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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 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]

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]

Lithium-ion (Li-ion) battery has played a key role for the development of electric vehicle (EV) at present, while the Li-ion batteries in the market come from different manufactures. Verifying the performance of the battery management system (BMS) for various battery chemistries is a complex undertaking. This paper proposes a high-fidelity Li-ion battery emulator for EV applications. The emulator utilizes a battery model parameterized by a series of performance tests and a special-designed hardware platform. A three-order battery equivalent circuit model (ECM) is selected to provide the voltage and current reference signal in the processor. Subsequently, the hardware generates the high voltage and current signal in accordance with the reference. To ensure the high accuracy of the battery ECM, a 37 Ah nickel manganese cobalt (NMC) battery was selected for testing under both charge and discharge conditions, as well as across a temperature range of − 30℃ to 45℃. The battery emulator is verified on charge and discharge mode for both accuracy and dynamic performance validations. [ABSTRACT FROM AUTHOR]

This study experimentally investigated the performance of an oscillating heat pipe as a heat exchanger in the thermal management system of the lithium-ion cell. Acetone, Deionized (DI) water, methanol and ethanol were used as a working fluid at various volumetric filling ratios of 30%, 50% and 70% for ambient temperatures of 25°C, 30°C and 35°C. Results have shown that the acetone-filled Heat Pipe outperformed the four working fluids used in oscillating heat pipe-based cell cooling. The maximum average cell temperature was reduced to 39.4°C from 47.5°C and the maximum temperature difference was reduced to 3.5°C from 10°C. The reduction was noted at about 17% and 65% for the maximum average cell temperature and temperature difference, respectively compared to the bare battery cell. [ABSTRACT FROM AUTHOR]

This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have seen significant growth due to the increasing awareness about environmental concerns and the negative impacts of internal combustion engine vehicles (ICEVs). The electric vehicle landscape is rapidly evolving, with EV policies, battery, and charging infrastructure and electric vehicle-to-everything (V2X) at its forefront. This review study used a bibliometric analysis of the Scopus database to investigate the development of EV technology. This bibliometric study specifically focuses on analyzing electric vehicle trends, policy implications, lithium-ion batteries, EV battery management systems, charging infrastructure, EV smart charging technologies, and V2X. Through this detailed bibliometric analysis discussion, we aim to provide a better understanding of holistic EV technology and inspire further research in electric vehicles. The analysis covers the period from 1990 to 2022. This bibliometric analysis underscores the interplay of electric vehicle policies, technology, and infrastructure, specifically focusing on developments in battery management and the possibility of V2X technology. In addition, this bibliometric analysis suggests the synchronization of international electric vehicle policy, advancement of battery technology, and promotion of the use of EV smart charging and V2X systems. This bibliometric analysis emphasizes that the expansion of EVs and sustainable mobility relies on a comprehensive strategy that encompasses policy, technology, and infrastructure. This bibliometric analysis recommends fostering collaboration between different sectors to drive innovation and advancements in electric vehicle technology. [ABSTRACT FROM AUTHOR]

In response to the global pursuit of net-zero carbon emissions, the electrification of agricultural machinery is becoming a significant research and development trend. This study introduces the overall design of a 4 kW air compressor aimed at achieving a green vision for agricultural machinery. The design focuses on providing continuous and stable power and air output using a lithium-ion battery. Durability and cost-effectiveness are prioritized, with a particular emphasis on the Arduino system for integrating battery and motor systems to withstand harsh conditions and ensure ease of maintenance. A permanent magnet brushless motor was selected as the power source, paired with an optimized pulley to supply the proper torque to the air compressor. The system employs an Arduino-based feedback control sensor for air pressure regulation, ensuring energy efficiency. The primary energy source is a 48 V lithium iron phosphate battery, known for its high energy density and safety. The battery design focuses on system integration, addressing specific environmental discharge requirements. The embedded battery management system provides thermal and lifecycle parameter estimation, guaranteeing long-duration power supply and safe operation under various conditions. Unlike traditional fuel-driven systems, lithium iron phosphate batteries do not emit harmful gases, aligning with environmental standards. System integration testing demonstrated that the air pressure feedback control effectively meets the energy-saving requirements by digitally reducing power output as air accumulates in the chamber. Bench testing confirmed that the system performs as designed, achieving the desired results and advancing the goal of sustainable agricultural machinery. [ABSTRACT FROM AUTHOR]

Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO2 battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications. [ABSTRACT FROM AUTHOR]

In order to solve the compatibility problem of lithium batteries thermal management and cabin comfort in electric vehicles, a refrigerant direct cooling thermal management system is designed in this article, which is expected to balance the temperature demands of entire vehicle at different driving conditions. A one‐dimensional model is established to investigate the system performance, whose accuracy has been verified by the testing bench of a prototype fixed with a 16‐battery module. By the aid of this model, the thermal characteristics of the battery pack and the passenger cabin are obtained, and the compressor speed as well as the valves are selected as control units by sensitivity analysis of the important components. Subsequently, detailed control strategies around the variable frequency of compress speed and switch regulation for the valves are proposed based on the temperature changes, torque, and operating power consumption of the system in different control modes. Under the test conditions of the global light‐duty vehicle test procedure, it is found that cabin temperature can be quickly stabilized about 26 °C under different ambient temperatures. Additionally, the battery pack can be effectively maintained within the safe operating temperature range of 20–40 °C. [ABSTRACT FROM AUTHOR]

The existing battery management systems perform many functions, such as simply monitoring the battery's voltage, current, and temperature for the most basic and compensating energy imbalances between battery cells for the most advanced systems. In this last example, the function balancing helps protect the battery from obtaining a better lifespan. However, these systems with such functions remain complex because they involve techniques specific to power electronics and energy conversion. The number of components, implementation complexity, and cost increased. The work presented in this paper fits directly into this context. The main objective is to provide a solution to the problem of battery management and careful pack cell balancing. The proposed system aims to balance the battery pack cells based on the intermediate state of charge by charging or discharging the imbalanced cell. The implementation of the proposed control strategy was for a battery pack composed of five cells under MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

A hybrid technique is proposed for electric vehicles (EVs) or hybrid electric vehicles (HEVs) with a battery thermal management system (BTMS). The proposed hybrid method is the combined execution of the Pelican Optimization Algorithm (POA) and Firebug Swarm Optimization (FSO). The hunting behavior of pelicans is improved with the help of the FSO technique; hence, it is named the POA-FSO strategy. Here, a battery of 36 lithium-ion (Li-ion) cells is considered. The proposed method of analyzing battery thermal performance analyzed factors like heat flux from the battery cell to the passage spacing size, cooling air and the cooling air's mass flow rate (MFR). The BTMS performance is studied under maximum battery unit temperature, pressure drop and temperature uniformity. The proposed method enhances the coolant passage spacing and decreases the temperature difference in the battery cells. The proposed approach is executed on the MATLAB platform, and its performance is compared with existing approaches. From the results, it is concluded that the MFR maximizes, and a nonuniform distribution of the MFR of the passage occurs. By using the proposed approach, the maximal temperature change of cooling air among the passages is 2.0 K, the maximal temperature change between the battery cells is 7.5 K and a pressure drop of 228.03 Pa is obtained. [ABSTRACT FROM AUTHOR]

The increasing threat of cyber-attacks on modern power systems highlights the need for a comprehensive examination through simulations. This study conducts an in-depth simulation of cyber-attacks on critical smart grid components, including smart meters, substation automation, and battery management systems, to expose and analyze potential disruptions to power system operations. We identify vulnerabilities that can lead to severe grid instabilities, such as voltage variations, system collapses, and inverter failures. Our analysis underscores the complex interactions between cyber threats and grid components, revealing how disruptions extend beyond mere load interruptions to affect the core infrastructure. We advocate for integrating established cybersecurity frameworks like NIST, ISO/IEC 27001, and IEC 62443, essential in fortifying grid stability against these dynamic threats. Our findings highlight the urgent need for continuous adaptation and enforcement of these frameworks to enhance resilience and ensure the reliability of modern power grids against cyber-attacks. [ABSTRACT FROM AUTHOR]

Accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for maintaining the safe and stable operation of rechargeable battery management systems. However, this task is often challenging due to the complex temporal dynamics. Recently, attention-based networks, such as Transformers and Informer, have been the popular architecture in time series forecasting. Despite their effectiveness, these models with abundant parameters necessitate substantial training time to unravel temporal patterns. To tackle these challenges, we propose a straightforward MLP-Mixer-based architecture named "Intra–Inter Patch Mixer" (IIP-Mixer), which leverages the strengths of multilayer perceptron (MLP) models to capture both local and global temporal patterns in time series data. Specifically, it extracts information using an MLP and performs mixing operations along both intra-patch and inter-patch dimensions for battery RUL prediction. The proposed IIP-Mixer comprises parallel dual-head mixer layers: the intra-patch mixing MLP, capturing local temporal patterns in the short-term period, and the inter-patch mixing MLP, capturing global temporal patterns in the long-term period. Notably, to address the varying importance of features in RUL prediction, we introduce a weighted loss function in the MLP-Mixer-based architecture, marking the first time such an approach has been employed. Our experiments demonstrate that IIP-Mixer achieves competitive performance in battery RUL prediction, outperforming other popular time series frameworks, such as Informer and DLinear, with relative reductions in mean absolute error (MAE) of 24% and 10%, respectively. [ABSTRACT FROM AUTHOR]

In this study, the heat transfer model of a radiation–conduction–convection coupled lithium-ion battery pack is established through theoretical analysis. The temperature distribution and flow field distribution inside the battery pack are obtained by simulation using ANSYS Fluent software 2022 R1, and the reasonableness of the simulation model is verified with an experiment. This study also analyzes in detail the improvement effect of adding heat dissipation ribs, applying heat dissipation coatings, and adjusting the fan speed on the heat dissipation performance of the system. Under the same heat sink rib height conditions, the relationship between its thickness and total heat dissipation and thermal efficiency is studied in depth, and the temperature distribution of the cell under different rib thicknesses is obtained. At the same time, the emissivity of the heat sink coating under different coating thicknesses was measured by infrared thermography, and the relevant design values were determined through simulation experiments. Finally, based on the experimental test results of fan performance, a corresponding control strategy is proposed to construct an efficient and high-performance multiple-input multiple-output (MIMO) battery thermal management system. The experimental results show that optimizing the structure of the forced air cooling system through the above measures can ensure that the Li-ion battery operates within the efficient operating temperature range, thus extending its cycle life, improving its stability, and reducing the risk of thermal runaway. Meanwhile, the problem of excessive temperature difference between different modules is improved, and the output capacity of the energy storage system is increased. [ABSTRACT FROM AUTHOR]

Battery voltage plateau characteristics are crucial for designing and controlling battery management systems. Utilising the plateau period attributes to their fullest extent can enable optimal battery control, enhance battery performance, and prolong battery lifespan. This research aimed to investigate the performance of cylindrical ternary lithium batteries at various discharge rates, focusing on the variations in terminal voltage, capacity, and temperature. The battery performance at different discharge rates was meticulously examined through cyclic charge/discharge experiments. The convexity of the voltage curve was used to analyse the voltage plateau characteristics at different rates. The findings revealed significant differences in battery performance under varying discharge rates. Higher discharge rates resulted in shorter discharge times and lower battery voltages at corresponding residual capacities. The discharge time, capacity, and voltage during the plateau phase decreased as the discharge rate increased. At discharge rates of 1 C, 3 C, 5 C, 7 C, 9 C, and 11 C, the proportion of discharged battery capacity ranged from 86.45% to 78.42%. At the same time, voltage and temperature variations during the plateau period decreased significantly compared to those before and after discharge. This research provides a crucial reference point for advancing battery design and thermal management systems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for rapidly balancing lithium-ion batteries using a single DC–DC converter, enabling direct energy transfer between high- and low-voltage cells. Utilizing relays for cell pair selection ensures cost-effectiveness in the switch network. The control system integrates a battery-monitoring IC and an MCU to oversee cell voltage and ensure battery protection. A prototype circuit with twelve lithium-ion batteries demonstrates the method's efficacy, achieving a remarkable balancing time of 48 min during charging with a maximum efficiency of 89.85%. Comparative analysis with other methods underscores the superior performance of the proposed balancing circuit in terms of balancing time and implementation cost. Furthermore, this paper delves into hardware aspects of battery management systems (BMSs) for electric vehicles and stationary applications. It offers an overview of prevailing concepts in state-of-the-art systems, aiding readers in assessing considerations essential for BMS design in various applications. The discussion includes examples of battery packs sourced from commercially available electric vehicles. Subsequently, the manuscript addresses implementation aspects concerning the measurement of critical physical variables such as voltage, current, and temperature, alongside balancing strategies. [ABSTRACT FROM AUTHOR]

Electric mobility is pervasive and strongly affects everyone in everyday life. Motorbikes, bikes, cars, humanoid robots, etc., feature specific battery architectures composed of several lithium nickel oxide cells. Some of them are connected in series and others in parallel within custom architectures. They need to be controlled against over current, temperature, inner pressure and voltage, and their charge/discharge needs to be continuously monitored and balanced among the cells. Such a battery management system exhibits embarrassingly parallel computing, as hundreds of cells offer the opportunity for scalable and decentralized monitoring and control. In recent years, tiny machine learning has emerged as a data-driven black-box approach to address application problems at the edge by using very limited energy, computational and storage resources to achieve under mW power consumption. Examples of tiny devices at the edge include microcontrollers capable of 10–100 s MHz with 100 s KiB to few MB embedded memory. This study addressed battery management systems with a particular focus on state-of-charge prediction. Several machine learning workloads were studied by using IEEE open-source datasets to profile their accuracy. Moreover, their deployability on a range of microcontrollers was studied, and their memory footprints were reported in a very detailed manner. Finally, computational requirements were proposed with respect to the parallel nature of the battery system architecture, suggesting a per cell and per module tiny, decentralized artificial intelligence system architecture. [ABSTRACT FROM AUTHOR]

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The battery needs to provide a large amount of power in a short time under the condition of a high pulse rate. Real time and accurate State of Charge (SOC) prediction can help the battery management system understand the current status of the battery better, optimize the battery charging and discharging strategy, and improve the efficiency of the battery. In order to prolong battery life and enhance battery safety, a real-time prediction algorithm for SOC of the power battery under a high pulse rate was proposed. The second order RC equivalent circuit is used to establish the model of the battery. The equivalent circuit model of the battery is designed online using the recursive least squares algorithm, and the time-varying parameter model of the battery is established. Its output value is used as the input to the gating recurrent cell neural network, and the neural network is used to output the predicted SOC value. The SOC prediction result is used as the observation vector of the adaptive extended Kalman filter algorithm to obtain the final real-time prediction result of lithium ion power battery SOC. The experimental results show that the parameters identified by the research algorithm for lithium-ion power batteries are as follows: the fluctuation range of ohmic internal resistance is 0.05–0.40 Ω, and the fluctuation range of electrochemical polarization is 0–4.5 F. The terminal voltage values collected by the research algorithm have higher accuracy, with the error being always less than 0.03 V. Moreover, the algorithm can effectively predict the SOC of lithium-ion power batteries in real time, with a maximum average absolute error of about 2%. [ABSTRACT FROM AUTHOR]

This research presents an advanced control approach for battery management in battery electric utility vehicles (BEUV) operating in indoor logistics environments. The proposed approach utilizes a combination of proportional-integral (PI), fuzzy PI, and interval type 2 fuzzy PI (IT2fuzzyPI) control structures to augment the state space model for battery management. The state space model incorporates the voltage and current of each battery cell as state variables and considers the current demand from the electric motor as an input. By integrating fuzzy logic with PI control and considering uncertainty, the IT2fuzzyPI structure offers improved control recital and system robustness in the occurrence of nonlinearities, uncertainties, and turbulences. The outcomes of the simulation validate the effectiveness of the proposed scheme in managing the battery pack system’s state of charge and controlling the rates of charging and discharging. The IT2fuzzyPI control significantly improves the overall proficiency and longevity of the battery system, making it suitable for battery electric utility vehicles in logistics environments. This research contributes to the field of battery management systems, providing a valuable tool for designing and evaluating high-performance electric vehicles with enhanced control capabilities. [ABSTRACT FROM AUTHOR]

With the growing adoption of battery energy storage systems in renewable energy sources, electric vehicles (EVs), and portable electronic devices, the effective management of battery systems has become increasingly critical. The advent of wireless battery management systems (wBMSs) represents a significant innovation in battery management technology. Traditional wired battery management systems (BMSs) face challenges, including complexity, increased weight, maintenance difficulties, and a higher chance of connection failure. In contrast, wBMSs offer a robust solution, eliminating physical connections. wBMSs offer enhanced flexibility, reduced packaging complexity, and improved reliability. Given that wBMSs are still in a preliminary stage, this review paper explores their evolution, current state, and future directions. A comprehensive survey of state-of-the-art wBMS technologies, including academic and commercial solutions, is elaborated in this paper. We compare wireless communication technologies like Bluetooth Low Energy (BLE), Zigbee, Near-Field Communication (NFC), Wi-Fi, and cellular networks in the context of wBMSs. We discuss their performance in terms of efficiency, reliability, scalability, and security. Despite its promising outlook, wBMSs still face challenges such as data security, signal interference, regulatory and standardization issues, and competition from the continued advancement of wired BMS technologies, making the advantages of wBMSs less evident. This paper concludes with guidelines for future research and development of wBMSs, aiming to address these challenges and pave the way for a broad adoption of wBMSs across various applications. This paper aims to inspire further research and innovation in the field, contributing to developing an industry-ready wBMS. [ABSTRACT FROM AUTHOR]

The primary objective pursued in this research is the creation and thorough evaluation of an inventive cooling system designed to uphold optimal temperatures within the batteries employed in electric vehicles. Nowadays, the prevailing equipment underpinning electrical motion hinges on Lithium-Ion cells. These cells frequently necessitate the expeditious delivery of substantial power, thereby giving rise to a consequential generation of heat. Consequently, there is an emergence of elevated operational temperatures, potentially causing noteworthy declines in battery performance, or, in extreme cases, operational failures. Furthermore, deviating from the recommended temperature range (20–40 °C) significantly expedites the aging process of the battery and elevates the probability of premature malfunction. In response to these crucial challenges, the implementation of a battery thermal management system assumes a pivotal role in maximizing battery efficiency. Within the framework of this investigation, we propose the adoption of a cooling system founded on a three-dimensional pulsating heat pipe as the designated thermal management technology for a battery pack. The preliminary phase of our study involved the assessment of the suggested pulsating heat pipe's performance. Following this, the efficacy of the pulsating heat pipe is subjected to rigorous scrutiny through practical experimentation on an authentic electric battery. The empirical findings conclusively highlight the substantial cooling capacity of the proposed system, thereby presenting a robust and efficacious solution for the thermal management challenges faced by electric vehicle batteries. [ABSTRACT FROM AUTHOR]

Abstract-In recent years, electric vehicles have garnered significant attention, with lithium-ion batteries (LIBs) being central to their operation. Researchers and scholars have prioritized the accurate estimation of the state of charge (SOC) within the battery management system (BMS) as a key area of study. In this paper, by analyzing different equivalent circuit models, we choose to use the second-order RC model, elaborate the Kalman filter (KF) principle, and propose the adaptive extended Kalman filter (AEKF) to construct the estimation model of SOC. MATLAB validates the AEKF estimation model under two different operating conditions, UDDS and LA92, and the results show that the designed model can efficiently and accurately estimate the battery charge state with high competitiveness and accurately predict the real SOC direction regardless of the initial state, AEKF is more competitive than KF in terms of SOC prediction accuracy, Despite the different initial values of SOC, the roof-mean-square error of prediction was able to be controlled around one percent. [ABSTRACT FROM AUTHOR]

The short‐term load prediction is the critical operation in the peak demand administration and power generation scheduling of buildings that integrated the smart solar microgrid (SSM). Many research studies have proved that hybrid deep learning strategies achieve more accuracy and feasibility in practical applications than individual algorithms. Moreover, many buildings have integrated the SSM on the rooftop with the battery management system (BMS) to enhance energy efficiency management. However, the traditional methodologies only processed the weather parameters and power demand information for short‐term load prediction, ignoring the collected data from SSM and BMS by the advanced metering infrastructures (AMI), which probably improved prediction accuracy. In this research, many accumulated data of building and SSM are collected before methodology implementation. Considering the diversities of accumulated parameters from SSM and BMS, an adaptive convolution neural network long short‐term memory (CNN‐LSTM) is proposed for hourly electrical load prediction. The CNN could extract the critical large‐scale input feature, while the LSTM could achieve better accurate forecasts. The Pearson correlation matrix is calculated for the feature selection scheme from different data units. The hyperparameter tuning is utilized for obtaining the optimized hybrid CNN‐LSTM algorithm. The K‐fold cross‐validation is employed for deep learning algorithm verification, which includes LSTM, GRU, CNN, and Bi‐LSTM methodologies. The results prove that the hybrid CNN‐LSTM achieved outperformed improvements, which are 20.57%, 29.63%, 19.06% in MSE, MAE, MAPE, and 21.24%, 22.02%, 3.82% in validating MSE, MAE, MAPE, respectively. The hybrid CNN and LSTM combined with the feature selection scheme achieve superior predicting accuracies, proving the adaptability ability for integrating into the energy management system (EMS) of the building's SSM. [ABSTRACT FROM AUTHOR]

The link between the world of communications and the world of racing is provided by the telemetry systems in electric racing cars. These systems send real-time data about the vehicle's behavior and systems to enable informed decisions during the race. The objective of this research was to integrate telemetry into the battery bank of an electric racing car in order to find the optimal values of current and voltage that optimize the charging process and thus improve the performance of the vehicle in competition using Response Surface Analysis. Specifically, the telemetry system consisted of an Arduino Mega, a digital wattmeter, and temperature sensors, all installed in the vehicle. Once the telemetry data were obtained, a response surface design was fitted with current, voltage, and temperature as factors varying from low to high values, with the objective function being to minimize the battery charging time. Using the response surface methodology and the steepest descent algorithm, it was found that all factors significantly affect the charging time, with the minimum charging time being 6961 s, obtained with a current of 2.4 amps and voltages of 50.5 volts and 43.6 volts. [ABSTRACT FROM AUTHOR]

The power battery is a key component of electric vehicles and its performance is greatly affected by temperature. Battery thermal management systems based on phase change materials can effectively control the battery temperature and at the same time have the advantages of simple structures, energy savings, and good temperature uniformity, and has broad development prospects. In this paper, expanded graphite–paraffin composite phase change materials were prepared, phase change material cooling experiments were carried out, and a phase change material cooling simulation model was also established using the Fluent software to study the influence of phase change material thermophysical parameters on thermal management performance. The results show that the phase change material thermal management method has excellent cooling performance. The best thermal management performance is achieved at the 3C discharge rate, with a phase change material filling thickness of 4 mm, a melting point of 40 °C above ambient temperature, and a thermal conductivity of 3 W/(m·K). When the phase change latent heat was increased from 150 J/g to 250 J/g, the liquid phase ratio decreased from 0.84 to 0.51, and the subsequent cooling performance was greatly improved, so the phase change latent heat should be increased as much as possible. [ABSTRACT FROM AUTHOR]

Traditional fuel vehicles are currently still the main means of transportation when people travel. It brings convenience to their travels, but it also causes energy shortages and environmental pollution. With the development of science and technology and the popularization of green environmental protection, electric vehicles have gradually entered people's lives, greatly alleviating these problems. As a power supply device for electric vehicles, the performance of batteries directly affects various indicators of vehicles. Due to their long lifespan and high energy density, lithium-ion batteries are now the preferred source of power for electric vehicles. However, due to various factors in the manufacturing and operation of lithium-ion batteries, there are often differences among individual cells. The power balance and performance of a battery pack are closely related. Thus, battery equalization is an important standard for a battery management system to work normally, and it is also one of the various battery management application problems. This paper reviews battery equalization systems and various active equalization circuits and summarizes the working principle and research progress of each active equalization circuit. Then, various active equalization circuits are analyzed and compared, and dynamic equalization for a second-life battery is introduced to enrich this review of equalization technology. Finally, the above contents are summarized and prospected. In order to obtain the best outcomes, different equalization circuits need to be chosen for various situations. [ABSTRACT FROM AUTHOR]

This paper proposes a test procedure for evaluating the degradation of cells in a battery pack. The test can be performed using only the charger's converters and the battery management system (BMS) without requiring sophisticated instrumentation. The method circumvents the difficulties related to the evaluation of derivative quantities for estimating the state of health (SOH) using integral quantities in the evaluation. The method introduces a 'degradation function' that is calculated with respect to the reference performance of pristine cells. The procedure was applied to the JuiceRoll Race Edition system, an innovative electric vehicle (EV) DC charger with internal storage, made in ENEL X and used during the MotoE championship races. Using this procedure, the degradation of performance in individual groups of cells composing the battery pack was quantified in comparison to the reference group. The procedure helps identify modules that have aged too early or show reliability issues. The method is mature for field operational applications. [ABSTRACT FROM AUTHOR]

The methodology presented in this work allows for the creation of a real-time adjustment of Kalman Filter process noise for lithium battery state-of-charge estimation. This work innovates by creating a methodology for adjusting the process ( Q ) and measurement ( R ) Kalman Filter noise matrices in real-time. The filter algorithm with this adaptative mechanism achieved an average accuracy of 99.56% in real tests by comparing the estimated battery voltage and measured battery voltage. A cell-balancing strategy was also implemented, capable of guaranteeing the safety and efficiency of the battery pack in all conducted tests. This work presents all the methods, equations, and simulations necessary for the development of a battery management system and applies the system in a practical, real environment. The battery management system hardware and firmware were developed, evaluated, and validated on a battery pack with eight LiFePO4 cells, achieving excellent performance on all conducted tests. [ABSTRACT FROM AUTHOR]

Batteries play a crucial role in the domain of energy storage systems and electric vehicles by enabling energy resilience, promoting renewable integration, and driving the advancement of eco-friendly mobility. However, the degradation of batteries over time remains a significant challenge. This paper presents a comprehensive review aimed at investigating the intricate phenomenon of battery degradation within the realm of sustainable energy storage systems and electric vehicles (EVs). This review consolidates current knowledge on the diverse array of factors influencing battery degradation mechanisms, encompassing thermal stresses, cycling patterns, chemical reactions, and environmental conditions. The key degradation factors of lithium-ion batteries such as electrolyte breakdown, cycling, temperature, calendar aging, and depth of discharge are thoroughly discussed. Along with the key degradation factor, the impacts of these factors on lithium-ion batteries including capacity fade, reduction in energy density, increase in internal resistance, and reduction in overall efficiency have also been highlighted throughout the paper. Additionally, the data-driven approaches of battery degradation estimation have taken into consideration. Furthermore, this paper delves into the multifaceted impacts of battery degradation on the performance, longevity, and overall sustainability of energy storage systems and EVs. Finally, the main drawbacks, issues and challenges related to the lifespan of batteries are addressed. Recommendations, best practices, and future directions are also provided to overcome the battery degradation issues towards sustainable energy storage system. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries, as the main energy storage component of electric vehicles (EVs), play a crucial role in ensuring the safe and reliable operation of the battery systems through monitoring their state of health (SOH). However, temperature variations and battery aging have significant impacts on the internal parameters of lithium-ion batteries, and these changes directly correlate with the accuracy of battery SOH estimation. To address these issues, this paper proposes an estimation method for lithium-ion battery SOH that considers the impact of temperature. The method begins with reconstructing a second-order hybrid equivalent circuit model for lithium-ion batteries, through which an adaptive update rate for battery model parameters is designed. On this basis, a nonlinear observer for battery states is introduced by integrating filters to estimate SOH. The proposed method considers the impact of capacity in the design of the parameter adaptive update rate, enabling the capacity to be dynamically adjusted based on the actual state of the batteries. This reduces the cumulative error in the SOC observer and improves the modeling accuracy of battery models. Experimental results demonstrate that the method proposed in this paper exhibits exceptional performance in SOH estimation under different temperature conditions. The mean absolute error for SOH estimation does not exceed 0.5%, and the root mean square error does not exceed 0.2%. This method can significantly improve the estimation accuracy of SOH, providing a more efficient and accurate solution for battery management systems in EVs. [ABSTRACT FROM AUTHOR]

Accurate prediction of remaining useful life (RUL) plays an important role in maintaining the safe and stable operation of Lithium-ion battery management systems. Aiming at the problem of poor prediction stability of a single model, this paper combines the advantages of data-driven and model-based methods and proposes a RUL prediction method combining convolutional neural network (CNN), bi-directional long and short-term memory neural network (Bi-LSTM), SE attention mechanism (AM) and adaptive unscented Kalman filter (AUKF). First, three types of indirect features that are highly correlated with RUL decay are selected as inputs to the model to improve the accuracy of RUL prediction. Second, a CNN-BLSTM-AM network is used to further extract, select and fuse the indirect features to form predictive measurements of the identified degradation metrics. In addition, we introduce the AUKF model to increase the uncertainty representation of the RUL prediction. Finally, the method is validated on the NASA dataset and the CALCE dataset and compared with other methods. The experimental results show that the method is able to achieve an accurate estimation of RUL, a minimum RMSE of up to 0.0030, and a minimum MAE of up to 0.0024, which has high estimation accuracy and robustness. [ABSTRACT FROM AUTHOR]

Abstract This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems.

This study proposes a battery thermal management system based on L-shaped heat pipes coupled with liquid cooling. Experimental and computational fluid dynamics (CFD) numerical simulation studies have been conducted on the performance of the thermal management system. The thermal performance of three heat dissipation methods including forced air cooling, bottom liquid cooling and heat pipe coupled liquid cooling were compared. The results demonstrate that the coupling system can control the maximum temperature and temperature difference of the module at 30.12°C and 2.02°C at a 3C discharge rate. Compared with forced air cooling and bottom liquid cooling, the maximum temperature was decreased by 30.16% and 17.01% and the temperature difference was decreased by 72.14% and 77.20%, respectively. Studied the impact of factors such as coolant flow rate, the number of liquid-cooled plate channels, and the coolant inlet temperature under different ambient temperatures on the thermal management performance of the coupled system. By monitoring the maximum temperature of the module and the ambient temperature, a method for controlling the flow rate and the inlet temperature of the cooling water has been developed to implement an intermittent liquid cooling strategy for the battery module. Intermittent liquid cooling at various ambient temperatures can obtain similar thermal management performance to continuous liquid cooling, while significantly reducing liquid cooling energy consumption. Compared to continuous liquid cooling, intermittent liquid cooling can reduce energy consumption by a maximum of 97.05% and a minimum of 30.00%. [ABSTRACT FROM AUTHOR]

Abstract Ancillary service provision and peak shaving (PS) play essential roles in the current day‐to‐day power system operation, which is challenged by the increasing renewable generation penetration. Providing these critical services using classical approaches such as peak load generators has been limited due to high operational costs and environmental impacts. The use of battery energy storage systems (BESS) is another popular method that is limited by high initial investment costs and high degradation rates. In this work, a novel approach to utilize industrial loads and BESS to provide multiple power system services in different stages is proposed. Industrial loads such as aluminium crushers are known for their intensive electricity consumption. Nevertheless, when applied in frequency regulation (FR), they perform poorly due to their discrete nature in operation. This drawback and the aforementioned BESS shortcomings are addressed by combining on‐site BESS with plant machinery to provide FR services and recover BESS related costs. Later, depending on the optimal capacity distribution, BESS usage is extended into the energy arbitrage market to provide PS services. This approach resulted in higher earnings for participating customers and network operators, as well as in less CO2 emissions, and minimal BESS degradations. An Australian case study of the South West Interconnected System, along with Worsley Alumina refinery data of Western Australia has been used to showcase the model performances.

Abstract This paper proposes a comprehensive control strategy for a battery energy storage system (BESS) participating in primary frequency modulation (FM) while considering the state of charge (SOC) recovery. On the one hand, when the frequency fluctuations are outside the dead zone of the FM, variable coefficient virtual sag control and variable coefficient virtual inertia control are coordinated for more accurate primary FM. Also, an assisted FM strategy is proposed, where the temporal alignment of assisted FM is determined by the regional segmentation of the frequency deviation and SOC, and it enables the BESS to recover SOC concurrently during its involvement in primary FM. On the other hand, when the frequency fluctuations are inside the dead zone of the FM, typical S‐type functions are deployed to dynamically establish the mutual constraints relationships of SOC recovery demand coefficient and frequency deviation. Subsequently, the recovery control coefficients are derived, and accordingly, a SOC recovery strategy is proposed. Finally, simulation results verify the efficacy of the proposed control strategy in the grid primary FM system and demonstrate that the strategy expedites the SOC recovery and concurrently diminishes the conventional unit output.