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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A charge and discharge management system for electric vehicles participating in vehicle-to-grid (V2G) applications to reduce battery degradation. The system calculates factors that accelerate battery deterioration like temperature, charging rate, and cycle life based on data from multiple vehicles. It then uses those values to control charging and discharging for individual vehicles to mitigate battery aging. This adaptive management reduces battery degradation compared to fixed charging profiles.

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Charging method and system that extends the life of batteries by dynamically adjusting the charging profile based on the battery's aging state. In the pre-charging stage, parameters like current and voltage are monitored to assess battery aging. During charging, target peak current and voltage values are determined based on the aging assessment. The battery is then charged using these optimized peak values instead of the maximum allowed by the specification. This reduces the stress on aging batteries and slows down aging compared to maximum charge rates.

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Method for charging a power battery like lithium-ion batteries used in electric vehicles. The method involves determining charging intervals based on battery health and state of charge (SOC) to prevent lithium plating that damages batteries. When charging, if the battery SOC crosses a threshold, it discharges or stops charging to prevent plating. The threshold is lower for degraded batteries and higher for healthy batteries. This improves charging performance and safety by preventing plating without degrading battery life.

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Optimized charging control for low-voltage lithium batteries in new energy vehicles to prevent overcharging and improve battery life. The charging algorithm uses three modules: battery state of health estimation, remaining power estimation, and DC/DC control. It estimates battery health based on SOC and temperature, detects overcharging, and adjusts DC/DC output voltage using proportional control to avoid overcharging during driving. This targeted optimization addresses issues with traditional charging methods like fixed voltage or constant current.

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Method for managing charging of lithium-ion batteries to enable safe and efficient charging by effectively utilizing the chargeable time. The method involves estimating the internal state of the battery during charging based on current and voltage measurements, target charging time, and relationship data linking state, charging conditions, and deterioration rate. This data is used to set a charging pattern that keeps deterioration below a threshold while meeting the target time. By optimizing charging parameters based on internal state estimation, it allows safe and appropriate charging while fully utilizing the chargeable time.

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Charging method for lithium-ion batteries that reduces charging time, prevents lithium plating, and improves battery life. The method involves charging in five stages with gradually increasing currents. The stages are: (1) a small current to open graphite layers, (2) a large current to form a dish-shaped lithium structure, (3-5) decreasing currents to relieve anode polarization. By using different currents based on the battery's charge state, it allows maximum lithium uptake without analyzing during charging. This prevents lithium plating and improves capacity retention.

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A battery management system that improves the lifetime and performance of secondary batteries like lithium-ion by accurately estimating degradation and adjusting charge levels accordingly. The system estimates battery degradation based on the change in state of charge during constant current charging. It then adjusts the target charge level to compensate for higher degradation. This prevents overcharging and undercharging that accelerate degradation.

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Adaptive burst power management for batteries to extend battery life while still meeting user expectations. The method involves monitoring battery degradation indicators like impedance change and comparing to a baseline. If degradation is below the baseline, burst power, charging speed, and limit are increased. If degradation exceeds the baseline, these are decreased. This adaptive management balances user performance needs with degradation prevention. It also sets a replacement limit based on degradation and disables excessive burst power when reached.

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Optimizing battery charging to reduce degradation over multiple charge cycles by estimating and minimizing total battery degradation. The method involves estimating stage-specific degradation for different current patterns during charging. It then selects the current pattern with least interval degradation across the charging stages to minimize overall degradation. This personalized charging profile is used for subsequent charge cycles to further reduce degradation compared to conventional charging methods.

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Optimizing the charging of electric vehicle batteries to extend battery life and improve efficiency by intelligently controlling the charging process based on factors like anticipated driving distance, battery temperature, battery health, and more. The vehicle determines a target state of charge (SOC) less than 100% based on expected driving needs before the next charge. Charging the battery to a lower optimized SOC reduces degradation compared to always charging to 100%.

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Real-time optimization control strategy for protecting the service life of a power battery in electric vehicles that actively adjusts the usage strategy of the battery according to its current state of health (SOH) during operation. This allows extending the usable life of batteries below 80% SOC without replacement. The strategy involves setting charging limits and rate reduction based on SOH degradation. It mitigates safety issues like over-discharge as batteries age.

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Aging-aware battery charging strategy to extend battery life and improve reliability by considering battery condition and aging mechanisms. The strategy involves detecting or predicting battery aging phenomena using a degradation model based on sensor data, and then calculating customized charging profiles for individual batteries based on their aging state. This allows optimizing charging parameters for different batteries to mitigate aging effects and balance overall vehicle performance, durability, and reliability.

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Charging management for batteries in electric vehicles to extend battery life. The method estimates the optimal charge level for the next day based on historical driving data, aging level, and optimal charge ranges. It then charges the battery to that level instead of fully charging. This prevents overcharging that can degrade batteries. The method uses driving data, aging estimates, and predefined optimal charge ranges to determine the optimal charge level for the next day.

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Charging control method and apparatus for batteries that uses real-time battery health information to optimize charging and prevent overcharging. The method involves acquiring battery voltage, current, and real-time battery health (SOH) from the battery management system. A charging reference voltage is calculated based on the SOH. This variable charging reference voltage is then used to generate control signals for the charging system. By dynamically adjusting the charging voltage based on battery health, it prevents overcharging as batteries age and degrade.

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Optimizing battery charging by dynamically adjusting the charging profile based on the battery's characteristics. The method involves determining battery characteristics and deriving weight information based on those characteristics. This weight information is then used to modify a basic charging profile to create a customized charging profile optimized for that specific battery.

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Battery charging control method for electric vehicles that prolongs battery life and mitigates degradation when the vehicle is parked for long periods. The method calculates an initial maintenance capacity target value based on the date interval between charges. It then determines the charging and maintenance capacity target based on this value and the pre-stored maintenance capacity. If the target is met, the battery charges in a maintenance mode with a small rate to complete charging. This prevents damage when resuming use after extended storage.

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Adaptive fast charging method and system for lithium-ion batteries that considers battery health degradation. The method involves calculating an adaptive maximum charging current based on battery state of health (SOH) in addition to temperature and state of charge (SOC). As batteries age, their performance degrades, so charging at the same rate can exceed safe limits. By adjusting the charging current based on SOH, it prevents overcharging and improves safety as batteries age. The adaptation is done by the battery management system (BMS) interacting with the charging station to limit charging current based on SOH.

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Battery charging control to improve life and efficiency of battery systems. The charging control calculates estimated battery temperature, capacity retention, and cumulative capacity up to the battery life based on factors like power usage, outside air temperature, and charging rate. It then determines the optimal charging voltage to maximize life and cumulative capacity. This allows charging optimization that balances deterioration and capacity retention versus economic charging to improve overall battery performance.

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Battery management method for electric vehicles that dynamically adjusts charging limits based on battery health to extend battery life and reduce safety risks as batteries age. The method involves determining a target charging upper limit based on the battery state parameter, which decreases linearly with increasing battery degradation. This allows setting different charging limits at each stage of battery life to keep the battery in a good operating state and delay aging. The method also considers vehicle usage patterns to optimize charging strategies.

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Battery management system that adjusts battery charging to mitigate degradation and maintain performance over time. It estimates battery health and determines a degraded state of charge based on the relationship between health and capacity loss. The charging is then adjusted to target that degraded state of charge. This prevents overcharging in later life when capacity loss is higher, avoiding excessive cell stress and further degradation.

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Battery charging system that adjusts the charging current based on the battery's health state of charge. The system includes a controller that stores an initial charging current magnitude. The controller reduces this magnitude based on the battery's state of health (SOH) to generate the charging current command. This prevents overloading a deteriorated battery with excessive current.

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Optimizing the estimation of state of charge (SOC) in lithium batteries during slow charging to accurately track the SOC over long charge times. The optimization involves monitoring the voltage change during charging, comparing it to known characteristics of the specific battery, and dynamically adjusting the SOC calculation based on the voltage trends. This avoids errors in SOC estimation when batteries are charged slowly for long periods.

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Intelligent battery charging technique to optimize battery life by predicting aging rate and dynamically adjusting charge limits. The method involves calculating an aging rate coefficient based on internal battery state and aging parameters. It then predicts the aging rate by modeling the coefficient change with battery aging. If the predicted rate exceeds a target, it adjusts the charge limit to prevent overcharging and reduce aging.

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Optimizing the useful life of a battery in vehicles by dynamically adjusting the target state of charge based on battery temperature to mitigate thermal degradation. The method involves determining a decreasing target state of charge as temperature increases, to match the relative increase in internal resistance from a reference degradation profile. This iterative estimation aims to keep the estimated relative resistance increase less than the reference profile. The target charge decreases with higher temperatures to control wear.

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A battery charging system that calculates an optimal charging rate based on battery usage history to mitigate wear and extend battery life. The system tracks voltage and temperature ranges the battery spends time in, assigns wear factors to those ranges, and calculates an accumulated battery degradation. It then charges the battery at reduced voltages if the degradation exceeds certain thresholds. This adaptive charging accounts for factors like excessive charging, overdischarging, and heat cycling that accelerate wear.

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Accurately estimating the charging time of a vehicle battery by predicting the voltage and temperature during charging. The method involves using a system with a prediction module that forecasts the battery voltage, state of charge, and heat generation based on factors like charger current, battery properties, and cooling/heating conditions. This allows more precise estimation compared to using pre-stored database values since it accounts for dynamic charging conditions.

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Optimizing battery charging and management for electric work vehicles to extend battery life and improve performance by carefully controlling charge rates and state of charge during extended periods of immobilization. The method involves charging the battery during immobilization to a lower state of charge than normal, using slower charge rates, and potentially warming the battery and other components. This is done to reduce aging effects from high charge levels and fast charging. During extended immobilization, other processes like warming the cab and performing service tasks can also be done. The charging strategy is adjusted based on battery health data to further optimize battery life.

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Charging control method and system for electric vehicles that improves charging efficiency and fuel efficiency by optimizing charging strategy based on auxiliary battery state. The method involves monitoring auxiliary battery parameters like SOC, temperature, and charging current. Based on the monitored state, the charging mode is determined. The charging voltage is set based on the SOC and temperature. This allows tailoring the charging strategy to the battery condition. For example, higher voltage and faster charging for low SOC versus lower voltage and slower charging for high SOC. This prevents overcharging and reduces heat generation for improved efficiency.

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Intelligent charging management for electric vehicle batteries that limits charging to less than full capacity based on the anticipated driving distance before the next recharge. The system estimates battery temperature during the upcoming drive, calculates the energy needed for that distance, factors in the impact on battery health and efficiency, and sets a target charge level. This avoids unnecessary full charging which can degrade battery life. The charge target is communicated to the vehicle charger to stop at that SOC.

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Method to dynamically estimate battery health during charging to improve electric vehicle safety. The method involves estimating battery performance indicators like state of health (SOH) during charging by the charging station. It sets initial SOC and gradient based on the battery's initial charge level. The charging station then divides the SOC range into intervals. It calculates the actual charge level at the end of charging and finds the corresponding SOC interval. By tracking the intervals traversed during charging cycles, it estimates the battery's health degradation over time. This allows real-time monitoring and maintenance recommendations for EVs.

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