Innovations in EV Battery Management Systems

A battery management system that balances battery life and charge rate requirements for electric vehicles. The system uses a multi-stage charging strategy with discharging and subcharging in addition to main charging. The charging rate targets are set to maintain an average low rate during the overall charging duration. This reduces battery degradation while allowing higher rates at the end to meet vehicle needs. The discharging uses battery output for vehicle functions like heating or cooling instead of wasting it.

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Battery management system for shared battery swapping infrastructure to proactively identify and recall faulty batteries in electric vehicles. The system identifies users with batteries that may have issues based on manufacturing data and prompts them to exchange the batteries. This allows proactive recall of potentially faulty batteries before they fail in the field. The system registers individual battery IDs when shared, identifies users with batteries to recall based on manufacturing data, and sends reminders to exchange those batteries using the user's device.

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Battery management system to prevent over-potential during charging by adaptively setting charge conditions based on history. The system stores over-potential management information from a previous charge cycle. During a new charge cycle, it uses the stored information to determine optimal charge parameters that suppress over-potential. Specifically, if the stored peak value is too high, indicating excessive over-potential, the system reduces the charge rate. If the new peak value is lower, indicating improved over-potential, the system updates the stored peak value for future charges.

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Dynamic adaptation of estimation gain in a battery management system to improve accuracy and stability of estimating battery characteristics like power capability and state-of-charge at low and high charge levels. The gain is adjusted based on battery state-of-charge (SOC) to match the changing battery dynamics at extreme SOCs and avoid overshooting. This prevents estimation errors like overestimating discharge power at low SOC or overestimating charge power at high SOC.

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Optimizing battery life of parked vehicles by intelligently managing cellular V2X and GPS when the engine is off. It turns off V2X and GPS when parked in a known safe location for long durations to conserve battery. But if parked at a hazardous location or with an unknown duration, it keeps V2X on to announce disabled vehicle status. The system determines hazard level based on location history and user input.

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Modular battery pack architecture that allows extending the life of a battery pack by selectively disabling and removing degraded cells while still providing power. A pack management unit senses cell conditions and identifies degraded cells. It then disables sensing of those cells and reconfigures the pack to exclude them, providing reduced capacity but still usable pack. This enables repurposing degraded cells and extending pack life compared to replacing the whole pack when one cell fails.

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This paper introduces a hybrid converter topology based on boost and push-pull configurations for electric vehicle (EV) charging applications. In the proposed structure, an RCD snubber circuit is used to reduce thermal losses in power electronic devices peak voltages individual components. Thus, by reducing switching components, efficiency of structure increased. The aims voltage stress components transformation ratio transformer converter. addition, another aim provide improved continuous rectified output current, thus extending system's life Moreover, tested conduction mode (CCM) compared with similar existing designs. To verify mathematical analysis, 500 W designed using estimation simulation environment. analysis yielded direct current. As result significant reduction stresses elements occurred topology. turns ratio, are reduced.

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The rapid growth of electric vehicles (EVs) has intensified the demand for accurate and interpretable battery health prediction systems. While machine learning models have demonstrated high accuracy in forecasting degradation, their black-box nature poses challenges real-world deployment safety-critical applications. This paper proposes an explainable artificial intelligence (XAI) framework degradation prediction, aiming to provide transparent reliable insights into energy storage dynamics EVs. study integrates data-driven such as Gradient Boosting Machines (GBMs) Long Short-Term Memory (LSTM) networks with post hoc explainability tools, including SHapley Additive exPlanations (SHAP) Local Interpretable Model-agnostic Explanations (LIME). Experimental evaluations on EV datasets show that proposed achieves strong predictive performance while offering outputs regarding feature influence dynamics. These findings suggest XAI-enabled can bridge gap between power trust, contributing smarter management systems sustainable transportation.

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This paper presents the design and simulation of fuzzy control rule-based for energy management in a parallel mild hybrid electric vehicle. The system must minimize fuel consumption while ensuring load maintenance component limitations. Equivalent is used to evaluate performance, which penalizes battery's use electrical but does not reward storage battery. presented model was tested two different driving cycles, Europe: NEDC USA: FTP-75 with an efficiency 17.6% 18.3% respectively. However, this result could be improved more in-depth testing error estimation, as well advanced simulations. In addition, rules can added proposed model, giving approach scalable functionality.

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Abstract - A Battery Monitoring System (BMS) is an electronic setup designed to track essential parameters of rechargeable batteries, such as voltage, current, and State-of-Charge (SoC). By preventing overcharging over-discharging, systems help extend the lifespan reliability batteries. However, commercially available BMoS solutions are often costly unsuitable for budget-friendly embedded systems. Given widespread use Arduino Uno its affordability, open-source platform, user-friendly programming environment, this study aims develop a using microcontroller. The proposed system includes voltage current sensors, board, liquid crystal display (LCD) real-time monitoring. To achieve this, set out three primary objectives. First, it was necessary mathematically establish relationship between sensors' input output values. These mathematical expressions were then validated by observing sensor outputs under varying load conditions connecting disconnecting monitoring corresponding readings. Following complete prototype assembled integrating sensors LCD with Uno. tested 11.1 V Lithium-ion ba... Read More

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Enhanced Battery Management Systems (BMS) are essential for improving operational efficacy and safety within Electric Vehicles (EVs), especially in tropical climates where traditional systems encounter considerable performance constraints. This research introduces a novel two-tiered deep learning framework that utilizes two-stage Long Short-Term Memory (LSTM) precise prediction of battery voltage SoC. The first tier employs LSTM-1 forecasts individual cell voltages across full-scale 120-cell Lithium Iron Phosphate (LFP) pack using multivariate time-series data, including history, vehicle speed, current, temperature, load metrics, derived from dynamometer testing. Experiments simulate real-world urban driving, with speeds 6 km/h to 40 variations 0, 10, 20%. second uses LSTM-2 SoC estimation, designed handle temperature-dependent fluctuations high-temperature environments. cascade design allows the system capture complex temporal inter-cell dependencies, making it effective under variable-load Empirical validation demonstrates 15% improvement estimation accuracy over methods driving co... Read More

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<title>Abstract</title> The inconsistency between power battery cells can seriously restrict the energy utilization efficiency of pack, accelerate aging batteries, and even significantly increase risk thermal runaway. Aiming at this inconsistency, an active equilibrium scheme combining dynamic programming (DP) model predictive control (MPC) is proposed. Firstly, topology circuit bidirectional flyback transformer with rapid equalization speed built, then a system prediction established through MPC to achieve rolling optimization. Finally, using DP solve cost function in conditional scenarios obtain global optimal current time, used predict state changes multiple sampling periods online correction balanced current. algorithm improves overall reduces loss while considering charging discharging equalizer ensure consistent convergence each individual SOC. Simulation experimental results show that strategy quickly effectively correct lithium packs under various conditions, thereby greatly improving balance pack.

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Smart vehicle systems for detecting and responding to severe thermal events in rechargeable battery packs. The systems employ wireless communication protocols to monitor battery temperature and surrounding environment, and automatically detect nearby vehicles or pedestrians within a defined proximity. They then assess the severity of the thermal event using data from both the battery and surrounding environment, and trigger appropriate vehicle responses such as alerts, system shutdown, or control actions to mitigate the thermal event.

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Preventing output limit of a battery in a vehicle by selectively raising the battery temperature based on the state of charge (SOC) and temperature during driving and restart. A method of preventing output limit of a battery of a vehicle includes monitoring the battery SOC and temperature while driving, determining the likelihood of output limit based on a map, and selectively increasing the battery temperature if output limit is likely. This prevents output limit when the SOC decreases in cold temperatures.

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Computationally efficient and accurate method for estimating battery states of electric vehicle energy storage systems using dual estimators. The dual estimators separately estimate battery state and parameter changes around nominal values. This allows tracking fast changes while reducing computation and storage compared to estimating total changes. The nominal values are also adaptively adjusted based on operational data to account for slow timescale variations. A lower frequency third estimator estimates the nominal values using downsampled dual estimator output. This reduces computation further by not using all historical data.

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Migrating interrupt handling between processing cores in a system-on-chip (SoC) to improve real-time responsiveness and reduce latency. The method involves dynamically assigning interrupt affinities and priorities at runtime based on real-time conditions and workload demands. This allows interrupts to be serviced by the most appropriate core without fixed affinity constraints. The technique involves monitoring core utilization, interrupt latency, and other metrics to determine optimal affinity and priority assignments. It also supports live migration of interrupt handling between cores during execution to further optimize performance. This enables dynamic interrupt management that can adapt to changing workloads and prioritize critical real-time interrupts over less time-sensitive ones.

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Battery management system for detecting cell abnormalities using just the cell voltage as input. The method involves generating an observation matrix of voltage histories for each cell, recovering it using principal components, and comparing the original and recovered matrices to find abnormal cells. This reduces computation, time, and power compared to monitoring multiple parameters.

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Thermal management system for electric vehicle batteries that allows individual cooling or heating of different zones within the battery to optimize performance and lifespan. The system uses multiple distinct circuits, each associated with a cooling zone, with independent flow control valves. A controller ranks the zones by temperature and adjusts the valves to balance cooling and heating based on the hottest and coldest zones. This provides customized cooling/heating to prevent hot spots and improve overall battery temperature management.

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Battery management system and method for optimizing battery utilization during storage. The system involves charging/discharging batteries in response to demand response signals from a power system. By cycling stored batteries this way, it allows effective utilization during storage periods rather than just sitting idle. The system also evaluates degradation of each battery based on charging/discharging data. This allows selecting batteries with the right condition for subsequent uses like reuse or rebuilding.

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Combining highly concentrated photovoltaic (HCPV) modules and battery storage systems attain high power- density than other ones using separate elements. This work aims to examine the performance of electric vehicle (EV) charging station utilizing an HCPV system integrated with lithium-ion batteries in Kuwait. is EV, storge system, AC grid as a four-component for EV at educational building located A theoretical technique developed calculate proposed efficiency. simple algorithm utilized reduction greenhouse emissions due use HCPV-EV. Energy produced from used charge four EVs, supplying energy electrical appliances feeding remaining grid. Present results reveal that represents 81% total load needed EVs about 90% required through utilization which can potentially reduce operating costs EVs. Only 10% (1218 kWh) be exported fully Also, provide 93% resulting big saving electricity bill. feed utility by 11458 kWh, additional decrease consumption costs. In all months, ratio (PR) higher 80%, indicating reliability system. Battery state (SOC) exceeds 70% most year months durability batteries.... Read More

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Abstract Lithium-based batteries (LiBs) are integral components in operating electric vehicles to renewable energy systems and portable electronic devices, thanks their unparalleled density, minimal self-discharge rates, favorable cycle life. However, the inherent safety risks performance degradation of LiB over time impose continuous monitoring facilitated by sophisticated battery management (BMS). This review comprehensively analyzes current state sensor technologies for smart LiBs, focusing on advancements, opportunities, potential challenges. Sensors classified into two primary groups based application: optimization. Safety sensors, including temperature, pressure, strain, gas, acoustic, magnetic focus detecting conditions that could lead hazardous situations. Performance optimization such as optical-based electrochemical-based, monitor factors charge health, emphasizing operational efficiency lifespan. The also highlights importance integrating these sensors with advanced algorithms control approaches optimize charging discharge cycles. Potential advancements driven nanotechnolo... Read More

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The global shift toward environmentally sustainable transportation has accelerated the development and adoption of Battery Electric Vehicles (BEVs). This paper presents a comprehensive evaluation BEVs using Estimation Based on Distance from Average Solution (EDAS) method, multi-criteria decision-making (MCDM) approach that facilitates an objective systematic comparison across multiple performance parameters. assessment considers critical technical user-centric criteria, including battery capacity, driving range, top speed, acceleration (0100 km/h), charging time, overall environmental impact. Eight popular BEV modelsKia EV6, Mahindra XUV400 EV, Hyundai Kona Electric, BMW i7, Jaguar I-Pace, Mercedes-Benz EQS, Audi e-tron GT, Porsche Taycanwere analyzed EDAS method to determine their relative scores. analysis revealed Kia EV6 ranks highest, demonstrating well-balanced profile all key indicators. Conversely, recorded lowest score due trade-offs in metrics. results offer practical implications for potential consumers, manufacturers, policymakers by identifying optimal models hig... Read More

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LiFePO4 batteries need a battery management system (BMS) to improve performance, extend their lifespan, and maintain safety by utilizing advanced monitoring, control, optimization techniques. This paper presents the design, development, implementation of an intelligent (i-BMS) that integrates real-time monitoring control batteries. The was extensively tested using multiple datasets, results show able temperature within set range, balance cell voltages, distribute energy according load prioritization. It uses fuzzy logic approach effectively manage farm requirements. Additionally, proposed method embedded three-level prioritization algorithm woven into rule allocate dynamically among essential, regular, non-essential loads.

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Accurate estimation of the state charge (SOC) lithium-ion batteries (LiBs) proportionally impacts efficiency battery management systems (BMS) considering dynamic and non-linear behavior LiBs. Changes in activities cathode anode materials internal resistance tend to impact capacity. When is operated at high or low temperatures under HWFET condition, capacity tends deteriorate drastically. Therefore, high-precision SOC required ensure safe stable operation. In this work, we propose a combined Improved Dung Beetle Optimization (IDBO) Extreme Learning Machine (ELM) framework for evaluate BMS. The novelty model stems from application IDBO algorithm, which incorporating Circle chaotic mapping, Golden sine strategy, Levy flight hyper-parameter optimization. This effectively resolves problems inconsistent performance instability arising randomly initialized hidden layer weights biases ELM, resulting enhanced prediction accuracy. proposed IDBO-ELM method validated context five parameters, namely, different ambient temperatures, operating conditions, materials, initial values, running time. ex... Read More

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Abstract The multistage constant current (MCC) charging protocol for lithiumion batteries is commonly used to balance lithium plating and time. Traditional methods depend on a predefined map without considering the feedback of subsequent selfregulation rate. To tackle this problem, an adaptive MCC method proposed, which based expansion force detect plating. By integrating experiments with simulations, results indicate that when occurs, experiences abnormal, accelerated increase. If rate reduced until ceases, decreases. Correspondingly, three thresholds, V1, V2, V3, in derivative (dF/dSOC), are identified. Utilizing these can be selfregulated. demonstrate speed increased by 50% causing irreversible proposed holds great promise integration into intelligent battery management systems, thereby enhancing performance fast charging.

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This paper shows that lithium-ion battery model parameters are vital for state-of-health assessment and performance optimization. Traditional evolutionary algorithms often fail to balance global local search. To address these challenges, this study proposes the Elite Opposition-Based Learning Snake Optimization (EOLSO) algorithm, which uses an elite opposition-based learning mechanism enhance diversity a non-monotonic temperature factor exploration exploitation. The algorithm is applied parameter identification of second-order RC equivalent circuit model. EOLSO outperforms some traditional optimization methods, including Gray Wolf Optimizer (GWO), Honey Badger Algorithm (HBA), Golden Jackal (GJO), Enhanced (ESO), (SO), in both standard functions HPPC experiments. experimental results demonstrate significantly SO, achieving reductions 43.83% Sum Squares Error (SSE), 30.73% Mean Absolute (MAE), 25.05% Root Square (RMSE). These findings position as promising tool modeling state estimation. It also potential applications management systems, electric vehicle energy management, other compl... Read More

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Abstract Batteries are commonly connected in series and parallel to create modules that fulfill the power energy requirements of specific applications. However, conclusions about battery performance degradation under different conditions, as well predictive models, often derived from single cell cycling results. In this study, we evaluate six series-parallel configurations commercial lithium nickel manganese cobalt cells over hundreds cycles. Each within was individually instrumented for voltage, current, temperature monitoring. We quantified impact module configuration on overall throughput, voltage-spread among series-connected cells, current heterogeneity parallel-connected cells. This one broadest reported date, supports systematic evaluation trade-offs, pack penalty, safety implications configurations.

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Safety concerns in lithiumion batteries pose significant challenges for electric vehicle systems. A reliable battery thermal management system is essential to maintain optimal performance. In this article, simulation carried out the design of aircooled packs with aligned, equally spaced staggered, and nonequally staggered arrangements, based on experimental validation. The spacing positioning are optimized cooling heating conditions determine heat dissipation configuration. results reveal that arrangements enhance transfer, configuration reducing maximum temperature difference by 15.6% energy consumption 36.6%, compared setup. Furthermore, considering control demands pack wind speed, state equation model predictive constructed solved particle swarm optimization algorithm. Subsequently, cosimulation employed verify its effectiveness. proposed strategy efficiently regulates reduces consumption, demonstrating potential improving practical applications.

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Multisine electrochemical impedance spectroscopy (EIS) represents a highly promising technique for the online characterization of battery functional states, offering potential to monitor, in real-time, key degradation phenomena such as aging, internal resistance variation, and state health (SoH) evolution. However, its widespread adoption embedded systems is currently limited by need balance measurement accuracy with strict energy constraints requirement short acquisition times. This work proposes novel broadband EIS approach based on multiband multisine excitation strategy which signal spectrum divided into multiple sub-bands that are sequentially explored. enables available be concentrated portion at time, thereby significantly improving signal-to-noise ratio (SNR) without substantially increasing total time. The result more energy-efficient method maintains high diagnostic precision. We further investigated optimal design these sequences, taking account realistic imposed sensing hardware limitations amplitude noise level. effectiveness proposed was demonstrated within comprehensiv... Read More

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Battery management system for electric vehicles with a refined voltage measurement technique to optimize battery performance and durability. The system manages an electric battery device with multiple modules connected in series, each containing cells. Some cells are connected by a busbar. The measurement technique accounts for busbar voltage offsets. It uses a single slave element to gather data from two modules. Measurements from cells on the busbar are adjusted based on the busbar resistance. This prevents overestimation due to busbar voltage. The adjusted cell voltages are used for safety methods like derating charging power. The technique improves accuracy by avoiding erroneous voltage readings from busbar cells.

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This study examines the thermal behaviour of a LiFePO battery pack in converted electric vehicle using GT-Suite simulation. The base vehicle, Toyota Avanza originally powered by 1.3-litre engine, was retrofitted with 60 kW synchronous AC motor and 268.8 V, 40.32 kWh pack. Simulations were conducted under NEDC WLTP driving cycles, both without passive cooling. Results showed that temperature peaked at 45.3C (NEDC) 71.6C (WLTP) cooling, reduced to 36.6C 48.0C respectively Temperature spikes coincided rapid acceleration high-speed phases, highlighting influence discharge current on heating. These findings demonstrate importance management EV conversions effectiveness Future work will focus experimental validation control via system (BMS) ensure safety longevity.

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Battery module housing with directed and controlled venting for lithium-ion battery packs in electric vehicles. The housing has two types of vents: burst vents to rapidly release excess gas buildup during cell operation, and selective permeability vents to slowly allow gas exchange between the cell stack and housing cavity. This enables directed venting of excess gases away from the vehicle cabin while still allowing controlled breathing of the cells. The burst vents open at high pressure thresholds, while the selective permeability vents have membranes that allow gas exchange but not moisture.

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Method for accurately estimating the initial state of charge of a battery in a hybrid or electric vehicle, particularly when the battery has not been at rest for a long time. The method involves estimating an initial value of the state of charge during an initialization phase. The initialization phase includes steps like measuring the battery's voltage and current at the start of charging or discharging, calculating the current polarization based on the voltage and current values, and using the polarization to adjust the initial state of charge estimate. This takes into account the battery's polarization state to provide a more accurate initial state of charge estimate when the battery has not been resting for a long time.

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Thermal management system for electric vehicle battery packs that provides efficient cooling and heating without adding significant weight or cost. The system uses a network of flexible tubes connecting intake and exhaust manifolds with channels tuned for even fluid flow distribution. It allows direct contact cooling/heating of individual battery cells by conforming tubes passing between them. The system connects to a pump and heat exchanger for circulating fluid through the pack. The flexible tubes fill and bleed easily for installation. The manifold design prevents fluid bypassing and ensures full inflation.

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Rechargeable battery monitoring system for electric vehicles that prevents damage to battery management unit (BMU) chips when disconnecting/connecting manual service disconnect (MSD) switches. The system has multiple cell monitoring circuits (CMCs) connected in groups via daisy chain buses to the BMU. The CMC groups are separated by MSD switches between adjacent battery unit groups. By balancing the number of battery units and CMCs in each group, the daisy chain length and line losses are equalized. Isolation transformers between adjacent CMCs further improve isolation and signal quality. This reduces voltage spikes and current surges when disconnecting/connecting MSDs that could damage CMC and BMU chips.

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Battery module design to prevent thermal runaway propagation in stacked battery cells. The module has thermal runaway preventers between the cells that disperse heat from an overheating cell and prevent direct transfer to neighboring cells. This prevents thermal runaway chain reactions that can occur when one cell overheats. The preventers are installed in the module case between the cells in the stack.

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Simplified and accurate method to measure battery resistance using existing vehicle components. It leverages the fact that when a battery is charging and discharging simultaneously, it outputs more current than it receives. By determining if this condition is met, the method can detect when the battery switches from a charged to discharged state. At this point, it starts measuring the resistance. This avoids the need for specialized equipment and allows using the vehicle's control system to enable battery resistance measurement during normal operation.

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Method and controller for accurately detecting voltages of battery cells in a battery pack using converters and switching units. The method involves routing the operating current from the battery cell anode through the converter to ground, while also routing a sampling current from the battery cell anode to the converter. This reduces the difference between currents through the anode and cathode paths, allowing the converters to accurately detect the cell voltage.

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Improving safety and reliability of battery packs by selectively supplying power to the cooling system when a thermal runaway occurs in one module. When a battery module undergoes thermal runaway, the system determines the module and surrounding modules without runaway based on voltage levels. It then powers the chiller using the non-runaway modules to cool the pack. This prevents overheating and gas generation in the runaway module. The vehicle control unit assists by checking the power supply circuit of the backup modules.

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Battery module and power system design to mitigate wireless communication interference and leakage in electric vehicle battery packs. The module has individual battery cells with detectors and communicators inside an electromagnetic shielded housing. The housing has a non-shielded portion for access. An electromagnetic shield opposite the non-shielded portion prevents wireless signals from leaking or entering. This isolates the wireless communication inside the shielded space.

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Abstract: Lithium batteries are the most commonly used energy storage devices in items such as electric vehicles, portable devices, and systems. However, if lithium will not continuously monitored, their performance could be degraded, lifetime becomes shortened, or severe damage explosion happen. To prevent types of accidents, we propose a battery state health(SoH) monitoring method charge estimation algorithm based on health results. And also speed control vehicles is mandatory Because it to influence rotational motors machinery. This has direct effect operation machine crucial for quality overall outcome work. Li-ion having lot into them, thermal runaway accelerates quicker more power present itself. If fully charged something happened inside it, then would happen really quickly. overcome this problem, fire protection vehicle necessary.

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Abstract Accurate and stable predictions of the state-of-health (SOH) lithium-ion batteries are essential for effective battery management extending lifespan. Two major issues exist in current lithium capacity prediction models. First original data captured from temperature, voltage, sensors contains a large number noises, which negatively impacts accuracy. Second, SOH is significantly influenced by factors such as charge discharge rates, voltage. These rapid changing can be more effectively using short-term dependence rather than long-term dependence. However, degradation not fully captured, coupling relationships among multivariable sequences, along with inaccurate resulting frequency information, adequately addressed. In this paper, we propose frequency-domain enhanced trans-variate recognition framework, FE-STDR, to solve these two problems predict batteries. The FE-STDR framework comprises modules including Stacked Denoised Autoencoder (SDAE) FEformer. SDAE module removes noise during acquisition cycles, automatically extracting high-level features capture complex state patterns... Read More

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Abstract The conventional method for assessing the health status of lithium batteries typically necessitates comprehensive data from complete charging and discharging cycles. prolonged duration required collection such may lead to issues including time inefficiency delays in battery state estimation processes. In response, this paper presents a rapid estimating based on local information short process. Additionally, address situation where correlation features is low specific regions entire voltage domain, complementary strategy proposed. This allows quick accurate using only process intervals across domain. First, that can represent full domain are extracted. Subsequently, multi-feature fusion approach combined with LightGBM algorithm employed construct model. Finally, effects various types, different operating conditions, diverse sampling window sizes accuracy analyzed through experiments, thereby demonstrating feasibility effectiveness proposed approach.

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This study presents a detailed simulation of lithium-ion battery charging using the Constant Current/Constant Voltage (CC/CV) method. MATLAB is used in conjunction with certain mathematical algorithms, such as numerical integration and curve fitting, to simulate process, utilizing parameters including constant current 1C voltage threshold 4.2V. The analyzes efficiency, usable capacity, internal impedance variation under various levels thresholds. CC/CV method compared findings from other studies that also technique, highlighting similarities differences results. analysis reveals while effective balancing speed safety, minimizing risk overcharging, some note challenges related temperature variations their impact on performance. While offers optimal management for charging, future research can focus investigating long-term effects life environmental conditions, considering methodologies similar studies.

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Abstract The capacity of a system for battery management (BMS) to evaluate the general condition pack is one its most important features. BMS monitors weakest cell and internal resistance each cell. Based on these variables, it calculates healthiness percent that ranges from 0% 100%. A fault code created freeze frame data kept later examination if any cells or entire fall below predetermined criteria when this health assessed against them. Numerous functions are designed protect pack. These systems use techniques make sure lasts longer ready give full power needed, in addition continuously monitoring safeguarding it. extends life improves performance by carefully controlling cycles charging discharging, balancing voltages, guarding situations could harm battery. This guarantees dependable operation maximum efficiency. Keywords State charge (SOC), simulation, electric vehicles, batteries systems, open circuit value (OCV).

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Enabling efficient and convenient battery swapping for electric vehicles to enable longer range and faster charging compared to battery charging. The method involves using vehicles themselves to transfer batteries between each other in a peer-to-peer fashion. When a vehicle's battery needs charging, it finds another nearby vehicle with a fully charged battery using onboard sensors and communication. The vehicles then physically connect and swap batteries. This allows a vehicle to quickly obtain a fully charged battery instead of waiting for its own battery to charge. The swapped battery can then be returned to the original vehicle for future use. This peer-to-peer battery swapping leverages the mobility of vehicles themselves to facilitate rapid and convenient battery swapping.

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Charging system for batteries that optimizes charging efficiency by dynamically controlling temperature during charging operations. The system monitors the external power source's output and compares it with the previous output value during charging. When the current output value exceeds the previous one, the system adjusts the battery temperature target based on the current output value. This approach prevents the repetitive stopping and restarting of charging operations that can occur when the target temperature is updated too frequently. The system maintains the target temperature at a previously set value when the output value does not exceed the previous one, ensuring consistent charging conditions.

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Battery module with enhanced thermal management through strategically integrated phase change materials (PCMs) that absorb heat generated in critical battery connections. The module features a bus bar with integrated phase change members that distribute heat from connecting areas between the cell tab and bus bar, while a secondary phase change member is positioned on the top surface of the cell. This dual-phase design enables targeted cooling of high-temperature areas, particularly the connecting region between the cell tab and bus bar, while maintaining overall system thermal balance. The phase change materials are designed to absorb and release heat efficiently, preventing thermal runaway and fire hazards.

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Diagnosing abnormalities in voltage behavior of batteries based on changes in differences between measured open circuit voltage data and estimated open circuit voltage data over time. The method involves generating open circuit voltage (OCV) data from the battery, deriving estimated OCV data based on the measured data, and diagnosing battery health based on the difference between the measured and estimated OCV values. This allows detecting subtle voltage abnormalities even when the battery voltage itself doesn't change significantly.

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Diagnosing lithium-ion battery degradation during charging using resistance measurements. The method involves monitoring voltage changes during charging to calculate resistance of each battery cell. Resistance variations indicate degradation like lithium plating. By calculating average resistances for each SOC class, probabilities of abnormal cells can be determined. This allows diagnosing cells during normal charging without extended rests.

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