Search

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]

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

Matching between low-voltage high-current capacity battery/fuel cell with high-voltage low-current motors in electric vehicles (EVs) requires paralleling-connection multi-stage power electronic converter with accurate modeling and control system. For high-voltage low-current drives of EVs with high-boosting/bucking gain, single-stage single-arm DC/DC converters suffer from unstable output voltage and the impractical choice of charging/discharging elements. Therefore, a multi-stage multi-arm DC/DC converter is researched to solve the problems of high boosting/bucking gain and minimized the voltage and current ripples. This paper presents parametric sizing, dynamic modeling, and analysis of all logical operational modes for a new high-gain DC/DC converter suitable for deployment in EV applications. The proposed topology composes of three-stage interleaved-input composed of cascaded two stages and double float-output as a third stage with bidirectional current capability. In this study, parametric design, and sizing for the three stages are performed according to the prerequisite ratings. Then, an analysis is conducted on rules of operating the DC/DC converter along with a discussion on considerations taken into account for such a critical design. An investigation of the dynamic behavior of the suggested DC/DC converter is carried out through a state-space model with switches’ states. Furthermore, a simulation and measurement-based validation of the derived mathematical models are conducted. The theoretical based analysis, design, and boosting gains are confirmed through a constructed and tested prototype given data on power supply and load utilization. A discussion on the choice of semiconductor equipment and reactive elements is provided for the proposed DC/DC converter. The experimental results show that the three stages can be used in a separate mode or in a cascaded booster.

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

Electric vehicles, especially cars, have been in the spotlight for some time now. In the focus of environmentalists, engineers, users, media, etc. With the growth and advancement of the market for such vehicles, other electric vehicles are also focussing on. One of such vehicles is boats, particularly smaller boats up to 8-10 meters in length. Of course, the biggest problem here is charging. The general idea is to use battery modules that can be easily carried and enable hot swapping. This paper investigates scenarios and simulations of the control system for hot swapping of the battery module. Simulations of connection of two and three battery modules to parallel operation and current control are presented in this paper, as well as applied control rules. [ABSTRACT FROM AUTHOR]

Aerodynamic characteristics are of great significance to the fuel economy and handling the stability of electric vehicles. The battery pack of electric vehicles has a huge structure and is usually arranged in the chassis area of the vehicle, which inevitably occupies the space at the bottom of the vehicle and affects the aerodynamic characteristics of the vehicle. To study the effect of the power battery pack installed in the chassis on the aerodynamics characteristics of the electric vehicle, the Computational Fluid Dynamics (CFD) method is used to study the flow and pressure fields of the SAE (Society of Automotive Engineers) hierarchical car model with battery packs mounted on chassis. The influence of the structure parameters of the battery pack on the automobile's aerodynamics are also analyzed in detail. Based on the simulation results, it can be seen that the battery pack installed on the chassis has a great impact on the flow and pressure field at the bottom and tail of the vehicle, causing the drag coefficient and lift coefficient to increase. The structural parameters of the battery pack have contradictory effects on the drag and lift coefficients. As the length of the battery pack increases, the drag coefficient decreases, and the lift coefficient increases. As the battery pack width and height increase, the drag coefficient increases, and the lift coefficient decreases. The research results provide a reference for the optimization of the aerodynamic characteristics of electric vehicles with battery packs mounted on chassis. [ABSTRACT FROM AUTHOR]

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

In the last decades of electric vehicle (EV) development, battery thermal management has become one of the remaining issues that must be appropriately handled to ensure robust EV design. Starting from researching safer and more durable battery cells that can resist thermal exposure, battery packing design has also become important to avoid thermal events causing an explosion or at least to prevent fatal loss if the explosion occurs. An optimal battery packing design can maintain the battery cell temperature at the most favorable range, i.e., 25–40 °C, with a temperature difference in each battery cell of 5 °C at the maximum, which is considered the best working temperature. The design must also consider environmental temperature and humidity effects. Many design strategies have been reported, including novel battery pack constructions, a better selection of coolant materials, and a robust battery management system. However, those endeavors are faced with the main challenges in terms of design constraints that must be fulfilled, such as material and manufacturing costs, limited available battery space and weight, and low energy consumption requirements. This work reviewed and analyzed the recent progress and current state-of-the-art in designing battery packs for superior thermal management. The narration focused on significant findings that have solved the battery thermal management design problem as well as the remaining issues and opportunities to obtain more reliable and enduring batteries for EVs. Furthermore, some recommendations for future research topics supporting the advancement of battery thermal management design were also discussed. [ABSTRACT FROM AUTHOR]

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

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

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

Electrical vehicles (EVs) are becoming more popular every day. Lithium ion (Li-ion) batteries in the cylindrical form are utilized as the power source of both electrical vehicles and hybrid electric vehicles due to their small size and high-power density. There is typically very little space between the batteries. Heat dissipation must be properly evaluated to ensure that the battery operates as intended. The study goal is examine how well a battery pack performs overall while using alternating cooling strategies. Three models were created in solidworks 2016 for the purpose of determine the best cooling approach. Three battery thermal management (BTMs) options were also chosen, analyzed, and simulated in Ansys Fluent 19.2 to ensure accurate and thermal modelling. According to the findings, geometry 2 ethynyl glycol exhibits a more efficient temperature distribution and maximum temperature than the other cooling methods. Furthermore, channel cooling based on BTMs, the consistent temperature distribution is carried out, and the maximum temperature is regulated to 306.66 K, with a minimum temperature of 293. 20 K being attained.

In the last decades of electric vehicle (EV) development, battery thermal management has become one of the remaining issues that must be appropriately handled to ensure robust EV design. Starting from researching safer and more durable battery cells that can resist thermal exposure, battery packing design has also become important to avoid thermal events causing an explosion or at least to prevent fatal loss if the explosion occurs. An optimal battery packing design can maintain the battery cell temperature at the most favorable range, i.e., 25–40 °C, with a temperature difference in each battery cell of 5 °C at the maximum, which is considered the best working temperature. The design must also consider environmental temperature and humidity effects. Many design strategies have been reported, including novel battery pack constructions, a better selection of coolant materials, and a robust battery management system. However, those endeavors are faced with the main challenges in terms of design constraints that must be fulfilled, such as material and manufacturing costs, limited available battery space and weight, and low energy consumption requirements. This work reviewed and analyzed the recent progress and current state-of-the-art in designing battery packs for superior thermal management. The narration focused on significant findings that have solved the battery thermal management design problem as well as the remaining issues and opportunities to obtain more reliable and enduring batteries for EVs. Furthermore, some recommendations for future research topics supporting the advancement of battery thermal management design were also discussed.

Transportation market once more put emphasis on electrical and hybrid vehicles to satisfy environmental regulations and disregard fossil fuel price variations. Leading companies in the market and the researchers study on critical units of electrical and hybrid vehicles such as electric motors and batteries which directly affect the vehicle performance on different operating modes. However, battery safety on unusual cases such as crash, explosion or fire caused by the short cut inside the battery pack could damage the other units and the human inside or around the vehicle. On that point, generally, protection of the battery system accomplished by the battery housing with the usage of steel or aluminum sheets. In this study, battery housing designed with sandwich panels constructed with different lattice structures, which could be used in aviation, aerospace, manufacturing industries and biomechanical applications. High strength and stiffness, thermal flow and mass reduction opportunities makes the lattices foremost solution when compared with plain sheet metals. In this scope, the lattice structures were introduced and six different lattice types designed in Solidworks and analyzed in HyperWorks from the point of static loading with 0.2 MPa on upper cover while keeping the lower one fix. Compliance value evaluated for each lattice structure to find linear static response when loaded. Results show that hexagonal honeycomb lattice have superiority on other structures regarding to the compliance value noted as 3.82 Nmm for the load case while 23% mass increase according to plain sheet metal. For the mass reduction output, cross semicirlcle and 3D kagome lattices presents superiority. [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 study presents a multidisciplinary end-to-end design, build, and test drive experience of a Formula Society of Automatic Engineers (FSAE) electric vehicle. The design team members’ background include computer engineering, electrical engineering, and mechanical engineering expertise. Each team member worked on a sub-team namely mechanical, drivetrain, supervisory control and data acquisition (SCADA), and battery management system (BMS). The mechanical team modified/redesigned a gasoline FSAE car frame to accommodate the battery pack, cooling systems, electric drive train, and SCADA hardware. The drivetrain team designed a combinational logic-based tractive system control board to enhance the safety and reliability of the drivetrain. The SCADA team used a model-view-controller software architecture and agile development practices to design and implement a custom electric vehicle operating system and the main brain hardware. The BMS team designed the sensing network and communication protocol to provide an update on the state of the health of the energy source. A thorough description of each sub-system is provided herein. System integration and field test results are also included.

This study presents a multidisciplinary end-to-end design, build, and test drive experience of a Formula Society of Automatic Engineers (FSAE) electric vehicle. The design team members' background include computer engineering, electrical engineering, and mechanical engineering expertise. Each team member worked on a sub-team namely mechanical, drivetrain, supervisory control and data acquisition (SCADA), and battery management system (BMS). The mechanical team modified/redesigned a gasoline FSAE car frame to accommodate the battery pack, cooling systems, electric drive train, and SCADA hardware. The drivetrain team designed a combinational logic-based tractive system control board to enhance the safety and reliability of the drivetrain. The SCADA team used a model-view-controller software architecture and agile development practices to design and implement a custom electric vehicle operating system and the main brain hardware. The BMS team designed the sensing network and communication protocol to provide an update on the state of the health of the energy source. A thorough description of each sub-system is provided herein. System integration and field test results are also included. [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]

Weight reduction has always been a challenge for the automotive industry, mainly to reduce consumption but also improve handling. In electric vehicle design, the battery packs, their shape and positioning are critical aspects that determine the overall weight, weight distribution and, as a consequence, the efficiency, dynamics and stability of the vehicle. This presented a new challenge, to manage this necessary and inflexible weight and volume, developing the vehicle chassis around it and in the best possible way, without compromising the overall efficiency and behavior. In this work, a methodology for nested topology optimization has been developed which combines structural topology optimization and battery pack shaping and positioning. The new methodology is implemented without limiting its applicability, into the framework of the commercial software Hyperstudy by Altair.

This study proposes a novel hybrid energy storage system (HESS) composed of a battery pack and a superconducting magnetic energy storage (SMES) for electric vehicle. Typically, the SMES has a higher power density and lower energy density than other energy storage devices, while battery has higher energy density. Thus, the overall HESS performance can be increased in power and energy density by combining SMES and batteries. The proposed HESS is designed based on bidirectional Z‐source inverter (ZSI). Compared to other SMES/battery‐based HESS topologies that are two stage designs (including DC/DC and AC/DC converters), in this topology, SMES and battery can be incorporated into the Z‐source network which results in lower cost and improved HESS performance. Furthermore, the battery converter has been eliminated due to the buck/boost feature of the ZSI. The fuzzy control method and filters are used to distribute power between the SMES and battery. This study also describes the proposed HESS performance principles and its operation in different modes. [ABSTRACT FROM AUTHOR]

Due to enormous growth of production of electric vehicles, it is estimated by the year 2020 about 250,000 tons of battery must be disposed or recycled. The technology to recycle this much amount of batteries in a single year does not exist., neither does the methods for recycling are standardized because of different configurations of battery packs. A challenge strictly poses on how to deal with lithium ion batteries, which are embedded in hundreds or more in a battery pack. Furthermore, the recovery of materials from the battery in the pack is essential to ensure the growth and sustainability of the electric vehicle market. It is desirable to establish a framework that is semi-automated/automated for ensuring faster disassembly of battery pack, identification and detection of residual energy of batteries in packs and recovery of materials from batteries. This review paper summarizes the two main basic aspects of recycling battery packs: mechanical procedure and chemical recycling (metallurgical). The work summarizes the existing recycling technology in these two aspects and identifies important research problems in the process of recycling of pack such as (i) automatic and intelligent recovery system, (ii) efficiency and safety disassemble of battery pack (iii) Adjustment of Chaos in recycling market (iv) Recovery processes for slag, electrolyte and anode, (v) Application in industrial scale, and (vi) development of recycling methods for new batteries having components with different properties. This paper also proposes a framework to push the recycling process from conception to practicality, both on government incentive polices and effective recycling technology. [ABSTRACT FROM AUTHOR]

Complex environments and variable working conditions lead to irreversible attenuation of battery pack capacity in electric vehicles (EVs). Online capacity estimation is of great significance for battery pack management and maintenance. This work proposes a state-of-health (SOH) attenuation model considering driving mileage and seasonal temperature for battery health estimation. Firstly, a variable forgetting factor recursive least square (VFFRLS) algorithm is proposed for battery model parameter identification. It adaptively adjusts the forgetting factor according to current fluctuations. Then, an extended Kalman-particle filter (EPF) algorithm is proposed for online capacity estimation. In addition, a battery pack SOH attenuation model is constructed considering seasonal temperature and driving mileage. Finally, the performance of the proposed model and algorithm is verified with nine months of actual vehicle data. The experimental results show that the proposed parameter identification and capacity estimation algorithm can accurately estimate the model parameters and capacity. The average capacity of the battery module decreases with the total mileage. The compensation of monthly driving mileage and ambient temperature factors effectively improves the accuracy of SOH model. • A SOH attenuation model considering temperature and mileage is proposed for EVs. • A variable forgetting factor RLS is proposed for battery parameter identification. • Multidimensional equation of state is constructed for capacity estimation with EPF. • Nine month real vehicle data are used to validate the proposed algorithm and model. [ABSTRACT FROM AUTHOR]

Abstract: This work aims to make a comparative analysis of the unbalanced discharging phenomenon for battery packs with series/parallel configurations due to the temperature differences among the cells. A theoretically‐based model is developed for the battery pack and constant power discharging processes are simulated by the model. At a constant temperature difference, lowering the operating temperature increases the divergence among the cell terminal voltages for the series pack and the cell discharging currents for the parallel pack. Increasing the temperature differences decreases the dischargeable energy of the series battery pack, whereas it has little effect on that of the parallel configuration. Given the same temperature difference, the cell energy differences within the parallel battery pack are 5–10 times higher than those within the series configuration, which shows that the temperature can be maintained much more uniformly for a parallel battery pack than for series configuration. [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]

Due to urban pollution, transport electrification is being currently promoted in different countries. Electric Vehicles (EVs) sales are growing all over the world, but there are still some challenges to be solved before a mass adoption of this type of vehicles occurs. One of the main drawbacks of EVs are their limited range, for that reason an accurate estimation of the state-of-charge (SOC) is required. The main contribution of this work is the design of a Nonlinear Autoregressive with External Input (NARX) artificial neural network to estimate the SOC of an EV using real data extracted from the car during its daily trips. The network is trained using voltage, current and four different battery pack temperatures as input and SOC as output. This network has been tested using 54 different real driving cycles, obtaining highly accurate results, with a mean squared error lower than 1e-6 in all situations [ABSTRACT FROM AUTHOR]

State-of-the-art researches on the modelling of lithium-ion batteries for electric vehicle have been conducted based on the physics and empirical-based models to estimate their states. However, less attention has been paid to evaluating the mechanical strength of the batteries when the battery pack is subjected to sudden external impact or crash. The present work, therefore, proposes a simulation-based probabilistic framework that combines artificial neural network and a moment-based uncertainty evaluation technique utilising the finite element model of a lithium-ion battery to evaluate its mechanical strength. The study was based on the following inputs: displacement, temperature and strain rate of the battery, and their uncertainties when the battery is subjected to sudden impact. The artificial neural network outperforms other well-known modelling methods, such as the radial basis function neural network and polynomial regression, for the global mechanical strength modelling, and the probability distribution obtained from the proposed uncertainty evaluation procedure is shown to be accurate. Further analysis employing the framework reveals that the mean mechanical strength of the battery decreases with increasing temperature, but increases with increasing displacement and strain rate. It was also found that the displacement and temperature have similarly high influence on the mechanical strength of the battery compared to the strain rate. The proposed framework and presented findings can help battery manufacturers improve the road safety of electric vehicles. [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 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]

An H-infinity observer (HIO) for state of charge (SOC) estimation is proposed for a series-connected battery pack in underground mine electric vehicles (UMEVs). In the proposed method, an average virtual cell (AVC) model is defined and the SOC of the AVC model is estimated to represent the pack SOC when all terminal voltage differences (TVDs) between each individual cell in the pack and the AVC are within a pre-set voltage threshold. To make sure all the TVDs are within the threshold, lithium iron phosphate cells are clustered into the group with similar characteristics and the cells in the same group are used to build the sorted battery pack. The experiments of pulse current and current profiles based on both urban dynamometer driving schedule and underground mine driving cycle are conducted on the sorted battery pack to verify the effectiveness of the proposed HIO for the pack SOC estimation under UMEVs. [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]

A battery pack is produced by connecting the cells in series and/or in parallel to provide the necessary power for electric vehicles (EVs). Those parameters affecting cost and reliability of the EVs, including cycle life, capacity, durability and warranty are highly dependent on the thermal management system. In this work, computational fluid dynamic analysis is performed to investigate the air cooling system for a 38,120 cell battery pack. The battery pack contained 24 pieces of 38,120 cells, copper bus bars, intake and exhaust plenum and holding plates with venting holes. Heat generated by the cell during charging is measured using an accelerating rate calorimeter. Thermal performances of the battery pack were analyzed with various mass flow rates of cooling air using steady state simulation. The correlation between Nu number and Re number were deduced from the numerical modeling results and compared with literature. Additionally, an experimental testing of the battery pack at different charging rates is conducted to validate the correlation. This method provides a simple way to estimate thermal performance of the battery pack when the battery pack is large and full transient simulation is not viable. [ABSTRACT FROM AUTHOR]

Battery technology is an enabling technology for electric vehicles (EVs), and improving its safety and reliability while reducing its cost will benefit its application to EVs. In this paper, a method on the design and analysis of lithium-ion (Li-ion) battery pack from the reliability perspective is presented. The analysis is based on the degradation of the battery pack, which is related to the cells configuration in the battery pack and the state of health (SoH) of all the Li-ion cells in the pack. Universal Generating Function (UGF) technique is used for reliability analysis. As adding new battery cells to the battery pack in the production process can improve its reliability but it also increases cost, tradeoff between the number of the redundant battery cells, the configuration of the redundant cells and their reliability is investigated in this work. [ABSTRACT FROM AUTHOR]

The present paper documents a comprehensive study on the ground impact of lithium-ion battery packs in electric vehicles. With the purpose of developing generic methodology, a hypothetic global finite element model is adopted. The force–displacement response of indentation process simulated by the global FE model is cross-validated with the earlier analytical solutions. The punching process after the armor plate perforation, the ensuing crack propagation of the armor plate as well as the local deformation modes of individual battery cells are clearly predicted by the global modeling. A parametric study is carried out, and a few underlying rules are revealed, providing important clues on the design of protective structure of battery packs against ground impact. In the next step, detailed FE models at the level of a single battery cell and shell casing are developed and simulations are performed using boundaries and loading conditions taken from the global solution. In the detailed modeling the failure of individual components is taken into account, which is an important indicator of electric short circuit of a battery cell and possible thermal runaway. The damage modes and the deformation tolerances of components in the battery cell under various loading conditions are observed and compared. [ABSTRACT FROM AUTHOR]

Abstract: Because of the inevitable inconsistency during manufacture and use of battery cells, cell variations in battery packs have significant impacts on battery pack capacities, durability and safety for electric vehicles (EVs). To reduce cell variations and increase pack capacity, cell equalization is essentially required. In the series of two papers, we discover that dissipative cell equalization (DCE) using dissipative resistances is a feasible on-line equalization method for battery packs in EVs. We subsequently propose on-line equalization algorithms for lithium-ion battery packs based on charging cell voltage curves (CCVCs). The objective of these algorithms is to maximize pack capacities by conditioning CCVCs. As the first paper of the series, we first briefly review equalization topologies and algorithms. We discover cell remaining charging capacity (RCC) can be on-line estimated and further propose DCE algorithm based on remaining charging capacity estimation (RCCE). We establish a pack model with 8 cells in series and simulate 4 scenes with different cell variations. RCCE–DCE algorithm is proved to be effective by comparing pack capacities with/without RCCE–DCE algorithm. The equalization capability and over-equalization prevention are further examined, and the result shows that RCCE–DCE algorithm is suitable for on-line equalization in EVs. [Copyright &y& Elsevier]