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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]

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 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]

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

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

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]

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]

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]