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

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

As lithium-ion battery (LIB) cells degrade over time and usage, it is crucial to understand their remaining capacity, also known as State of Health (SoH), and inconsistencies between cells in a pack, also known as cell-to-cell variation (CtCV), to appropriately operate and maintain LIB packs. This study outlines efforts to model pack SoH and SoH CtCV of nickel-cobalt-aluminum (NCA) and lithium-iron-phosphate (LFP) battery packs consisting of four cells in series using pack-level voltage data. Using small training data sets and rapid testing procedures, partial least squares regression (PLS) models were built and achieved a mean absolute error of 0.38% and 1.43% pack SoH for the NCA and LFP packs, respectively. PLS models were also built that correctly categorized the packs as having low, medium, and high-ranked SoH CtCV 72.5% and 65% of the time for the NCA and LFP packs, respectively. This study further investigates the relationships between pack SoH, SoH CtCV, and the voltage response of the NCA and LFP packs. The slope of the discharge voltage response of the NCA packs was shown to have a strong correlation with pack dynamics and pack SoH, and the lowest SoH cell within the NCA packs was shown to dominate the dynamic response of the entire pack.

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.

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.

Unmanned robots being remotely controlled or autonomous are spread worldwide and used in different purposes. Inhabited robots are brought very close to users and accessibility to these tools is very high, moreover, at very low costs. Regardless to emphasize the increasing popularity of these robots. However, any robot system being electrically driven and controlled has bottleneck in the amount of the electrical energy stored aboard in the battery packs. In other words, due to limited amount of the electrical energy available special issues related to use energy best way with maximum effectiveness are needed and considered. Additionally, the battery management system is needed to control the processes of the discharge and the charge ensuring technical data and parameters set by the manufacturer. This paper addresses robot applications in regions out of the calculated when special environmental testing is needed to confirm battery pack technical data. Among those of the environmental tests required the temperature test is in the focus of attention. The main idea and purpose of this paper is to set up new concept of the low-cost environmental temperature test chamber, to define its technical parameters and other properties needed for its preliminary design and prototype manufacturing.

Numerical simulation on the U-shaped channel with various inlet positions using air cooling is investigated to solve heat dissipation issue of battery pack in electric vehicle. The mechanism of the influences of inlet position and inlet air velocity on the cooling performance of battery pack is revealed. The results show that the maximum temperature of battery pack decreases by 4.17 K at inlet position of 30 mm compared with that of 20 mm due to the increase of airflow velocity in the second half of the battery pack caused by flow around effect. The maximum temperature of battery pack decreases by 8.5–18.5 K with increasing inlet air velocity from 2 m/s to 5 m/s. The cooling performance improves because of flow around effect and high convective heat transfer using reasonable matching between inlet position (10–30 mm) and inlet air velocity (3–6 m/s). This paper provides valuable guidelines for cooling design of battery pack in electric vehicle.

Today, electric driven motorbikes (e-motorbikes) are facing multiple safety, functionality and operating challenges, particularly in hot climatic conditions. One of them is the increasing demand for efficient battery cooling to avoid the potential thermal stability concerns due to extreme temperatures and the conventional plastic enclosure of the battery pack. A reliable and efficient thermal design can be formulated by accommodating the battery within an appropriate battery housing supported by a cooling configuration. The proposed design includes a battery pack housing made of high conductive materials, such as copper (Cu) and aluminum (Al), with an adequate liquid cooling system. This study first proposes a potted cooling structure for the e-motorbike battery and numerical studies are carried out for a 72 V, 42 Ah battery pack for different ambient temperatures, casing materials, discharge rates, coolant types, and coolant temperatures. Results reveal that up to 53 °C is achievable with only the Cu battery housing material. Further temperature reduction is possible with the help of a liquid cooling system, and in this case, with the use of coolant temperature of 20 °C, the battery temperature can be maintained within 28 °C. The analysis also suggests that the proposed cooling system can keep a safe battery temperature up to a 5C rate. The design was also validated for different accelerated driving scenarios. The proposed conceptual design could be exploited in future e-motorbike battery cooling for optimum thermal stability. [ABSTRACT FROM AUTHOR]

Saving energy is a fundamental topic considering the growing energy requirements with respect to energy availability. Many studies have been devoted to this question, and life cycle assessment (LCA) is increasingly acquiring importance in several fields as an effective way to evaluate the energy demand and the emissions associated with products' life cycles. In this work, an LCA analysis of an existent lithium-ion battery pack (BP) unit is presented with the aim to increase awareness about its consumption and offering alternative production solutions that are less energy intensive. Exploiting the literature data about cradle-to-grave and cradle-to-gate investigations, and after establishing reasonable approximations, the main BP sub-elements were considered for this study, such as the plastic cells support, the Li-ion cells brick, the PCBs for a battery management system (BMS), the liquid-based battery thermal management system (BTMS) and the BP container. For each of these components, the impacts of the extraction, processing, assembly, and transportation of raw materials are estimated and the partial and total values of the energy demand (ED) and global warming potential (GWP) are determined. The final interpretation of the results allows one to understand the important role played by LCA evaluations and presents other possible ways of reducing the energy consumption and C O 2 emissions. [ABSTRACT FROM AUTHOR]

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

Unmanned robots being remotely controlled or autonomous are spread worldwide and used in different purposes. Inhabited robots are brought very close to users and accessibility to these tools is very high, moreover, at very low costs. Regardless to emphasize the increasing popularity of these robots. However, any robot system being electrically driven and controlled has bottleneck in the amount of the electrical energy stored aboard in the battery packs. In other words, due to limited amount of the electrical energy available special issues related to use energy best way with maximum effectiveness are needed and considered. Additionally, the battery management system is needed to control the processes of the discharge and the charge ensuring technical data and parameters set by the manufacturer. This paper addresses robot applications in regions out of the calculated when special environmental testing is needed to confirm battery pack technical data. Among those of the environmental tests required the temperature test is in the focus of attention. The main idea and purpose of this paper is to set up new concept of the low-cost environmental temperature test chamber, to define its technical parameters and other properties needed for its preliminary design and prototype manufacturing. [ABSTRACT FROM AUTHOR]

The article is devoted to solving the problem of improving the energy efficiency of power plants of high-speed lowtonnage (planing) vessels used for passenger transportation between coastal settlements on the example of the port of Vladivostok located on the Muravyov-Amursky Peninsula, which has mainly land communication with settlements located on the coast. The oversaturation of motor transport, due to the need for communication between localities, which are usually characterized by dense buildings, is one of the main causes of many hours of traffic congestion, which worsen the environmental situation and increase social tension in society. At the same time, the tourist attractiveness of Primorye is also decreasing. One of the promising areas for solving this transport problem is the use of low-tonnage passenger vessels, in particular planing vessels, with hybrid electrified power propulsion systems. According to the available data, such vessels have reduced consumption of fuel and lubricants necessary for the operation of power plants and reduction in harmful emissions into the surrounding atmosphere by more than two times compared to similar traditional diesel vessels. In addition, the prospects of the proposed scientific and technical solution are due to the reduction in the time of passenger transportation while reducing the cost of travel.

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

In order to suppress leakage current caused in the traditional multi-cells series Li-ion battery pack protection system, a new battery voltage transfer method is presented in this paper, which uses the current generated in the transfer process of one of the batteries to compensate for the leakage of itself and other cells except the top cell. Based on the 0.18 µm Bipolar and CMOS and DMOS technology, this proposed circuit has been integrated into a 7-cells Li-ion battery charge and discharge protection chip successfully. The experimental results of cell balancing confirm that the balancing algorithm can help with the shortage of the voltage transfer circuit. Furthermore, analysis of 20 samples shows that the new method can greatly suppress the battery leakage. Compared with the traditional solution, the voltage transfer circuit proposed in this paper greatly reduces the leakage current, which helps to extend the service life of the battery. [ABSTRACT FROM AUTHOR]

This paper proposes a method to estimate state of charge (SoC) for Lithium-ion battery pack (LIB) with N series-connected cells. The cell’s model is represented by a second-order equivalent circuit model taking into account the measurement disturbances and the current sensor bias. By using two sigma point Kalman filters (SPKF), the SoC of cells in the pack is calculated by the sum of the pack’s average SoC estimated by the first SPKF and SoC differences estimated by the second SPKF. The advantage of this method is the SoC estimation algorithm performed only two times instead of N times in each sampling time interval, so the computational burden is reduced. The test of the proposed SoC estimation algorithm for 7 samsung ICR18650 Lithium-ion battery cells connected in series is implemented in the continuous charge and discharge scenario in one hour time. The estimated SoCs of the cells in the pack are quite accurate, the 3-sigma criterion of estimated SoC error distributions is 0.5%. [ABSTRACT FROM AUTHOR]

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

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

To reduce the impact of series battery pack inconsistency on energy utilization, an active state of charge (SOC) balancing method based on an inductor and capacitor is proposed. Only one inductor and one capacitor can achieve a direct transfer of balanced energy between the highest power cell and the lowest power cell. This method has the characteristics of a simple structure, small size, simple control, fast balancing speed, and low topology cost. First, the topology, working principle, parameter design, and control strategy of the proposed balancing method are explained. Second, the characteristics of the speed and efficiency are analyzed through the simulation models, and the advantages of its low topology cost and simple control are explained. Finally, by building an experimental platform for a four-cell series battery pack, the effectiveness of the new balancing method in the charging/discharging process and the dynamic process of the battery pack is verified. After the end of the balancing, the SOC difference is less than 4%.

Battery capacity fade analysis was conducted to enhance both battery life and safety for different battery thermal managements based on natural convection, forced convection, finned-natural convection, PCM, and liquid cooling channels combined with PCM. The numerical model was developed according to Newman's pseudo two-dimensional (P2D) model, which included the generation of electrochemical heat. The capacity fade analysis showed that the temperature limit of 45 °C had the least negative impact on the battery life during charge/discharge cycles. In addition, the capacity fade was considerable as the battery temperature rose, showing 70% capacity loss after 500 cycles at 55 °C. The PCM provided a more uniform temperature distribution across the battery pack, however required extra heat dissipation mechanism. PCM cooling with liquid cooling channels could satisfy the temperature limits and significantly enhance the performance of battery heat management system.

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

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

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

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.

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

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

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

The electric-motorcycle is one of the promising technology that can improve environmental quality as well as reduce dependency on oil imports in Indonesia. The major challenge of electric-motorcycle implementation is the safety of the lithium-ion battery pack. This research aims to ascertain the over-temperature protection, and also to carry out shock & cycling test for the battery thermal safety. The testing procedures and apparatus comply with the United Nations Regulation No 136 (UN R-136), namely a temperature humidity chamber, a battery load unit, a blower, and a charger. The first step is the designing and prototyping of the battery pack with 100 V and 30 Ah, which contains a module of cells, Battery Management System (BMS), wiring, isolator, socket, and aluminum casing. The second step is the battery thermal safety test. The results showed that the battery pack satisfied the acceptance criteria, with evidence of no electrolyte leakage, rupture, fire, and explosion during and after testing. Result of over-temperature protection, BMS can cut-off the discharge rate at a temperature of 45.89°C. Meanwhile, for thermal shock & cycling tests, the battery pack did not fail when the temperature changes from 59.72°C and -40.25°C for 60 hours.

With the aging of Lithium-ion batteries (LIBs) of electric vehicles in the near future, research on the second use of retired LIBs is becoming more and more critical. The classification method of the retired LIBs is challenging before the second use due to large cell variations. This paper proposes a rapid classification method based on battery capacity and internal resistance, because batteries with different capacities and internal resistances have different voltage curves during charge/discharge. First, the piecewise linear fitting method established by the specified tested batteries with capacities and their corresponding characteristic voltages is used to sort the batteries. Then combined with the nonlinear function approximation ability of the radial basis function neural network (RBFNN) model, battery capacity and internal resistance are predicted after the model training. 108 cells are used for the simulation classification with experimental classification performed on 12 cells. The results prove that the classification method is accurate.

In this work, a numerical model is developed for simulating the compression molding process of a hybrid composite material, alternately laminated with continuous and discontinuous fiber-reinforced layers. Although various process simulation models are already available for plastic materials embedding each type of the reinforcements, they are incapable of simultaneously dealing with the continuity and discontinuity. Here, thermomechanical behavior of the continuous fiber-reinforced layer and rheological behavior of the discontinuous fiber-reinforced layer are separately modeled and eventually integrated assuming perfectly bonded interfaces. The unified process model is applied to the simulation of compression molding of a full-scale battery pack structure of an electric vehicle. A simple yet robust rheology test is utilized to measure rheological properties necessary for the numerical simulation. In the full-scale simulation, thermoforming process of the hybrid charge is successfully simulated and fiber direction changes due to the suspension flow are also predicted. It is found that the reoriented fibers significantly affect stress distributions at the final stage of the process. The process model developed in the present study can be implemented into either the Lagrangian or Eulerian framework.

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]

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.

With Abundant power available in the form of solar energy, it is not only necessary to utilize that energy but also important to extract maximum power from that source and minimize the losses. So in this paper we will discuss how maximum power is extracted from the solar array connected in Total Cross Tied (TCT) configuration using Perturb and Observe Method and fed to an Active DC load along with a Battery pack in order to maintain power to load regularly during low irradiance conditions and maintain constant DC voltage across the load terminals. Simulation of above conditions is achieved using MATLAB SIMULINK. [ABSTRACT FROM AUTHOR]

The design and performance optimization of fully electric trucks constitute an integral goal of the transport sector to meet climate emergency measures and local air quality requirements. Most studies in the literature have determined the optimum pack size based on economic factors, without accounting for the details of pack behavior when varying the size. In this paper, the effect of battery pack sizing and cargo capacity of a class 8, 41-ton truck on its overall energy performance and technical parameters of its powertrain is investigated. For this purpose, the proposed electric truck is designed and mathematically modelled using AVL CRUISE M software. The second-order equivalent circuit model is developed to predict the battery packs’ parameters. The proposed battery pack model is extracted from experimental analysis on SONY VTC6 lithium-ion batteries performed in the lab. The weight changes due to adding the battery packs to the truck are also estimated and have been taken into account. The mathematical model of the powertrain is simulated in the long-haul driving cycle considering different cargo capacities and battery pack sizes. The results of this study revealed that the battery pack voltage reached its minimum value when the maximum cargo capacity was applied for the 399 kWh battery pack. In addition, increasing the occupied cargo capacity from 10% to 100% resulted in an increase in the regenerative brake energy of up to 9.87 kWh, while changing the battery size imposed minimal impacts on regenerative brake energy recovery as well as energy consumption.

Saving energy is a fundamental topic considering the growing energy requirements with respect to energy availability. Many studies have been devoted to this question, and life cycle assessment (LCA) is increasingly acquiring importance in several fields as an effective way to evaluate the energy demand and the emissions associated with products’ life cycles. In this work, an LCA analysis of an existent lithium-ion battery pack (BP) unit is presented with the aim to increase awareness about its consumption and offering alternative production solutions that are less energy intensive. Exploiting the literature data about cradle-to-grave and cradle-to-gate investigations, and after establishing reasonable approximations, the main BP sub-elements were considered for this study, such as the plastic cells support, the Li-ion cells brick, the PCBs for a battery management system (BMS), the liquid-based battery thermal management system (BTMS) and the BP container. For each of these components, the impacts of the extraction, processing, assembly, and transportation of raw materials are estimated and the partial and total values of the energy demand (ED) and global warming potential (GWP) are determined. The final interpretation of the results allows one to understand the important role played by LCA evaluations and presents other possible ways of reducing the energy consumption and CO2 emissions.

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

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

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

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

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

Abstract 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.

Today, electric driven motorbikes (e-motorbikes) are facing multiple safety, functionality and operating challenges, particularly in hot climatic conditions. One of them is the increasing demand for efficient battery cooling to avoid the potential thermal stability concerns due to extreme temperatures and the conventional plastic enclosure of the battery pack. A reliable and efficient thermal design can be formulated by accommodating the battery within an appropriate battery housing supported by a cooling configuration. The proposed design includes a battery pack housing made of high conductive materials, such as copper (Cu) and aluminum (Al), with an adequate liquid cooling system. This study first proposes a potted cooling structure for the e-motorbike battery and numerical studies are carried out for a 72 V, 42 Ah battery pack for different ambient temperatures, casing materials, discharge rates, coolant types, and coolant temperatures. Results reveal that up to 53 °C is achievable with only the Cu battery housing material. Further temperature reduction is possible with the help of a liquid cooling system, and in this case, with the use of coolant temperature of 20 °C, the battery temperature can be maintained within 28 °C. The analysis also suggests that the proposed cooling system can keep a safe battery temperature up to a 5C rate. The design was also validated for different accelerated driving scenarios. The proposed conceptual design could be exploited in future e-motorbike battery cooling for optimum thermal stability.

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]

Purpose: Battery electric vehicles (BEVs) have been widely publicized. Their driving performances depend mainly on lithium-ion batteries (LIBs). Research on this topic has been concerned with the battery pack's integrative environmental burden based on battery components, functional unit settings during the production phase, and different electricity grids during the use phase. We adopt a synthetic index to evaluate the sustainability of battery packs. Methods: A life cycle assessment (LCA) is used to reveal the aspects of global warming potential (GWP), water consumption, and ecological impact during the two phases. An integrative indicator, the footprint-friendly negative index (FFNI), is combined with footprint family indicators of battery packs and electricity sources. We investigate two cases of 1 kg battery production and 1 kWh battery production to assess nickel–cobalt–manganese (NMC) and lithium–iron phosphate (LFP) battery packs and compare their degrees of environmental friendliness. Then, we break down the battery pack to identify the key factors influencing the environmental burden and use sensitivity analysis to analyze the causes. Moreover, we evaluate the environmental impact of battery packs during the use phase among different regions. Results and discussion: Regardless of the functional unit (FU), the weights of the carbon footprint (CF), water footprint (WF), and ecological footprint (EF) are approximately the same. The results of the integrative environmental indicator, the FFNI, illustrate that the LFP is approximately 0.014, which is lower than that of the NMC battery pack in the mass production case. When using energy units as the FU, the FFNI of the NMC is 0.015, which reflects a lower environmental burden than that of other battery packs. In the use phase, 1kWh electricity consumption in China and Europe has the highest and lowest FFNI, respectively. When breaking down the battery-pack components, the simplified model advocates the cathode as the major contributor that determines the total environmental performance. In the following sensitivity analysis, the battery management system (BMS) is found to be the most intensive part of the footprint of most battery packs. Conclusion: FU can influence the evaluation results. Developing proper renewable energy sources can reduce the footprints of battery packs during the use phase. The positive electrode pastes in the battery cell, BMS, and packaging in the battery pack can influence the environmental burden. Adopting green materials in sections like the BMS may be a specific measure to enhance the environmental friendliness of a battery pack during the production phase. [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.

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

It is more convenient and safer to employ wireless power transfer (WPT) systems to charge the battery pack of electric scooters (ESs) than conventional plug‐in systems. A charging equaliser suitable for ESs is proposed to achieve constant current (CC) output without communication link between the transmitter side and the receiver side. The full‐bridge rectifier and select switches utilised at the receiver side are employed to be operated once to change from the balancing mode to charging modes. The characteristics of the load‐independent current output in the CC mode are achieved by properly selecting the parameters of the circuit so that no sophisticated control strategies are required to regulate the output as per the charging profile. The experimental results show that when the voltage of battery pack is charged at 32 or 4.2 V, the current is 1.02 or 1.12 A, respectively. Therefore, the proposed system can be considered as a CC system. [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]