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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