Range Prediction Improvement in Lithium Iron Phosphate for EV

Adaptive SOC estimation for lithium iron phosphate battery cells using a third-order RC model. The method calculates the average electromotive force of the battery model based on its state parameters, then uses this value to derive a correction factor for voltage estimation. The correction factor is determined by temperature and current factors, which are determined using a lookup table. The correction factor is applied to the measured voltage to estimate the battery state of charge (SOC).

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Real-time predictive modeling and intelligent control for lithium-ion batteries using deep learning and optimization techniques. The system employs structured data processing, predictive models, and real-time control strategies to enhance battery performance, safety, and lifespan. It leverages machine learning algorithms to accurately predict critical battery parameters like state of charge, state of health, and remaining useful life, while implementing dynamic control strategies to mitigate aging and thermal management issues. The system integrates with existing battery management systems, enabling seamless integration while minimizing hardware modifications.

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Intelligent control and management system for chemical battery energy storage in electric vehicles, optimizing power management through advanced state estimation and predictive analytics. The system monitors and analyzes real-time battery state, including charge and discharge cycles, balance, and health metrics, using machine learning algorithms. It employs a recurrent neural network to predict battery state and optimize charging and discharging strategies based on predicted conditions, ensuring safe and efficient energy storage and utilization. The system dynamically adjusts power management to maintain optimal battery state, including preventing over-discharge and maintaining sufficient power reserves for vehicle functions.

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Intelligent power buffer module that optimizes energy storage through advanced predictive modeling and real-time control. The module combines energy flow control modeling with advanced predictive analytics to accurately predict battery energy states, while incorporating real-time monitoring and adaptive learning mechanisms to dynamically adjust charging and discharging strategies. This enables enhanced energy conversion efficiency, improved battery performance, and enhanced system adaptability compared to conventional power buffer systems.

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A method for detecting and controlling the status of energy storage power supplies like battery packs. The method involves monitoring voltage, current, temperature, and using algorithms to estimate state-of-charge (SOC) and predictive control. It uses sensor data fusion, machine learning, and adaptive control to improve battery performance, safety, and longevity by accurately tracking state, predicting SOC, adapting charging strategies based on temperature, and providing intelligent protection against overcharge/discharge and temperature abnormalities.

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Intelligent lithium battery control system that improves performance, reliability, and safety of lithium battery packs used in electric vehicles, drones, and energy storage systems. The system monitors battery status, optimizes charge/discharge strategies, predicts battery health, manages power peaks, balances energy use, provides remote monitoring, and implements short circuit protection. Algorithmic optimization, temperature management, and data analysis enhance battery performance and longevity.

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Method for optimizing battery charging to prevent lithium precipitation and maintain capacity while controlling battery state of charge (SOC). The method determines optimal discharge parameters based on battery state parameters such as SOC and health, and applies these parameters during charging to prevent lithium precipitation while maintaining capacity. The method continuously monitors battery state and adjusts charging parameters accordingly to maintain optimal operating conditions.

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A lithium iron phosphate battery state estimation method that improves accuracy through correction of the traditional ampere-hour integration method. The method employs an error covariance estimation framework that incorporates both the state estimation error and the measurement noise covariance to enhance the accuracy of the battery state estimation. This correction approach enables more precise state estimation in the battery's voltage plateau region, where traditional methods may suffer from significant errors due to measurement noise and initial state inaccuracies.

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A lithium iron phosphate battery discharge management system that optimizes battery life through precise control of the discharge cut-off point. The system employs a pulse discharge method to identify the battery's nominal state of charge (NSOC) by analyzing the characteristic behavior of the battery's discharge curve. By comparing the pulse discharge characteristics to established thresholds, the system determines the optimal discharge cut-off point to prevent deep discharge while maintaining battery health. This approach eliminates the need for series capacitors and enables more accurate state of charge monitoring.

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Power lithium battery state-of-charge (SOC) estimation model that improves accuracy through temperature adaptation and battery shutdown considerations. The model incorporates a modified RC Thevenin equivalent circuit that accounts for temperature-dependent battery characteristics, while incorporating battery shutdown factors to mitigate potential errors. This approach enables more accurate SOC predictions across varying temperatures and battery states, particularly in applications where battery management systems rely on precise SOC estimation.

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Real-time monitoring and management system for lithium iron phosphate batteries that enables comprehensive DC system control through advanced cell-level monitoring. The system comprises a battery pack with multiple cells connected in series, a monitoring management unit that includes an isolation amplifier, analog-to-digital converter (ADC), microprocessor, CAN bus, host computer, temperature sensor, control gating unit, protection unit, and voltage switch matrix. The isolation amplifier processes each cell's voltage signal, while the ADC converts it into a digital signal. The microprocessor integrates this data with cell-level current and temperature measurements, enabling precise state of charge and temperature monitoring. The CAN bus transmits this comprehensive data to the host computer, which performs real-time analysis and control.

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Real-time monitoring and management of lithium iron phosphate batteries through an integrated system that combines traditional battery monitoring with advanced power system control. The system enables comprehensive monitoring of battery state of charge, voltage, and capacity, while also monitoring the DC system of the substation and detecting potential issues before they become operational problems. This integrated approach ensures safe and reliable battery operation through real-time monitoring and precise control of the DC system.

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A lithium iron phosphate battery management system for railway applications that enables real-time monitoring and control of battery pack performance through a distributed architecture. The system comprises a series of lithium iron phosphate batteries connected in parallel, each with its own dedicated monitoring module. These modules collect voltage and temperature data from individual cells, which are then combined through a central convergence module to provide comprehensive battery state of charge (SOC) and state of health (SOH) monitoring. The system incorporates current monitoring and Hall sensor detection capabilities, enabling accurate power management even under intermittent power supply conditions. This distributed architecture eliminates the traditional centralized BMS design limitations, providing enhanced reliability and flexibility for battery management in harsh railway environments.

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A novel SOC estimation algorithm for electric vehicles that addresses the challenges of battery state-of-charge estimation. The method leverages a combination of the Kalman filter and a novel internal resistance model to achieve more accurate SOC predictions. Unlike traditional methods that require initial battery state, the algorithm can estimate SOC from the beginning without requiring prior calibration. This approach enables real-time SOC estimation even when the battery's internal resistance is uncertain, significantly improving the accuracy of battery state-of-charge monitoring.

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Active balancing architecture for battery management systems that enables consistent power output across thousands of battery cells in parallel connections. The method employs active equalization techniques to manage cell voltage variations between battery packs, ensuring the system's total capacity is maintained. By dynamically adjusting cell voltages through equalization, the system can recover losses and maintain its rated capacity even when individual pack voltages deviate significantly. This approach enables efficient operation of large-scale battery systems with thousands of cells connected in parallel.

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Estimating lithium iron phosphate battery life (LOC) through adaptive neural network analysis. The method employs a scalable wavelet neural network framework that incorporates adaptive node determination to optimize network structure for accurate LOC prediction. The network architecture is built using a hierarchical approach, with each level of the network learning from previous outputs. This adaptive learning enables the network to adaptively determine the optimal number of hidden layers and parameters while maintaining high accuracy. The network is trained using input parameters related to lithium iron phosphate battery performance, enabling precise LOC estimation.

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Determining battery state of charge through temperature-dependent mapping of battery capacity and voltage. The method compares the battery's remaining capacity to its optimal charge level based on ambient temperature, enabling accurate discharge detection. The temperature-dependent mapping is configured to match the specific characteristics of different battery types, ensuring reliable state of charge monitoring across various environmental conditions.

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A closed-loop residual power estimation method for lithium-ion batteries that improves accuracy beyond traditional Kalman filter-based approaches. The method employs an adaptive observer gain control mechanism that maintains a constant coefficient L during battery state estimation, while dynamically adjusting the observer gain to effectively suppress non-Gaussian disturbances. This approach enables precise residual power estimation even in complex battery systems with nonlinear dynamics.

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A neural network-based method for precise lithium-ion battery state of charge (S0C) estimation. The method employs a deep neural network to predict S0C from battery state-of-charge (SoC) data, leveraging the inherent characteristics of lithium-ion batteries. The network achieves high accuracy in the range of full charge (10% to 100%) and improves significantly beyond this point, with a maximum error of less than 3% at full charge. The network's performance degrades as SoC approaches 0% or 100%, where the accuracy drops to around 8%. This enables reliable S0C estimation for both full charge and partial charge states, while maintaining accuracy across the entire charge range.

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A battery management system for electric vehicles that dynamically monitors and controls battery pack performance through advanced algorithms and hardware protection. The system incorporates magnetic isolation technology, ESD protection, and EMI filtering to ensure reliable operation across various environmental conditions. It employs real-time monitoring of battery state-of-charge, temperature, and other critical parameters to predict potential issues before they occur. The system integrates with the vehicle's CAN bus and provides isolated power supply for CAN communication, enabling precise control of battery pack operations.

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