A quick charging device for power battery

Abstract

The application discloses a kind of quick charging equipment for power battery, it is related to power battery technical field, including intelligent charging module, including intelligent charging module, the intelligent charging module comprehensively considers charging time, battery temperature rise and capacity attenuation, and optimizes control charging process;The intelligent charging module includes: state perception unit, advance prediction unit and target optimization unit.The application is realized by designing intelligent charging module, and the collaborative promotion of safety, efficiency, life, solves the problem that safety and life are difficult to weigh under the scene of traditional charging equipment in extremely fast charging, maintains the high efficiency and safety of charging process.

Description

Technical Field

[0001] This invention relates to the field of power battery technology, specifically to a fast charging device for power batteries. Background Technology

[0002] With global attention focused on environmental protection and the energy crisis, electric vehicles, due to their zero emissions, high energy conversion efficiency, and low operating costs, are gradually becoming an important alternative to traditional gasoline-powered vehicles. Consumers' urgent demand for faster charging speeds for electric vehicles is placing higher demands on the charging of power batteries.

[0003] Traditional charging devices are prone to battery overheating or lithium plating risks when pursuing fast charging, while overly conservative charging strategies will prolong charging time, making it difficult to balance the conflict between charging speed, temperature rise control and lifespan extension.

[0004] Patent CN119765584B discloses a method, device, and equipment for equalizing charging a power battery pack. The patent achieves temperature adaptive control of the equalization circuit, effectively prevents overheating of the devices, extends the service life of the system, and realizes quantitative evaluation and strategy optimization of the equalization effect.

[0005] The aforementioned patent addresses the problem that traditional equalization methods struggle to accurately identify cells requiring balancing, leading to wasted balancing resources, by comprehensively evaluating voltage standard deviation, temperature distribution range, and state of charge consistency indicators. However, there is still room for optimization in the formulation of charging strategies. This application dynamically adjusts the charging strategy based on the real-time state of the battery, solving the problem of balancing safety and lifespan in traditional charging devices during high-speed charging scenarios.

[0006] Therefore, this application proposes a fast charging device for power batteries that achieves multi-objective collaborative optimization. Summary of the Invention

[0007] The purpose of this invention is to provide a fast charging device for power batteries, so as to solve the technical problem mentioned in the background art that it is difficult to balance safety and lifespan in the case of ultra-fast charging.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a fast charging device for power batteries, comprising an intelligent charging module, wherein the intelligent charging module comprehensively considers charging time, battery temperature rise and capacity decay, and optimizes the control of the charging process; The intelligent charging module includes: a state sensing unit, an advanced prediction unit, and a target optimization unit. The state sensing unit is connected to the advanced prediction unit and the target optimization unit via signals, and the advanced prediction unit is connected to the target optimization unit via signals. The state perception unit collects the state information of the power battery in real time through the battery management system, and queries the safety boundary based on the real-time temperature and state of charge of the power battery. The advanced prediction unit predicts the temperature rise of the power battery based on the battery lumped parameter thermal model. Based on the current and temperature prediction results, it uses a semi-empirical aging model to predict the capacity decay of the battery and calculates the required charging time considering the equalization charging demand. The target optimization unit constructs a multi-objective optimization model based on the prediction results and the current battery state, combined with the optimization objective and battery characteristic constraints. It also considers historical charging performance evaluation reports, performs multi-objective optimization based on the non-dominated sorting genetic algorithm II, generates a Pareto optimal solution set, and selects a charging strategy.

[0009] Preferably, the charging performance evaluation report within the target optimization unit is generated by the charging analysis module; The charging analysis module includes: a current-voltage analysis unit, a performance mapping unit, and an evaluation feedback unit. The current-voltage analysis unit is connected to the performance mapping unit via a signal, and the performance mapping unit is connected to the evaluation feedback unit via a signal. The voltage and current analysis unit acquires the voltage and current data of the power battery during the power charging process, generates and analyzes the charging voltage-current curve, calculates the performance indicators of the power battery, integrates the changes in battery state, and generates complete charging data. The performance mapping unit associates the charging strategy used in each charge with the charging data to generate a complete charging record. Based on the preset efficiency evaluation model, it generates the comprehensive efficiency value and ranking for each charging cycle. The evaluation feedback unit identifies charging cycles with excellent and insufficient charging performance based on the overall efficiency value and ranking, and generates a detailed charging performance evaluation report.

[0010] Preferably, the multi-objective optimization model within the target optimization unit is dynamically adjusted through a satisfaction optimization module, which is used to transform the user's subjective needs into quantifiable optimization objectives. The satisfaction optimization module includes: a demand perception unit, a strategy mapping unit, and a strategy evaluation unit. The demand perception unit is connected to the strategy mapping unit via signals, and the strategy mapping unit is connected to the strategy evaluation unit via signals. The demand perception unit obtains the user's expected charging completion time and target state of charge through the interactive interface, matches the user's weight preferences, and establishes a satisfaction function between charging time and charging capacity. The strategy mapping unit combines the user satisfaction function, matches the charging mode based on the predicted results of power battery temperature and capacity decay, determines the weight of each component in the optimization objective, and dynamically adjusts the multi-objective optimization model. The strategy evaluation unit will include the actual charging time and the actual charging result of the initial state of charge, compare it with the user's initial expectations, and calculate the actual user satisfaction score for charging.

[0011] Preferably, the charging strategy within the target optimization unit is adjusted by a charging optimization module, which is used to optimize the grid load and the operating efficiency of the charging equipment. The charging optimization module includes: a real-time analysis unit, a demand analysis unit, and an energy management unit. The real-time analysis unit is connected to the demand analysis unit via a signal, and the demand analysis unit is connected to the energy management unit via a signal. The real-time analysis unit obtains grid load data, time-of-use electricity price information, weather data, and traffic conditions through a cloud platform, analyzes the correlation between the current grid load, electricity price period, and battery status, and generates a real-time analysis report. The demand analysis unit combines the real-time analysis report with the status information of the power battery to identify factors affecting the charging demand of the power battery and generate a priority queue. Based on real-time analysis reports and priority queues, the energy management unit generates charging strategy adjustment plans, configures intelligent control mechanisms, and dynamically allocates charging equipment resources.

[0012] Preferably, the equalization charging demand in the advanced prediction unit is generated by the equalization charging module, which is used to balance the charging state of each cell in the power battery. The equalization charging module includes a state evaluation unit and an equalization strategy unit, wherein the state evaluation unit is connected to the equalization strategy unit via a signal. Based on the charging state information of the power battery, the state assessment unit generates five-dimensional evaluation indicators of voltage, temperature, internal resistance, capacity and charge. It uses principal component analysis to reduce dimensionality and uses clustering algorithm to identify performance clusters to generate consistent evaluation results. Based on the consistency evaluation results, the equalization strategy unit determines the individual cells that need to be equalized, selects the equalization strategy, compares the battery charging status information before and after equalization, and evaluates the effect of equalization charging.

[0013] Preferably, the method for generating the overall efficiency value within the performance mapping unit includes the following steps: Data association: The charging voltage, capacity, and time of each charge are associated with the battery capacity, power, and efficiency performance data generated during that charge to form a complete charging record; Model transportation: Using charging record data over a period of time as decision units, and substituting it into the ultra-efficient DEA model for calculation; Results generated: The super-efficiency DEA model calculates a comprehensive efficiency value for each charging record.

[0014] Preferably, the charging time satisfaction function within the demand sensing unit is: ; Where T is the actual charging time, T1 is the user's expected charging time, L1 is the lower limit of acceptable time deviation when the user is very satisfied, and L1 is the upper limit of time deviation when the user is very dissatisfied.

[0015] Preferably, the charging power satisfaction function within the demand sensing unit is: ; Wherein, SOC is the final charged capacity, SOC1 is the user's desired target state of charge, L2 is the lower limit of the acceptable capacity deviation when the user is very satisfied, and L3 is the upper limit of the capacity deviation when the user is very dissatisfied.

[0016] Preferably, the optimization objectives within the target optimization unit include: minimizing charging speed, minimizing capacity decay, and minimizing battery temperature rise.

[0017] Preferably, the formula for calculating user satisfaction score within the strategy evaluation unit is as follows: , where a and b are the user's preference weights for charging time and charging capacity, respectively, and a+b=1.

[0018] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention, through the design of an intelligent charging module, achieves a synergistic improvement in safety, efficiency, and lifespan, solving the problem of balancing safety and lifespan in traditional charging devices under high-speed charging scenarios, and maintaining the high efficiency and safety of the charging process; 2. This invention, through the design of a charging analysis module, realizes data-driven optimization and closed-loop decision-making in the charging process, solves the problem of strategy blindness caused by the lack of historical data, and improves the adaptability of charging strategies and the performance of the battery throughout its entire life cycle. 3. This invention, through the design of a satisfaction optimization module, realizes the generation of personalized charging strategies, solves the problem of poor experience caused by traditional charging strategies ignoring user preferences, and improves the synergy between user satisfaction, charging efficiency and battery life; 4. This invention, by incorporating a charging optimization module, solves the problem of strategy formulation under multiple constraints of grid load, user demand, and battery status, thereby improving charging economy, grid coordination, and equipment utilization efficiency. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the intelligent charging module of the present invention; Figure 2 This is a schematic diagram of the charging analysis module of the present invention; Figure 3 This is a schematic diagram of the satisfaction optimization module of the present invention; Figure 4 This is a schematic diagram of the charging optimization module of the present invention; Figure 5 This is a schematic diagram of the equalization charging module of the present invention; Figure 6 This is a schematic diagram of the overall efficiency value generation process of the present invention; Figure 7 This is a schematic diagram illustrating the optimization objective of the present invention; Figure 8 This is a schematic diagram of the working process of the charging device of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 , Figure 7 and Figure 8 This invention provides an embodiment of a fast charging device for power batteries, comprising an intelligent charging module. The intelligent charging module includes a state sensing unit, a predictive unit, and a target optimization unit. The state sensing unit collects real-time state information of the power battery through a battery management system and queries the safety boundary based on the real-time temperature and state of charge of the power battery. The predictive unit predicts the temperature rise of the power battery based on a lumped parameter thermal model of the battery, and predicts the capacity decay of the battery using a semi-empirical aging model based on the current and temperature prediction results, calculating the required charging time considering equalization charging requirements. The target optimization unit constructs a multi-objective optimization model based on the prediction results and the current battery state, combined with the optimization objective and battery characteristic constraints. Considering historical charging performance evaluation reports, it performs multi-objective optimization based on a non-dominated sorting genetic algorithm II to generate a Pareto optimal solution set and select a charging strategy.

[0022] Furthermore, when the charging gun of the charging device is inserted into the fast charging port of the vehicle, the charging device and the vehicle establish a connection through their respective pins, thereby constructing a communication network. Under this network architecture, the power battery management system equipped in the vehicle is responsible for transmitting the real-time charging status information of the power battery to the status sensing unit of the charging device, including key parameters such as the temperature, state of charge, voltage, current and historical charge and discharge data of the power battery. Based on the real-time collected temperature and state of charge information, the status sensing unit queries the lithium plating boundary database pre-calculated and stored in the P2D electrochemical model. The boundary defines the maximum allowable charging current rate that does not trigger the negative electrode lithium plating side reaction under different temperatures and states of charge. Combined with the set maximum allowable temperature under different charge and discharge rates, the limit that the battery can withstand under the current conditions is determined, and the maximum allowable charging current and temperature safety upper limit of the battery under the current state are obtained. For example, when the battery temperature is high, the maximum allowable charging current is reduced to prevent the battery from overheating. The advance prediction unit interacts with the state perception unit and the target optimization unit. The advance prediction unit receives real-time current, voltage, and initial temperature from the state perception unit, and receives candidate charging strategies from the target optimization unit. It then calculates the instantaneous heat generation power Q=I(VU) using the Bernardi equation. ocv )+IT2(dU ocv / dT2), where I is the charging and discharging current of the power battery, V is the terminal voltage of the power battery, and U ocv T1 is the open-circuit voltage of the power battery, T2 is the absolute temperature of the power battery, and dU is the open-circuit voltage of the power battery. ocv / dT2 is the entropy-heat coefficient. Substituting the generated heat power into the heat network state equation derived from Kirchhoff's laws, the equation is: T3 and T4 are the temperatures of the core of the power battery and the external insulation material, respectively. ∞ R represents ambient temperature, C1 and C2 represent the heat capacities of the power battery itself and the external insulation material, respectively. i R0 and R0 represent the internal thermal resistance of the power battery and the thermal resistance of the external insulation material, respectively. This allows for the prediction of the battery's temperature distribution and temperature rise at different time points, generating a predicted temperature sequence. Based on the battery temperature rise prediction results, a semi-empirical aging model Q0 is used. loss =B(I)·exp(-E a / RT (k) )·(Ah) z Q loss B(I) is the capacity decay percentage, B(I) is the pre-exponential factor, and E is the capacity decay percentage. a The activation energy is T, R is the molar gas constant, and T is the activation energy. (k)Let k be the temperature at time k, Ah be the cumulative charge / discharge ampere-hour throughput, and z be the power-law factor. This predicts the capacity decay of the battery during fast charging. During the prediction process, the advance prediction unit identifies model parameters to improve model accuracy and reduce the mean absolute error between predicted and measured values. In the identification step, the optimal parameter set is searched using the whale optimization algorithm. The optimal solution is searched in the parameter space, and the parameter values ​​are adjusted to minimize the error between the model's prediction and the actual measured data. The advance prediction unit simulates charging the battery model according to candidate charging strategies to calculate the required equalization charging time considering equalization charging needs. The main charging time and equalization charging time are combined, and the advance prediction unit transmits the key indicators of the required charging time to the target optimization unit. The target optimization unit receives the current battery state data, maximum allowable charging current, and temperature safety limit from the state perception unit. Its optimization objectives are minimizing charging time, maximum battery temperature rise, and capacity decay. Current and temperature constraints are based on the maximum allowable charging current and temperature safety limit, while charging starts at the target state of charge as a charge constraint. Current monotonicity constraints are based on a gradual decrease in current at each stage. According to the historical charging performance evaluation report of the power battery (i.e., the comprehensive efficiency value and ranking under historical charging strategies), the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) algorithm is used for multi-objective optimization. In this process, multiple initial populations of multi-stage charging strategies are randomly generated. Each charging strategy is input into the advance prediction unit for prediction. The prediction results from the advance prediction unit are received, and a fast non-dominated sorting is performed, dividing the charging strategies in the population into different non-dominated levels. For example, if strategy A is superior to strategy B in terms of time and decay, then strategy A dominates B. Based on the non-dominated levels, the superior strategies are used to generate offspring populations through genetic operations. This process is iterated and looped to output a Pareto optimal solution set, thus obtaining the charging strategy.

[0023] Please see Figure 1 , Figure 2 , Figure 6 and Figure 8This invention provides an embodiment of a fast charging device for power batteries. The charging performance evaluation report within the target optimization unit is generated by a charging analysis module. The charging analysis module includes a voltage-current analysis unit, a performance mapping unit, and an evaluation feedback unit. The voltage-current analysis unit acquires the voltage and current data of the power battery during charging, generates and analyzes the charging voltage-current curve, calculates the performance indicators of the power battery, integrates battery state changes, and generates complete charging data. The performance mapping unit associates the charging strategy used for each charge with the charging data, generates a complete charging record, and generates a comprehensive efficiency value and ranking for each charging cycle based on a preset efficiency evaluation model. The evaluation feedback unit, based on the comprehensive efficiency value and ranking, identifies charging cycles with excellent and insufficient charging performance and generates a detailed charging performance evaluation report.

[0024] Furthermore, during the charging process of the power battery, the current-voltage analysis unit receives real-time battery status data from the current-voltage analysis unit and plots the voltage and current data in the collected real-time status data into a charging current-voltage curve. The curve reflects the external electrical performance of the internal electrochemical reaction during the charging process of the power battery. The charging capacity is calculated by integrating the current over time, the charging power is calculated by multiplying the voltage and current, and the charging efficiency is calculated by the ratio of the energy absorbed by the power battery to the energy input by the external circuit. This achieves direct and effective measurement of charging performance. By continuously recording the current-voltage curve of each charging process and the calculated charging capacity, power, and efficiency performance index values, the decay and change trends of the index with the increase of charging cycles are identified, and a complete charging performance data package spanning the entire life cycle is generated. The performance mapping unit receives charging performance data from the current-voltage analysis unit and combines it with the charging strategy adopted by the intelligent charging module for this charging session. It binds charging strategy data (including parameters such as charging voltage, charging current, and charging time) with charging performance data (including battery capacity, battery power, and charging efficiency), generating a complete charging record for each charging process. This record documents the charging effect achieved by the used charging strategy. The performance mapping unit uses a pre-set efficiency evaluation model within the device, employing the recorded charging strategy as the model's input indicator and the recorded charging performance data as the model's output indicator to calculate a comprehensive efficiency value. This assigns a quantified comprehensive efficiency value θ to each charging record. Based on the θ value, all charging records are sorted. When θ equals 1, it indicates that the charging process is DEA effective, meaning the output / input efficiency is relatively high. When θ is greater than 1, it indicates super-efficient effectiveness, with higher values ​​indicating higher efficiency. When θ is less than 1, it indicates non-DEA effectiveness, suggesting room for improvement. The evaluation feedback unit receives the comprehensive efficiency value of each charging cycle and the global ranking of all charging cycles from the performance mapping unit. It identifies excellent cycles with θ greater than or equal to 1 and cycles with θ less than 1 that need improvement. It summarizes the characteristic patterns of efficient charging strategies. For example, under a certain voltage and current condition, the charging time is slightly longer, but the battery capacity decay is the slowest and the comprehensive efficiency over the entire life cycle is the highest. It generates a charging performance evaluation report, thereby improving the efficient charging cycles and their corresponding optimal charging parameter specifications for the smart charging module.

[0025] Please see Figure 3 and Figure 8 This invention provides an embodiment of a fast charging device for power batteries. The multi-objective optimization model within the target optimization unit is dynamically adjusted by a satisfaction optimization module. This module transforms the user's subjective needs into quantifiable optimization objectives. The satisfaction optimization module includes a demand perception unit, a strategy mapping unit, and a strategy evaluation unit. The demand perception unit obtains the user's desired charging completion time and target state of charge through an interactive interface, matches the user's weight preferences, and establishes a satisfaction function between charging time and charging capacity. The strategy mapping unit, combined with the user satisfaction function, matches the charging mode based on the predicted results of power battery temperature and capacity decay, determines the weight of each component in the optimization objective, and dynamically adjusts the multi-objective optimization model. The strategy evaluation unit compares the actual charging result, including the actual charging time and initial state of charge, with the user's initial expectations to calculate the actual user satisfaction score for charging.

[0026] Furthermore, the demand perception unit obtains two key parameters input by the user through an interactive interface, such as the human-machine interface of the charging device or a mobile terminal application: the expected charging completion time and the target state of charge, i.e., the time point when the battery is expected to be fully charged and the state of charge the user expects the battery to reach. It receives the user's preferences for charging speed and charging capacity, and dynamically allocates weights accordingly. For example, fast charging mode prioritizes time, and healthy charging mode prioritizes lifespan. The demand perception unit then transmits the quantified satisfaction function and weight parameters to the strategy mapping unit. The strategy mapping unit receives the battery temperature rise prediction based on the current candidate strategy and battery state from the advance prediction unit. The capacity degradation prediction results, combined with the quantified satisfaction function and weight parameters, are used by the strategy mapping unit to comprehensively analyze the user's urgency regarding time, the intensity of their demand for electricity, and the battery temperature rise and degradation feedback from the advanced prediction unit. The current charging demand is then mapped to three preset charging modes: fast charging mode, healthy charging mode, or balanced charging mode. If the user expects a very short charging time and has extremely high speed requirements, the fast charging mode may be prioritized. In this mode, the weight of minimizing charging time in the optimization objective is increased, while the restrictions on battery degradation are appropriately relaxed, and safety constraints are followed, thereby achieving personalized service and improving user experience. When the strategy mapping unit determines the charging mode, it assigns a set of dynamic weights to the multi-objective optimization model within the objective optimization unit. This transforms the multi-objective problem into a weighted single-objective optimization problem, enabling the rapid selection of a unique optimal solution from the Pareto solution set. For example, when the strategy mapping unit determines the fast charging mode, it assigns the highest priority weight to charging time, and the objective optimization unit selects the strategy with the shortest charging time from the Pareto front. The strategy evaluation unit calculates the charging time and current satisfaction scores based on the constructed satisfaction function, thereby generating a comprehensive user satisfaction score, providing a data foundation for the generation of subsequent charging strategies.

[0027] Please see Figure 4 and Figure 8This invention provides an embodiment of a fast charging device for power batteries. The charging strategy within the target optimization unit is adjusted by a charging optimization module, which optimizes the grid load and the operating efficiency of the charging equipment. The charging optimization module includes a real-time analysis unit, a demand analysis unit, and an energy management unit. The real-time analysis unit acquires grid load data, time-of-use electricity price information, weather data, and traffic conditions through a cloud platform, analyzes the correlation between the current grid load, electricity price period, and battery status, and generates a real-time analysis report. The demand analysis unit combines the real-time analysis report with the power battery status information to identify factors affecting the power battery charging demand and generates a priority queue. The energy management unit generates a charging strategy adjustment plan based on the real-time analysis report and the priority queue, configures an intelligent control mechanism, and dynamically allocates charging equipment resources.

[0028] Furthermore, the real-time analysis unit acquires key real-time data through a cloud platform, linking it to grid load and electricity price periods, weather and battery status, and traffic conditions and charging demand. For example, during peak hours, high grid load and high electricity prices can remind users to avoid charging during peak periods; during off-peak hours, low load and low electricity prices can guide users to charge, generating a real-time analysis report that includes grid load assessment, electricity price period suggestions, and risk warnings. The risk warnings include suggestions to reduce fast charging power in hot weather or adjust charging queues at bus charging stations during traffic congestion. The demand analysis unit combines the real-time status information of the power battery with the real-time analysis report to identify and quantify key factors affecting power battery charging demand. For example, for the charging status of ordinary private car power batteries, referring to the time-of-use electricity price information in the real-time analysis report, vehicles are prioritized for charging during off-peak hours. Charging is implemented to reduce costs. For the charging status of bus power batteries, the system considers the bus's departure time, traffic conditions, and state of charge to identify whether a vehicle is in an emergency. During peak grid hours, the charging power is reduced or charging for non-emergency vehicles is temporarily suspended. The real-time analysis unit, combined with user requirements from the satisfaction optimization module, calculates a comprehensive priority score for the power batteries of different vehicles waiting to be charged, thereby generating a dynamically updated priority queue. Based on the real-time analysis report and the priority queue, the energy management unit identifies the charging urgency of different power batteries, introduces optimization objectives such as minimizing total charging costs and optimizing grid load, and further generates charging strategies. Through an intelligent control mechanism, charging equipment is allocated according to the charging urgency, determining which power battery should be charged first and adjusting the total power output of the entire charging station.

[0029] Please see Figure 5 and Figure 8This invention provides an embodiment of a fast charging device for power batteries. The equalization charging demand within the advanced prediction unit is generated by an equalization charging module, which is used to balance the charging state of each cell in the power battery. The equalization charging module includes a state evaluation unit and an equalization strategy unit. The state evaluation unit is connected to the equalization strategy unit via a signal. Based on the power battery charging state information, the state evaluation unit generates five-dimensional evaluation indicators of voltage, temperature, internal resistance, capacity, and charge. It performs dimensionality reduction using principal component analysis and identifies performance clusters using a clustering algorithm to generate a consistent evaluation result. Based on the consistent evaluation result, the equalization strategy unit determines the cells that need to be equalized, selects an equalization strategy, compares the battery charging state information before and after equalization, and evaluates the effect of equalization charging.

[0030] Furthermore, the state assessment unit receives real-time charging state information of the power battery from the intelligent charging module, including five-dimensional evaluation indicators: voltage, temperature, internal resistance, capacity, and charge. Dimensionality reduction is performed using principal component analysis. Through linear transformation, the five highly correlated original indicators are recombined into a set of uncorrelated principal components. The first principal component represents the direction with the largest variance contribution from the linear combination of the original five-dimensional variables, while the second principal component has the second largest variance contribution. Specifically, the first principal component mainly reflects the variation of indicators strongly correlated with charging and discharging performance, such as voltage and internal resistance, while the second principal component focuses on the differences in indicators related to thermal management and aging characteristics, such as temperature and capacity. Clustering algorithms are used to perform cluster analysis on the dimensionality-reduced data, grouping battery cells with similar performance into the same cluster and identifying cells with significant performance differences within the battery pack, outputting a consistency evaluation result. Based on the consistency evaluation result, the balancing strategy unit identifies the performance differences of each cell in the battery pack, determines the battery cells requiring equalization charging, and selects a balancing strategy based on the type and degree of inconsistency. For example, after the balancing operation is completed, the balancing strategy unit compares and analyzes the data before and after balancing to evaluate the balancing effect and determine whether cells that have abnormally deviated from their clusters have returned to the main group.

[0031] Working principle: Before charging, the satisfaction optimization module and the charging optimization module respectively obtain user needs and external information to determine the urgency of charging the power battery and determine the allocated charging mode and charging equipment resources. After the charging device establishes communication with the power battery, the intelligent charging module obtains the charging status information of the power battery. The intelligent charging module combines the historical charging performance evaluation report and generates multiple initial candidate charging strategies through a multi-objective optimization model. Based on the charging parameters in the initial candidate charging strategies, it predicts the charging response information and selects the optimal charging strategy. During the charging process, the equalization charging module assesses the consistency status of the individual power battery cells and dynamically adjusts the charging current of each cell to achieve equalization charging. When charging is complete, the charging analysis module generates charging records that are associated with the charging strategy and charging performance data, and outputs a charging performance evaluation report that includes a comprehensive efficiency value and ranking.

[0032] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A rapid charging device for a power battery, characterized in that: It includes an intelligent charging module, which comprehensively considers charging time, battery temperature rise and capacity decay to optimize the control of the charging process; The intelligent charging module includes: a state sensing unit, an advanced prediction unit, and a target optimization unit. The state sensing unit is connected to the advanced prediction unit and the target optimization unit via signals, and the advanced prediction unit is connected to the target optimization unit via signals. The state perception unit collects the state information of the power battery in real time through the battery management system, and queries the safety boundary based on the real-time temperature and state of charge of the power battery. The advanced prediction unit predicts the temperature rise of the power battery based on the battery lumped parameter thermal model. Based on the current and temperature prediction results, it uses a semi-empirical aging model to predict the capacity decay of the battery and calculates the required charging time considering the equalization charging demand. The target optimization unit constructs a multi-objective optimization model based on the prediction results and the current battery state, combined with the optimization objective and battery characteristic constraints. It considers historical charging performance evaluation reports, performs multi-objective optimization based on the non-dominated sorting genetic algorithm II, generates a Pareto optimal solution set, and selects a charging strategy. The charging performance evaluation report within the target optimization unit is generated by the charging analysis module. The charging analysis module includes: a current-voltage analysis unit, a performance mapping unit, and an evaluation feedback unit. The current-voltage analysis unit is connected to the performance mapping unit via a signal, and the performance mapping unit is connected to the evaluation feedback unit via a signal. The voltage and current analysis unit acquires the voltage and current data of the power battery during the power charging process, generates and analyzes the charging voltage-current curve, calculates the performance indicators of the power battery, integrates the changes in battery state, and generates complete charging data. The performance mapping unit associates the charging strategy used in each charge with the charging data to generate a complete charging record. Based on the preset efficiency evaluation model, it generates the comprehensive efficiency value and ranking for each charging cycle. The evaluation feedback unit identifies charging cycles with excellent and insufficient charging performance based on the overall efficiency value and ranking, and generates a detailed charging performance evaluation report. The equalization charging demand in the advanced prediction unit is generated by the equalization charging module, which is used to balance the charging state of each cell in the power battery. The equalization charging module includes a state evaluation unit and an equalization strategy unit, wherein the state evaluation unit is connected to the equalization strategy unit via a signal. Based on the charging state information of the power battery, the state assessment unit generates five-dimensional evaluation indicators of voltage, temperature, internal resistance, capacity and charge. It uses principal component analysis to reduce dimensionality and uses clustering algorithm to identify performance clusters to generate consistent evaluation results. The equalization strategy unit determines the individual cells that need to be equalized based on the consistency evaluation results, selects the equalization strategy, compares the battery charging status information before and after equalization, and evaluates the effect of equalization charging. The method for generating the overall efficiency value within the performance mapping unit includes the following steps: Data association: The charging voltage, capacity, and time of each charge are associated with the battery capacity, power, and efficiency performance data generated during that charge to form a complete charging record; Model transportation: Using charging record data over a period of time as decision units, and substituting it into the ultra-efficient DEA model for calculation; Results generated: The super-efficiency DEA model calculates a comprehensive efficiency value for each charging record.

2. The rapid charging device for power batteries according to claim 1, characterized in that: The multi-objective optimization model within the target optimization unit is dynamically adjusted through the satisfaction optimization module, which transforms the user's subjective needs into quantifiable optimization objectives. The satisfaction optimization module includes: a demand perception unit, a strategy mapping unit, and a strategy evaluation unit. The demand perception unit is connected to the strategy mapping unit via signals, and the strategy mapping unit is connected to the strategy evaluation unit via signals. The demand perception unit obtains the user's expected charging completion time and target state of charge through the interactive interface, matches the user's weight preferences, and establishes a satisfaction function between charging time and charging capacity. The strategy mapping unit combines the user satisfaction function, matches the charging mode based on the predicted results of power battery temperature and capacity decay, determines the weight of each component in the optimization objective, and dynamically adjusts the multi-objective optimization model. The strategy evaluation unit will include the actual charging time and the actual charging result of the initial state of charge, compare it with the user's initial expectations, and calculate the actual user satisfaction score for charging.

3. The rapid charging device for power batteries according to claim 1, characterized in that: The charging strategy within the target optimization unit is adjusted by the charging optimization module, which is used to optimize the grid load and the operating efficiency of the charging equipment. The charging optimization module includes: a real-time analysis unit, a demand analysis unit, and an energy management unit. The real-time analysis unit is connected to the demand analysis unit via a signal, and the demand analysis unit is connected to the energy management unit via a signal. The real-time analysis unit obtains grid load data, time-of-use electricity price information, weather data, and traffic conditions through a cloud platform, analyzes the correlation between the current grid load, electricity price period, and battery status, and generates a real-time analysis report. The demand analysis unit combines the real-time analysis report with the status information of the power battery to identify factors affecting the charging demand of the power battery and generate a priority queue. Based on real-time analysis reports and priority queues, the energy management unit generates charging strategy adjustment plans, configures intelligent control mechanisms, and dynamically allocates charging equipment resources.

4. The rapid charging device for power batteries according to claim 2, characterized in that: The charging time satisfaction function within the demand perception unit: Wherein, T is the actual charging duration, T1 is the user's desired charging time, L1 is the lower limit of the time deviation acceptable when the user is very satisfied, L2 is the upper limit of the time deviation when the user is very dissatisfied.

5. The rapid charging device for power batteries according to claim 2, characterized in that: The charging power satisfaction function within the demand perception unit is: wherein SOC is the final charge amount, SOC1 is the target state of charge desired by the user, L2 is the lower limit of the charge amount deviation when the user is very satisfied, is the upper limit of the charge amount deviation when the user is very dissatisfied.

6. The rapid charging device for power batteries according to claim 1, characterized in that: The optimization objectives within the target optimization unit include: minimizing charging speed, minimizing capacity decay, and minimizing battery temperature rise.

7. The fast charging device for power battery according to claim 2, characterized in that: The user satisfaction score calculation formula in the policy evaluation unit is Wherein a and b are respectively the preference weights of the user for charging time and charging power, and a+b=1.

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