Real-Time EV Battery State Monitoring

A method for estimating battery pack state of charge (SOC) in electric vehicles that enables faster and more accurate charging and discharging compared to waiting for equilibrium. The method involves estimating open circuit voltage (OCV) for each cell using sensor measurements after vehicle deactivation. This estimated OCV is then used to calculate cell SOC instead of waiting for actual OCV stabilization. A decay parameter based on time and voltages since deactivation improves accuracy.

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Using binding immunoglobulin protein (BiP) or BiP analogs to modulate inflammatory responses in intestinal inflammation, such as inflammatory bowel diseases (IBD) like ulcerative colitis and Crohn's disease. The BiP or analogs can be administered to prevent or treat IBD by reducing inflammation. BiP is a cellular protein that binds denatured proteins during stress. BiP analogs with altered amino acid sequences can have similar anti-inflammatory effects.

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Mobile electric vehicle charging that accounts for battery health and state to optimize charging and discharging between vehicles. The system monitors battery cell states and identifies cells beyond a threshold for remediation. These cells are continued to be used to degrade further. This targeted cell selection allows replacing only some cells instead of all when they reach end of life. It also enables balancing charge distribution between cells to prevent uneven degradation. The system uses battery metrics and historical data to make charging decisions based on cell health.

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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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Estimating battery cell temperature using electrochemical impedance spectroscopy (EIS) to provide accurate and wide-range temperature estimation without additional sensors. The method involves estimating the battery cell impedance from frequency-domain measurements of voltage and current, and then estimating the cell temperature based on features of the impedance. A linear programming solver is used to find optimal weighting of the impedance features that provides accurate temperature estimation over a wide frequency range.

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Battery deterioration diagnosis method and device that accurately estimates the deterioration state of a battery in a short time by leveraging the temperature dependence of internal resistance. The method involves raising the battery temperature to a diagnosis temperature before charging/discharging. This reduces the internal resistance effect compared to lower temperatures, allowing distinct peaks to appear in the differential voltage curve without reducing the charge/discharge current. This enables more accurate deterioration diagnosis.

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A control device for monitoring battery degradation during power pumping, a technique used to recover undercharged batteries. The control device estimates the full charge capacity of the main battery during power pumping by measuring its open circuit voltage (OCV) just before and after the pumping. This allows accurate monitoring of battery degradation during pumping, unlike measuring OCV during pumping which is less accurate due to the auxiliary battery as a load. The OCV measurements are taken close to the pumping start and end times.

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Auxiliary component for easily releasing conduits from joints in push-fit conduit systems. The component consists of a sleeve that slides over the joint and a releaser that inserts between the sleeve and the propeller inside the joint. The sleeve limits the sleeve position to outside the joint end and side wall. When the releaser is inserted, it pushes the propeller to extrude the gear ring inside, loosening the joint and allowing the conduit to be easily removed.

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Equalizing current distribution among multiple power supply units (PSUs) in a system to prevent any single PSU from overloading. Each PSU senses the load voltage, calculates its local impedance, and compares it to a reference impedance. It then adjusts its output voltage based on the difference between the calculated and reference impedance values to equalize current sharing. This prevents a PSU with higher output voltage from dominating and allows each PSU to provide an equalized share of the total output current.

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Battery pack with a temperature sensing configuration that allows detecting the temperature of multiple battery cells using fewer temperature sensors compared to conventional packs. The battery pack has a case with parallel-arranged battery cells orthogonal to their axial direction. A single temperature sensor is placed in the center of the pack to detect the average cell temperature. This reduces the number of temperature sensors needed compared to placing one in each cell.

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The rapid expansion of battery-operated signal processing systems across domains such as biomedical monitoring, environmental sensing, and portable instrumentation necessitates the development microcontroller-based architectures optimized for power efficiency. This chapter presents a comprehensive exploration design methodologies architectural techniques aimed at minimizing energy consumption while maintaining real-time computational performance. focus is placed on system-level co-design strategies, including intelligent gating, dynamic voltage frequency scaling, low-leakage memory architectures, energy-aware task scheduling. Emphasis also laid hardware-software co-optimization approaches that leverage profiling, adaptive clocking, modular subsystem activation achieving application-specific power-performance trade-offs. A detailed analysis microcontroller core provided, addressing register access schemes, sleep mode hierarchies, impact peripheral total draw. integration analog monitors budget tracking highlighted critical enabler feedback-driven system control, novel acquisition fram... Read More

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Accurate estimation of the state health (SOH) and remaining useful life (RUL) lithium-ion batteries (LiBs) is critical for ensuring battery reliability safety in applications such as electric vehicles energy storage systems. However, existing methods developed estimating SOH RUL LiBs often rely on full-cycle charging data, which are difficult to obtain engineering practice. To bridge this gap, paper proposes a novel data-driven method estimate only using partial curve features. Key features extracted from constant voltage (CV) process relaxation, validated through Pearson correlation analysis SHapley Additive exPlanations (SHAP) interpretability. A hybrid framework combining CatBoost particle swarm optimization-support vector regression (PSO-SVR) developed. Experimental validation public datasets demonstrates superior performance methodology described above, with an root mean square error (RMSE) absolute (MAE) below 1.42% 0.52% relative (RE) under 1.87%. The proposed also exhibits robustness computational efficiency, making it suitable management systems (BMSs) LiBs.

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Battery prognostics tool that can predict and alert for thermal runaway events in batteries even when the battery management system (BMS) is turned off or failed. The tool uses cell temperature sensors to monitor cells when the BMS is not operational. It compares the cell temps to a thermal model to predict runaway risk. If a runaway is predicted, an alarm is output to alert of the potential issue. This allows early warning of runaway even when the BMS is not functional.

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Vehicle battery management system that optimizes battery health in electric vehicles by dynamically controlling battery temperature and charge/discharge rates based on battery SOH. When the SOH is below a reference value, the system increases temperature to prevent further SOH loss, decreases temperature to prevent overheating, or reduces charge/discharge rates to mitigate degradation. This helps extend battery life by compensating for higher-than-normal SOH degradation in certain vehicle types or driving conditions. The system also provides user notifications to allow manual control if desired. The SOH reference values are determined through battery durability evaluations for each vehicle type.

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Battery management system that allows individual monitoring and control of battery cells without a complex web of wires. The system uses existing power cables between cells to transmit battery parameters and commands. A monitoring board on each cell monitors status and sends parameters to a central controller. The controller adjusts cell performance based on received data. This eliminates the need for extra wiring between cells and allows individual cell monitoring without grouping them into modules. The data is transmitted by injecting modulated signals onto the existing power cables.

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Detecting aging and malfunctions in cells of an energy storage system like batteries in an electric vehicle or grid storage. The method involves regulating power through the system to vary cell current and voltage, then calculating cell impedance from the signals. Cells with high impedance are flagged as potentially malfunctioning. This allows simultaneous inspection of all cells in the system.

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Accurately estimating the state of health of a storage battery by considering the specific use conditions of the battery. The method involves identifying the current use state of the battery and then calculating the state of health separately for each identified use state. This allows more accurate estimation compared to using a single calculation for all use states as the state of health can vary significantly depending on factors like charge/discharge cycling patterns, temperature, and depth of discharge.

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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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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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Diagnosing battery degradation in assembled packs like those found in electric vehicles. The method involves controlled charging and discharging cycles with voltage monitoring. When a cell reaches the end voltage during charging or discharging, the remaining cells are charged or discharged to that cell's end voltage. This ensures all cells are cycled to the same level. This allows accurately estimating pack degradation based on voltage transitions of any cell, rather than over-discharging or over-charging specific cells.

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The increasing penetration of distributed energy resources (DERs) such as solar photovoltaic (PV) systems, battery storage systems (BESSs), and electric vehicles (EVs) in low-voltage (LV) medium-voltage (MV) distribution networks is reshaping traditional grid operations. This shift introduces challenges including voltage violations, thermal overloading, power quality issues due to bidirectional flows. Hosting capacity (HC) assessment has become essential for quantifying optimizing DER integration while ensuring stability. paper reviews state-of-the-art HC methods, deterministic, stochastic, time-series, AI-based approaches. Techniques enhancing HCsuch on-load tap changers, reactive control, network reconfigurationare also discussed. A key focus the emerging concept dynamic operating envelopes (DOEs), which enable real-time allocation by dynamically adjusting import/export limits DERs based on operational conditions. examines benefits, challenges, implementation DOEs, supported insights from Australian projects. Technical, regulatory, social aspects are addressed, visibility, un... Read More

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Abstract - Smart sensors are at the forefront of a major transformation in instrumentation engineering. These advanced devices go beyond simple data collectionthey capable processing information, drawing insights, and communicating results without human intervention. When combined with connectivity Internet Things (IoT) analytical power Artificial Intelligence (AI), smart enable real-time monitoring, early fault detection, adaptive control wide range applications. From remote patient monitoring healthcare to precision farming agriculture energy optimization cities, integration these technologies is redefining way systems designed operated. This paper explores foundational principles, system architectures, key use cases sensor networks. It also addresses practical challenges such as efficiency, security, interoperability. By examining both current capabilities future possibilities, this study provides clear understanding how sensors, empowered by IoT AI, reshaping intelligent instrumentation. Key Words: Sensors, Instrumentation, (IoT), Edge Computing, Wireless Sensor Networks, Real... Read More

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Accurate State-of-Health (SOH) estimation is a key technology for ensuring battery safety, optimizing energy management, and enhancing lifecycle value. This paper proposes novel SOH method lithium-ion batteries, utilizing incremental features hybrid deep learning model that combines Convolutional Neural Network (CNN), KolmogorovArnold (KAN), Bidirectional Long Short-Term Memory (BiLSTM) (CNN-KAN-BiLSTM). First, the batterys voltage, current, temperature, other data during charging stage were measured recorded through experiments. Incremental Energy Analysis (IEA) was conducted on to extract various characteristics. The Pearson correlation used verify strong between proposed characteristics SOH. includes experimental verification of both cells pack. For cell, complete multi-feature sequence formed based curve combined with temperature pack, supplemented Variance Voltage Means (VVM) as an inconsistent feature, Standard Deviation Temperature (SDTM), create sequence. then input into CNN-KAN-BiLSTM developed in this study training, successfully estimating lithium batteries. results ... Read More

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Abstract Estimating the state of charge lithium-ion battery systems is important for efficient management systems. This work conducts a thorough evaluation multiple SOC estimate methods, including both classic approaches Coulomb Counting and extended Kalman filter machine learning techniques under different load profile on pouch cell. The assessment included variety experimental data collected from entire cycles, shallow dynamic operations utilizing Worldwide Harmonized Light Vehicles Test Procedure Hybrid Pulse Power Characterization tests done 100% to 10% SOC. While traditional performed well ordinary settings, they had severe limits during cycling. In contrast, technologies, notably random forest method, better across all testing conditions. approach showed outstanding accuracy while minimizing error metrics (RMSE: 0.0229, MSE: 0.0005, MAE: 0.0139) effectively handled typical issues such as drift ageing effects. These findings validate dependable robust real-time estimation in

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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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Lithiumion batteries are the dominant battery type for emerging technologies in efforts to slow climate change. Accurate and quick estimations of state charge (SOC) internal cell temperature vital battery-management systems enable effective operation portable electronics electric vehicles. Therefore, a long short-term memory (LSTM) recurrent-neural network is proposed which completes estimation SOC average (Tavg) lithiumion under varying current loads. The trained evaluated using data compiled from newly developed extended single-particle model coupled with thermal dynamic model. Results promising, root mean square values typically 2% 1.2 K Tavg, while maintaining training testing times. In addition, we examined comparison single-feature versus multi-feature network, as well two different approaches partitioning.

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Abstract Battery lifetime and reliability depend on accurate state-of-health (SOH) estimation, while complex degradation mechanisms varying operating conditions strengthen this challenge. This study presents two physics-informed neural network (PINN) configurations, PINN-Parallel PINN-Series, designed to improve SOH prediction by combining an equivalent circuit model (ECM) with a long short-term memory (LSTM) network. process input data through parallel ECM LSTM modules combine their outputs for estimation. On the other hand, PINN-Series uses sequential approach that feeds ECM-derived parameters into supplement temporal analysis physics information. Both models utilize easily accessible voltage, current, temperature match realistic battery monitoring constraints. Experimental evaluations show outperforms baseline in accuracy robustness. It also adapts well different conditions. demonstrates simulated dynamic states from increase LSTM's ability capture patterns model's explain behavior. However, trade-off between robustness training efficiency of PINNs is discussed. The research findi... Read More

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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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Real-time analysis of multi-dimensional time series data from sensors using a time series service that allows efficient and scalable computation and visualization of time series data from heterogeneous sources with different time units and sampling rates. The time series service receives queries, identifies the required data from the time series databases, computes transforms, and provides real-time output. It enables contextual analysis of multi-dimensional time series data for real-time monitoring and alerting using computations like correlation and regression.

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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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Method to estimate battery health of electric vehicles accurately and online without removing the battery. The method involves estimating the state of charge (SOC) of the battery by relaxing it, discharging it, and checking the max charge. This allows precise SOC estimation even as the battery ages without full discharge. The SOC is used to estimate battery health. The steps are: 1. Relax battery at max charge, monitoring voltage and temperature. 2. Discharge to a target SOC while monitoring voltage and temperature. 3. Check if SOC reached max charge. If yes, proceed with health estimation, else repeat steps 1-2. By relaxing before discharge, the SOC error from aging resistance is avoided.

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Battery management system for accurately determining if a battery is defective in an electric vehicle. The system diagnoses the battery during normal operation to determine if it has abnormalities. Based on the results, it adjusts the diagnostic conditions for a second diagnosis to further evaluate the battery. This allows more accurate detection of defects like increased internal resistance, leakage current, or decreased state of health. It also prevents false positives by checking if the second diagnosis result is significantly different from the first. This provides a more reliable diagnosis of battery defects compared to single-diagnosis methods.

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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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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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Battery management system and method that provides real-time battery monitoring and predicts battery performance based on accumulated usage. The system connects a display panel to a battery pack to show battery status like voltage, current, and temperature in real time. It calculates cumulative usage by comparing actual battery data to a model. Using this, it predicts battery performance and adjusts charging/discharging to extend life. When a battery is nearing end-of-life, it determines recyclability based on the accumulated usage.

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Battery monitoring and management system for predicting battery degradation and making maintenance recommendations. The system uses big data analysis to evaluate battery health and predict degradation. It acquires battery voltage, current, temperature, capacity, discharge rate, etc. through sensors. The data is stored and analyzed to calculate health indices and degradation rates. A decision tree algorithm uses these to generate maintenance reminders, warnings, and charging strategies. A user interface displays battery status and alerts.

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Battery management system that estimates battery state of charge (SOC) more accurately by leveraging nonlinear characteristics of the battery's open circuit voltage (OCV) near full discharge. The system uses an extended Kalman filter to estimate SOC from battery measurements. But instead of just using the Kalman filter output, it also calculates OCV based on the provisional SOC estimate. Then it adjusts the SOC estimate using a measurement update with both the provisional SOC and OCV. This leverages the strong nonlinear OCV-SOC relationship near full discharge to improve SOC estimation accuracy.

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Battery monitoring and management system that uses compacted sensor data to generate insights and alarms for battery health and performance. The system receives sensor data from temperature, voltage, and current sensors of a battery. It compacts the data into coefficients and generates performance indicators based on those coefficients. Alarms are then generated when performance indicators exceed thresholds. This allows efficient monitoring and management of large amounts of battery sensor data by compacting and analyzing key performance metrics instead of storing and processing raw sensor data.

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Vehicle-pile collaborative state monitoring system for electric vehicles that accurately predicts battery life during charging and discharging. The system monitors parameters of the vehicle battery, charging pile, and charging/discharging state. It uses a model to predict battery life based on these parameters. Multiple influencing factors like charging cycles are combined to correct and improve the life prediction accuracy.

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An online adaptive method to estimate and control battery parameters like remaining useful life (RUL) and state of charge (SOC) for electric vehicles. It uses a combination of extended Kalman filtering and recurrent neural networks (GRU) to provide accurate and adaptive battery parameter estimation. The method involves predicting SOC using Kalman filters, then using the predicted SOC along with current battery and environmental data to predict RUL using a GRU neural network. The adaptive component lies in dynamically adjusting battery internal and external parameters based on the predicted RUL.

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A method for estimating the health status of batteries in energy storage systems that combines Kalman filtering with a neural network to accurately estimate battery health using only online sensor data. The method involves using Kalman filters to estimate key battery parameters like state of charge, then feeding that data into a long short-term memory neural network to predict battery health. This allows estimating internal battery degradation without requiring invasive measurements.

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Online monitoring and management of lithium-ion batteries using deep learning networks to improve efficiency, longevity, and safety of battery systems. The method involves real-time monitoring of battery parameters, generating balanced charging profiles, analyzing performance, predicting battery life, and detecting abnormalities using deep learning models. It leverages data processing, trend analysis, feature extraction, and machine learning techniques to provide accurate battery condition assessment and maintenance recommendations.

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Integrated battery management system for lithium batteries in electric vehicles that improves charging and discharging efficiency and protects battery health. The system monitors battery parameters during charging and discharging to enable pre-regulation. It determines charging power based on time and battery state, synchronizes with redundant power, and calculates charging time using user habit analysis. This allows lower charging currents to protect the battery while ensuring sufficient charging.

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Adaptive battery monitoring system for electric vehicles that provides accurate and real-time battery state estimation and prediction using a multi-parameter monitoring approach. The system uses sensors to measure voltage, current, and temperature of the battery. It calculates state of charge, capacity, and available energy based on voltage. The temperature data improves capacity estimation accuracy. An adaptive algorithm designs optimal monitoring parameters based on battery type and conditions. The system provides adaptive and accurate battery monitoring compared to static methods like open circuit voltage measurement.

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Real-time monitoring and control of individual rechargeable battery cells in a battery bank to detect and prevent faults, optimize performance, and extend life. The method involves measuring voltage, current, and temperature of each cell using onboard sensors, and sending the data to a central unit. The unit calculates cell state, health, charge/discharge times, and replacement time. It stores the data and compares against ranges. If outside, it initiates preventive/corrective actions like regulating energy flow or alerting. This allows precise real-time monitoring and control of each cell to anticipate and address issues before they spread to the bank.

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Power battery monitoring method that provides early warning of battery degradation by tracking battery usage patterns. The method involves calculating the state of charge, output power, and operating temperature during discharge, constructing scatter plots, and fitting curves to analyze trends. It predicts future values and generates warnings if they exceed normal ranges. This provides more comprehensive monitoring compared to just capacity or state of charge.

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Cloud-based Internet of Things (IoT) enabled battery management system that provides real-time battery data, state of charge (SOC), state of health (SOH) estimation, battery cooling control, and outlier detection. The system uses IoT sensors to collect battery data, cloud storage to store the data, and a mobile app to access it. Finite state machines calculate SOH based on factors like outlier current/voltage and cycle count. A thresholding technique identifies outliers. If battery temperature exceeds a threshold, a cooling system engages.

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Predicting battery condition of a lithium-ion battery in a vehicle to enable early detection of battery degradation and failure. The prediction method involves calculating quaternionic state variables from measured battery parameters like current, voltage, and time derivatives. These quaternionic variables better reflect the battery's time-varying behavior compared to static variables like impedance or SOC. By tracking the time course of these quaternionic variables, critical battery conditions like degradation or safety issues can be detected earlier.

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A system for monitoring charge level and predicting remaining life of lithium-ion batteries to improve safety and reliability. The system uses sensors to continuously track voltage, temperature, and current in battery packs. It estimates state of charge using a Kalman filter algorithm. Then, a battery life prediction algorithm calculates remaining useful life based on the estimated state of charge. The system allows online monitoring and prediction of battery health to mitigate safety issues like overcharging and overdischarging.

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A system for managing battery packs in electric vehicles using KNN machine learning to predict battery aging and optimize pack performance. The system monitors charging/discharging states of the pack cells and uses KNN to forecast aging changes. It then controls pack operation to mitigate aging and maintain optimal performance. By predicting cell aging and taking proactive measures, it aims to extend pack life beyond normal limits.

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