Portable electric vehicle rapid charging current balancing system based on self-adaptive control algorithm

Abstract

A portable electric vehicle rapid charging current balancing system based on a self-adaptive control algorithm comprises a data acquisition and sensor module, a power management and conversion module, a portable 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 portable 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 portable electric vehicle rapid charging current balancing system based on the self-adaptive control 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] The invention relates to the technical fields of competition analysis, adaptive control and current balancing, and in particular to a portable electric vehicle fast charging current balancing system based on an adaptive control algorithm. Background Art

[0002] Competition analysis technology is an optimization method based on dynamic resource allocation, which aims to solve the problems of low battery management efficiency and insufficient energy utilization during the charging process of electric vehicles. Competition analysis technology combines adaptive control technology to monitor charging load, grid voltage fluctuations and battery status in real time, comprehensively analyze the charging demand between different battery cells, and adaptively and dynamically adjust the charging power distribution to ensure the optimal power distribution of battery management and improve the overall energy efficiency of the charging system. Combined with adaptive control methods, this technology can dynamically optimize according to real-time data during the charging process, realize intelligent power management, and further improve charging stability and safety.

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

[0004] However, the existing portable electric vehicle fast charging current balancing system based on an adaptive control algorithm has the problem that the charging system cannot be flexibly adjusted according to the real-time load conditions and battery status, resulting in low energy utilization and long charging time, as well as overcharging and undercharging due to unbalanced charging, which accelerates battery aging and affects the overall life of the battery pack. Summary of the invention

[0005] The purpose of the present invention is to provide a portable electric vehicle fast charging current balancing system based on an adaptive control algorithm to solve the problem that the charging system of the existing portable electric vehicle fast charging current balancing system based on an adaptive control algorithm proposed in the above background technology cannot be flexibly adjusted according to the real-time load conditions 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 life of the battery pack.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: 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 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 suitable for the electric vehicle battery to ensure 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, and the structure and heat dissipation optimization unit is used to optimize the physical structure of the charging device The structure and heat dissipation performance ensure efficient heat dissipation and maintain stable operation of the equipment. The charging interface protection module is used to protect the charging interface from damage caused by overcurrent and overvoltage abnormal conditions to ensure 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 status of the battery and environmental changes to ensure 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 unit. The data storage and remote monitoring module is used to store charging process data and support remote monitoring and fault diagnosis to ensure 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 to ensure 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 obtains key parameters of battery current, voltage, temperature, humidity and external temperature, and provides comprehensive data support for charging control and current balance.

[0008] Preferably, the power management and conversion module proposes an adaptive power management algorithm based on competition analysis to ensure optimal power allocation for battery management, improve power utilization and reduce energy consumption by real-time analysis of charging load, grid voltage fluctuation and battery status.

[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. s, which means that the system is idle for more than T s Shutting down the computer at this time can save energy. The specific formula is:

[0010]

[0011] Among them, P b It is the power consumption of the charging system in idle state, in watts, E a It is expressed as the wake-up energy consumption of the charging system, including the total energy consumption of circuit startup and capacitor charging, in joules. By quantifying the shutdown threshold, it provides a benchmark for the subsequent adaptive management strategy to ensure that the charging system achieves a preliminary balance between energy waste and delay. Then, an offline adaptive power management strategy and a corresponding online adaptive power management strategy are constructed. It is assumed that the time interval for predicting all charging current requirements through offline measurement is expressed as σ={t1,t2,…,t n}, t1, t2, …, t n It is expressed as the time interval of independent charging current demand at each moment. The decision formula and total energy consumption formula are expressed as:

[0012] If the idle time Δt j ≥T s , then shut down immediately; otherwise, keep idle.

[0013]

[0014] Among them, E opt denoted as 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, so it can make the optimal decision. n is the total number of requests in the time interval of all charging current demands, j is the request index in the time interval of all charging current demands, min(·) is the minimization function, Δt j It is represented as the time interval from the completion of the previous request to the arrival of the jth request, and δ(·) is represented as the indicator function, which represents the time interval from the completion of the previous request to the arrival of the jth request. j ≥T s When , the function value is 1, indicating that the system chooses to shut down, otherwise it is 0, indicating that the system remains idle. The corresponding online adaptive power management strategy is different in that the time interval of all charging current requirements is expressed as σ distribution is unknown but correlated, and real-time decision-making is required. The specific formula is expressed as:

[0015]

[0016] E online It is expressed as the total energy consumption calculated by the online adaptive power management strategy, t idleIt is expressed as the time that the system actually remains idle before the jth 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] Among them, γ represents the competition ratio, max σ It is expressed as a maximization function. The worst performance of the algorithm is quantified through competitive analysis to provide a theoretical basis for the dynamic adjustment strategy, ensuring the stability of the portable charging system under variable loads. Then, the shutdown threshold is dynamically optimized based on historical load data to improve battery management efficiency and current balancing ability. The specific formula for weighted update of historical data is expressed as:

[0019]

[0020] in, It is represented by the adaptive adjustment threshold when the jth request arrives, obtained by using the exponentially weighted moving average method EWMA, Δt j-1 It is represented as the time interval from the completion of the previous request to the arrival of the j-1th request, and α is the weight of the decay factor controlling the historical data. It is expressed as the adaptive adjustment threshold when the j-1th request arrives obtained by the exponential weighted moving average method EWMA. Through dynamic threshold adjustment, the energy waste and frequent wake-up problems caused by the fixed threshold are reduced, and the current balance efficiency of the portable charging system is optimized. Then, by constructing a delay constraint integration and real-time correction strategy, it is ensured that the power management strategy meets the maximum delay constraint, avoiding charging interruption and battery life reduction. The specific formula for delay model construction is expressed as:

[0021] L j =max(0,T w +W j -Δt j )

[0022] Among them, L j It is expressed as the actual delay consisting of wake-up time and queue waiting time, T w It is expressed as the wake-up time of the charging system in seconds, W j Denote the intrinsic waiting time of request j, Δt j It is expressed as the time interval from the completion of the previous request to the arrival of the jth request, calculated by queuing theory. The specific formula of the real-time correction mechanism is expressed as:

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

[0024] Among them, E' online It is expressed as the total energy consumption calculated by the real-time correction mechanism, L max It is expressed as the maximum delay allowed by the system. If the predicted delay is L j >L max , prohibit the current shutdown operation, force to maintain the idle state, strike a balance between energy optimization and user experience, ensure the high responsiveness and safety of the portable electric vehicle charging system, and finally, dynamically adjust the algorithm parameters through real-time feedback to achieve long-term stable optimization, calculate the multi-objective optimization function, and minimize the total energy consumption and delay penalty terms at the same time. The specific formula is expressed as:

[0025]

[0026] Among them, F(α,β) represents the comprehensive index multi-objective optimization function reflecting the system performance, E total It is represented by the total energy consumption calculated by the online adaptive power management strategy, and β is represented by the delay penalty coefficient, which reflects the weight of the delay on the system performance. Through the multi-objective optimization function, the energy consumption and delay are balanced to ensure that the system can meet the user experience requirements while managing energy efficiently. The gradient descent method is used to dynamically optimize the attenuation factor α and the delay penalty coefficient β. The specific formula is expressed as follows:

[0027]

[0028] Among them, α (k+1) It is represented as the attenuation factor of the k+1th iteration, k is the number of algorithm iterations, α (k) It is represented as the decay factor of the kth iteration, η is represented as the learning rate of the control parameter update step, Expressed as the partial derivative of the multi-objective optimization function with respect to α, β (k+1) Denotes the delay penalty coefficient for the k+1th iteration, β (k) Expressed as the delay penalty coefficient for the kth iteration, It is expressed as the partial derivative of the multi-objective optimization function with respect to β. The gradient descent method is used to dynamically adjust α and α to minimize the multi-objective optimization function F(α, β) and achieve continuous optimization of system performance. The closed-loop optimization engine is used to adaptively adjust the algorithm parameters 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, which ensures that the temperature of the charging device is stable when working for a long time by adopting high thermal conductivity materials, air cooling and liquid cooling solutions, thereby avoiding thermal runaway affecting charging safety.

[0030] Preferably, the portable charging device design module includes a charging interface protection module, and the charging interface protection unit is optimized by short circuit prevention, overcurrent, and contact reliability design 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, and performs intelligent adjustment in combination with the battery charging curve and environmental factors to ensure the optimal charging rate under different charging conditions, improve the fast charging efficiency and reduce battery loss.

[0032] Preferably, the fast charging algorithm based on adaptive dynamic adjustment is specifically as follows: dynamically initialize and set the charging parameters, and initialize the maximum allowable charging current I according to the battery type and historical data. max 、Target charging voltage V tar And the adaptive adjustment coefficient matrix β = [β1, β2, β3], where β1 represents the current adjustment rate factor, β2 represents the voltage deviation compensation factor, and β3 represents the temperature attenuation coefficient. Calculate the initial charging current I init , the specific formula is:

[0033]

[0034] Among them, min(·) represents the minimization function, V tar Expressed as the initial battery voltage, R bat It is expressed as the equivalent internal resistance of the battery. By dynamically initializing and setting the charging parameters, the initial parameter benchmark is established for the charging system. Then, according to the battery status parameters output by the power management and conversion module, dynamic input is provided for the adaptive fast charging adjustment to ensure the safety and efficiency of the charging process. The charging requirements of different batteries are quickly matched through the adaptive adjustment mechanism to ensure the real-time and stability of subsequent adjustments. The specific formula is expressed as follows:

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

[0036] Among them, I adj It is represented by the dynamic regulation current calculated by the algorithm, I curIt represents the current charging current of the system, ΔV represents the voltage deviation, ΔT represents the temperature deviation, and |·| 0.5 It is expressed as the 0.5 power of the absolute value function, sgn(·) is expressed as the sign function to ensure that the current is attenuated when the temperature exceeds the limit, so that the current does not exceed the safety threshold after constraint adjustment. The specific formulas of 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 Expressed as the real-time battery voltage, T cur Represents the real-time battery temperature, T safe It is represented as the preset safety temperature threshold. Secondly, for the scenario of multiple battery packs being charged in parallel, the current of each channel is dynamically allocated to avoid local overload and achieve system-level current balance. The current allocation weight of the kth channel is set to w k , the total number of channels is K, and the specific formula of the equalization target is expressed as:

[0039]

[0040] Where, ΔV k It is represented as the voltage deviation of the kth channel, ∈ is represented as the anti-zero denominator coefficient, I k It is expressed as the actual charging current of the kth channel, which represents the result of current balancing distribution, I new It is expressed as the total charging current, which represents the global current value after adaptive adjustment. By dynamically optimizing the charging curve, the total charging time is shortened, and the mode is smoothly switched to the trickle charging mode when the termination condition is reached. The specific formula for calculating the current charging efficiency η is expressed as:

[0041]

[0042] Among them, SOC cur Indicated as initial state of charge, SOC init represents the current state of charge, t represents the cumulative charging time, I avg It is expressed as the average current of the charging process. If SOC_cur≥95%, it switches to constant voltage trickle charging. e is expressed as an exponential function. Expressed as the decay rate coefficient, I finalRepresented as the charging current after adaptive adjustment, the application of the algorithm in the portable fast charging system not only greatly shortens the charging time and improves the charging efficiency, but also effectively prevents battery overheating, overload and uneven charging, ensures the safety and reliability of electric vehicle batteries, and extends the battery life, providing electric vehicle users with a more intelligent, efficient and safe charging experience.

[0043] Preferably, the adaptive charging control module includes a current balancing control unit. The current balancing 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 to ensure the charging consistency between different battery modules and avoid overcharging and undercharging.

[0044] Preferably, the data storage and remote monitoring module ensures long-term storage and remote access to charging process data through cloud storage and wireless communication technology, which facilitates analysis and optimization by users and management systems.

[0045] Preferably, the user interaction and control module ensures that the user can check the charging status, adjust the charging strategy, and receive abnormal alarms at any time by designing a convenient system interaction interface, thereby improving the charging experience.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. Power management and conversion module An adaptive power management algorithm based on competition analysis is proposed. First, the algorithm defines the key energy consumption parameters of the portable electric vehicle fast charging system and establishes a dynamic shutdown threshold model to optimize the energy conversion efficiency. The model is based on the energy consumption balance principle and calculates the shutdown threshold to ensure that the charging system enters the low power mode only when the idle time exceeds the set parameters, thereby reducing unnecessary energy consumption and providing a benchmark for subsequent adaptive management strategies to achieve a balance between energy waste and delay in the charging system. Secondly, the algorithm constructs an offline adaptive power management strategy and an online adaptive power management strategy. In the offline strategy, the system pre-measures the time intervals of all charging current requirements and makes the optimal shutdown decision based on the data to achieve minimum energy consumption. In the online strategy, facing the unknown but relevant charging current demand time distribution, the system dynamically adjusts the shutdown strategy through real-time decision-making to adapt to the 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 performance of the power management strategy under different load conditions to ensure the system Stability under complex charging requirements. In addition, the algorithm optimizes the shutdown threshold based on historical load data, and dynamically adjusts it through the exponentially weighted moving average method to reduce energy waste and frequent wake-up problems caused by fixed thresholds, and further improves the current balance efficiency of the charging system. While ensuring battery management efficiency, the algorithm also combines delay constraint integration and real-time correction strategies to avoid charging interruptions and battery life degradation. By building a delay model, the system can calculate the waiting time of charging requests and dynamically adjust the shutdown strategy when it predicts that it exceeds the maximum allowable delay, thereby achieving a balance between energy optimization and user experience. Overall, the algorithm uses a real-time feedback mechanism to dynamically adjust the algorithm parameters to achieve long-term stable optimization, and through a multi-objective optimization method, it minimizes total energy consumption while taking into account user experience requirements. The gradient descent method is used to optimize the attenuation factor and delay penalty coefficient to ensure that the system always 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 capabilities and stable charging performance under dynamic loads for a long time.

[0048] 2. The adaptive charging strategy unit proposes a fast charging algorithm based on adaptive dynamic regulation. The algorithm first dynamically initializes and sets the benchmark for the charging parameters. According to the battery type, historical data and current status, the maximum allowable charging current, target charging voltage and adaptive adjustment coefficient matrix are set. By initializing the charging parameters, the system can quickly match the charging requirements of different types of batteries at the beginning of charging, provide an accurate benchmark for subsequent dynamic regulation, and ensure the safety and stability of the charging process. Based on the adaptive regulation mechanism, the algorithm can dynamically adjust the charging current according to the real-time feedback of the power management and conversion modules, so that the charging current responds to the battery voltage deviation, temperature changes and current current level at any time, 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 the charging current and increase the charging rate; when the temperature exceeds the safety threshold, the system can adaptively reduce the charging current. Flow to prevent overheating and damage. In addition, for the scenario of parallel charging of multiple battery packs, the algorithm dynamically allocates the current of each charging channel to ensure that the voltage deviation of each channel is minimized, avoid local overload, achieve system-level current balance, and improve the overall charging consistency and balance. As the charging process progresses, the algorithm can also dynamically optimize the charging curve to ensure the optimal distribution of charging current in different state of charge intervals and improve the overall charging efficiency. When it is close to full, the algorithm will automatically detect the state of charge and gradually adjust the charging current, smoothly transitioning to trickle charging mode to prevent battery overcharging and increase battery life. Overall, the application of this algorithm in portable fast charging systems not only greatly shortens the charging time and improves charging efficiency, but also can effectively prevent battery overheating, overloading and uneven charging, ensure the safety and reliability of electric vehicle batteries, and extend the battery life, providing electric vehicle users with a more intelligent, efficient and safe charging experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a structural schematic diagram of the present invention; DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] See also Figure 1The present invention provides a portable electric vehicle fast charging current balancing system based on an adaptive control algorithm, including 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 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 suitable for the electric vehicle battery to ensure 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, and the structure and heat dissipation optimization unit is used to optimize the physical structure and heat dissipation of the charging device The charging interface protection module is used to protect the charging interface from damage caused by overcurrent and overvoltage abnormal conditions, 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 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 unit; the data storage and remote monitoring module is used to store charging process data and support remote monitoring and fault diagnosis to ensure 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 to ensure that the user can control and monitor the charging process.

[0052] See also 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 obtains the key parameters of battery ,current, voltage, temperature, humidity, and external temperature, providing comprehensive ,data support for

[0053] See also Figure 1 ,Furthermore, the power management and conversion module proposes an ,adaptive power management algorithm based on competition analysis ,through real-time analysis of charging load, grid voltage fluctuation and battery status, ,ensuring the optimal power allocation for battery management, ,improving power utilization and reducing energy consumption.

[0054] See also Figure 1 ,Furthermore, the adaptive power management algorithm based on competition ,analysis is specifically as follows: ,First, the key energy consumption parameters of the portable ,electrical vehicle fast charging system are defined, a dynamic shutdown threshold model is ,established to optimize the energy conversion efficiency, and the shutdown threshold T is ,calculated based on the energy s, which means that the system is idle for more than T s Shutting down the computer at this time can save energy. The specific formula is:

[0055]

[0056] Among them, P b It is the power consumption of the charging system in idle state, in watts, E a It is expressed as the wake-up energy consumption of the charging system, including the total energy consumption of circuit startup and capacitor charging, in joules. By quantifying the shutdown threshold, it provides a benchmark for the subsequent adaptive management strategy to ensure that the charging system achieves a preliminary balance between energy waste and delay. Then, an offline adaptive power management strategy and a corresponding online adaptive power management strategy are constructed. It is assumed that the time interval for predicting all charging current requirements through offline measurement is expressed as σ={t1,t2,…,t n}, t1, t2, …, t n It is expressed as the time interval of independent charging current demand at each moment. The decision formula and total energy consumption formula are expressed as:

[0057] If the idle time Δt j ≥T s , then shut down immediately; otherwise, keep idle.

[0058]

[0059] Among them, E opt denoted as 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, so it can make the optimal decision. n is the total number of requests in the time interval of all charging current demands, j is the request index in the time interval of all charging current demands, min(·) is the minimization function, Δt j It is represented as the time interval from the completion of the previous request to the arrival of the jth request, and δ(·) is represented as the indicator function, which represents the time interval from the completion of the previous request to the arrival of the jth request. j ≥T s When , the function value is 1, indicating that the system chooses to shut down, otherwise it is 0, indicating that the system remains idle. The corresponding online adaptive power management strategy is different in that the time interval of all charging current requirements is expressed as σ distribution is unknown but correlated, and real-time decision-making is required. The specific formula is expressed as:

[0060]

[0061] E online It is expressed as the total energy consumption calculated by the online adaptive power management strategy, t idleIt is expressed as the time that the system actually remains idle before the jth 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:

[0062]

[0063] Among them, γ represents the competition ratio, max σ It is expressed as a maximization function. The worst performance of the algorithm is quantified through competitive analysis to provide a theoretical basis for the dynamic adjustment strategy, ensuring the stability of the portable charging system under variable loads. Then, the shutdown threshold is dynamically optimized based on historical load data to improve battery management efficiency and current balancing ability. The specific formula for weighted update of historical data is expressed as:

[0064]

[0065] in, It is represented by the adaptive adjustment threshold when the jth request arrives, obtained by using the exponentially weighted moving average method EWMA, Δt j-1 It is represented as the time interval from the completion of the previous request to the arrival of the j-1th request, and α is the weight of the decay factor controlling the historical data. It is expressed as the adaptive adjustment threshold when the j-1th request arrives obtained by the exponential weighted moving average method EWMA. Through dynamic threshold adjustment, the energy waste and frequent wake-up problems caused by the fixed threshold are reduced, and the current balance efficiency of the portable charging system is optimized. Then, by constructing a delay constraint integration and real-time correction strategy, it is ensured that the power management strategy meets the maximum delay constraint, avoiding charging interruption and battery life reduction. The specific formula for delay model construction is expressed as:

[0066] L j =max(0,T w +W j -Δt j )

[0067] Among them, L j It is expressed as the actual delay consisting of wake-up time and queue waiting time, T w It is expressed as the wake-up time of the charging system in seconds, W j Denote the intrinsic waiting time of request j, Δt j It is expressed as the time interval from the completion of the previous request to the arrival of the jth request, calculated by queuing theory. The specific formula of the real-time correction mechanism is expressed as:

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

[0069] Among them, E' online It is expressed as the total energy consumption calculated by the real-time correction mechanism, L max It is expressed as the maximum delay allowed by the system. If the predicted delay is L j >L max , prohibit the current shutdown operation, force to maintain the idle state, strike a balance between energy optimization and user experience, ensure the high responsiveness and safety of the portable electric vehicle charging system, and finally, dynamically adjust the algorithm parameters through real-time feedback to achieve long-term stable optimization, calculate the multi-objective optimization function, and minimize the total energy consumption and delay penalty terms at the same time. The specific formula is expressed as:

[0070]

[0071] Among them, F(α,β) represents the comprehensive index multi-objective optimization function reflecting the system performance, E total It is represented by the total energy consumption calculated by the online adaptive power management strategy, and β is represented by the delay penalty coefficient, which reflects the weight of the delay on the system performance. Through the multi-objective optimization function, the energy consumption and delay are balanced to ensure that the system can meet the user experience requirements while managing energy efficiently. The gradient descent method is used to dynamically optimize the attenuation factor α and the delay penalty coefficient β. The specific formula is expressed as follows:

[0072]

[0073] Among them, α (k+1) It is represented as the attenuation factor of the k+1th iteration, k is the number of algorithm iterations, α (k) It is represented as the decay factor of the kth iteration, η is represented as the learning rate of the control parameter update step, Expressed as the partial derivative of the multi-objective optimization function with respect to α, β (k+1) Denotes the delay penalty coefficient for the k+1th iteration, β (k) Expressed as the delay penalty coefficient for the kth iteration, It is expressed as the partial derivative of the multi-objective optimization function with respect to β. The gradient descent method is used to dynamically adjust α and α to minimize the multi-objective optimization function F(α, β) and achieve continuous optimization of system performance. The closed-loop optimization engine is used to adaptively adjust the algorithm parameters to ensure the long-term stability and efficiency of the portable charging system under dynamic loads.

[0074] See also Figure 1Furthermore, the portable charging device design module includes a structure and heat dissipation optimization unit, which ensures the temperature stability of the charging device when it works for a long time by adopting high thermal conductivity materials, air cooling and liquid cooling solutions, and avoids thermal runaway affecting charging safety.

[0075] See also Figure 1 Furthermore, the portable charging device design module includes a charging interface protection module. The charging interface protection unit is optimized by short circuit prevention, overcurrent, and contact reliability to ensure stable connection and safe charging of the charging interface under high-frequency plugging and unplugging and different environmental conditions.

[0076] See also 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, and performs intelligent adjustment in combination with the battery charging curve and environmental factors to ensure the optimal charging rate under different charging conditions, improve the fast charging efficiency and reduce battery loss.

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

[0078]

[0079] Among them, min(·) represents the minimization function, V tar Expressed as the initial battery voltage, R bat It is expressed as the equivalent internal resistance of the battery. By dynamically initializing and setting the charging parameters, the initial parameter benchmark is established for the charging system. Then, according to the battery status parameters output by the power management and conversion module, dynamic input is provided for the adaptive fast charging adjustment to ensure the safety and efficiency of the charging process. The charging requirements of different batteries are quickly matched through the adaptive adjustment mechanism to ensure the real-time and stability of subsequent adjustments. The specific formula is expressed as follows:

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

[0081] Among them, I adj It is represented by the dynamic regulation current calculated by the algorithm, I cur It represents the current charging current of the system, ΔV represents the voltage deviation, ΔT represents the temperature deviation, and |·| 0.5 It is expressed as the 0.5 power of the absolute value function, sgn(·) is expressed as the sign function to ensure that the current is attenuated when the temperature exceeds the limit, so that the current does not exceed the safety threshold after constraint adjustment. The specific formulas of 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 Expressed as the real-time battery voltage, T cur Represents the real-time battery temperature, T safe It is represented as the preset safety temperature threshold. Secondly, for the scenario of multiple battery packs being charged in parallel, the current of each channel is dynamically allocated to avoid local overload and achieve system-level current balance. The current allocation weight of the kth channel is set to w k , the total number of channels is K, and the specific formula of the equalization target is expressed as:

[0084]

[0085] Where, ΔV k It is represented as the voltage deviation of the kth channel, ∈ is represented as the anti-zero denominator coefficient, I k It is expressed as the actual charging current of the kth channel, which represents the result of current balancing distribution, I new It is expressed as the total charging current, which represents the global current value after adaptive adjustment. By dynamically optimizing the charging curve, the total charging time is shortened, and the mode is smoothly switched to the trickle charging mode when the termination condition is reached. The specific formula for calculating the current charging efficiency η is expressed as:

[0086]

[0087] Among them, SOC cur Indicated as initial state of charge, SOC init represents the current state of charge, t represents the cumulative charging time, I avg It is represented as the average current of the charging process. If SOC_cur≥95%, it switches to constant voltage trickle charging. e is represented as an exponential function, θ is represented as the decay rate coefficient, I finalRepresented as the charging current after adaptive adjustment, the application of the algorithm in the portable fast charging system not only greatly shortens the charging time and improves the charging efficiency, but also effectively prevents battery overheating, overload and uneven charging, ensures the safety and reliability of electric vehicle batteries, and extends the battery life, providing electric vehicle users with a more intelligent, efficient and safe charging experience.

[0088] See also Figure 1 Furthermore, the adaptive charging control module includes a current balancing control unit. The current balancing control unit monitors the charging status of each battery cell in real time according to the output information of the adaptive charging strategy unit and dynamically adjusts the current distribution to ensure the charging consistency between different battery modules and avoid overcharging and undercharging.

[0089] See also Figure 1 Furthermore, the data storage and remote monitoring module ensures long-term storage and remote access to charging process data through cloud storage and wireless communication technology, which is convenient for users and management systems to analyze and optimize.

[0090] See also Figure 1 Furthermore, the user interaction and control module ensures that users can check the charging status, adjust the charging strategy, and receive abnormal alarms at any time by designing a convenient system interaction interface, thereby improving the charging experience.

[0091] During specific use, first, 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; secondly, the power management and conversion module proposes an adaptive power management algorithm based on competition analysis to manage power and convert external power into charging voltage and current suitable for electric vehicle batteries to ensure 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 is used to optimize 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 is used to protect the charging interface from damage caused by overcurrent and overvoltage abnormalities. , ensuring safety during the charging process; secondly, 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 unit; then, the data storage and remote monitoring module is used to store charging process data and support remote monitoring and fault diagnosis to ensure 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 to ensure that the user can control and monitor the charging process.

[0092] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in 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 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 suitable for the electric vehicle battery to ensure 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 is used to optimize 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 is used to protect the charging interface from damage caused by overcurrent and overvoltage abnormalities to ensure 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 unit; the data storage and remote monitoring module is used to store charging process data and support remote monitoring and fault diagnosis to ensure 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 to ensure that the user can control and monitor the charging process.

2. According to claim 1, a portable electric vehicle fast charging current balancing system based on an adaptive control algorithm is 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 obtains key parameters of battery current, voltage, temperature, humidity and external temperature, and provides comprehensive data support for charging control and current balance.

3. The portable electric vehicle fast charging current balancing system based on adaptive control algorithm according to claim 1 is characterized in that: The power management and conversion module proposes an adaptive power management algorithm based on competition analysis to ensure optimal power allocation for battery management, improve power utilization and reduce energy consumption by real-time analysis of charging load, grid voltage fluctuation and battery status.

4. A portable electric vehicle fast charging current balancing system based on an adaptive control algorithm according to claim 3, characterized in that: Firstly, the key energy consumption parameters of the portable electric vehicle fast charging system are defined, a dynamic shutdown threshold model is established to optimize the energy conversion efficiency, and the shutdown threshold T is calculated based on the energy consumption balance principle. s , which means that the system is idle for more than T s Shutting down the computer at this time can save energy. The specific formula is: Among them, P b It is the power consumption of the charging system in idle state, in watts, E a It is expressed as the wake-up energy consumption of the charging system, including the total energy consumption of circuit startup and capacitor charging, in joules. By quantifying the shutdown threshold, it provides a benchmark for the subsequent adaptive management strategy to ensure that the charging system achieves a preliminary balance between energy waste and delay. Then, an offline adaptive power management strategy and a corresponding online adaptive power management strategy are constructed. It is assumed that the time interval for predicting all charging current requirements through offline measurement is expressed as σ={t1,t2,…,t n }, t1, t2, …, t n It is expressed as the time interval of independent charging current demand at each moment. The decision formula and total energy consumption formula are expressed as: If the idle time Δt j ≥T s , then shut down immediately; otherwise, keep idle. Among them, E opt denoted as 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, so it can make the optimal decision. n is the total number of requests in the time interval of all charging current demands, j is the request index in the time interval of all charging current demands, min(·) is the minimization function, Δt j It is represented as the time interval from the completion of the previous request to the arrival of the jth request, and δ(·) is represented as the indicator function, which represents the time interval from the completion of the previous request to the arrival of the jth request. j ≥T s When , the function value is 1, indicating that the system chooses to shut down, otherwise it is 0, indicating that the system remains idle. The corresponding online adaptive power management strategy is different in that the time interval of all charging current requirements is expressed as σ distribution is unknown but correlated, and real-time decision-making is required. The specific formula is expressed as: E online It is expressed as the total energy consumption calculated by the online adaptive power management strategy, t idle It is expressed as the time that the system actually remains idle before the jth 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: Among them, γ represents the competition ratio, max σ It is expressed as a maximization function. The worst performance of the algorithm is quantified through competitive analysis to provide a theoretical basis for the dynamic adjustment strategy, ensuring the stability of the portable charging system under variable loads. Then, the shutdown threshold is dynamically optimized based on historical load data to improve battery management efficiency and current balancing ability. The specific formula for weighted update of historical data is expressed as: in, It is represented by the adaptive adjustment threshold when the jth request arrives, obtained by using the exponentially weighted moving average method EWMA, Δt j-1 It is represented as the time interval from the completion of the previous request to the arrival of the j-1th request, and α is the weight of the decay factor controlling the historical data. It is expressed as the adaptive adjustment threshold when the j-1th request arrives obtained by the exponential weighted moving average method EWMA. Through dynamic threshold adjustment, the energy waste and frequent wake-up problems caused by the fixed threshold are reduced, and the current balance efficiency of the portable charging system is optimized. Then, by constructing a delay constraint integration and real-time correction strategy, it is ensured that the power management strategy meets the maximum delay constraint, avoiding charging interruption and battery life reduction. The specific formula for delay model construction is expressed as: L j =max(0,T w +W j -Δt j ) Among them, L j It is expressed as the actual delay consisting of wake-up time and queue waiting time, T w It is expressed as the wake-up time of the charging system in seconds, W j Denote the intrinsic waiting time of request j, Δt j It is expressed as the time interval from the completion of the previous request to the arrival of the jth request, calculated by queuing theory. The specific formula of the real-time correction mechanism is expressed as: AND' online =And online -P b ·(T s -Δt j )·δ(L j >L max ) Among them, E' online It is expressed as the total energy consumption calculated by the real-time correction mechanism, L max It is expressed as the maximum delay allowed by the system. If the predicted delay is L j >L max , prohibit the current shutdown operation, force to maintain the idle state, strike a balance between energy optimization and user experience, ensure the high responsiveness and safety of the portable electric vehicle charging system, and finally, dynamically adjust the algorithm parameters through real-time feedback to achieve long-term stable optimization, calculate the multi-objective optimization function, and minimize the total energy consumption and delay penalty terms at the same time. The specific formula is expressed as: Among them, F(α,β) represents the comprehensive index multi-objective optimization function reflecting the system performance, E total It is represented by the total energy consumption calculated by the online adaptive power management strategy, and β is represented by the delay penalty coefficient, which reflects the weight of the delay on the system performance. Through the multi-objective optimization function, the energy consumption and delay are balanced to ensure that the system can meet the user experience requirements while managing energy efficiently. The gradient descent method is used to dynamically optimize the attenuation factor α and the delay penalty coefficient β. The specific formula is expressed as follows: Among them, α (k+1) It is represented as the attenuation factor of the k+1th iteration, k is the number of algorithm iterations, α (k) It is represented as the decay factor of the kth iteration, η is represented as the learning rate of the control parameter update step, Expressed as the partial derivative of the multi-objective optimization function with respect to α, β (k+1) Denotes the delay penalty coefficient for the k+1th iteration, β (k) Expressed as the delay penalty coefficient for the kth iteration, It is expressed as the partial derivative of the multi-objective optimization function with respect to β. The gradient descent method is used to dynamically adjust α and α to minimize the multi-objective optimization function F(α, β) and achieve continuous optimization of system performance. The closed-loop optimization engine is used to adaptively adjust the algorithm parameters to ensure the long-term stability and efficiency of the portable charging system under dynamic loads.

5. A portable electric vehicle fast charging current balancing system based on an adaptive control algorithm according to claim 1, characterized in that: The portable 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 ensures the temperature stability of the charging device when it works for a long time by adopting high thermal conductivity materials, air cooling and liquid cooling solutions to avoid thermal runaway affecting charging safety; the charging interface protection unit ensures the stable connection and safe charging of the charging interface under high-frequency plugging and unplugging and different environmental conditions through the optimization design of anti-short circuit, overcurrent and contact reliability.

6. A portable electric vehicle fast charging current balancing system based on an adaptive control algorithm according to claim 5, 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, and performs intelligent adjustment in combination with the battery charging curve and environmental factors to ensure the optimal charging rate under different charging conditions, improve the fast charging efficiency and reduce battery loss; the current balance control unit monitors the charging status of each battery cell in real time according to the output information of the adaptive charging strategy unit and dynamically adjusts the current distribution to ensure the charging consistency between different battery modules and avoid overcharging and undercharging.

7. A portable electric vehicle fast charging current balancing system based on an adaptive control algorithm according to claim 1, characterized in that: Dynamically initialize and benchmark charging parameters, and initialize the maximum allowable charging current I according to battery type and historical data max 、Target charging voltage V tar And the adaptive adjustment coefficient matrix β = [β1, β2, β3], where β1 represents the current adjustment rate factor, β2 represents the voltage deviation compensation factor, and β3 represents the temperature attenuation coefficient. Calculate the initial charging current I init , the specific formula is: Among them, min(·) represents the minimization function, V tar Expressed as the initial battery voltage, R bat It is expressed as the equivalent internal resistance of the battery. By dynamically initializing and setting the charging parameters, the initial parameter benchmark is established for the charging system. Then, according to the battery status parameters output by the power management and conversion module, dynamic input is provided for the adaptive fast charging adjustment to ensure the safety and efficiency of the charging process. The charging requirements of different batteries are quickly matched through the adaptive adjustment mechanism to ensure the real-time and stability of subsequent adjustments. The specific formula is expressed as follows: I adj =β1I cur +β2·ΔV-β3·sgn(ΔT)·|ΔT| 0.5 Among them, I adj It is represented by the dynamic regulation current calculated by the algorithm, I cur It represents the current charging current of the system, ΔV represents the voltage deviation, ΔT represents the temperature deviation, |·| 0.5 It is expressed as the 0.5 power of the absolute value function, sgn(·) is expressed as the sign function to ensure that the current is attenuated when the temperature exceeds the limit, so that the current does not exceed the safety threshold after constraint adjustment. The specific formulas of voltage deviation and temperature deviation are expressed as follows: ΔV=V tar -V cur ,ΔT=T cur -T safe Among them, V cur Expressed as the real-time battery voltage, T cur Represents the real-time battery temperature, T safe It is represented as the preset safety temperature threshold. Secondly, for the scenario of multiple battery packs being charged in parallel, the current of each channel is dynamically allocated to avoid local overload and achieve system-level current balance. The current allocation weight of the kth channel is set to w k , the total number of channels is K, and the specific formula of the equalization target is expressed as: Where, ΔV k It is represented as the voltage deviation of the kth channel, ∈ is represented as the anti-zero denominator coefficient, I k It is expressed as the actual charging current of the kth channel, which represents the result of current balancing distribution, I new It is expressed as the total charging current, which represents the global current value after adaptive adjustment. By dynamically optimizing the charging curve, the total charging time is shortened, and the mode is smoothly switched to the trickle charging mode when the termination condition is reached. The specific formula for calculating the current charging efficiency η is expressed as: Among them, SOC cur Indicated as initial state of charge, SOC init represents the current state of charge, t represents the cumulative charging time, I avg It is represented as the average current of the charging process. If SOC_cur≥95%, it switches to constant voltage trickle charging. e is represented as an exponential function, θ is represented as the decay rate coefficient, I final Represented as the charging current after adaptive adjustment, the application of the algorithm in the portable fast charging system not only greatly shortens the charging time and improves the charging efficiency, but also effectively prevents battery overheating, overload and uneven charging, ensures the safety and reliability of electric vehicle batteries, and extends the battery life, providing electric vehicle users with a more intelligent, efficient and safe charging experience.

8. The portable electric vehicle fast charging current balancing system based on adaptive control algorithm according to claim 1, characterized in that: The data storage and remote monitoring module ensures long-term storage and remote access to charging process data through cloud storage and wireless communication technology, which is convenient for users and management systems to perform analysis and optimization.

9. The portable electric vehicle fast charging current balancing system based on adaptive control algorithm according to claim 1, characterized in that: The user interaction and control module ensures that users can check the charging status, adjust the charging strategy, and receive abnormal alarms at any time by designing a convenient system interaction interface, thereby improving the charging experience.

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