Charging Protocols to Minimize EV Battery Degradation

Accurate prediction of remaining charging time for batteries during multistage constant current charging. The prediction takes into account the charger's maximum output power to improve accuracy compared to assuming full charging rate. The method involves determining the appropriate charging curve and current for the battery's estimated state of charge (SOC). If the charger's max power is less than the curve's minimum, constant power charging is used. If it's less than max, it's constant current and constant power. If equal or greater, just constant current. The predicted times for each stage are added to find the total time.

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A charging control method for battery packs that prevents cell voltage from exceeding upper limits during charging while maintaining rapid charging speeds. The method involves deriving a charging rate based on target SOC and pack temperature, generating an error voltage between cell voltage and OCV for target SOC, and compensating the charging rate through PID control based on the error voltage. This allows adjusting the charging speed dynamically to prevent cells from overvoltage issues as they degrade over time.

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Battery charging method that improves charging efficiency and fault monitoring by dynamically adjusting charging power and monitoring parameters during charging based on the battery's current conditions. It combines dynamic charging with fault monitoring to improve sensitivity and accuracy. Charging power is adjusted using recommended rates based on the battery's age and temperature. If any parameter falls outside the expected range, charging stops for fault monitoring. This flexible dynamic charging with macroscopic time monitoring improves charging efficiency by adapting to the battery's condition and preventing damage.

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Battery charging method and management system to prevent lithium plating and overheating during charging of lithium-ion batteries used in electric vehicles. The method involves monitoring battery parameters like state of charge (SOC) and open circuit voltage (OCV) during charging. If the battery parameters exceed certain thresholds, indicating potential issues like lithium plating or overheating, the charging is stopped or the battery is discharged to prevent damage. The threshold values are determined based on the battery temperature. This prevents lithium plating and overheating during charging by proactively addressing issues as they arise.

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Battery management system using artificial neural networks to optimize charging and discharging of batteries. The system involves training a neural network model to predict charge amount based on battery state. Charging is stopped when the predicted charge is reached. This prevents overcharging. The model learns charging behavior from labeled training data and can adapt to different batteries.

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Vehicle battery charging and discharging system that optimizes charging and discharging speed of electric vehicle batteries based on the battery's state of deterioration and power system frequency fluctuations. The system measures battery parameters, calculates deterioration state, maps optimal charging/discharging rates for current deterioration and frequency, and adjusts charging/discharging power accordingly. This enables delaying battery degradation when charging/discharging rapidly on unstable power grids.

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Method and battery management system to improve the performance and safety of electric vehicle batteries during charging. The method involves discharging the battery briefly during charging to prevent lithium plating. The discharge interval and parameters are determined based on factors like state of charge, state of health, and temperature. This allows optimizing discharge for safety and performance while charging.

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Battery fast charging control method and device based on feedback that can adapt charging strategies to battery health and detect internal faults to prevent failures. The method involves establishing a battery model based on multiple aspects of physical behavior. It uses feedback control to optimize charging time, minimize temperature rise, and adjust discharge current based on estimated faults. This adaptive feedback charging extends fault tolerance, reduces failures, and ensures battery safety throughout life.

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A charging system that adapts the charging current and voltage based on the battery's health and state of charge (SOC) to improve charging efficiency and prevent damage. The charging command generator takes real-time battery data like SOH and temperature to generate optimized charging parameters. It uses learning models for the charging current command and a lookup table for the charging voltage command. This allows customized charging profiles tailored to the battery's condition.

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Dynamic charging control for batteries to improve safety and performance by adjusting charge levels based on cell health. The system monitors cell conditions and determines an optimal charge level for each cell based on its degradation. This reduces the risk of thermal runaway propagation by preventing overcharging cells that are approaching end-of-life. It charges cells to lower SoCs than normal to compensate for capacity fade. The controller commands the charger to stop when the target voltage is reached. This dynamically adjusted EOCV prevents overcharging cells that are degrading.

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Dynamic optimization charging method for lithium-ion batteries that balances charging speed and battery life improvement. The method uses a coupled electrochemistry-thermal-aging model of the battery to accurately predict its state during charging. A state observer estimates internal parameters that cannot be measured. Model predictive control optimizes the charging current iteratively within constraints to balance charging time and aging capacity loss. This dynamically optimizes the charging strategy to accelerate charging while suppressing aging reactions.

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Adaptive battery charging for heavy-duty freight trains that improves charging efficiency, reduces battery degradation, and enables faster charging in low temperature environments. The method involves building a battery coupling model that considers electrical, thermal, and aging interactions. The model parameters are identified based on battery state and temperature. This allows adaptive multi-stage charging with varying numbers of stages and stage transition conditions based on initial and real-time battery state. Particle swarm optimization is used to find the optimal charging sequence. This self-adaptive charging provides faster, safer charging across temperature ranges, improves low temp performance, and extends battery life compared to fixed stage charging.

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Charging method for lithium-ion batteries that balances charging speed and safety. The method involves adjusting the charging current based on the battery's state parameters like SOC, temperature, and health. A negative electrode potential safety threshold is determined based on the state parameters. As the battery charges, the requested charging current is adjusted based on the negative electrode potential and safety threshold. Lower charging current when potential drops to prevent lithium plating, higher current when potential is stable to speed charge. This balances charge time and safety by optimizing current based on the battery's condition.

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Battery charging method to improve battery life by reducing expansion force during charging. The method involves adjusting the charge rate when the battery's state of charge (SOC) reaches a certain range where expansion force is maximized. The charge rate is lowered when SOC is close to the range to reduce expansion force and prolong battery life. When SOC exceeds the range, the charge rate is raised to ensure efficiency. This optimizes charging near the expansion limit to extend battery cycle life.

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Predicting an optimized charging profile for a battery of an electric vehicle that considers environmental conditions, dynamic parameters, and user-defined parameters like expected charge time and range. A battery management system monitors battery parameters, a powertrain controller receives the data, user input, and historical profiles. It estimates optimal charge range, temperature rise, and time based on all inputs, then creates an optimized profile to charge the battery efficiently, fast, and with lower thermal stress.

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Predicting an optimized charging profile for an electric vehicle battery that balances charging time, temperature, and health. The system uses a battery management system (BMS) to monitor battery parameters during charging cycles. It also receives user-defined parameters like expected charge time and range. The Common Powertrain Controller (CPC) unit combines this data with historical charging profiles to estimate charge time, temperature rise, and SOC/SOH ranges for the current charge. It then creates an optimized charging profile to charge the battery efficiently within the user-defined timeframe while minimizing temperature rise and degradation.

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Battery charging method that dynamically adjusts charging strategy based on battery health and cycle count to optimize charging and reduce aging. The charging method involves determining if the battery's health and cycle count meet certain conditions. If so, a first charging strategy is used with high charging rate. If not, a second charging strategy is used with reduced charging rate to mitigate aging. This adaptive charging extends battery life by selecting appropriate charging methods based on battery condition.

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Charging method for secondary batteries with lithium replenishment during cycling to improve energy density and cycle life. The method involves detecting the State of Health (SOH) of the battery at specific charging nodes. If SOH is below a threshold, lithium supplementation is activated. This involves charging at higher voltage and temperature to replenish lithium. After supplementation, normal charging continues once the battery's lithium content reaches standard levels. This allows targeted lithium replenishment to compensate for capacity loss during cycling without the drawbacks of pre-lithiation.

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Battery charging method to appropriately charge a battery even when the battery capacity estimation error is large. The method involves estimating a coefficient indicating the degree of deterioration of the battery's capacity, calculating a coefficient indicating the degree of deterioration over time, and using the smaller of the two coefficients to calculate a limited charging current. This allows charging based on the actual deterioration state rather than just the estimated capacity. If the capacity estimation error is large, using the aging coefficient prevents excessive charging due to overestimated capacity.

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Fast charging electric vehicles using model predictive control to balance charging speed, safety, and battery life. The charging method involves optimizing the charging current based on a lithium-ion battery model that considers electrochemical and thermal effects. It uses a predictive control algorithm with a discrete-time model of the battery to find the optimal charging current sequence over multiple time steps. This ensures charging meets physical and chemical limits to avoid overheating, degradation, and safety issues.

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