Real-Time EV Battery State Monitoring

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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