Electric vehicle rapid charging current balancing system based on adaptive control algorithm and rapid charging algorithm

Abstract

An electric vehicle rapid charging current balancing system based on a self-adaptive control algorithm and a rapid charging algorithm comprises a data acquisition and sensor module, a power management and conversion module, a charging device design module, a self-adaptive charging control module, a data storage and remote monitoring module and a user interaction and control module. The data acquisition and sensor module is used for data acquisition, the power management and conversion module is used for power management and energy conversion, the charging device design module is used for charging structure and interface design, the adaptive charging control module is used for adjusting the charging process, and the data storage and remote monitoring module is used for storage and monitoring. According to the electric vehicle rapid charging current balancing system based on the self-adaptive control algorithm and the rapid charging algorithm, the self-adaptive power management algorithm based on competitive analysis is provided for optimizing battery management; a fast charging algorithm based on adaptive dynamic adjustment is provided for adaptive adjustment of a charging process.

Description

Technical Field

[0001] This invention relates to the fields of competition analysis, adaptive control, and current balancing technology, specifically to a fast charging current balancing system for electric vehicles based on adaptive control algorithms and fast charging algorithms. Background Technology

[0002] Competition analysis technology is an optimization method based on dynamic resource allocation, designed to solve the problems of low battery management efficiency and insufficient energy utilization during electric vehicle charging. By combining adaptive control technology, competition analysis technology monitors charging load, grid voltage fluctuations, and battery status in real time, comprehensively analyzes the charging needs among different battery cells, and adaptively and dynamically adjusts the charging power allocation to ensure optimal power allocation for battery management, thereby improving the overall energy efficiency of the charging system. Combined with adaptive control methods, this technology can dynamically optimize based on real-time data during the charging process, achieving intelligent power management and further improving charging stability and safety.

[0003] Current balancing technology is an optimization strategy based on intelligent charging control, designed to solve the problems of uneven current distribution and accelerated battery cell aging during electric vehicle battery charging. By combining adaptive control technology, the charging system can adjust the current distribution in real time according to battery status, temperature and charging progress, achieving precise current balancing control. Through intelligent algorithms, the charging strategy is dynamically adjusted to ensure that each battery cell is charged in the best way, thereby improving charging efficiency and extending battery life.

[0004] The existing electric vehicle fast charging current balancing system based on adaptive control algorithm and fast charging algorithm has the following problems: the charging system cannot flexibly adjust according to real-time load and battery status, resulting in low energy utilization and long charging time; and the uneven charging leads to overcharging and undercharging, which accelerates battery aging and affects the overall lifespan of the battery pack. Summary of the Invention

[0005] The purpose of this invention is to provide a fast charging current balancing system for electric vehicles based on adaptive control algorithms and fast charging algorithms, in order to solve the problems mentioned in the background art, such as the charging system's inability to flexibly adjust according to real-time load conditions and battery status, resulting in low energy utilization and long charging times, as well as the problems caused by overcharging and undercharging due to uneven charging, which accelerate battery aging and affect the overall lifespan of the battery pack.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a fast-charging current balancing system for electric vehicles based on adaptive control and fast-charging algorithms, comprising a data acquisition and sensor module, a power management and conversion module, a charging device design module, an adaptive charging control module, a data storage and remote monitoring module, and a user interaction and control module. The system is characterized in that: the data acquisition and sensor module is used to collect battery status, current, voltage information, and environmental data in real time, ensuring the accuracy and real-time nature of the data collected during charging; the power management and conversion module proposes an adaptive power management algorithm based on competition analysis to manage the power supply and convert external power into charging voltage and current suitable for the electric vehicle battery, ensuring a stable and reliable power supply; the charging device design module includes a structure and heat dissipation optimization unit and a charging interface protection module, wherein the structure and heat dissipation optimization unit is used to optimize the physical structure of the charging device. The charging interface protection module ensures efficient heat dissipation and stable operation of the device by protecting the charging interface from damage caused by overcurrent and overvoltage abnormalities, thus ensuring safety during charging. The adaptive charging control module includes an adaptive charging strategy unit and a current balance control unit. The adaptive charging strategy unit proposes a fast charging algorithm based on adaptive dynamic adjustment to automatically optimize the charging strategy according to the real-time battery status and environmental changes, ensuring high charging efficiency and battery life. The current balance control unit intelligently adjusts the current distribution according to the adaptive charging strategy to ensure balanced charging of each battery cell. The data storage and remote monitoring module stores charging process data and supports remote monitoring and fault diagnosis, ensuring the reliability of real-time monitoring and data analysis. The user interaction and control module interacts with the user and provides charging status feedback, ensuring that the user can control and monitor the charging process.

[0007] Preferably, the data acquisition and sensor module monitors the charging status and external environmental conditions in real time through high-precision current, voltage, temperature and environmental sensors, ensuring that the system accurately acquires key parameters such as battery current, voltage, temperature, humidity and external temperature, providing comprehensive data support for charging control and current balancing.

[0008] Preferably, the power management and conversion module proposes an adaptive power management algorithm based on competition analysis. By analyzing the charging load, grid voltage fluctuations and battery status in real time, it ensures optimal power allocation for battery management, improves power utilization and reduces energy consumption.

[0009] Preferably, the adaptive power management algorithm based on competition analysis is as follows: First, define the key energy consumption parameters of the electric vehicle fast charging system, establish a dynamic shutdown threshold model to optimize energy conversion efficiency, and calculate the shutdown threshold based on the energy balance principle. This means the system has been idle for more than [time period]. Turning off the device at certain times can save energy. The specific formula is as follows:

[0010]

[0011] in, This represents the power consumption of the charging system in the idle state, measured in watts. The wake-up energy consumption of the charging system, including the total energy consumption of circuit startup and capacitor charging, is expressed in joules. By quantifying the shutdown threshold, a benchmark is provided for subsequent adaptive management strategies, ensuring that the charging system achieves an initial balance between energy waste and latency. Then, offline adaptive power management strategies and corresponding online adaptive power management strategies are constructed. It is assumed that the time interval for offline measurement to predict all charging current demands is expressed as... for, Let represent the time interval of the independent charging current demand at each moment. The decision formula and the total energy consumption formula are expressed as follows:

[0012]

[0013]

[0014] in, The power consumption is represented by the offline optimal strategy. The offline algorithm assumes that the time intervals of all charging current requirements are known in advance. Therefore, it is able to make the optimal decision. This represents the total number of requests over all time intervals of charging current demand. This represents the request index within the time interval of all charging current requirements. Represented as a minimization function, This represents the time from the completion of the previous request to the [number]th request. The time interval between the arrival of each request Represented as an indicator function, it means that when When the function value is 1, it indicates that the system has chosen to shut down; otherwise, it is 0, indicating that the system remains idle. The difference in the corresponding online adaptive power management strategy lies in the time interval of all charging current requirements. The distribution is unknown but correlated, requiring real-time decision-making. The specific formula is as follows:

[0015]

[0016] This is expressed as the total energy consumption calculated using an online adaptive power management strategy. Represented as the first The actual idle time of the system before the first request; secondly, the performance gap between the offline adaptive power management strategy and the corresponding online adaptive power management strategy is evaluated through competition analysis to ensure the robustness of the power management strategy. The specific formula is expressed as:

[0017]

[0018] in, Expressed as the competition ratio, Represented as a maximization function, the worst-case performance of the algorithm is quantified through competitive analysis, providing a theoretical basis for dynamic adjustment strategies and ensuring the stability of the charging system under varying loads. Then, the shutdown threshold is dynamically optimized based on historical load data to improve battery management efficiency and current balancing capability. The specific formula for weighted update of historical data is expressed as follows:

[0019]

[0020] in, Represented as the first value obtained using the Exponentially Weighted Moving Average (EWMA) method. Adaptive threshold adjustment when a request arrives This represents the time from the completion of the previous request to the [number]th request. The time interval between the arrival of each request This represents the weight of the decay factor used to control historical data. Represented as the first value obtained using the Exponentially Weighted Moving Average (EWMA) method. An adaptive threshold adjustment is implemented when a request arrives. This dynamic threshold adjustment reduces energy waste and frequent wake-ups caused by fixed thresholds, optimizing the current balance efficiency of the charging system. Then, by constructing a delay constraint integration and real-time correction strategy, the power management strategy is ensured to meet the maximum delay constraint, avoiding charging interruptions and battery life degradation. The specific formula for the delay model is expressed as follows:

[0021]

[0022] in, This is expressed as the actual delay consisting of wake-up time and queuing time. This represents the wake-up time of the charging system, expressed in seconds. Indicated as a request The inherent waiting time, This represents the time from the completion of the previous request to the [number]th request. The time interval between the arrival of each request is calculated using queuing theory, and the specific formula for the real-time correction mechanism is as follows:

[0023]

[0024] in, This is expressed as the total energy consumption calculated through a real-time correction mechanism. This represents the maximum allowable delay of the system, if the predicted delay is... This approach prohibits current shutdown operations and forces the system to remain in an idle state, striking a balance between energy optimization and user experience to ensure the high responsiveness and safety of the electric vehicle charging system. Finally, it dynamically adjusts algorithm parameters through real-time feedback to achieve long-term stable optimization, calculating a multi-objective optimization function that minimizes both total energy consumption and latency penalties. The specific formula is as follows:

[0025]

[0026] in, This is expressed as a comprehensive index reflecting system performance, a multi-objective optimization function. This represents the current total energy consumption of the system calculated using an online adaptive power management strategy. The attenuation factor is represented as a latency penalty coefficient, reflecting the weight of latency's impact on system performance. Through a multi-objective optimization function, energy consumption and latency are balanced to ensure the system meets user experience requirements while achieving efficient energy management. The gradient descent method is used to dynamically optimize the attenuation factor. and delay penalty coefficient The specific formula is expressed as follows:

[0027]

[0028]

[0029] in, Represented as the first The decay factor of the next iteration. This is represented by the number of algorithm iterations. Represented as the first The decay factor of the next iteration. The learning rate is represented as the step size for updating the control parameters. Represented as a multi-objective optimization function pair The partial derivatives, Represented as the first The delay penalty coefficient for the next iteration Represented as the first The delay penalty coefficient for the next iteration Represented as a multi-objective optimization function pair The partial derivatives are dynamically adjusted using the gradient descent method. and This makes the multi-objective optimization function Minimize and continuously optimize system performance. Through a closed-loop optimization engine, adaptive adjustment of algorithm parameters is achieved to ensure the long-term stability and efficiency of the charging system under dynamic loads.

[0030] Preferably, the charging device design module includes a structure and heat dissipation optimization unit. The structure and heat dissipation optimization unit uses high thermal conductivity materials and air cooling and liquid cooling solutions to ensure that the temperature of the charging device is stable during long-term operation and avoid thermal runaway from affecting charging safety.

[0031] Preferably, the charging device design module includes a charging interface protection module. The charging interface protection module is designed with short-circuit protection, overcurrent protection, and contact reliability optimization to ensure stable connection and safe charging of the charging interface under high-frequency plugging and unplugging and different environmental conditions.

[0032] Preferably, the adaptive charging control module includes an adaptive charging strategy unit, which proposes a fast charging algorithm based on adaptive dynamic adjustment. This algorithm intelligently adjusts the battery charging curve and environmental factors to ensure the optimal charging rate under different charging conditions, thereby improving fast charging efficiency and reducing battery wear.

[0033] Preferably, the fast charging algorithm based on adaptive dynamic adjustment is as follows: dynamically initialize and set the benchmark for charging parameters, and initialize the maximum allowable charging current according to the battery type and historical data. Target charging voltage and adaptive adjustment coefficient matrix ,in, Expressed as the current regulation rate factor, This is expressed as the voltage deviation compensation factor. Expressed as the temperature decay coefficient, used to calculate the initial charging current. The specific formula is expressed as follows:

[0034]

[0035] in, Represented as a minimization function, This is represented as the initial battery voltage. This is expressed as the battery's equivalent internal resistance. By dynamically initializing and setting the charging parameters, an initial parameter benchmark is established for the charging system. Then, based on the battery state parameters output by the power management and conversion module, dynamic input is provided for adaptive fast charging adjustment, ensuring the safety and efficiency of the charging process. The adaptive adjustment mechanism quickly matches the charging needs of different batteries, ensuring the real-time performance and stability of subsequent adjustments. The specific formula is as follows:

[0036]

[0037] in, This is represented by the dynamically adjusted current calculated by the algorithm. This represents the current charging current of the system. This is expressed as voltage deviation. This is expressed as temperature deviation. It is expressed as the absolute value function raised to the power of 0.5. This represents the sign function used to ensure current decay when the temperature exceeds the limit, so that the current does not exceed the safety threshold after constraint adjustment. The specific formulas for voltage deviation and temperature deviation are expressed as follows:

[0038]

[0039] in, This is expressed as the real-time battery voltage. This is expressed as the real-time battery temperature. This is represented by a preset safe temperature threshold. Secondly, for multi-battery pack parallel charging scenarios, system-level current balance is achieved by dynamically allocating the current to each channel to avoid local overload. Let the first... The current allocation weights for each channel are: The total number of channels is The specific formula for the equilibrium objective is expressed as:

[0040]

[0041] in, Represented as the first Voltage deviation of each channel This is represented as the zero-prevention denominator coefficient. Represented as the first The actual charging current of each channel represents the result of current balance distribution. The total charging current represents the adaptively adjusted global current value. By dynamically optimizing the charging curve, the total charging time is shortened, and a smooth switch to trickle charging mode is achieved when the termination condition is reached. The current charging efficiency is then calculated. The specific formula is expressed as follows:

[0042]

[0043] in, This is represented as the initial state of charge. This represents the current state of charge. This represents the cumulative charging time. It is expressed as the average current during the charging process, if it satisfies Then switch to constant voltage trickle charging. Represented as an exponential function, Represented as the decay rate coefficient, This represents the adaptively adjusted charging current. The application of this algorithm in fast charging systems not only significantly shortens charging time and improves charging efficiency, but also effectively prevents problems such as battery overheating, overload, and uneven charging, ensuring the safety and reliability of electric vehicle batteries and extending battery life, thus providing electric vehicle users with a more intelligent, efficient, and safe charging experience.

[0044] Preferably, the adaptive charging control module includes a current balance control unit. The current balance control unit monitors the charging status of each battery cell in real time and dynamically adjusts the current distribution according to the output information of the adaptive charging strategy unit, so as to ensure the charging consistency between different battery modules and avoid overcharging and undercharging.

[0045] Preferably, the data storage and remote monitoring module uses cloud storage and wireless communication technology to ensure long-term storage and remote access to charging process data, facilitating analysis and optimization by users and management systems.

[0046] Preferably, the user interaction and control module is designed with a user-friendly system interface to ensure that users can check the charging status, adjust the charging strategy, and receive abnormal alarms at any time, thereby improving the charging experience.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] 1. The power management and conversion module proposes an adaptive power management algorithm based on competition analysis. First, the algorithm defines key energy consumption parameters of the electric vehicle fast charging system and establishes a dynamic shutdown threshold model to optimize energy conversion efficiency. This model, based on the principle of energy balance, calculates the shutdown threshold to ensure that the charging system only enters low-power mode when idle time exceeds the set parameters, thereby reducing unnecessary energy loss and providing a benchmark for subsequent adaptive management strategies, achieving a balance between energy waste and delay in the charging system. Second, the algorithm constructs offline and online adaptive power management strategies. In the offline strategy, the system pre-measures the time intervals of all charging current demands and makes optimal shutdown decisions based on the data to achieve minimum energy consumption. In the online strategy, facing unknown but correlated charging current demand time distributions, the system dynamically adjusts the shutdown strategy through real-time decisions to adapt to changing load conditions. Through competition analysis, the algorithm quantifies the performance gap between offline and online strategies, calculates the competition ratio, and evaluates the worst-case performance of the power management strategy under different load conditions to ensure the system's performance. In addition to ensuring stability under complex charging demands, the algorithm optimizes the shutdown threshold based on historical load data and dynamically adjusts it using an exponentially weighted moving average method. This reduces energy waste and frequent wake-ups caused by fixed thresholds, further improving the current balance efficiency of the charging system. While ensuring battery management efficiency, the algorithm also incorporates delay constraint integration and real-time correction strategies to avoid charging interruptions and battery life degradation. By constructing a delay model, the system can calculate the waiting time for charging requests and dynamically adjust the shutdown strategy when the maximum allowable delay is predicted, thus achieving a balance between energy optimization and user experience. Overall, the algorithm uses a real-time feedback mechanism to dynamically adjust algorithm parameters, achieving long-term stable optimization. Through a multi-objective optimization method, it minimizes total energy consumption while considering user experience requirements. Gradient descent is used to optimize the attenuation factor and delay penalty coefficient, ensuring that the system maintains efficient and stable charging performance under different load conditions. Through a closed-loop optimization engine, the algorithm achieves adaptive adjustment, enabling the charging system to maintain efficient current management and stable charging performance under dynamic loads over a long period of time.

[0049] 2. The adaptive charging strategy unit proposes a fast charging algorithm based on adaptive dynamic adjustment. This algorithm first dynamically initializes and sets a benchmark for charging parameters. Based on battery type, historical data, and current state, it sets the maximum allowable charging current, target charging voltage, and adaptive adjustment coefficient matrix. Through initializing the charging parameters, the system can quickly match the charging needs of different battery types at the start of charging, providing an accurate benchmark for subsequent dynamic adjustment and ensuring the safety and stability of the charging process. Based on the adaptive adjustment mechanism, the algorithm can dynamically adjust the charging current according to real-time feedback from the power management and conversion module. This allows the charging current to respond to battery voltage deviations, temperature changes, and the current current level, ensuring that the battery health is maintained while optimizing the charging rate. When the battery voltage is lower than the target value, the algorithm can automatically compensate for the charging current, increasing the charging rate; when the temperature exceeds the safety threshold, the system can adaptively reduce the charging current. This algorithm not only minimizes voltage deviations in each charging channel to prevent overheating and damage, but also addresses the issue of multiple battery packs charging in parallel. By dynamically allocating the current to each channel, it ensures system-level current balance, preventing localized overload and improving overall charging consistency and uniformity. As charging progresses, the algorithm dynamically optimizes the charging curve, ensuring optimal current distribution across different states of charge (SOCs) to improve overall charging efficiency. Near full charge, the algorithm automatically detects SOCs and gradually adjusts the charging current, smoothly transitioning to trickle charging mode to prevent overcharging and extend battery life. Overall, the application of this algorithm in fast charging systems significantly shortens charging time and improves charging efficiency. It also effectively prevents overheating, overload, and uneven charging, ensuring the safety and reliability of electric vehicle batteries and extending battery life, providing electric vehicle users with a more intelligent, efficient, and safe charging experience. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the structure of the present invention; Detailed Implementation

[0051] 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.

[0052] Please see Figure 1This invention provides a fast-charging current balancing system for electric vehicles based on adaptive control and fast-charging algorithms. The system includes a data acquisition and sensor module, a power management and conversion module, a charging device design module, an adaptive charging control module, a data storage and remote monitoring module, and a user interaction and control module. The key features are: the data acquisition and sensor module collects battery status, current, voltage information, and environmental data in real time, ensuring the accuracy and real-time nature of the data collected during charging; the power management and conversion module proposes an adaptive power management algorithm based on competition analysis to manage the power supply and convert external power into charging voltage and current suitable for the electric vehicle battery, ensuring a stable and reliable power supply; and the charging device design module includes a structure and heat dissipation optimization unit and a charging interface protection module. The structure and heat dissipation optimization unit optimizes the physical structure and heat dissipation performance of the charging device. To ensure efficient heat dissipation and stable operation of the equipment, the charging interface protection module protects the charging interface from damage caused by overcurrent and overvoltage, ensuring safety during charging. The adaptive charging control module includes an adaptive charging strategy unit and a current balance control unit. The adaptive charging strategy unit proposes a fast charging algorithm based on adaptive dynamic adjustment to automatically optimize the charging strategy according to the real-time status of the battery and environmental changes, ensuring high charging efficiency and battery life. The current balance control unit intelligently adjusts the current distribution according to the adaptive charging strategy to ensure balanced charging of each battery cell. The data storage and remote monitoring module stores charging process data and supports remote monitoring and fault diagnosis, ensuring the reliability of real-time monitoring and data analysis. The user interaction and control module interacts with the user and provides charging status feedback, ensuring that the user can control and monitor the charging process.

[0053] See Figure 1 Furthermore, the data acquisition and sensor module monitors the charging status and external environmental conditions in real time through high-precision current, voltage, temperature and environmental sensors, ensuring that the system accurately acquires key parameters such as battery current, voltage, temperature, humidity and external temperature, providing comprehensive data support for charging control and current balancing.

[0054] See Figure 1 Furthermore, the power management and conversion module proposes an adaptive power management algorithm based on competition analysis. By analyzing the charging load, grid voltage fluctuations and battery status in real time, it ensures optimal power allocation for battery management, improves power utilization and reduces energy consumption.

[0055] See Figure 1 Furthermore, the adaptive power management algorithm based on competition analysis is as follows: First, the key energy consumption parameters of the electric vehicle fast charging system are defined, a dynamic shutdown threshold model is established to optimize energy conversion efficiency, and the shutdown threshold is calculated based on the energy balance principle. This means the system has been idle for more than [time period]. Turning off the device at certain times can save energy. The specific formula is as follows:

[0056]

[0057] in, This represents the power consumption of the charging system in the idle state, measured in watts. The wake-up energy consumption of the charging system, including the total energy consumption of circuit startup and capacitor charging, is expressed in joules. By quantifying the shutdown threshold, a benchmark is provided for subsequent adaptive management strategies, ensuring that the charging system achieves an initial balance between energy waste and latency. Then, offline adaptive power management strategies and corresponding online adaptive power management strategies are constructed. It is assumed that the time interval for offline measurement to predict all charging current demands is expressed as... for, Let represent the time interval of the independent charging current demand at each moment. The decision formula and the total energy consumption formula are expressed as follows:

[0058]

[0059]

[0060] in, The power consumption is represented by the offline optimal strategy. The offline algorithm assumes that the time intervals of all charging current requirements are known in advance. Therefore, it is able to make the optimal decision. This represents the total number of requests over all time intervals of charging current demand. This represents the request index within the time interval of all charging current requirements. Represented as a minimization function, This represents the time from the completion of the previous request to the [number]th request. The time interval between the arrival of each request Represented as an indicator function, it means that when When the function value is 1, it indicates that the system has chosen to shut down; otherwise, it is 0, indicating that the system remains idle. The difference in the corresponding online adaptive power management strategy lies in the time interval of all charging current requirements. The distribution is unknown but correlated, requiring real-time decision-making. The specific formula is as follows:

[0061]

[0062] This is expressed as the total energy consumption calculated using an online adaptive power management strategy. Represented as the first The actual idle time of the system before the first request; secondly, the performance gap between the offline adaptive power management strategy and the corresponding online adaptive power management strategy is evaluated through competition analysis to ensure the robustness of the power management strategy. The specific formula is expressed as:

[0063]

[0064] in, Expressed as the competition ratio, Represented as a maximization function, the worst-case performance of the algorithm is quantified through competitive analysis, providing a theoretical basis for dynamic adjustment strategies and ensuring the stability of the charging system under varying loads. Then, the shutdown threshold is dynamically optimized based on historical load data to improve battery management efficiency and current balancing capability. The specific formula for weighted update of historical data is expressed as follows:

[0065]

[0066] in, Represented as the first value obtained using the Exponentially Weighted Moving Average (EWMA) method. Adaptive threshold adjustment when a request arrives This represents the time from the completion of the previous request to the [number]th request. The time interval between the arrival of each request This represents the weight of the decay factor used to control historical data. Represented as the first value obtained using the Exponentially Weighted Moving Average (EWMA) method. An adaptive threshold adjustment is implemented when a request arrives. This dynamic threshold adjustment reduces energy waste and frequent wake-ups caused by fixed thresholds, optimizing the current balance efficiency of the charging system. Then, by constructing a delay constraint integration and real-time correction strategy, the power management strategy is ensured to meet the maximum delay constraint, avoiding charging interruptions and battery life degradation. The specific formula for the delay model is expressed as follows:

[0067]

[0068] in, This is expressed as the actual delay consisting of wake-up time and queuing time. This represents the wake-up time of the charging system, expressed in seconds. Indicated as a request The inherent waiting time, This represents the time from the completion of the previous request to the [number]th request. The time interval between the arrival of each request is calculated using queuing theory, and the specific formula for the real-time correction mechanism is as follows:

[0069]

[0070] in, This is expressed as the total energy consumption calculated through a real-time correction mechanism. This represents the maximum allowable delay of the system, if the predicted delay is... This approach prohibits current shutdown operations and forces the system to remain in an idle state, striking a balance between energy optimization and user experience to ensure the high responsiveness and safety of the electric vehicle charging system. Finally, it dynamically adjusts algorithm parameters through real-time feedback to achieve long-term stable optimization, calculating a multi-objective optimization function that minimizes both total energy consumption and latency penalties. The specific formula is as follows:

[0071]

[0072] in, This is expressed as a comprehensive index reflecting system performance, a multi-objective optimization function. This represents the current total energy consumption of the system calculated using an online adaptive power management strategy. The attenuation factor is represented as a latency penalty coefficient, reflecting the weight of latency's impact on system performance. Through a multi-objective optimization function, energy consumption and latency are balanced to ensure the system meets user experience requirements while achieving efficient energy management. The gradient descent method is used to dynamically optimize the attenuation factor. and delay penalty coefficient The specific formula is expressed as follows:

[0073]

[0074]

[0075] in, Represented as the first The decay factor of the next iteration. This is represented by the number of algorithm iterations. Represented as the first The decay factor of the next iteration. The learning rate is represented as the step size for updating the control parameters. Represented as a multi-objective optimization function pair The partial derivatives, Represented as the first The delay penalty coefficient for the next iteration Represented as the first The delay penalty coefficient for the next iteration Represented as a multi-objective optimization function pair The partial derivatives are dynamically adjusted using the gradient descent method. and This makes the multi-objective optimization function Minimize and continuously optimize system performance. Through a closed-loop optimization engine, adaptive adjustment of algorithm parameters is achieved to ensure the long-term stability and efficiency of the charging system under dynamic loads.

[0076] See Figure 1 Furthermore, the charging device design module includes a structure and heat dissipation optimization unit. This unit uses high thermal conductivity materials and air-cooling and liquid-cooling heat dissipation solutions to ensure that the charging device maintains a stable temperature during long-term operation and avoids thermal runaway from affecting charging safety.

[0077] See Figure 1 Furthermore, the charging device design module includes a charging interface protection module. The charging interface protection module is designed to ensure stable connection and safe charging of the charging interface under high-frequency plugging and unplugging and different environmental conditions through short-circuit protection, overcurrent protection, and contact reliability optimization.

[0078] See Figure 1 Furthermore, the adaptive charging control module includes an adaptive charging strategy unit, which proposes a fast charging algorithm based on adaptive dynamic adjustment. This algorithm intelligently adjusts the battery charging curve and environmental factors to ensure the optimal charging rate under different charging conditions, thereby improving fast charging efficiency and reducing battery wear.

[0079] See Figure 1 Furthermore, the fast charging algorithm based on adaptive dynamic adjustment is specifically as follows: charging parameters are dynamically initialized and a baseline is set; based on battery type and historical data, the maximum allowable charging current is initialized. Target charging voltage and adaptive adjustment coefficient matrix ,in, Expressed as the current regulation rate factor, This is expressed as the voltage deviation compensation factor. Expressed as the temperature decay coefficient, used to calculate the initial charging current. The specific formula is expressed as follows:

[0080]

[0081] in, Represented as a minimization function, This is represented as the initial battery voltage. This is expressed as the battery's equivalent internal resistance. By dynamically initializing and setting the charging parameters, an initial parameter benchmark is established for the charging system. Then, based on the battery state parameters output by the power management and conversion module, dynamic input is provided for adaptive fast charging adjustment, ensuring the safety and efficiency of the charging process. The adaptive adjustment mechanism quickly matches the charging needs of different batteries, ensuring the real-time performance and stability of subsequent adjustments. The specific formula is as follows:

[0082]

[0083] in, This is represented by the dynamically adjusted current calculated by the algorithm. This represents the current charging current of the system. This is expressed as voltage deviation. This is expressed as temperature deviation. It is expressed as the absolute value function raised to the power of 0.5. This represents the sign function used to ensure current decay when the temperature exceeds the limit, so that the current does not exceed the safety threshold after constraint adjustment. The specific formulas for voltage deviation and temperature deviation are expressed as follows:

[0084]

[0085] in, This is expressed as the real-time battery voltage. This is expressed as the real-time battery temperature. This is represented by a preset safe temperature threshold. Secondly, for multi-battery pack parallel charging scenarios, system-level current balance is achieved by dynamically allocating the current to each channel to avoid local overload. Let the first... The current allocation weights for each channel are: The total number of channels is The specific formula for the equilibrium objective is expressed as:

[0086]

[0087] in, Represented as the first Voltage deviation of each channel This is represented as the zero-prevention denominator coefficient. Represented as the first The actual charging current of each channel represents the result of current balance distribution. The total charging current represents the adaptively adjusted global current value. By dynamically optimizing the charging curve, the total charging time is shortened, and a smooth switch to trickle charging mode is achieved when the termination condition is reached. The current charging efficiency is then calculated. The specific formula is expressed as follows:

[0088]

[0089] in, This is represented as the initial state of charge. This represents the current state of charge. This represents the cumulative charging time. It is expressed as the average current during the charging process, if it satisfies Then switch to constant voltage trickle charging. Represented as an exponential function, Represented as the decay rate coefficient, This represents the adaptively adjusted charging current. The application of this algorithm in fast charging systems not only significantly shortens charging time and improves charging efficiency, but also effectively prevents problems such as battery overheating, overload, and uneven charging, ensuring the safety and reliability of electric vehicle batteries and extending battery life, thus providing electric vehicle users with a more intelligent, efficient, and safe charging experience.

[0090] See Figure 1 Furthermore, the adaptive charging control module includes a current balance control unit. The current balance control unit monitors the charging status of each battery cell in real time and dynamically adjusts the current distribution based on the output information of the adaptive charging strategy unit, ensuring charging consistency between different battery modules and avoiding overcharging and undercharging.

[0091] See Figure 1 Furthermore, the data storage and remote monitoring module uses cloud storage and wireless communication technologies to ensure long-term storage and remote access to charging process data, facilitating analysis and optimization by users and management systems.

[0092] See Figure 1 Furthermore, the user interaction and control module is designed with a user-friendly system interface to ensure that users can check the charging status, adjust the charging strategy, and receive abnormal alarms at any time, thereby improving the charging experience.

[0093] In practical use, firstly, the data acquisition and sensor module collects battery status, current, voltage information, and environmental data in real time to ensure the accuracy and real-time nature of the data collected during charging. Secondly, the power management and conversion module proposes an adaptive power management algorithm based on competition analysis to manage the power supply and convert external power into charging voltage and current suitable for the electric vehicle battery, ensuring a stable and reliable power supply. Then, the charging device design module includes a structure and heat dissipation optimization unit and a charging interface protection module. The structure and heat dissipation optimization unit optimizes the physical structure and heat dissipation performance of the charging device to ensure efficient heat dissipation and maintain stable operation of the equipment. The charging interface protection module protects the charging interface from damage caused by overcurrent and overvoltage abnormalities. First, it ensures safety during the charging process. Second, the adaptive charging control module includes an adaptive charging strategy unit and a current balance control unit. The adaptive charging strategy unit proposes a fast charging algorithm based on adaptive dynamic adjustment to automatically optimize the charging strategy according to the real-time status of the battery and environmental changes, ensuring high charging efficiency and battery life. The current balance control unit is used to intelligently adjust the current distribution according to the adaptive charging strategy to ensure balanced charging of each battery cell. Third, the data storage and remote monitoring module stores charging process data and supports remote monitoring and fault diagnosis, ensuring the reliability of real-time monitoring and data analysis. Finally, the user interaction and control module interacts with the user and provides charging status feedback, ensuring that the user can control and monitor the charging process.

[0094] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fast-charging current balancing system for electric vehicles based on adaptive control and fast-charging algorithms, comprising a data acquisition and sensor module, a power management and conversion module, a charging device design module, an adaptive charging control module, a data storage and remote monitoring module, and a user interaction and control module, characterized in that: The data acquisition and sensor module is used to collect battery status, current, voltage information and environmental data in real time to ensure that the data collected during the charging process is accurate and real-time; the power management and conversion module proposes an adaptive power management algorithm based on competition analysis to manage the power supply and convert the external power supply into a charging voltage and current that is compatible with the electric vehicle battery to ensure a stable and reliable power supply. The charging device design module includes a structure and heat dissipation optimization unit and a charging interface protection module. The structure and heat dissipation optimization unit optimizes the physical structure and heat dissipation performance of the charging device to ensure efficient heat dissipation and maintain stable operation. The charging interface protection module protects the charging interface from damage caused by overcurrent and overvoltage abnormalities, ensuring safety during the charging process. The adaptive charging control module includes an adaptive charging strategy unit and a current balance control unit. The adaptive charging strategy unit proposes a fast charging algorithm based on adaptive dynamic adjustment to automatically optimize the charging strategy according to the real-time battery status and environmental changes, ensuring high charging efficiency and battery life. The current balance control unit intelligently adjusts the current distribution according to the adaptive charging strategy to ensure balanced charging of each battery cell. The data storage and remote monitoring module stores charging process data and supports remote monitoring and fault diagnosis, ensuring the reliability of real-time monitoring and data analysis. The user interaction and control module interacts with the user and provides charging status feedback, ensuring that the user can control and monitor the charging process. The power management and conversion module proposes an adaptive power management algorithm based on competition analysis to ensure optimal power allocation for battery management by analyzing charging load, grid voltage fluctuations, and battery status in real time, improving power utilization and reducing energy consumption.

2. The electric vehicle fast charging current balancing system based on adaptive control algorithm and fast charging algorithm according to claim 1, characterized in that: The data acquisition and sensor module monitors the charging status and external environmental conditions in real time through high-precision current, voltage, temperature and environmental sensors, ensuring that the system accurately acquires key parameters such as battery current, voltage, temperature, humidity and external temperature, providing comprehensive data support for charging control and current balancing.

3. The electric vehicle fast charging current balancing system based on adaptive control algorithm and fast charging algorithm according to claim 1, characterized in that, First, key energy consumption parameters for electric vehicle fast charging systems are defined, a dynamic shutdown threshold model is established to optimize energy conversion efficiency, and the shutdown threshold is calculated based on the energy balance principle. This means the system has been idle for more than [time period]. Turning off the device at certain times can save energy. The specific formula is as follows: in, This represents the power consumption of the charging system in the idle state, measured in watts. The wake-up energy consumption of the charging system, including the total energy consumption of circuit startup and capacitor charging, is expressed in joules. By quantifying the shutdown threshold, a benchmark is provided for subsequent adaptive management strategies, ensuring that the charging system achieves an initial balance between energy waste and latency. Then, offline adaptive power management strategies and corresponding online adaptive power management strategies are constructed. It is assumed that the time interval for offline measurement to predict all charging current demands is expressed as... for, Let represent the time interval of the independent charging current demand at each moment. The decision formula and the total energy consumption formula are expressed as follows: in, The power consumption is represented by the offline optimal strategy. The offline algorithm assumes that the time intervals of all charging current requirements are known in advance. Therefore, it is able to make the optimal decision. This represents the total number of requests over all time intervals of charging current demand. This represents the request index within the time interval of all charging current requirements. Represented as a minimization function, This represents the period from the completion of the previous request to the completion of the current request. The time interval between the arrival of each request Represented as an indicator function, it means that when When the function value is 1, it indicates that the system has chosen to shut down; otherwise, it is 0, indicating that the system remains idle. The difference in the corresponding online adaptive power management strategy lies in the time interval of all charging current requirements. The distribution is unknown but correlated, requiring real-time decision-making. The specific formula is as follows: This is expressed as the total energy consumption calculated using an online adaptive power management strategy. Represented as the first The actual idle time of the system before the first request; secondly, the performance gap between the offline adaptive power management strategy and the corresponding online adaptive power management strategy is evaluated through competition analysis to ensure the robustness of the power management strategy. The specific formula is expressed as: in, Expressed as the competition ratio, Represented as a maximization function, the worst-case performance of the algorithm is quantified through competitive analysis, providing a theoretical basis for dynamic adjustment strategies and ensuring the stability of the charging system under varying loads. Then, the shutdown threshold is dynamically optimized based on historical load data to improve battery management efficiency and current balancing capability. The specific formula for weighted update of historical data is expressed as follows: in, Represented as the first value obtained using the Exponentially Weighted Moving Average (EWMA) method. Adaptive threshold adjustment when a request arrives This represents the period from the completion of the previous request to the completion of the current request. The time interval between the arrival of each request This represents the weight of the decay factor used to control historical data. Represented as the first value obtained using the Exponentially Weighted Moving Average (EWMA) method. An adaptive threshold adjustment is implemented when a request arrives. This dynamic threshold adjustment reduces energy waste and frequent wake-ups caused by fixed thresholds, optimizing the current balance efficiency of the charging system. Then, by constructing a delay constraint integration and real-time correction strategy, the power management strategy is ensured to meet the maximum delay constraint, avoiding charging interruptions and battery life degradation. The specific formula for the delay model is expressed as follows: in, This is expressed as the actual delay consisting of wake-up time and queuing time. This represents the wake-up time of the charging system, expressed in seconds. Indicated as a request The inherent waiting time, This represents the period from the completion of the previous request to the completion of the current request. The time interval between the arrival of each request is calculated using queuing theory, and the specific formula for the real-time correction mechanism is as follows: in, This is expressed as the total energy consumption calculated through a real-time correction mechanism. This represents the maximum allowable delay of the system, if the predicted delay is... This approach prohibits current shutdown operations and forces the system to remain in an idle state, striking a balance between energy optimization and user experience to ensure the high responsiveness and safety of the electric vehicle charging system. Finally, it dynamically adjusts algorithm parameters through real-time feedback to achieve long-term stable optimization, calculating a multi-objective optimization function that minimizes both total energy consumption and latency penalties. The specific formula is as follows: in, This is expressed as a comprehensive index reflecting system performance, a multi-objective optimization function. This represents the current total energy consumption of the system calculated using an online adaptive power management strategy. The attenuation factor is represented as a latency penalty coefficient, reflecting the weight of latency's impact on system performance. Through a multi-objective optimization function, energy consumption and latency are balanced to ensure the system meets user experience requirements while achieving efficient energy management. The gradient descent method is used to dynamically optimize the attenuation factor. and delay penalty coefficient The specific formula is expressed as follows: in, Represented as the first The decay factor of the next iteration. This is represented by the number of algorithm iterations. Represented as the first The decay factor of the next iteration. The learning rate is represented as the step size for updating the control parameters. Represented as a multi-objective optimization function pair The partial derivatives, Represented as the first The delay penalty coefficient for the next iteration Represented as the first The delay penalty coefficient for the next iteration Represented as a multi-objective optimization function pair The partial derivatives are dynamically adjusted using the gradient descent method. and This makes the multi-objective optimization function Minimize and continuously optimize system performance. Through a closed-loop optimization engine, adaptive adjustment of algorithm parameters is achieved to ensure the long-term stability and efficiency of the charging system under dynamic loads.

4. The electric vehicle fast charging current balancing system based on adaptive control algorithm and fast charging algorithm according to claim 1, characterized in that: The charging device design module includes a structure and heat dissipation optimization unit and a charging interface protection module. The structure and heat dissipation optimization unit uses high thermal conductivity materials and air cooling and liquid cooling solutions to ensure the temperature of the charging device is stable during long-term operation and avoid thermal runaway affecting charging safety. The charging interface protection module uses short-circuit protection, overcurrent protection, and contact reliability optimization design to ensure stable connection and safe charging of the charging interface under high-frequency plugging and unplugging and different environmental conditions.

5. The electric vehicle fast charging current balancing system based on adaptive control algorithm and fast charging algorithm according to claim 4, characterized in that: The adaptive charging control module includes an adaptive charging strategy unit and a current balance control unit. The adaptive charging strategy unit proposes a fast charging algorithm based on adaptive dynamic adjustment, which intelligently adjusts the charging rate by combining the battery charging curve and environmental factors to ensure the optimal charging rate under different charging conditions, thereby improving fast charging efficiency and reducing battery wear. The current balance control unit monitors the charging status of each battery cell in real time based on the output information of the adaptive charging strategy unit and dynamically adjusts the current distribution to ensure charging consistency between different battery modules and avoid overcharging and undercharging.

6. The electric vehicle fast charging current balancing system based on adaptive control algorithm and fast charging algorithm according to claim 1, characterized in that, The charging parameters are dynamically initialized and baseline set. Based on the battery type and historical data, the maximum allowable charging current is initialized. Target charging voltage and adaptive adjustment coefficient matrix ,in, Expressed as the current regulation rate factor, This is expressed as the voltage deviation compensation factor. Expressed as the temperature decay coefficient, used to calculate the initial charging current. The specific formula is expressed as follows: in, Represented as a minimization function, This is represented as the initial battery voltage. This is expressed as the battery's equivalent internal resistance. By dynamically initializing and setting the charging parameters, an initial parameter benchmark is established for the charging system. Then, based on the battery state parameters output by the power management and conversion module, dynamic input is provided for adaptive fast charging adjustment, ensuring the safety and efficiency of the charging process. The adaptive adjustment mechanism quickly matches the charging needs of different batteries, ensuring the real-time performance and stability of subsequent adjustments. The specific formula is as follows: in, This is represented by the dynamically adjusted current calculated by the algorithm. This represents the current charging current of the system. This is expressed as voltage deviation. This is expressed as temperature deviation. It is expressed as the absolute value function raised to the power of 0.

5. This represents the sign function used to ensure current decay when the temperature exceeds the limit, so that the current does not exceed the safety threshold after constraint adjustment. The specific formulas for voltage deviation and temperature deviation are expressed as follows: in, This is expressed as the real-time battery voltage. This is expressed as the real-time battery temperature. This is represented by a preset safe temperature threshold. Secondly, for multi-battery pack parallel charging scenarios, system-level current balance is achieved by dynamically allocating the current to each channel to avoid local overload. Let the first... The current allocation weights for each channel are: The total number of channels is The specific formula for the equilibrium objective is expressed as: in, Represented as the first Voltage deviation of each channel This is represented as the zero-prevention denominator coefficient. Represented as the first The actual charging current of each channel represents the result of current balance distribution. The total charging current represents the adaptively adjusted global current value. By dynamically optimizing the charging curve, the total charging time is shortened, and a smooth switch to trickle charging mode is achieved when the termination condition is reached. The current charging efficiency is then calculated. The specific formula is expressed as follows: in, This is represented as the initial state of charge. This represents the current state of charge. This represents the cumulative charging time. It is expressed as the average current during the charging process, if it satisfies Then switch to constant voltage trickle charging. Represented as an exponential function, Represented as the decay rate coefficient, This represents the adaptively adjusted charging current. The application of this algorithm in fast charging systems not only significantly shortens charging time and improves charging efficiency, but also effectively prevents problems such as battery overheating, overload, and uneven charging, ensuring the safety and reliability of electric vehicle batteries and extending battery life, thus providing electric vehicle users with a more intelligent, efficient, and safe charging experience.

7. The electric vehicle fast charging current balancing system based on adaptive control algorithm and fast charging algorithm according to claim 1, characterized in that: The data storage and remote monitoring module uses cloud storage and wireless communication technologies to ensure long-term storage and remote access to charging process data, facilitating analysis and optimization by users and management systems.

8. The electric vehicle fast charging current balancing system based on adaptive control algorithm and fast charging algorithm according to claim 1, characterized in that: The user interaction and control module is designed with a user-friendly system interface to ensure that users can check the charging status, adjust the charging strategy, and receive abnormal alarms at any time, thereby improving the charging experience.

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