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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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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With the developing technology, energy storage systems, especially lithium-ion batteries (LiBs), play a critical role in electric vehicles and renewable energy applications. However, the performance and life of batteries are significantly affected by their operating temperatures. In this context, battery thermal management systems (BTMS) are of vital importance to ensure temperature control of batteries and to create a safe operating environment. BTMSs are divided into main two groups active and passive which require and does not require extra energy consumption, respectively. In this review, the basic principles, design criteria and application areas of battery thermal management systems are examined. First of all, the components of BTMS, passive and active cooling methods, heat dissipation and temperature monitoring techniques are detailed. In addition, the effects of different BTMS approaches on efficiency and performance are compared. The analysis of existing studies in the literature reveals the positive contributions of BTMS on battery life, charge-discharge efficiency and safety. In addition, future research areas and development opportunities are also highlighted. In conclusion, an effective thermal management system is a critical factor in the development of battery technology and has great potential in terms of sustainability of energy systems. [ABSTRACT FROM AUTHOR]

Formula Student (FS) competitions aim to prepare and encourage engineering students to participate in the progression of automotive and motorsport industries. The built racing cars adhere to strict regulations set by competition guidelines to ensure the safety of both teams and spectators. For electric racing cars, the high-voltage (HV) battery system usually operates within a voltage range between 100 V to 600 V to supply the motor and its controller with the required electrical power. It is essential to ensure that these components are operating effectively to minimize battery and motor current as well as to ensure efficient and reliable performance throughout the race. A low-voltage control system (LVCS), usually operating at 12 V, is used to coordinate a wide array of critical operational and safety functions to control the HV system. These functions include: (1) turning on/off procedures, (2) monitoring speed, voltage, and current, (3) interfacing with pedals, (4) controlling dashboard features, (5) managing lighting, (6) facilitating data communication, and (7) implementing safety protocols. The design and operation of the LVCS are crucial for compliance with safety regulations and enhancing the FS electric racing car (FSERC) performance. This details and discusses the design procedures of the LVCS, using the Lancaster E-Racing (LER) FSERC as a case study. The LER car employs a 400 V battery system to power a 68-kW permanent manet synchronous motor (PMSM) using a three-phase voltage source inverter. Using mathematical analysis, SIMULINK/MATLAB® computer simulations, and the experimental real-data results provided by the LER FSERC, this study seeks to offer valuable insights regarding the LVCS practical implementation and optimization. [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]

This paper presents a novel integrated control architecture for automotive battery management systems (BMSs). The primary focus is on estimating the state of charge (SoC) and the state of health (SoH) of a battery pack made of sixteen parallel-connected modules (PCMs), while actively balancing the system. A key challenge in this architecture lies in the interdependence of the three algorithms, where the output of one influences the others. To address this control problem and obtain a solution suitable for embedded applications, the proposed algorithms rely on an equivalent circuit model. Specifically, the SoCs of each module are computed by a bank of extended Kalman filters (EKFs); with respect to the SoH functionality, the internal resistances of the modules are estimated via a linear filtering approach, while the capacities are computed through a total least squares algorithm. Finally, a model predictive control (MPC) was employed for the active balancing. The proposed controller was calibrated with Samsung INR18650-20R lithium-ion cells data. The control system was validated in a simulation environment through typical automotive dynamic scenarios, in the presence of measurement noise, modeling uncertainties, and battery degradation. [ABSTRACT FROM AUTHOR]

The global reliance on electric vehicles (EVs) has been rapidly increasing due to the excessive use of fossil fuels and the resultant CO2 emissions. Moreover, EVs facilitate using alternative energy sources, such as energy storage systems (ESSs) and renewable energy sources (RESs), promoting mobility while reducing dependence on fossil fuels. However, this trend is accompanied by multiple challenges related to EVs' traction systems, storage capacity, chemistry, charging infrastructure, and techniques. Additionally, the requisite energy management technologies and the standards and regulations needed to facilitate the expansion of the EV market present further complexities. This paper provides a comprehensive and up-to-date review of the state of the art concerning EV-related components, including energy storage systems, electric motors, charging topologies, and control techniques. Furthermore, the paper explores each sector's commonly used standards and codes. Through this extensive review, the paper aims to advance knowledge in the field and support the ongoing development and implementation of EV technologies. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are cornerstones of renewable technologies, which is why they are used in many applications, specifically in electric vehicles and portable electronics. The accurate estimation of the remaining useful life (RUL) of a battery is pertinent for durability, efficient operation, and stability. In this study, we have proposed an approach to predict the RUL of a battery using partial discharge data from the battery cycles. Unlike other studies that use complete cycle data and face reproducibility issues, our research utilizes only partial data, making it both practical and reproducible. To analyze this partial data, we applied various deep learning methods and compared multiple models, among which ConvLSTM showed the best performance, with an RMSE of 0.0824. By comparing the performance of ConvLSTM at various ratios and ranges, we have confirmed that using partial data can achieve a performance equal to or better than that obtained when using complete cycle data. [ABSTRACT FROM AUTHOR]

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load. [ABSTRACT FROM AUTHOR]

Electric vehicle systems and smart grid systems are setting stringent development targets to respond to global trends in energy saving, carbon reduction, and sustainable environmental development. In the field of batteries, there has been extensive discussion on the estimation of battery charge. In battery management systems (BMSs) and charging/discharging systems, the accuracy of the measurement of battery physical parameters is critical, as it directly affects the system, alongside the algorithm's estimation and error correction. Therefore, this paper proposes incorporating a low-power continuous-time delta-sigma analogue-to-digital converter into a battery measurement system to support deep learning algorithms for battery state estimation. This approach aims to maintain the accuracy of battery state estimation while reducing latency and overall system power consumption. Implemented using the UMC 0.18 μm CMOS 1P6M process, the proposed design achieves a measured signal-to-noise distortion ratio (SNDR) of 78.42 dB, an effective number of bits (ENOB) of 12.73 bits, and a power consumption of approximately 15.97 μW. The chip layout area is 0.67 mm × 0.56 mm. By applying delta-sigma modulators to energy management systems, this solution aims to increase the total number of battery monitoring units while reducing overall power consumption and construction costs. [ABSTRACT FROM AUTHOR]

This study proposes a deep learning-based long short-term memory (LSTM) model to predict the state of charge (SOC) of lithium-ion batteries. The purpose of the research is to accurately model the complex nonlinear behavior that occurs during the charging and discharging processes of batteries to predict the SOC. The LSTM model was trained using battery data collected under various temperature and load conditions. To evaluate the performance of the artificial intelligence model, measurement data from the CS2 lithium-ion battery provided by the University of Maryland College of Engineering was utilized. The LSTM model excels in learning long-term dependencies from sequence data, effectively modeling temporal patterns in battery data. The study trained the LSTM model based on battery data collected from various charge and discharge cycles and evaluated the model's performance by epoch to determine the optimal configuration. The proposed model demonstrated high SOC estimation accuracy for various charging and discharging profiles. As training progressed, the model's predictive performance improved, with the predicted SOC moving from 14.8400% at epoch 10 to 12.4968% at epoch 60, approaching the actual SOC value of 13.5441%. Simultaneously, the mean absolute error (MAE) and root mean squared error (RMSE) decreased from 0.9185% and 1.3009% at epoch 10 to 0.2333% and 0.5682% at epoch 60, respectively, indicating continuous improvement in predictive performance. In conclusion, this study demonstrates the effectiveness of the LSTM model for predicting the SOC of lithium-ion batteries and its potential to enhance the performance of battery management systems. [ABSTRACT FROM AUTHOR]

The design of an efficient thermal management system for a lithium-ion battery pack hinges on a deep understanding of the cells' thermal behavior. This understanding can be gained through theoretical or experimental methods. While the theoretical study of the cells using electrochemical and numerical methods requires expensive computing facilities and time, the Equivalent Circuit Model (ECM) offers a more direct approach. However, upfront experimental cell characterization is needed to determine the ECM parameters. In this study, the behavior of a cell is characterized experimentally, and the results are used to build a second-order equivalent electrical circuit model of the cell. This model is then integrated with the cooling system of the battery pack for effective thermal management. The Equivalent Circuit Model estimates the internal heat generation inside the cell using instantaneous load current, terminal voltage, and temperature data. By extrapolating the heat generation data of a single cell, we can determine the heat generation of the cells in the pack. With the implementation of the ECM in the cooling system, the coolant flow rate can be adjusted to ensure the attainment of a safe operating cell temperature. Our study confirms that 14% of pumping power can be reduced when compared to the conventional constant flow rate cooling system, while still maintaining the temperature of the cells within safe limits. [ABSTRACT FROM AUTHOR]

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Lithium-ion batteries (LIBs) have the lead as the most used power source for electric vehicles and grid storage systems, and a battery thermal management system (BTMS) can ensure the efficient and safe operation of lithium-ion batteries. Epoxy resin board (ERB) offers a wide range of applications in LIBs due to its significant advantages such as high dielectric strength, electrical insulation, good mechanical strength, and stiffness. This study proposes an air-cooled battery module comprised of sixteen prismatic batteries incorporating an ERB layer between the batteries. To compare the performance of the ERB-based air-cooling system, two other air-cooling structures are also assessed in this study. Three-dimensional numerical models for the three cases are established in this paper, and the heat dissipation processes of the battery module under varying discharge rates (1C, 2C, and 5C) are simulated and analyzed to comprehensively evaluate the performance of the different cooling systems. Comparative simulations reveal that incorporating ERB into the battery assembly significantly reduces battery surface temperatures and promotes temperature uniformity across individual batteries and the entire pack at various discharge rates. Notably, under 5C discharge conditions, the ERB-based thermal management system achieves a maximum battery surface temperature increase of 16 °C and a maximum temperature difference of 8 °C between batteries. Additionally, this paper also analyzes the impact of battery arrangement on air-cooling system performance. Therefore, further optimization of the structural design or the integration of supplementary cooling media might be necessary for such demanding conditions. [ABSTRACT FROM AUTHOR]

This study uses an equivalent circuit model (ECM) and real-time data to model lithium iron phosphate (LFP) batteries to accurately represent their thermo-electrical behavior. In particular, the focus is on a thermal management perspective in high-performance electric vehicles (EVs). The ECM-based battery management system, which effectively captures the non-linear behavior of Li-ion batteries, is developed to optimize the safety, lifespan and overall performance of the EV battery management system. The ECM-based battery model is validated using real-time drive cycle data to enhance the understanding of battery management systems, contributing to improved overall performance and reliability. In addition, advanced estimation algorithms, such as the extended Kalman filter, are integrated to further improve the predictive capabilities of battery parameters. Battery terminal voltage prediction with an average RMSE error of 0.015% is achieved, highlighting the critical role of ECMs and advanced numerical simulation methods in optimizing the performance of automotive battery management systems. The achieved results provide important guidance for model-based design validation and functional development of battery management for mobility applications. [ABSTRACT FROM AUTHOR]

Lithium-ion battery state of health (SOH) estimation is critical in battery management systems (BMS), with data-driven methods proving effective in this domain. However, accurately estimating SOH for lithium-ion batteries remains challenging due to the complexities of battery cycling conditions and the constraints of limited data. This paper proposes an estimation approach leveraging partial incremental capacity curves and transfer learning to tackle these challenges. First, only partial voltage segments are utilized for incremental capacity analysis, which are then fed into a stacked bidirectional gated recursive unit (SBiGRU) network, and finally, transfer learning is utilized to address issues related to limited data availability and differing data distributions. The method is further enhanced through hyperparameter optimization to refine estimation accuracy. The proposed method is validated in two publicly available datasets. For the base model, the root mean square error is 0.0033. With the transfer learning method, which utilized only 1.6% of the target domain data, the root mean square error is 0.0039. Experimental results demonstrate that the proposed method can accurately estimate SOH and works well in training and testing over different voltage ranges. The results underscore the potential of the proposed SOH estimation method for lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Technologies like data-driven methods for battery state estimation, fleet monitoring and cloud-based BMSs are emerging. However, challenges in data compression and storage hinder their widespread adoption. This paper addresses these issues by proposing a novel, efficient lossy voltage data compression method for measurements in electric vehicles. The method is grounded in polynomial regression and enables the use of the adaptive method without the need for parameters or training of the model which, representing an improvement over existing techniques. At a compression rate of 99.75% in an ambient temperature of 25 °C on average across all drive cycles compared, the root mean square error (RMSE) was 5.62 mV. Impressively, at a compression rate of 99%, the RMSE decreased to 3.12 mV. Furthermore, an implementation on a low-power STM32 microcontroller can compress 600 data points in just 35 milliseconds, demonstrating its suitability for real-time applications. These results highlight the potential of our approach to significantly improve the efficiency and accuracy of voltage measurement compression in electric vehicles, paving the way for advancements in electric vehicle technology. [ABSTRACT FROM AUTHOR]

A Y-type air-cooled structure has been proposed to improve the heat dissipation efficiency and temperature uniformity of battery thermal management systems (BTMSs) by reducing the flow path of air. By combining computational fluid dynamics (CFD) methods, the influence of the depths of the distribution and convergence plenums on the airflow velocity through battery cells was analyzed to improve heat dissipation efficiency. Adjusting the width of the first and ninth cooling channels can change the air velocity of these two channels, thereby improving the temperature uniformity of the BTMS. Further discussion was conducted regarding the influences of inlet and outlet depths. When the inlet width and outlet width were 20 mm, the maximum temperature and maximum temperature difference of the Y-type BTMS were 39.84 °C and 0.066 °C at a discharge rate of 2.5 °C, respectively; these temperatures were 1.537 °C (3.68%) and 0.059 °C (47.2%) lower than those of the T-type model. Meanwhile, the energy consumption of the sample also decreased by 13.1%. The results indicate that the heat dissipation performance of the proposed Y-type BTMS was improved, achieving excellent temperature uniformity, and the energy consumption was also reduced. [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]

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]

Exact state-of-charge estimation is necessary for every application related to energy storage systems to protect the battery from deep discharging and overcharging. This leads to an improvement in discharge efficiency and extends the battery lifecycle. Batteries are a main source of energy and are usually monitored by management systems to achieve optimal use and protection. Coming up with effective methods for battery management systems that can adequately estimate the state-of-charge of batteries has become a great challenge that has been studied in the literature for some time. Hence, this paper analyses the different energy storage technologies, highlighting their merits and demerits. The various estimation methods for state-of-charge are discussed, and their merits and demerits are compared, while possible applications are pointed out. Furthermore, factors affecting the battery state-of-charge and approaches to managing the same are discussed and analysed. The different modelling tools used to carry out simulations for energy storage experiments are analysed and discussed. Additionally, a quantitative comparison of different technical and economic modelling simulators for energy storage applications is presented. Previous research works have been found to lack accuracy under varying conditions and ageing effects; as such, integrating hybrid approaches for enhanced accuracy in state-of-charge estimations is advised. With regards to energy storage technologies, exploring alternative materials for improved energy density, safety and sustainability exists as a huge research gap. The development of effective battery management systems for optimisation and control is yet to be fully exploited. When it comes to state-of-the-art simulators, integrating multiscale models for comprehensive understanding is of utmost importance. Enhancing adaptability across diverse battery chemistries and rigorous validation with real-world data is essential. To sum up the paper, future research directions and a conclusion are given. [ABSTRACT FROM AUTHOR]

This study examines the thermal behavior of lithium-ion battery modules intended for Urban Air Mobility (UAM), a forthcoming urban transport system designed to facilitate efficient and secure passenger and cargo transport within city centers. UAM applications necessitate batteries with high energy densities capable of sustaining elevated discharge rates during critical phases such as takeoff and landing. The battery module evaluated in this study comprises four cells arranged in series and configured as a submodule for UAM applications. A three-dimensional thermal model was utilized to analyze the impact of external temperature fluctuations and high discharge rates on the performance of the battery module. The numerical findings indicated considerable variations in temperature and internal resistance among the cells, especially under high discharge rates at low temperatures, with a maximum temperature deviation of 32.952 °C observed at an 8 C discharge rate. These thermal non-uniformities were attributed to variations in cell capacity and internal resistance, which were amplified by manufacturing inconsistencies and operational conditions. The study underscores the necessity of robust thermal management strategies to mitigate the risk of thermal runaway and ensure the operational safety and reliability of UAM systems. The results emphasize the critical role of advanced Battery Management Systems (BMS) in monitoring and controlling cell voltage and temperature to achieve consistent performance across the battery module. This research contributes valuable insights into the design of more efficient and reliable battery modules for UAM, highlighting the importance of addressing cell-to-cell performance discrepancies to enhance overall module efficacy and durability. [ABSTRACT FROM AUTHOR]

This document discusses the optimization of operating conditions for battery thermal management systems (BTMS) in electric vehicles. The operational temperature of lithium-ion batteries has a significant impact on their efficiency and safety. The ideal temperature range for efficient operation is 20-40 °C, with a recommended temperature difference between batteries of no more than 5 °C. Researchers have explored various methods to regulate battery temperature, including using phase change materials (PCM) and heat dissipation techniques. These studies aim to improve battery performance in both low and high-temperature environments and prevent thermal runaway. The findings from these studies can contribute to the development of more efficient BTMS for lithium batteries. [Extracted from the article]

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]