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The development of lithium-ion battery technology has ensured that battery thermal management systems are an essential component of the battery pack for next-generation energy storage systems. Using dielectric immersion cooling, researchers have demonstrated the ability to attain high heat transfer rates due to the direct contact between cells and the coolant. However, feedback control has not been widely applied to immersion cooling schemes. Furthermore, current research has not considered battery pack plant design when optimizing feedback control. Uncertainties are inherent in the cooling equipment, resulting in temperature and flow rate fluctuations. Hence, it is crucial to systematically consider these uncertainties during cooling system design to improve the performance and reliability of the battery pack. To fill this gap, we established a reliability-based control co-design optimization framework using machine learning for immersion cooled battery packs. We first developed an experimental setup for 21700 battery immersion cooling, and the experiment data were used to build a high-fidelity multiphysics finite element model. The model can precisely represent the electrical and thermal profile of the battery. We then developed surrogate models based on the finite element simulations in order to reduce computational cost. The reliability-based control co-design optimization was employed to find the best plant and control design for the cooling system, in which an outer optimization loop minimized the cooling system cost while an inner loop ensured battery pack reliability. Finally, an optimal cooling system design was obtained and validated, which showed a 90% saving in cooling system energy consumption. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) battery cells are used as the major power source for every electric vehicle (EV) industry because of their properties like density and voltage. Their optimal operating temperature ranges between 15 and 45 °C. The charge mobility and chemical reaction in Li-ion batteries cause excessive heat generation leading to thermal runaway and ultimately their capacity diminishes over the life cycle. The main idea of the present study is to control the thermal runaway of the Li-ion batteries using nano-enhanced phase change materials (Ne-PCM). Hence, there is a need for the development of a battery thermal management system (BTMS) using either air, liquid, or phase change material (PCM). An 18650 battery cell (normal capacity: 2700 mAh; rated capacity: 2600 mAh; normal voltage: 3.7 V; rated power: 9.62 Wh; anode material: lithiated graphite (LiC6); cathode material: lithium-nickel-manganese-cobalt-oxide (LiNiMnCoO2); electrolyte material: lithium hexafluorophosphate (LiPF6)) along with a complete battery pack (4 cells) is considered in the present study. Transient numerical simulations (using both MATLAB R2022a and ANSYS 2020 R2) are carried out with and without using the paraffin wax. Three different nanoparticles (copper oxide (CuO), aluminum oxide (Al2O3), and titanium oxide (TiO2)) at various concentrations (0%, 3%, 7%, and 10%) are added to the paraffin wax to enhance their thermal conductivity value. However, the experiments are conducted only with and without using the paraffin wax, but not with the nano-enhanced paraffin wax. Hence, this (Ne-PCM case) acts only as a support to the numerical study. For both the numerical and experimental analysis, the temperature and voltage characteristics of the battery packs are measured for a specific time to understand their charging and discharging characteristics. It is found that paraffin wax is a better candidate for maintaining the battery temperature in an optimal range when the battery generates excess heat. Paraffin wax gives a 41% increase in battery life compared to air cooling. The hybrid cooling (combination of paraffin wax and air) technique reduces the battery temperature rise by 4 °C compared to only paraffin wax and by 8 °C compared to only air cooling. [ABSTRACT FROM AUTHOR]

To achieve efficient cooling capabilities in electric vehicle (EV) batteries, battery thermal management systems with higher power density have garnered significant attention. This work introduces a novel computational analysis method to assess the temperature distribution within the designed multiple EV battery cooling module's, investigating the flow of both water and silver-based nanofluids as coolants. The EV battery module under study comprises ten cylindrical lithium-ion batteries of model 18,650 types. This comprehensive simulation considered various factors, such as the coolant's flow path, flow rate and type, appear to have significantly influencing temperature distribution. The analysis identifies "Case IV" as the most effective cooling configuration utilizing water as the coolant and demonstrating superior cooling capabilities compared to the conventional cooling module ("Case I"). This finding marks a critical step toward optimizing battery cooling methods and achieving efficient thermal management. The incorporation of silver nanoparticles in the base fluid (water) enhances the nanofluid's thermal conductivity and heat transfer efficiency, showcasing improved cooling capabilities beyond that of water alone. The performance of different nanofluid concentrations including 0.25% and 0.50% by volume was evaluated to demonstrate their impact on battery cooling efficiency. To contextualize the results, the cooling performance of silver nanofluids has been compared with that of TiO2 nanofluids reported in previous studies. The outcomes of this research underscore the potential of the computational analysis technique to advance temperature management systems for EV batteries, improving EVs performance and reliability and contributing to a more sustainable and efficient future of transportation. [ABSTRACT FROM AUTHOR]

This study explores the performance of a steady-state flow single-phase non-conductive liquid immersion cooling system in a single-cell Li-ion battery under a variety of thermal environments such as 25°C, 40°C, and 60°C and tested at different charge C-rates. The experimental results show that the proposed cooling system exhibited the best cooling performance of less than 5°C and a significant temperature rise reduction of 34% at 3C rate under the ambient temperature of 25°C. Also, it provided uniform temperature distribution within the cell when charged at fast charge rates of 2C and 3C operations. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are obstacles to the widespread use of small electric vehicles (EVs) in Rwanda, including concerns regarding the battery range and lifespan. Lithium-ion batteries (LIBs) play an important role in EVs. However, their performance declines over time because of several factors. To optimize battery management systems and extend the range of EVs in Rwanda, it is essential to understand the influence of the driving profiles on lithium-ion battery degradation. This study analyzed the degradation patterns of a lithium-ion battery cell that propels an E-bike using various real-world E-bike driving cycles that represent Rwandan driving conditions under deep discharge (> 80 %). By being aware of these variables, battery failure can be slowed and improved battery performance can be achieved to promote the transition to cleaner transportation in Rwanda for the productive use of energy. The analyzed parameters that affect battery performance are temperature, driving cycles, and state of charge. It was found that the higher the temperature, the higher was the rate of fading. On the other hand, the EVs that operate in the region with higher elevation (hilly region) combined with a flat surface where the riders use their physical forces to propel the E-bike and their batteries lose their capacity rapidly compared to those operating in regions where the energy from the lithium-ion battery assists for the entire mileage. By draining the battery to 10% and charging it to 90% of its initial capacity, the capacity fading decreased by 5%. [ABSTRACT FROM AUTHOR]

The battery management system (BMS) in electric vehicles (EVs) relies on State of Health (SOH) assessment to monitor battery health and ensure performance. BMS can detect and prevent cell balance, thermal runaway, and extend its lifespan. Methodical charging and discharging under different circumstances reduce battery degradation. Grid Partitioning (GP), Subtractive Clustering (SC), and Fuzzy C-Means (FCM) from the Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to predict battery SOH in this work. This study examines charge-discharge cycles of 8 lithium-ion pouch cells from the Oxford Battery Degradation Dataset. Charging cycle data extracts the five vital health indicators (VHIs), whereas discharging cycle data calculates cell capacity. For analyzing the effects of VHIs on SOH, a correlation matrix is formed. Three instances are defined depending on the individual VHIs impacts. Instance 1 utilizes VHI 1, VHI 2, and VHI 5, instance 2 utilizes VHI 3 and VHI 4, and instance 3 uses all VHIs as inputs. Due to enormous data points, only even-numbered cell data is utilized for training the ANFIS model, while odd-numbered cell data is used for validation and testing. In the best-case scenario, instance 1 gives the lowest root mean square error (RMSE) from all the methods. [ABSTRACT FROM AUTHOR]

Thermal runaway propagation (TRP) in lithium batteries poses significant risks to energy‐storage systems. Therefore, it is necessary to incorporate insulating materials between the batteries to prevent the TRP. However, the incorporation of insulating materials will impact the battery thermal management system (BTMS). In this article, the influence of aerogel insulation on liquid‐cooled BTMS is analyzed employing experiments and simulations. In the experiment results, it is revealed that aerogel reduces heat dissipation from liquid‐cooled battery packs, leading to elevated peak temperatures and steeper temperature gradients. Simulation of battery pack discharge warming based on the 3D model shows that the result matches very well with that in the experiment., indicating a maximum temperature rise from 34.92 to 42.57 °C at 2C when aerogel thickness is increased to 5 mm, alongside a temperature differential expansion from 11.11 to 17.50 °C. Nonetheless, beyond 3 mm thickness, further increases in aerogel thickness cause negligible (<0.1 °C) temperature alterations, defining the saturation thickness of aerogel. Furthermore, maintaining consistent thickness and stacking more aerogel layers do not mitigate its detrimental effects. Interestingly, augmenting the battery's through‐thickness thermal conductivity counteracts the adverse outcomes of aerogel usage. [ABSTRACT FROM AUTHOR]

Thermal management systems for power batteries based on phase change materials (PCM) are limited by low heat transfer efficiency, leakage issues, and high rigidity, and most of them cannot meet the needs of all‐climate thermal management. A double‐layer flexible phase change material (FPCM) sleeve structure for all‐climate thermal management is proposed in this study for the first time. Innovations in both material and design have enhanced the adaptability of the thermal management system. The outer layer FPCM achieves high thermal conductivity (4.23 W m−1 K−1) and electrical conductivity (0.95 S m−1), the inner layer FPCM achieves insulation (22.76 MΩ) and flexibility, the thermal contact resistance (TCR) between the double‐layer sleeve structure and the battery are measured to be only 0.15 °C W−1, significantly improved thermal management performance. Experimental results show that at 30 °C ambient temperature, 5 C discharge, the system reduces the maximum temperature by 14.5 °C, from 61.4 to 46.9 °C, compared to natural convection. Additionally, during heating, the system achieves heating rates of 7.0, 8.3, and 8.7 °C min−1 at −20, −10, and 0 °C, respectively, extending battery discharge duration by 32.5%, 65.9%, and 33.6%. [ABSTRACT FROM AUTHOR]

Herein, the synthesis of a novel microencapsulated phase change material via a sol–gel method is presented, featuring disodium hydrogen phosphate dodecahydrate as the core and a SiO2/graphene oxide composite as the shell. The morphology and core–shell microstructure of the synthesized microcapsules are characterized using scanning electron microscopy. The phase change performance and thermal stability are evaluated by differential scanning calorimetry and a high/low temperature test chamber, respectively. Energy dispersive X‐ray spectroscopy and Fourier transform infrared spectroscopy are employed to determine the chemical composition of the microcapsules. The findings reveal that the MEPCM exhibited a high melting enthalpy of 174.3 J g−1. The degree of supercooling is reduced by 2.1 °C, and after 600 thermal cycles, the phase change enthalpy of the microcapsules showed a minor decrease of ≈6.0%. Notably, the thermal conductivity of the microcapsules is significantly enhanced, increasing by up to 51.8%. When integrated into the thermal management systems of lithium‐ion batteries, the MEPCM effectively maintained the battery temperature below 46 °C under a 3 C charging rate. In summary, these results suggest that the hydrated salt microcapsule with its hybrid silica and graphene oxide shell is a promising material for the thermal management of lithium‐ion batteries. [ABSTRACT FROM AUTHOR]

In recent years, lithium-ion batteries (LIB) have become the de facto energy storage means for electric vehicles (EVs) due to their high energy density. However, LIBs require state of charge (SOC) monitoring to ensure safe operating conditions and for an enhanced lifespan. Since SOC cannot be directly measured, various estimation methods have been proposed in recent literature, most notably the recent rise in popularity of long short-term memory-recurrent neural networks (LSTM-RNN). Current research in the use of LSTM-RNNs typically applies a single strong data-driven model that can produce accurate predictions at the expense of lengthy model training times. As LSTM-RNNs must be retrained as LIBs age to maintain reasonable estimation accuracies, this poses a problem for EV battery management system processors. To address this research gap, this work proposes a homogeneous ensemble learning model based on several LSTM-RNN base models, as a solution to reduce the training time. The LSTM base models are fused by a meta-learner, to overcome the shortcomings of traditional ensemble fusion methods. Data diversification methods for homogeneous ensembles are also reviewed and benchmarked in this paper. The proposed method achieves a low model training time by 2.6–3.5 times while maintaining a similar mean absolute error (MAE) of 1.4% when compared to conventional shallow and deep LSTM-RNN models. The proposed model was also successfully validated with battery discharge data collected from a custom build battery tester. It is anticipated that the proposed LIB SOC estimation approach can contribute to increased feasibility of using artificial intelligence in EVs in general and improve EV battery management. [ABSTRACT FROM AUTHOR]

Accurately estimating the state of charge of a lithium-ion battery plays an important role in managing the health of a battery and estimating its charging state. Traditional state-of-charge estimation methods encounter difficulties in processing the diverse temporal data sequences and predicting adaptive results. To address these problems, we propose a spatial transformer network (STN) for multi-temperature state-of-charge estimation of lithium-ion batteries. The proposed STN consists of a convolutional neural network with a temporal–spatial module and a long short-term memory transformer network, which together are able to efficiently capture the spatiotemporal features. To train the STN under multi-temperature conditions, denoising augmentation and attention prediction are proposed to enhance the model's generalizability within a unified framework. Experimental results show that the proposed method reduces the mean absolute error and root mean square error by 41% and 43%, respectively, compared with existing methods; in the semi-supervised setting, the respective reductions are 23% and 38%, indicating that effective extraction of the spatiotemporal features along with denoising augmentation is beneficial for estimating the state of charge and can promote the development of battery management systems using semi-supervised learning methods. [ABSTRACT FROM AUTHOR]

Passive battery thermal management systems (BTMSs) are critical for mitigation of battery thermal runaway (TR). Phase change materials (PCMs) have shown promise for mitigating transient thermal challenges. Fluid leakage and low effective thermal conductivity limit PCM adoption. Furthermore, the thermal capacitance of PCMs diminishes as their latent load is exhausted, creating an unsustainable cooling effect that is transitory. Here, an expanded graphite/PCM/graphene composite that solves these challenges is proposed. The expanded graphite/PCM phase change composite eliminates leakage and increases effective thermal conductivity while the graphene coating enables radiative cooling for PCM regeneration. The composite demonstrates excellent thermal performance in a real BTMS and shows a 26% decrease in temperature when compared to conventional BTMS materials. The composite exhibits thermal control performance comparable with active cooling, resulting in reduced cost and increased simplicity. In addition to BTMSs, this material is anticipated to have application in a plethora of engineered systems requiring stringent thermal management. [ABSTRACT FROM AUTHOR]

The accurate prediction of the state of health (SOH) for lithium-ion batteries is a key factor for improving the performance of battery management systems (BMS). However, traditional point prediction methods are difficult to effectively eliminate errors due to the uncertainty of variables and application environments. This paper presents a model for predicting the interval of lithium-ion batteries based on health indicators (HIs) during charging, which addresses the limitations of current point prediction in practical applications. First, twelve HIs are extracted from the current and voltage variables of the charging process. Secondly, feature selection is performed by random forest (RF) training, and the selected HIs are dimensioned using partial least squares (PLS). Finally, a long short-term memory network (LSTM) combined with quantile regression (QR) is used to derive the quantile values of the prediction points and each quantile is employed as input information for Gaussian kernel density estimation (KDE) to obtain the SOH probability density distribution. The experimental results based on the NASA PCOE Li-ion battery dataset and CALCE Li-ion battery dataset show that the SOH interval coverage is more than 90% and the average width of the interval is less than 0.294. [ABSTRACT FROM AUTHOR]

Since battery systems typically account for over 40% of the cost of an electric vehicle, their mid-life replacements are exceptional. Therefore, the battery's lifespan must exceed that of the vehicle. To ensure long-term and safe use, accurate state-of-charge (SOC) estimation must be maintained throughout the battery's lifespan. This requires appropriate updates to parameters, such as capacity, in the battery model. In this context, dual extended Kalman filters, which simultaneously estimate both states and parameters, have gained interest. While existing reports on simultaneous estimators seemed promising, our study found that they performed well under low levels of battery aging but encountered issues at higher levels. Accurately reflecting the actual physicochemical changes of the parameters in aging cells is challenging for two reasons: the limited number of measurements of terminal voltage available for numerous parameters, and the weak observability of the capacity. Therefore, we combined the simultaneous estimator with a capacity estimator operated separately during charging and a sequential estimator specialized for an enhanced self-correcting model, achieving SOC accuracy within 5% even when the SOH decreased by 30%. However, there is still much work to be carried out to implement sequential estimators in battery management systems operating in real time with limited computational resources. [ABSTRACT FROM AUTHOR]

The escalating demand for electric vehicles (EVs) arises from apprehensions regarding fossil fuel depletion and the ecological repercussions of conventional combustion engines, propelling the transition towards environmentally sustainable alternatives. EVs are conventionally comprised of a powertrain, battery management system, and communication infrastructure, with the battery playing a pivotal role. Achieving an efficient EV battery charger necessitates the implementation of a proficient charging algorithm and a high-power converter capable of adeptly regulating battery parameters. Among the array of available options, a bi-directional DC/DC converter is often employed for this purpose. This study predominantly focuses on the Bi-Directional Dual Active Bridge Converter with Single-Phase Shift Control. It is renowned for attaining a broad voltage range through transformer turn ratio adjustments. Its controllable parameters, such as phase shift ratio and duty cycle, bolster its versatility. Moreover, due to its input–output isolation and minimal passive elements, this converter exhibits superior efficiency due to its soft-switching capabilities. The analytical framework encompasses steady-state and dynamic assessments, small-signal modeling, and system transfer function analysis, all of which contribute to formulating an optimized controller design. Additionally, the study conducts simulations of various battery charging techniques, including constant current (CC), constant voltage (CV), and CCCV mode, employing a 1.5 kW Dual Active Bridge DAB-based charger through MATLAB/SIMULINK. Furthermore, voltage mode control simulations for a 5 kW DAB converter augment the study's insights into effective EV charging strategies. [ABSTRACT FROM AUTHOR]

Li-ion batteries generate a large amount of heat in the electric vehicles. The poor heat dissipation from the battery causes temperature rise and affects its performance and life. If the battery temperature is not controlled, it may lead to serious damage to the battery cells; in extreme scenario, it may lead to fire hazards. A properly designed battery thermal management system (BTMS) controls the battery temperature ensuring its safe and efficient operation. In the present work, a nanoparticle assisted phase change material (PCM) and active cooling based BTMS technique has been investigated. The study employs both experimental and numerical approaches in the development of a water-composite PCM-based hybrid BTMS (combination of active and passive cooling techniques). Firstly, the investigation is performed to optimize the amount of PCM in the BTMS as it affects the thermal performance and mass of the system. It is found that an excess amount of PCM results in heat accumulation at the heating surface, which leads to rise in the cell temperature. The reduction in thickness of PCM from 40 to 5 mm results in 26.98% reduction in the maximum temperature. Moreover, it is found that introduction of nanoparticles between 1 and 20% (by volume) in PCM results in improved thermal conductivity. The melting fraction of PCM is improved by 29.33% and 28.0% with 20% concentration of CuO and Al2O3 in the PCM, respectively. It further helps in 70.79% improvement in the thermal non-uniformity in PCM-based hybrid BTMS. [ABSTRACT FROM AUTHOR]

This study investigated the application of nanophase change material emulsions (NPCMEs) for thermal management in high-capacity ternary lithium-ion batteries. We formulated an NPCME of n-octadecane (n-OD) and n-eicosane (n-E) with a mass fraction of 10%, whose phase change temperatures are 25.5 °C and 32.5 °C, respectively, with specific heat capacities 2.1 and 2.4 times greater than water. Experiments were conducted to evaluate the thermal control performance and latent heat utilization efficiency of these NPCMEs. The NPCMEs with an n-OD mass fraction of 10% (NPCME-n-OD), particularly reduced the battery pack's maximum temperature and temperature difference to 41.6 °C and 3.72 °C under a 2 C discharge rate, lower than the water-cooled group by 1.3 °C and 0.3 °C. This suggests that nano emulsions with phase change temperatures close to ambient temperatures exhibit superior cooling performance. Increased flow rates from 50 mL/min to 75 mL/min significantly lowered temperatures, resulting in temperature reductions of 2.73 °C for the NPCME-n-OD group and 3.37 °C for the NPCME-n-E group. However, the latent heat utilization efficiency of the nano emulsions decreased, leading to increased system energy consumption. Also, it was found that the inlet temperature of the NPCMEs was very important for good thermal management. The right inlet temperatures make it easier to use phase change latent heat, while excessively high temperatures may make thermal management less effective. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation. [ABSTRACT FROM AUTHOR]

Electric power systems are currently undergoing a transformation towards a decentralized paradigm by actively involving prosumers, through the utilization of distributed multi‐energy sources. This research introduces a fully decentralized multi‐community peer‐to‐peer electricity trading mechanism, which integrates iterative auction and pricing methods within local electricity markets. The mechanism classifies peers in all communities on an hourly basis depending on their electricity surplus or deficit, facilitating electricity exchange between sellers and buyers. Moreover, communities engage in energy exchange not only within and between themselves but also with the grid. The proposed mechanism adopts a fully decentralized approach known as the alternating direction method of multipliers. The key advantage of this approach is that it eliminates the need for a supervisory node or the disclosure of private information of the involved parties. Furthermore, this study incorporates the flexibility provided by residential heating systems and energy storage systems into the energy scheduling of some prosumers. Case studies illustrate that the proposed multi‐community peer‐to‐peer electricity trading mechanism effectively enhances local energy balance. Specifically, the proposed mechanism reduces average daily electricity costs for individual prosumers by 63% compared to scenarios where peer‐to‐peer electricity trading is not employed. [ABSTRACT FROM AUTHOR]

The management of electric car batteries has taken front stage in automotive research and development as more people choose electric vehicles (EVs). Optimal performance, safety, and lifetime of electric vehicles depend on effective battery management systems (BMS). To address this, we developed software for system functionality testing and a BMS prototype with an AVR ATmega family micro-controller. The originality of this work is the practical application of Arduino platform for BMS development, offering a scalable and reasonably priced solution that goes beyond past efforts by including the most recent technological developments and addressing modern challenges in the field. Conclusions drawn show that overcoming present battery performance constraints by developing BMS technology will open the path for more general EV acceptance. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Advancements in the reduction of carbon dioxide emissions from ships are driving the development of more efficient onboard power systems. The proposed non-equivalent parallel running operation system is explored in this study, which improves the efficiency of the main power generation source compared with traditional equal load-sharing methods used in power management systems. However, the asymmetric method reduces the efficiency of the auxiliary power sources. To address this issue, we propose a control method that integrates a battery system with an efficiency-based algorithm to optimize the overall system performance. The proposed approach involves establishing operation command values based on the characteristics of the power generation source and adjusting these commands according to the battery's state of charge (SOC). MATLAB/Simulink simulations confirmed the effectiveness of this method across various operating modes and revealed no operational issues. When applied to a ship's operating profile over 222 h, the method reduced fuel consumption by approximately 2.98 tons (5.57%) compared with conventional systems. Over 38 annual voyages, this reduction equates to savings of 115.96 tons of fuel or approximately 96.47 million Korean won. This study demonstrates that integrating an optimal efficiency algorithm into the energy management system significantly enhances both the propulsion and overall energy efficiency of ships. [ABSTRACT FROM AUTHOR]

Battery management systems (BMSs) play a vital role in understanding battery performance under extreme conditions such as high C-rate testing, where rapid charge or discharge is applied to batteries. This study presents a novel BMS tailored for continuous monitoring, transmission, and storage of essential parameters such as voltage, current, and temperature in an NCA 18650 4S lithium-ion battery (LIB) pack during high C-rate testing. By incorporating deep learning, our BMS monitors external battery parameters and predicts LIB's health in terms of discharge capacity. Two experiments were conducted: a static experiment to validate the functionality of BMS, and an in operando experiment on an electrically propelled vehicle to assess real-world performance under high C-rate abuse testing with vibration. It was found that the external surface temperatures peaked at 55 °C during in operando flight, which was higher than that during static testing. During testing, the deep learning capacity estimation algorithm detected a mean capacity deviation of 0.04 Ah, showing an accurate state of health (SOH) by predicting the capacity of the battery. Our BMS demonstrated effective data collection and predictive capabilities, mirroring real-world conditions during abuse testing. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles. [ABSTRACT FROM AUTHOR]

To protect the environment and reduce dependence on fossil fuels, the world is shifting towards electric vehicles (EVs) as a sustainable solution. The development of fast charging technologies for EVs to reduce charging time and increase operating range is essential to replace traditional internal combustion engine (ICE) vehicles. Lithium-ion batteries (LIBs) are efficient energy storage systems in EVs. However, the efficiency of LIBs depends significantly on their working temperature range. However, the huge amount of heat generated during fast charging increases battery temperature uncontrollably and may lead to thermal runaway, which poses serious hazards during the operation of EVs. In addition, fast charging with high current accelerates battery aging and seriously reduces battery capacity. Therefore, an effective and advanced battery thermal management system (BTMS) is essential to ensure the performance, lifetime, and safety of LIBs, particularly under extreme charging conditions. In this perspective, the current review presents the state-of-the-art thermal management strategies for LIBs during fast charging. The serious thermal problems owing to heat generated during fast charging and its impacts on LIBs are discussed. The core part of this review presents advanced cooling strategies such as indirect liquid cooling, immersion cooling, and hybrid cooling for the thermal management of batteries during fast charging based on recently published research studies in the period of 2019–2024 (5 years). Finally, the key findings and potential directions for next-generation BTMSs toward fast charging are proposed. This review offers an in-depth analysis by providing recommendations and potential solutions to develop reliable and efficient BTMSs for LIBs during fast charging. [ABSTRACT FROM AUTHOR]

Although phase change materials (PCMs) exhibit effective performance in the thermal management of lithium-ion batteries (LIBs), their development is limited by low thermal conductivity and susceptibility to leakage during the solid–liquid phase transition. To address these challenges and enhance thermal management capabilities, this study introduces a novel composite phase change material (CPCM) synthesized by physically mixing paraffin (PA), expanded graphite (EG), and bacterial cellulose (BC). The thermal performance of CPCMs with varying BC proportions is evaluated, and their impact on temperature control in battery thermal management systems (BTMS) is assessed. The results show that the addition of EG and BC significantly improves the thermal conductivity of the CPCM, reaching a value of 1.39 W·m−1·K−1. This also enhances the uniformity of temperature distribution within the battery module and reduces CPCM leakage. By comparing temperature variations within the battery module under different operating conditions, it was found that the intricate network structure of the CPCM promotes uniform temperature distribution, effectively mitigating temperature rise. Consequently, the maximum temperature and maximum temperature difference within the battery module were maintained below 47 °C and 4 °C, respectively. Compared to a system without phase change material at a 3C discharge rate, the maximum cell temperature, maximum module temperature, and maximum temperature difference were reduced by 32.38%, 26.92%, and 34.94%, respectively. These findings provide valuable insights for the design and optimization of BTMS. [ABSTRACT FROM AUTHOR]

Accurately predicting the state of health (SOH) of lithium-ion batteries is crucial for optimizing battery performance and achieving efficient energy management, especially in electric vehicle applications. However, the existing incremental capacity analysis methods, which are mostly based on curve multi-parameter analysis, still have limitations in terms of computation, prediction accuracy, and adaptability to actual operating conditions. This paper conducts an in-depth analysis of the incremental capacity (IC) curve and proposes a feature parameter based on the area under the IC curve. By incorporating charge and discharge data, a weighted health indicator sequence is constructed and three mathematical models are proposed to link health indicators with cycle number, capacity, and SOH. The feasibility of using impedance as an additional input is also explored, despite the challenges of measurement, revealing its potential applications. Validation of the models with different datasets shows that the proposed method achieves both average relative error and root mean square error within 5%, outperforming other methods in terms of minimizing error and ensuring stability. The results demonstrate that the area-weighted incremental capacity method significantly enhances battery health monitoring accuracy, contributing to the development of sustainable and efficient energy storage systems. [ABSTRACT FROM AUTHOR]

This study presents a bionic structure-based liquid cooling plate designed to address the heat generation characteristics of prismatic lithium-ion batteries. The size of the lithium-ion battery is 148 mm × 26 mm × 97 mm, the positive pole size is 20 mm × 20 mm × 3 mm, and the negative pole size is 22 mm × 20 mm × 3 mm. Experimental testing of the Li-ion battery's heat generation model parameters, in conjunction with bionic structure and micro-channel features, has led to the development of this innovative cooling system. The traditional bionic liquid cooling plate's structure is often singular; however, the flow path of the liquid cooling plate designed in this paper is based on the combination of the distribution of human blood vessel branches and the structure of insect wing veins. The external dimension of the liquid cooling plate is 152 mm × 100 mm × 6 mm (length × width × height). Utilizing numerical simulation and thermodynamic principles, we analyzed the heat transfer efficacy of the bionic liquid cooling module for power batteries. Specifically, we investigated the impact of varying coolant flow rates and the contact radius between flow channels on the thermal performance of the bionic battery modules. Our findings indicate that a liquid flow rate of 0.6 m/s achieves a stable maximum surface temperature and temperature differential across the bionic battery liquid cooling module, with a relatively low overall system power consumption, suggesting room for further enhancement of heat transfer performance. By augmenting the contact radius between flow channels, we observed an initial increase in the maximum surface temperature, temperature differential, and inlet–outlet pressure differential at a flow rate of 0.2 m/s. However, at flow rates equal to or exceeding 0.4 m/s, these parameters stabilized across different design Scenarios. Notably, the pump power consumption remained consistent across various scenarios and flow rates. This study's outcomes offer valuable insights for the development of liquid-cooled battery thermal management systems that are energy-efficient and offer superior heat transfer capabilities. [ABSTRACT FROM AUTHOR]

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system's energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia's electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS). [ABSTRACT FROM AUTHOR]

Battery thermal management system (BTMS) based on phase change materials (PCMs) is simple in structure while presenting outstanding performance, but the core bottleneck hindering the industrialization of which is the poor performance of PCMs' pivotal properties. Apart from that, under extreme conditions, single passive phase change temperature‐control technology apparently could not meet the demands. Therefore, modification strategies to improve PCM's pivotal properties suitable for BTMS are thoroughly reviewed. Moreover, the optimization of as‐mentioned passive systems by integrating them with other active heating or cooling devices to obtain advanced active and passive full‐temperature responsive capability is also summarized. Profound opinions concerning about the prospect and challenges of PCM‐BTMS are given. It is expected to provide some innovative ideas for the advancement of such promising technology. [ABSTRACT FROM AUTHOR]

Battery pack provides the backup power supply for DC system of power substations. In the event of an AC power outage or other accidents, it is an important guarantee for the reliable operation of power substation. To prevent possible failures, batteries usually require careful maintenance. Common methods are online monitoring, condition assessments, and health management. Among these, model-based techniques are widely used for battery monitoring and prognostics optimization. Data-driven methods are a good alternative solution when no mathematical models are available. As substations develop towards intelligent and unmanned modes, this paper proposes an online battery monitoring and management system based on the "cloud-network-edge-end" Internet of Things (IoT) architecture. Firstly, advanced battery monitoring system based on IoT architecture is reviewed in depth. It provides basis for later designing. Secondly, the battery online monitoring and management system is designed considering functional requirements and data link. Designing functions include ledger management, basic battery information display, real-time display of battery monitoring data, and the visualization of battery alarm information. It can implement online monitoring and intelligent maintenance management for battery operating status. Finally, the designed and developed system is applied in a 110 kV offshore substation, mainly providing battery maintenance suggestions and fault alarm prompts. Typical results of ledger information management, key parameter monitoring and alarm prompt are presented. This verifies the effectiveness and convenience of IoT-based system for the monitoring and management of batteries. [ABSTRACT FROM AUTHOR]

This article presents a data-driven methodology for modeling lithium-ion batteries, which includes the estimation of the open-circuit voltage and state of charge. Using the proposed methodology, the dynamics of a battery cell can be captured without the need for explicit theoretical models. This approach only requires the acquisition of two easily measurable variables: the discharge current and the terminal voltage. The acquired data are used to build a linear differential system, which is algebraically manipulated to form a space-state representation of the battery cell. The resulting model was tested and compared against real discharging curves. Preliminary results showed that the battery's state of charge can be computed with limited precision using a model that considers a constant open-circuit voltage. To improve the accuracy of the identified model, a modified recursive least-squares algorithm is implemented inside the data-driven method to estimate the battery's open-circuit voltage. These last results showed a very precise tracking of the real battery discharging dynamics, including the terminal voltage and state of charge. The proposed data-driven methodology could simplify the implementation of adaptive control strategies in larger-scale solutions and battery management systems with the interconnection of multiple battery cells. [ABSTRACT FROM AUTHOR]

In this paper, we propose a battery management algorithm to maximize the lifetime of a parallel-series connected battery pack with heterogeneous states of health in a battery energy storage system. The growth of retired lithium-ion batteries from electric vehicles increases the applications for battery energy storage systems, which typically group multiple individual batteries with heterogeneous states of health in parallel and series to achieve the required voltage and capacity. However, previous work has primarily focused on either parallel or series connections of batteries due to the complexity of managing diverse battery states, such as state of charge and state of health. To address the scheduling in parallel-series connections, we propose a cooperative multi-agent deep Q network framework that leverages multi-agent deep reinforcement learning to observe multiple states within the battery energy storage system and optimize the scheduling of cells and modules in a parallel-series connected battery pack. Our approach not only balances the states of health across the cells and modules but also enhances the overall lifetime of the battery pack. Through simulation, we demonstrate that our algorithm extends the battery pack's lifetime by up to 16.27% compared to previous work and exhibits robustness in adapting to various power demand conditions. [ABSTRACT FROM AUTHOR]

This paper conducts an in-depth study of a wireless, hierarchical structure-based active balancing system for power batteries, aimed at addressing the rapid advancements in battery technology within the electric vehicle industry. The system is designed to enhance energy density and the reliability of the battery system, developing a balancing system capable of managing cells with significant disparities in characteristics, which is crucial for extending the lifespan of lithium-ion battery packs. The proposed system integrates wireless self-networking technology into the battery management system and adopts a more efficient active balancing approach, replacing traditional passive energy-consuming methods. In its design, inter-group balancing at the upper layer is achieved through a soft-switching LLC resonant converter, while intra-group balancing among individual cells at the lower layer is managed by an active balancing control IC and a bidirectional buck–boost converter. This configuration not only ensures precise control but also significantly enhances the speed and efficiency of balancing, effectively addressing the heat issues caused by energy dissipation. Key technologies involved include lithium-ion batteries, battery management systems, battery balancing systems, LLC resonant converters, and wireless self-networking technology. Tests have shown that this system not only reduces energy consumption but also significantly improves energy transfer efficiency and the overall balance of the battery pack, thereby extending battery life and optimizing vehicle performance, ensuring a safer and more reliable operation of electric vehicle battery systems. [ABSTRACT FROM AUTHOR]

In recent years, large power batteries have been widely used not only in automobiles and other vehicles but also in maritime vessels. The thermal uniformity of large marine battery packs significantly affects the performance, safety, and longevity of the electric ship. As a result, the thermal management of large power batteries has become a crucial technical challenge with traditional battery management system (BMS) that cannot effectively solve the battery heating problem caused by electrochemical reactions and joule heating during operation. To address this gap, a battery thermal management system (BTMS) has been newly designed. This article presents the design of a large marine battery pack, which features a liquid cooling system integrated into both the bottom and side plates of each pack. The flow plate is constructed from five independent units, each connected by manifold structures at both ends. These connections ensure the formation of a stable and cohesive flow plate assembly. Although research on the BTMS is relatively advanced, there is a notable lack of studies examining the effects of liquid temperature, flow rate, and battery discharge rate on the temperature consistency and uniformity of large marine battery packs. This work seeks to design the cooling system for the battery pack and analyzes the impact of the temperature, flow rate, and battery discharge rate of the liquid fluid on the consistency and uniformity of the battery pack temperature on the overall structure of the battery pack. It was found that, in low discharge conditions, there was good temperature consistency between the battery packs and between the different batteries within the battery pack, and the temperature difference did not exceed 1 °C. However, under high discharge rates, a C-rate of 4C, there might have been a decrease in temperature consistency; the temperature rise rate even exceeded 50% compared to when the discharge rate was low. The flow rate in the liquid flow characteristics had little effect on the temperature consistency between the batteries and the temperature uniformity on the battery surface, and the temperature fluctuation was maintained within 1 °C. Conversely, the liquid flow temperature had little effect on the temperature distribution between the batteries, but it caused discrepancies in the surface temperature of the batteries. In addition, the liquid flow temperature could cause the overall temperature of the battery to increase or decrease, which also occurs under different discharge rates. [ABSTRACT FROM AUTHOR]

This study focuses on the transformation of Jaguar XJ40 vehicles to electric power, with the main equipment being a permanent-magnet synchronous motor (PMSM), lithium iron phosphate (LFP) batteries, an on-board charger (OBC) system, and a battery management system (BMS). The process involves integrating the PMSM with the vehicle's existing transmission system. This research compares the driving range of battery electric vehicles (BEVs) using different testing methods under the same conditions: simulation, dynamometer (dino), and actual on-road testing. Based on Raminthra's public roads (RITA drive cycle), one drive cycle covers 7.64 km in 11.25 min. The simulation test by MATLAB/SIMULINK R2016a predicts a driving distance of up to 282.14 km. The dino test, using a chassis dynamometer to simulate driving conditions while the vehicle remains stationary, indicates a driving distance of 264.68 km. In contrast, actual on-road tests show a driving distance of 259.09 km, accounting for real-world driving conditions, including variations in speed, road types, weather, and traffic. The motor achieves 95% efficiency at 2400 rpm and 420 Nm torque. The simulated distance differs from the actual road distance by approximately 8.17%, suggesting reasonable accuracy of the model. [ABSTRACT FROM AUTHOR]

As embedded systems become increasingly complex, traditional reliability analysis methods based on text alone are no longer adequate for meeting the requirements of rapid and accurate quantitative analysis of system reliability. This article proposes a method for automatically generating and quantitatively analyzing dynamic fault trees based on an improved system model with consideration for temporal characteristics and redundancy. Firstly, an "anti-semantic" approach is employed to automatically explore the generation of fault modes and effects analysis (FMEA) from SysML models. The evaluation results are used to promptly modify the system design to meet requirements. Secondly, the Profile extension mechanism is used to expand the SysML block definition diagram, enabling it to describe fault semantics. This is combined with SysML activity diagrams to generate dynamic fault trees using traversal algorithms. Subsequently, parametric diagrams are employed to represent the operational rules of logic gates in the fault tree. The quantitative analysis of dynamic fault trees based on probabilistic models is conducted within the internal block diagram of SysML. Finally, through the design and simulation of the power battery management system, the failure probability of the top event was obtained to be 0.11981. This verifies that the design of the battery management system meets safety requirements and demonstrates the feasibility of the method. [ABSTRACT FROM AUTHOR]

The state of health (SOH) of lithium-ion batteries is a critical parameter of the battery management system. An accurate estimation of it can improve the battery's overall life. The data-driven method has the problems of low long-term prediction accuracy and difficult feature recognition. Therefore, this paper proposes a SOH estimation method based on the convolutional gated recurrent neural network with the self-attention mechanism. Firstly, the convolutional neural network (CNN) is applied to the input data, and its convolution operation is used to extract important local features. Then, the self-attention mechanism is added later to give greater weight to the more important features in the training process. Finally, the gated recurrent unit (GRU) recurrent neural network extracts the long-term correlation from the weighted output data. The experimental data of lithium-ion batteries from three sources are used to verify the correctness and effectiveness of this method. Experiments were based on whether the self-attention mechanism is present, different prediction starting points and ambient temperature, and analyzed and compared with the common machine learning methods. The experimental results show that the method exhibits good estimation accuracy, with a maximum mean absolute error below 1.78% and a maximum root mean square error below 2.3%. [ABSTRACT FROM AUTHOR]

Achieving precise estimates of battery cycle life is a formidable challenge due to the nonlinear nature of battery degradation. This study explores an approach using machine learning (ML) methods to predict the cycle life of lithium‐metal‐based rechargeable batteries with high mass loading LiNi0.8Mn0.1Co0.1O2 electrode, which exhibits more complicated and electrochemical profile during battery operating conditions than typically studied LiFePO₄/graphite based rechargeable batteries. Extracting diverse features from discharge, charge, and relaxation processes, the intricacies of cell behavior without relying on specific degradation mechanisms are navigated. The best‐performing ML model, after feature selection, achieves an R2 of 0.89, showcasing the application of ML in accurately forecasting cycle life. Feature importance analysis unveils the logarithm of the minimum value of discharge capacity difference between 100 and 10 cycle (Log(|min(ΔDQ 100–10(V))|)) as the most important feature. Despite the inherent challenges, this model demonstrates a remarkable 6.6% test error on unseen data, underscoring its robustness and potential for transformative advancements in battery management systems. This study contributes to the successful application of ML in the realm of cycle life prediction for lithium‐metal‐based rechargeable batteries with practically high energy density design. [ABSTRACT FROM AUTHOR]

Battery modeling and state of charge (SoC) estimation are critical functions in the effective battery management system (BMS) operation. Temperature directly affects the performance and changes the model accuracy of a battery. Most studies have focused on estimating the internal temperature of the battery from the surface temperature of the battery with the help of sensors. However, due to the high number of cells in battery packs, the increase in sensor costs and the number of parameters have been ignored. Therefore, this article presents a new framework for the temperature effect using the electrical circuit model. The terminal voltage of the battery includes the effect under different operating conditions. This effect was associated with internal resistance in the battery model. The developed temperature-effective battery model was tested at different temperatures and operating currents. The model was validated with a maximum average root mean square error of 0.05% from the test results. The SoC of the LTO battery was estimated with the sigma-point Kalman (SPK) filter incorporating the developed model. The maximum average root mean square error in the estimation results is 0.11%. It is suitable for practical applications due to its low cost, simplicity, and reliability. [ABSTRACT FROM AUTHOR]

In view of the problem of rapid estimation of capacity attenuation after multiple charge and discharge cycles of LIB(lithium-ion battery), a new method based on the capacity attenuation model of lithium-ion battery is proposed to rapidly estimate its internal capacity attenuation using the external expansion displacement of the battery. Based on the capacity attenuation model of LIB, the radial irreversible expansion model is derived, and the cylindrical LIB Sanyo UR18650E is modeled and solved by COMSOL Multiphysics, and the numerical results are compared with the experimental data, so as to verify the proposed model. Based on the above model, the causes of capacity attenuation and irreversible expansion of battery due to side reactions during the charge-discharge cycle are analyzed. The results show that the change of side reaction rate causes the gradient distribution of the concentration of side reaction products in the negative electrode, and the accumulation of side reaction products causes the expansion displacement of the battery to increase linearly with the cycle. The function formula of the cell capacity attenuation and its radial displacement is obtained by using the side reaction product as a bridge, which provides a new method for the rapid estimation of the capacity attenuation. [ABSTRACT FROM AUTHOR]

Batteries, particularly lithium‐ion batteries, are sensitive to temperature changes. Battery thermal management systems (BTMS) are essential in various battery‐powered applications, especially electric vehicles (EVs) and portable electronic devices. This study examines the importance of phase change material (PCM) in battery packs using numerical analysis. An examination is conducted on a battery pack consisting of 18 650 battery cells arranged in a 5 × 5 configuration. A comparative analysis is performed to evaluate the thermal efficiency of the battery pack with and without PCM. The study examines the influence of ambient conditions and charging rates on the selection of PCM for battery packs. A hybrid cooling solution utilizing PCM and a water tube has also been investigated against conventional passive PCM‐based BTMS. Additionally, two types of fins, namely circular and spiral fins, are introduced to improve the heat transfer rate. PCM‐based cooling systems are most effective when the ambient temperature is below the melting temperature of the PCM. However, when the ambient temperature exceeds the melting temperature of the PCM, this cooling system outperforms conventional PCM‐based cooling. The maximum temperature is found as 319, 316.9, and 315.3 K using without fin, circular fin and spriral fina, respectively. The spiral fins are found more effective than circular fins under high ambient temperature. In conclusion, The PCM‐with spiral‐fin system demonstrates notable benefits in high‐temperature environments. [ABSTRACT FROM AUTHOR]

Modern electric vehicle (EV) batteries need phase change materials (PCM) that are capable of efficient battery cooling. In this work, a composite PCM is prepared by mixing Fe3O4 nanoparticles (1 wt.%) in paraffin, and the effects of these nanoparticles on the enthalpy and melting point of PCM are studied. It is found that the Fe3O4 nanoparticle additives reduce the onset of melting from 61.46°C to 57.03°C. The composite PCM is used for the cooling of a battery module of 6 substitute‐18 650 batteries, and the cooling performance is experimentally and numerically investigated. The hybrid battery thermal management system (BTMS) utilizing composite paraffin demonstrates a significant reduction of 11.2°C in lithium‐ion battery (LIB) temperature compared with natural convection cooling at a heat generation rate of 2W. The numerical results in this study are in good agreement with the experimental temperature values, with a modest mean absolute error of 1.35°C detected between experimentally obtained and simulated battery temperature values. In order to deal with the low thermal conductivity of liquid PCM after PCM melting, a numerical investigation is conducted to study the effect of a graphite fin on the battery temperature. The use of a fin in hybrid BTMS considerably reduces the temperature of LIBs and temperature difference in the module. The numerical simulations capture the behavior of the phase change phenomenon, showing the evolution of liquid PCM under constant heating. This work presents the dynamic melting patterns of PCM along the length of LIB with and without a fin, which is useful for the effective design of BTMS. [ABSTRACT FROM AUTHOR]

The thermal performance of a lithium-ion battery pack for an electric vehicle by adding straight rib turbulators in battery cooling plate channels has been numerically investigated in this paper and the numerical model of the battery pack has been validated by experimental data, which exhibits a satisfactory prediction accuracy. The effects of rib shapes, rib angles, rib spacings, and irregular gradient rib arrangement configurations on the flow and heat transfer behaviors of battery pack cooling plates have been thoroughly explored and analyzed in this paper. In addition, the thermal performance of the ribbed battery cooling plates was examined at actual high-speed climbing and low-temperature heating operating conditions. The results indicate that compared to the original smooth cooling plate, the square-ribbed battery cooling plate with a 60° angle and 5 mm spacing reduced the maximum battery temperature by 0.3 °C, but increased the cross-sectional temperature difference by 0.357 °C. To address this issue, a gradient rib arrangement was proposed, which slightly reduced the maximum battery temperature and lowered the cross-sectional temperature difference by 0.445 °C, significantly improving temperature uniformity. The thermal performance of the battery thermal management system with this gradient rib configuration meets the requirements for typical electric vehicle operating conditions, such as high-speed climbing and low-temperature heating conditions. [ABSTRACT FROM AUTHOR]

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery's surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency. [ABSTRACT FROM AUTHOR]

The state of charge (SOC) is a core parameter in the battery management system for LMFP batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. In order to achieve better SOC estimation accuracy, this article proposes an adaptive whale optimization algorithm (WOA) with chaotic mapping to improve the BP neural network (BPNN) model. The SOC estimation accuracy of the BPNN model was improved by utilizing WOA to find the optimal target weight values and thresholds. Comparative simulation experiments (including constant current and working condition discharge experiments) were conducted in Matlab/Simulink R2018a to verify the proposed algorithm and the other four algorithms. The experimental results show that the proposed algorithm had higher SOC estimation accuracy than the other four algorithms, and its prediction errors were less than 1%. This indicates that the proposed SOC estimation method has better prediction accuracy and stability, and has certain theoretical research significance. [ABSTRACT FROM AUTHOR]

In this article, the effects of circular, cylindrical, square, and hexagonal crater shapes on cooling performance were analyzed by numerical simulation and experimental verification, and then a new honeycomb cooling structure for cylindrical batteries (18650 type for example) was proposed. The effects of honeycomb depth, side length and wall thickness on the cooling performance were investigated. A quadratic response regression model was numerically calculated based on the experimental scheme obtained from the Box-Behnken central combination test. The results showed that the hexagonal crater cooling structure can reduce the maximum battery temperature by 5.3% compared to the non-crater cooling structure. Therefore, the proposed honeycomb structure was reasonable. In addition, increasing the depth and the hedge length could effectively reduce the maximum battery temperature and temperature difference, and when a certain value was exceeded, a reverse temperature growth trend occurs. Within the range of variation, wall thickness had little effect on the cooling performance. The cooling performance was optimal when the depth is 0.475 mm, the side length is 1.250 mm, and the wall thickness is 0.125 mm. The novel cooling structure proposed in this study can provide a new idea for the structural design of air-cooled thermal management system for cylindrical batteries. [ABSTRACT FROM AUTHOR]

The profitable commercialization and swift growth in electrified transportation requires load management controllers. Effective load management controllers can reduce the peak load on the power grid, balance the load demand and improve the stability of the power grid. Several techniques are available to optimize the load, including battery swapping, vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-home (V2H) approaches. This paper addresses effects of rising adoption of electric vehicles (EVs) and the potential for an increased load on the distribution system, which requires load optimization to ensure sustainable development. This paper offers a comprehensive review of all possible energy management controllers used in EVs for load optimization and power split controllers for splitting power between the battery and super-capacitor in hybrid energy storage systems (HESS) to increase battery life span. These controllers are classified into three categories, fuzzy logic controller, voltage-based controller and soft computing-based controller, with their constraints, advantages and disadvantages and finally sum up with their current challenges and future recommendation. The paper also reveals the wireless charging with their pros and cons. The findings have implications for the design and implementation of controllers for EVs to ensure their sustainable development and reduce fossil fuel usage. This study will be highly helpful to the researchers, industrialist and investors as a ready literature of energy management controllers for EVs, with information of their recommendations and challenges. [ABSTRACT FROM AUTHOR]