannot meet power and driving range requirement in an electric vehicle, the battery packs with hundreds of single cells connected in parallel and series should be constructed. The most significant difference between a single cell and a battery pack is cell variation. Not only does cell variation affect pack energy density and power density, but also it causes early degradation of battery and potential safety issues. The cell variation effects on battery packs are studied, which are of great significant to battery pack screening and management scheme. In this study, the description for the consistency characteristics of battery packs was first proposed and a pack model with 96 cells connected in series was established. A set of parameters are introduced to study the cell variation and their impacts on battery packs are analyzed through the battery pack capacity loss simulation and experiments. Meanwhile, the capacity loss composition of the battery pack is obtained and verified by the temperature variation experiment. The results from this research can demonstrate that the temperature, self-discharge rate and coulombic efficiency are the major affecting parameters of cell variation and indicate the dissipative cell equalization is sufficient for the battery pack. [ABSTRACT FROM AUTHOR]

This paper presents a numerical model used for analyzing heat propagation as a safety feature in a custom-made battery pack. The pack uses a novel technology consisting of an internal short circuit device implanted in a cell to trigger thermal runaway. The goal of the study is to investigate the importance of wrapping cylindrical battery cells (18650 type) in a thermally and electrically insulating mica sleeve, to fix the cells in a thermally conductive aluminum heat sink. By modeling the full-scale pack using a 2D model and coupling the thermal model with an electrochemical model, good agreement with a 3D model and experimental data was found (less than 6%). The 2D modeling approach also reduces the computation time considerably (from 11 h to 25 min) compared to using a 3D model. The results showed that the air trapped between the cell and the boreholes of the heat sink provides a good insulation which reduces the temperature of the adjacent cells during thermal runaway. At the same time, a highly conductive matrix dissipates the heat throughout its thermal mass, reducing the temperature even further. It was found that for designing a safe battery pack which mitigates thermal runaway propagation, a combination of small insulating layers wrapped around the cells, and a conductive heat sink is beneficial. [ABSTRACT FROM AUTHOR]

This paper presents a method for an online real-time electrochemical impedance spectroscopy (EIS) measurement of batteries using closed-loop control of power converter. Unlike the previously proposed method which allows the measurement of the ac impedance for a single frequency, the presented method in this paper allows for obtaining the EIS for a spectrum of frequencies by using the information included in a single perturbation cycle, or a few cycles of perturbation to obtain a more accurate EIS with a very wide frequency range. This will result in faster EIS measurement for a spectrum of frequencies and under the same battery operating conditions. The presented method utilizes closed-loop control operation for the EIS measurement functionality, which allows for better control of the output voltage and for upgrading the concept to be able to achieve no added perturbation ripple at the output of the system. The presented online real-time EIS measurement method utilizes a power converter with closed-loop control in order to create an output voltage step-function perturbation at a given frequency to generate battery voltage and current responses. By applying Fourier analysis to these responses, an EIS can be obtained for a range of frequencies equal or higher than the perturbation frequency of the step function. In addition, this paper presents a method to eliminate the added perturbation ripple when two or more power converters are used. The theoretical basis and experimental prototype results are provided to illustrate and validate the presented method. [ABSTRACT FROM PUBLISHER]

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]

In a battery pack, cell-to-cell chemical variation, or the variation in operating conditions, can possibly lead to current imbalance which can accelerate pack ageing. In this paper, the Pseudo-Two-Dimensional(P2D) porous electrode model is extended to a battery pack layout, to predict the overall behaviour and the cell-to-cell variation under constant voltage charging and discharging. The algorithm used in this model offers the flexibility in extending the layout to any number of cells in a pack, which can be of different capacities, chemical characteristics and physical dimensions. The coupled electro-thermal effects such as differential cell ageing, temperature variation, porosity change and their effects on the performance of the pack, can be predicted using this modelling algorithm. The pack charging voltage is found to have an impact on the performance as well as the SEI layer growth. Numerical studies are conducted by keeping the cells at different thermal conditions and the results show the necessity to increase the heat transfer coefficient to cool the pack, compared to single cell. The results show that the thermal imbalance has more impact than the change in inter-connecting resistance on the split current distribution, which accelerates the irreversible porous filling and ageing. [ABSTRACT FROM AUTHOR]

In this study, a three dimensional (3D) modeling has been built for a lithium ion battery pack using the field synergy principle to obtain a better thermal distribution. In the model, the thermal behavior of the battery pack was studied by reducing the maximum temperature, improving the temperature uniformity and considering the difference between the maximum and maximum temperature of the battery pack. The method is further verified by simulation results based on different environmental temperatures and discharge rates. The thermal behavior model demonstrates that the design and cooling policy of the battery pack is crucial for optimizing the air-outlet patterns of electric vehicle power cabins. [ABSTRACT FROM AUTHOR]

As a key factor for the safety of electric vehicles, electric vehicles are focused on battery pack stability rather than vehicle dynamics. The stability of battery pack depends on the performance of suspensions and battery supporting system. Automobile suspensions can reduce the bumps and vibrations that come from road unevenness. Its purposes are to prevent the shock and vibration from transferring to the body, and reduce the vibration of the battery pack. To improve the performance of suspension systems of the electric vehicle and the stability of battery pack, the paper proposes a study on the interaction of electric vehicle roll and battery pack stability. 9-DOF vehicle vibration and roll model was established based on analysis of movement of the battery pack. Then simulation model was established to check the result to get the reasonable suspension stiffness by using Matlab/Simulink. The results show that the stiffness of the suspension is increased by configuring a high performance shock absorber and a short spring. Although it will inhibit roll, simply increasing the hardness of the shock absorber and the spring will cause a lot of negative effects. [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]

Modern applications of lithium-ion batteries such as smartphones, hybrid & electric vehicles and grid scale electricity storage demand long lifetime and high performance which typically makes them the limiting factor in a system. Understanding the state-of-health during operation is important in order to optimise for long term durability and performance. However, this requires accurate in-operando diagnostic techniques that are cost effective and practical. We present a novel diagnosis method based upon differential thermal voltammetry demonstrated on a battery pack made from commercial lithium-ion cells where one cell was deliberately aged prior to experiment. The cells were in parallel whilst being thermally managed with forced air convection. We show for the first time, a diagnosis method capable of quantitatively determining the state-of-health of four cells simultaneously by only using temperature and voltage readings for both charge and discharge. Measurements are achieved using low-cost thermocouples and a single voltage measurement at a frequency of 1 Hz, demonstrating the feasibility of implementing this approach on real world battery management systems. The technique could be particularly useful under charge when constant current or constant power is common, this therefore should be of significant interest to all lithium-ion battery users. [ABSTRACT FROM AUTHOR]

Thermal management system is of critical importance for a Li-ion battery pack, as high performance and long battery pack life can be simultaneously achieved when operated within a narrow range of temperature around the room temperature. An efficient thermal management system is required to keep the battery temperature in this range, despite widely varying operating conditions. A novel liquid coolant based thermal management system, for 18,650 battery pack has been introduced herein. This system is designed to be compact and economical without compromising safety. A coupled three-dimensional (3D) electrochemical thermal model is constructed for the proposed Li-ion battery pack. The model is used to evaluate the effects of different operating conditions like coolant flow-rate and discharge current on the pack temperature. Contact resistance is found to have the strongest impact on the thermal performance of the pack. From the numerical solution, a simple and novel temperature correlation of predicting the temperatures of all the individual cells given the temperature measurement of one cell is devised and validated with experimental results. Such coefficients have great potential of reducing the sensor requirement and complexity in a large Li-ion battery pack, typical of an electric vehicle. [ABSTRACT FROM AUTHOR]

This paper established the three degree of freedom mathematical model, based on the two degree of freedom linear model with two wheel steering. Then the dynamics differential equation is obtained. With a step input of the front wheel, observing and analyzing the yaw rate response and the side slip angle response in the whole process based on MATLAB/Simulink. Finally, by changing the position of the battery pack to transform the location of the gravity center of the whole vehicle, analyzing the impact on the electric vehicle's steering characteristic while the location of battery pack changed. [ABSTRACT FROM AUTHOR]

Vehicle battery systems are usually designed with a high number of cells connected in parallel to meet the stringent requirements of power and energy. The self-balancing characteristic of parallel cells allows a battery management system (BMS) to approximate the cells as one equivalent cell with a single state of health (SoH) value, estimated either as capacity fade (SoH E ) or resistance increase (SoH P ). A single SoH value is however not applicable if the initial SoH of each cell is different, which can occur when cell properties change due to inconsistent manufacturing processes or in-homogeneous operating environments. As such this work quantifies the convergence of SoH E and SoH P due to initial differences in cell SoH and examines the convergence factors. Four 3 Ah 18650 cells connected in parallel at 25 °C are aged by charging and discharging for 500 cycles. For an initial SoH E difference of 40% and SoH P difference of 45%, SoH E converge to 10% and SoH P to 30% by the end of the experiment. From this, a strong linear correlation between ΔSoH E and ΔSoH P is also observed. The results therefore imply that a BMS should consider a calibration strategy to accurately estimate the SoH of parallel cells until convergence is reached. [ABSTRACT FROM AUTHOR]

Incremental capacity analysis (ICA) is a widely used technique for lithium-ion battery state-of-health (SOH) evaluation. The effectiveness and robustness of ICA for single cell diagnostics have been reported in many published work. In this study, we extend the ICA based SOH monitoring approach from single cells to battery modules, which consist of battery cells with various aging conditions. In order to achieve on-board implementation, an IC peak tracking approach based on the ICA principles is proposed. Analytical, numerical and experimental results are presented to demonstrate the utility of the IC peak tracking framework on multi-cell battery SOH monitoring and the effects of cell non-uniformity on the proposed method. Results show that the methods developed for single cell capacity estimation can also be used for a module or pack that has parallel-connected cells. [ABSTRACT FROM AUTHOR]