A Portable Electric Vehicle Fast Charging Current Balancing System Based on Adaptive Control Algorithm

Abstract

A portable electric vehicle fast-charging current balancing system based on an adaptive control algorithm includes a data acquisition and sensor module, a power management and conversion module, a portable charging device design module, an adaptive charging control module, a data storage and remote monitoring module, and a user interaction and control module. The data acquisition and sensor module collects data; the power management and conversion module manages power supply and energy conversion; the portable charging device design module designs the charging structure and interface; the adaptive charging control module adjusts the charging process; the data storage and remote monitoring module stores and monitors data; and the user interaction and control module facilitates user interaction. This invention proposes an adaptive power management algorithm based on competition analysis to optimize battery management and an adaptive dynamic adjustment-based fast-charging algorithm to adaptively adjust the charging process.

Description

Technical Field

[0001] This invention relates to the fields of competition analysis, adaptive control, and current balancing technology, specifically a portable electric vehicle fast charging current balancing system based on an adaptive control algorithm. 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 portable electric vehicle fast charging current balancing system based on adaptive control algorithm has problems such as the charging system being unable to flexibly adjust according to real-time load and battery status, resulting in low energy utilization and long charging time, as well as overcharging and undercharging due to uneven charging, 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 portable electric vehicle fast charging current balancing system based on an adaptive control algorithm, 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 and battery status, resulting in low energy utilization and long charging time, as well as the problems caused by uneven charging leading to overcharging and undercharging, accelerating battery aging and affecting the overall lifespan of the battery pack.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a portable electric vehicle fast charging current balancing system based on an adaptive control algorithm, comprising a data acquisition and sensor module, a power management and conversion module, a portable 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 portable charging device design module includes a structure and heat dissipation optimization unit and a charging interface protection unit, wherein the structure and heat dissipation optimization unit is used to optimize the physical structure of the charging device. The system incorporates structural and heat dissipation performance to ensure efficient heat dissipation and stable operation of the device. The charging interface protection unit 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 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 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 portable electric vehicle fast charging system, establish a dynamic shutdown threshold model to optimize energy conversion efficiency, and calculate the shutdown threshold T based on the energy consumption balance principle. sThis means the system has been idle for more than T hours. s Turning off the device at certain times can save energy. The specific formula is as follows:

[0010]

[0011] Among them, P b This represents the power consumption of the charging system in its idle state, measured in watts (E). a The energy consumption for waking up the charging system is expressed as the total energy consumption for circuit startup and capacitor charging, measured 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 represented as σ={t1,t2,…,t… n Let t1, t2, ..., t be the values ​​of t1, t2, ..., t2. n 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] If the idle time Δt j ≥T s If the device is idle, shut it down immediately; otherwise, leave it idle.

[0013]

[0014] Among them, E opt Let represent the power consumption of the offline optimal strategy. The offline algorithm assumes that the time interval σ of all charging current demands is known in advance, thus enabling it to make optimal decisions. Let n represent the total number of requests in all charging current demand time intervals, j represent the request index in all charging current demand time intervals, and min(·) represent the minimization function, Δt. j Let δ(·) represent the time interval from the completion of the previous request to the arrival of the j-th request, and let δ(·) represent the indicator function, indicating when Δt... j ≥T s When the function value is 1, it indicates that the system chooses 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 fact that the time interval of all charging current demand is represented by an unknown but correlated σ distribution, requiring real-time decision-making. The specific formula is as follows:

[0015]

[0016] E online The total energy consumption, t, is calculated using an online adaptive power management strategy. idleThis represents the actual idle time of the system before the j-th request. Secondly, performance gaps between the offline adaptive power management strategy and the corresponding online adaptive power management strategy are evaluated through competition analysis to ensure the robustness of the power management strategy. The specific formula is as follows:

[0017]

[0018] Where γ represents the competition ratio, max σ 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 portable charging systems 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, Let Δt be the adaptive adjustment threshold for the arrival of the j-th request, obtained using the exponentially weighted moving average (EWMA) method. j-1 This represents the time interval from the completion of the previous request to the arrival of the (j-1)th request, and α represents the weight of the decay factor controlling historical data. This represents the adaptive adjustment threshold when the (j-1)th request arrives, obtained using the Exponentially Weighted Moving Average (EWMA) method. Dynamic threshold adjustment reduces energy waste and frequent wake-ups caused by fixed thresholds, optimizing the current balance efficiency of the portable charging system. Then, by constructing a delay constraint integration and real-time correction strategy, it ensures that the power management strategy meets the maximum delay constraint, avoiding charging interruptions and battery life degradation. The specific formula for constructing the delay model is expressed as follows:

[0021] L j =max(0,T) w +W j -Δt j )

[0022] Among them, L j T is the actual delay consisting of wake-up time and queuing time. w This represents the wake-up time of the charging system, expressed in seconds (W). j Let Δt be the inherent waiting time for request j. j Represented as the time interval from the completion of the previous request to the arrival of the j-th request, calculated using queuing theory, the specific formula for the real-time correction mechanism is as follows:

[0023] E′ online =E online -P b ·(Ts -Δt j )·δ(L j >L max )

[0024] Among them, E′ online L is expressed as the total energy consumption calculated through a real-time correction mechanism. max This represents the maximum allowable delay of the system. If the predicted delay L... j >L max This approach prohibits current shutdown operations and forces the system to remain idle, striking a balance between energy optimization and user experience. It ensures high responsiveness and safety for the portable electric vehicle charging system. Finally, it dynamically adjusts algorithm parameters through real-time feedback to achieve long-term stable optimization. A multi-objective optimization function is calculated, minimizing both total energy consumption and latency penalties. The specific formula is as follows:

[0025]

[0026] Where F(α,β) represents a multi-objective optimization function that reflects the comprehensive performance of the system, and E total Let α represent the total energy consumption of the system calculated through the online adaptive power management strategy, and β represent the latency penalty coefficient, reflecting the weight of latency's impact on system performance. A multi-objective optimization function is used to balance energy consumption and latency, ensuring that the system meets user experience requirements while achieving efficient energy management. The gradient descent method is used to dynamically optimize the attenuation factor α and the latency penalty coefficient β, as shown in the specific formula:

[0027]

[0028] Where, α (k+1) Let α represent the decay factor in the (k+1)th iteration, where k represents the number of algorithm iterations, and α ( k ) Let η represent the decay factor in the k-th iteration, and let η represent the learning rate for controlling the step size of the parameter update. Let β be the partial derivative of the multi-objective optimization function with respect to α. (k+1) Let β be the delay penalty coefficient for the (k+1)th iteration. (k) Let be the delay penalty coefficient for the k-th iteration. It is represented as the partial derivative of the multi-objective optimization function with respect to β. By dynamically adjusting α and α using the gradient descent method, the multi-objective optimization function F(α,β) is minimized, thereby achieving continuous optimization of system performance. Through a closed-loop optimization engine, the algorithm parameters are adaptively adjusted to ensure the long-term stability and efficiency of the portable charging system under dynamic loads.

[0029] Preferably, the portable charging device design module includes a structure and heat dissipation optimization unit. The structure and heat dissipation optimization unit uses high thermal conductivity materials, 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.

[0030] Preferably, the portable charging device design module includes a charging interface protection unit. The charging interface protection unit 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.

[0031] 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.

[0032] Preferably, the fast charging algorithm based on adaptive dynamic adjustment is as follows: dynamic initialization and baseline setting of charging parameters are performed; based on battery type and historical data, the maximum allowable charging current I is initialized. max Target charging voltage V tar And the adaptive adjustment coefficient matrix β=[β1,v2,β3], where β1 represents the current regulation rate factor, β2 represents the voltage deviation compensation factor, and β3 represents the temperature decay coefficient, and the initial charging current I is calculated. init The specific formula is expressed as follows:

[0033]

[0034] Where min(·) represents the minimization function, V tar Represented as the initial battery voltage, R bat 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:

[0035] I adj =β1I cur +β2·ΔV-β3·sgn(ΔT)·|ΔT| 0.5

[0036] Among them, I adj Represented as the dynamically adjusted current calculated by the algorithm, I curThe current charging current of the system is represented by ΔV, the voltage deviation by ΔT, and the temperature deviation by |·|. 0.5 Expressed as the absolute value function raised to the power of 0.5, sgn(·) represents the sign function 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:

[0037] ΔV=V tar -V cur ,ΔT=T cur -T safe

[0038] Among them, V cur T represents the real-time battery voltage. cur Represented as real-time battery temperature, T safe 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 w be the current allocation weight of the k-th channel. k The total number of channels is K, and the specific formula for the equilibrium objective is expressed as:

[0039] I k =w k ·I new

[0040] Where, ΔV k Let I represent the voltage deviation of the k-th channel, ∈ represent the zero-prevention denominator coefficient, and I k I represents the actual charging current of the k-th channel, indicating the result of current equalization. new The total charging current is represented by 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 specific formula for calculating the current charging efficiency η is as follows:

[0041]

[0042] Among them, SOC cur Represented as the initial state of charge, SOC init I represents the current state of charge, t represents the cumulative charging time, and I represents the current state of charge. avg The current is expressed as the average current during the charging process. If SOC_cur ≥ 95%, then constant voltage trickle charging is switched to the constant voltage form. e represents an exponential function, θ represents the decay rate coefficient, and I... finalRepresented as the adaptively adjusted charging current, the application of this algorithm in portable 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, while extending battery life and providing electric vehicle users with a more intelligent, efficient, and safe charging experience.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] 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 for the portable 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 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 portable charging system to maintain efficient current management and stable charging performance under dynamic loads over a long period of time.

[0048] 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 ensures that the charging current responds to battery voltage deviation, temperature changes, and the current current level, maintaining battery health 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 prevents overheating and damage. Furthermore, for multi-battery pack parallel charging scenarios, it dynamically allocates the current to each charging channel to minimize voltage deviations, avoid local overload, achieve system-level current balance, and improve overall charging consistency and uniformity. As charging progresses, the algorithm dynamically optimizes the charging curve to ensure optimal current allocation across different states of charge (SOCs), improving 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 portable fast charging systems not only significantly shortens charging time and improves charging efficiency but also effectively prevents battery overheating, overload, and uneven charging, ensuring the safety and reliability of electric vehicle batteries while extending battery life, providing electric vehicle users with a more intelligent, efficient, and safe charging experience. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the structure of the present invention; Detailed Implementation

[0050] 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.

[0051] Please see Figure 1This invention provides a portable electric vehicle fast charging current balancing system based on an adaptive control algorithm, comprising a data acquisition and sensor module, a power management and conversion module, a portable 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 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 portable charging device design module includes a structure and heat dissipation optimization unit and a charging interface protection unit, the structure and heat dissipation optimization unit being used to optimize the physical structure and heat dissipation of the charging device. The system ensures efficient heat dissipation and stable operation of the device. The charging interface protection unit 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 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.

[0052] 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.

[0053] 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.

[0054] See Figure 1 Furthermore, the adaptive power management algorithm based on competition analysis is as follows: First, the key energy consumption parameters of the portable electric vehicle fast charging system are defined, a dynamic shutdown threshold model is established to optimize energy conversion efficiency, and the shutdown threshold T is calculated based on the energy balance principle. sThis means the system has been idle for more than T hours. s Turning off the device at certain times can save energy. The specific formula is as follows:

[0055]

[0056] Among them, P b This represents the power consumption of the charging system in its idle state, measured in watts (E). a The energy consumption for waking up the charging system is expressed as the total energy consumption for circuit startup and capacitor charging, measured 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 represented as σ={t1,t2,…,t… n Let t1, t2, ..., t be the values ​​of t1, t2, ..., t2. n 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:

[0057] If the idle time Δt j ≥T s If the device is idle, shut it down immediately; otherwise, leave it idle.

[0058]

[0059] Among them, E opt Let represent the power consumption of the offline optimal strategy. The offline algorithm assumes that the time interval σ of all charging current demands is known in advance, thus enabling it to make optimal decisions. Let n represent the total number of requests in all charging current demand time intervals, j represent the request index in all charging current demand time intervals, and min(·) represent the minimization function, Δt. j Let δ(·) represent the time interval from the completion of the previous request to the arrival of the j-th request, and let δ(·) represent the indicator function, indicating when Δt... j ≥T s When the function value is 1, it indicates that the system chooses 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 fact that the time interval of all charging current demand is represented by an unknown but correlated σ distribution, requiring real-time decision-making. The specific formula is as follows:

[0060]

[0061] E online The total energy consumption, t, is calculated using an online adaptive power management strategy. idleThis represents the actual idle time of the system before the j-th request. Secondly, performance gaps between the offline adaptive power management strategy and the corresponding online adaptive power management strategy are evaluated through competition analysis to ensure the robustness of the power management strategy. The specific formula is as follows:

[0062]

[0063] Where γ represents the competition ratio, max σ 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 portable charging systems 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:

[0064]

[0065] in, Let Δt be the adaptive adjustment threshold for the arrival of the j-th request, obtained using the exponentially weighted moving average (EWMA) method. j-1 This represents the time interval from the completion of the previous request to the arrival of the (j-1)th request, and α represents the weight of the decay factor controlling historical data. This represents the adaptive adjustment threshold when the (j-1)th request arrives, obtained using the Exponentially Weighted Moving Average (EWMA) method. Dynamic threshold adjustment reduces energy waste and frequent wake-ups caused by fixed thresholds, optimizing the current balance efficiency of the portable charging system. Then, by constructing a delay constraint integration and real-time correction strategy, it ensures that the power management strategy meets the maximum delay constraint, avoiding charging interruptions and battery life degradation. The specific formula for constructing the delay model is expressed as follows:

[0066] L j =max(0,T) w +W j -Δt j )

[0067] Among them, L j T is the actual delay consisting of wake-up time and queuing time. w This represents the wake-up time of the charging system, expressed in seconds (W). j Let Δt be the inherent waiting time for request j. j Represented as the time interval from the completion of the previous request to the arrival of the j-th request, calculated using queuing theory, the specific formula for the real-time correction mechanism is as follows:

[0068] E′ online =E online -P b ·(Ts -Δt j )·δ(L j >L max )

[0069] Among them, E′ online L is expressed as the total energy consumption calculated through a real-time correction mechanism. max This represents the maximum allowable delay of the system. If the predicted delay L... j >L max This approach prohibits current shutdown operations and forces the system to remain idle, striking a balance between energy optimization and user experience. It ensures high responsiveness and safety for the portable electric vehicle charging system. Finally, it dynamically adjusts algorithm parameters through real-time feedback to achieve long-term stable optimization. A multi-objective optimization function is calculated, minimizing both total energy consumption and latency penalties. The specific formula is as follows:

[0070]

[0071] Where F(α,β) represents a multi-objective optimization function that reflects the comprehensive performance of the system, and E total Let α represent the total energy consumption of the system calculated through the online adaptive power management strategy, and β represent the latency penalty coefficient, reflecting the weight of latency's impact on system performance. A multi-objective optimization function is used to balance energy consumption and latency, ensuring that the system meets user experience requirements while achieving efficient energy management. The gradient descent method is used to dynamically optimize the attenuation factor α and the latency penalty coefficient β, as shown in the specific formula:

[0072]

[0073] Where, α (k+1) Let α represent the decay factor in the (k+1)th iteration, where k represents the number of algorithm iterations, and α (k) Let η represent the decay factor in the k-th iteration, and let η represent the learning rate for controlling the step size of the parameter update. Let β be the partial derivative of the multi-objective optimization function with respect to α. (k+1) Let β be the delay penalty coefficient for the (k+1)th iteration. (k) Let be the delay penalty coefficient for the k-th iteration. It is represented as the partial derivative of the multi-objective optimization function with respect to β. By dynamically adjusting α and α using the gradient descent method, the multi-objective optimization function F(α,β) is minimized, thereby achieving continuous optimization of system performance. Through a closed-loop optimization engine, the algorithm parameters are adaptively adjusted to ensure the long-term stability and efficiency of the portable charging system under dynamic loads.

[0074] See Figure 1Furthermore, the portable 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.

[0075] See Figure 1 Furthermore, the portable charging device design module includes a charging interface protection unit. The charging interface protection unit 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.

[0076] 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.

[0077] See Figure 1 Furthermore, the fast charging algorithm based on adaptive dynamic adjustment is specifically as follows: The charging parameters are dynamically initialized and a baseline is set; based on the battery type and historical data, the maximum allowable charging current I is initialized. max Target charging voltage V tar And the adaptive adjustment coefficient matrix β=[β1,β2,β3], where β1 represents the current regulation rate factor, β2 represents the voltage deviation compensation factor, and β3 represents the temperature decay coefficient, to calculate the initial charging current I. init The specific formula is expressed as follows:

[0078]

[0079] Where min(·) represents the minimization function, V tar Represented as the initial battery voltage, R bat 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:

[0080] I adj =β1I cur +β2·ΔV-β3·sgn(ΔT)·|ΔT| 0.5

[0081] Among them, I adj Represented as the dynamically adjusted current calculated by the algorithm, I cur The current charging current of the system is represented by ΔV, the voltage deviation by ΔT, and the temperature deviation by |·|. 0.5 Expressed as the absolute value function raised to the power of 0.5, sgn(·) represents the sign function 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:

[0082] ΔV=V tar -V cur ,ΔT=T cur -T safe

[0083] Among them, V cur T represents the real-time battery voltage. cur Represented as real-time battery temperature, T safe 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 w be the current allocation weight of the k-th channel. k The total number of channels is K, and the specific formula for the equilibrium objective is expressed as:

[0084] I k =w k ·I new

[0085] Where, ΔV k Let I represent the voltage deviation of the k-th channel, ∈ represent the zero-prevention denominator coefficient, and I k I represents the actual charging current of the k-th channel, indicating the result of current equalization. new The total charging current is represented by 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 specific formula for calculating the current charging efficiency η is as follows:

[0086]

[0087] Among them, SOC cur Represented as the initial state of charge, SOC init I represents the current state of charge, t represents the cumulative charging time, and I represents the current state of charge. avg The current is expressed as the average current during the charging process. If SOC_cur ≥ 95%, then constant voltage trickle charging is switched to the constant voltage form. e represents an exponential function, θ represents the decay rate coefficient, and I... finalRepresented as the adaptively adjusted charging current, the application of this algorithm in portable 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, while extending battery life and providing electric vehicle users with a more intelligent, efficient, and safe charging experience.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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 the external power supply into charging voltage and current suitable for the electric vehicle battery, ensuring a stable and reliable power supply. Then, the portable charging device design module includes a structure and heat dissipation optimization unit and a charging interface protection unit. 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 device. The charging interface protection unit 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. Then, the data storage and remote monitoring module is used to store charging process data and support remote monitoring and fault diagnosis, ensuring the reliability of real-time monitoring and data analysis. Finally, the user interaction and control module is used to interact with the user and provide charging status feedback, ensuring that the user can control and monitor the charging process.

[0092] 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 portable electric vehicle fast charging current balancing system based on an adaptive control algorithm, comprising a data acquisition and sensor module, a power management and conversion module, a portable 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, 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 portable charging device design module includes a structure and heat dissipation optimization unit and a charging interface protection unit. The structure and heat dissipation optimization unit optimizes the physical structure and heat dissipation performance of the charging device, ensuring efficient heat dissipation and maintaining stable operation. The charging interface protection unit protects the charging interface from damage caused by overcurrent and overvoltage abnormalities, 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 is used to intelligently adjust the current distribution according to the adaptive charging strategy to ensure balanced charging of each battery cell. The data storage and remote monitoring module is used to store charging process data and support remote monitoring and fault diagnosis, ensuring the reliability of real-time monitoring and data analysis. The user interaction and control module is used to interact with the user and provide charging status feedback, ensuring that the user can control and monitor the charging process.

2. The portable electric vehicle fast charging current balancing system based on adaptive control 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 portable electric vehicle fast charging current balancing system based on adaptive control algorithm according to claim 2, characterized in that: 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.

4. The portable electric vehicle fast charging current balancing system based on adaptive control algorithm according to claim 3, characterized in that: The portable charging device design module includes a structure and heat dissipation optimization unit and a charging interface protection unit. 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 unit 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. A portable electric vehicle fast charging current balancing system based on an adaptive control 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. A portable electric vehicle fast charging current balancing system based on an adaptive control algorithm according to claim 5, 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 I is initialized. max Target charging voltage V tar And the adaptive adjustment coefficient matrix β=[β1,β2,β3], where β1 represents the current regulation rate factor, β2 represents the voltage deviation compensation factor, and β3 represents the temperature decay coefficient, to calculate the initial charging current I. init The specific formula is expressed as follows: Where min(·) represents the minimization function, V tar Represented as the initial battery voltage, R bat 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: I adj =β1I cur +β2·ΔV-β3·sgn(ΔT)·|ΔT| 0.5 Among them, I adj Represented as the dynamically adjusted current calculated by the algorithm, I cur The current charging current of the system is represented by ΔV, the voltage deviation by ΔT, and the temperature deviation by |·|. 0.5 Expressed as the absolute value function raised to the power of 0.5, sgn(·) represents the sign function 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: ΔV=V tar -V cur ,ΔT=T cur -T safe Among them, V cur T represents the real-time battery voltage. cur Represented as real-time battery temperature, T safe 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 w be the current allocation weight of the k-th channel. k The total number of channels is K, and the specific formula for the equilibrium objective is expressed as: Where, ΔV k Let I represent the voltage deviation of the k-th channel, ∈ represent the zero-prevention denominator coefficient, and I k I represents the actual charging current of the k-th channel, indicating the result of current equalization. new The total charging current is represented by 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 specific formula for calculating the current charging efficiency η is as follows: Among them, SOC cur Represented as the initial state of charge, SOC init I represents the current state of charge, t represents the cumulative charging time, and I represents the current state of charge. avg The current is expressed as the average current during the charging process. If SOC_cur ≥ 95%, then constant voltage trickle charging is switched to the constant voltage form. e represents an exponential function, θ represents the decay rate coefficient, and I... final Represented as the adaptively adjusted charging current, the application of this algorithm in portable 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, while extending battery life and providing electric vehicle users with a more intelligent, efficient, and safe charging experience.

7. A portable electric vehicle fast charging current balancing system based on an adaptive control algorithm according to claim 6, 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. A portable electric vehicle fast charging current balancing system based on an adaptive control algorithm according to claim 7, 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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