Energy distribution optimization method and system for a stationary battery

Abstract

The application provides a kind of parking battery energy distribution optimization method and system, by real-time monitoring vehicle three-axis acceleration and angular velocity, calculate the initial flow direction of battery electrolyte, combined with fluid mechanics simulation to predict electrolyte concentration gradient distribution.When the concentration gradient is over the threshold, dynamically divide the electrode area and optimize the activation ratio, calculate the current density distribution and electrochemical reaction rate synchronously, use equivalent circuit model to deduce the real-time output power of battery.By comparing the real-time power demand of electrical equipment, use pulse width modulation technology to generate the initial energy distribution scheme, and based on the current density demand, fine adjustment the switching frequency distribution of electrode activation area, finally realize the dynamic regulation and control of battery management system to the output of electric energy.The method effectively solves the output power fluctuation problem caused by uneven distribution of electrolyte due to vehicle shaking in parking state, greatly improves the energy distribution efficiency and power supply stability of special-shaped structure battery under complex working conditions.

Description

Technical Field

[0001] This application relates to the field of vehicle energy management technology, and in particular to a method and system for optimizing energy distribution of a parking battery. Background Technology

[0002] With the popularization of new energy vehicles, optimizing the energy distribution of parking batteries has become a key area for improving vehicle performance and user experience. As the core component of electric vehicles, the battery's energy management directly affects the vehicle's range and efficiency, especially in complex parking scenarios such as slopes or uneven terrain. Optimizing energy distribution is crucial to ensuring battery life and equipment operation. Research in this area can not only improve the intelligence level of vehicles but also promote overall progress in energy efficiency, providing technological support for green travel.

[0003] Chinese Patent, Publication No. CN119382298B, Publication Date: July 1, 2025, discloses a cloud-based BMS (Battery Management System) remote monitoring and data analysis system, relating to the field of remote monitoring technology. This system addresses the shortcomings of existing technologies, such as the lack of detailed data monitoring, which hinders in-depth analysis of the performance and losses of various battery components, thus affecting overall battery health assessment and management. Furthermore, the battery state is typically a fixed, static value, neglecting the dynamic impact of management frequency and energy loss on battery state. By combining management frequency and energy loss, the system can accurately allocate the battery state of each functional component, optimizing battery usage strategies based on the actual conditions of different modules and reducing unnecessary energy loss. It also reduces unnecessary interference with the battery; through automated monitoring and adjustment mechanisms, it minimizes manual intervention in battery management, making battery management more intelligent and efficient.

[0004] The above technical solutions have the following problems: When the vehicle is parked, the electrolyte distribution inside the battery is uneven, which will lead to reduced battery efficiency, local overheating, and unstable equipment operation. The above methods ignore dynamic environmental changes and cannot adjust the energy distribution scheme based on the actual condition of the battery. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this application is to provide a method and system for optimizing the energy distribution of parking batteries, which can adjust the energy distribution scheme based on the actual condition of the battery.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] This application provides a method for optimizing energy distribution in a parking battery, the method comprising the following steps:

[0008] S101, real-time acquisition of acceleration and angular velocity values ​​of the vehicle's three axes, and calculation of the initial flow direction of electrolyte inside the battery when the vehicle is parked;

[0009] S102, based on the initial flow direction and battery cavity geometry, uses a fluid dynamics simulation method to obtain the electrolyte velocity field, and calculates the electrolyte concentration gradient distribution based on the velocity field;

[0010] S103, If the concentration gradient in the concentration gradient distribution exceeds the preset threshold, the battery electrode region is divided, the activation ratio of the electrodes in each region is optimized, and the current density distribution of the battery is calculated.

[0011] S104. Based on the current density distribution and electrode material characteristic parameters, calculate the electrochemical reaction rate distribution of the battery; based on the electrochemical reaction rate distribution and the battery's internal impedance, use an equivalent circuit model to calculate the battery's real-time output power.

[0012] S105, obtain the real-time power demand of the vehicle's electrical equipment, and determine whether the real-time output power of the battery meets the real-time power demand of the electrical equipment.

[0013] S106 If the real-time output power of the battery meets the real-time power demand of the electrical equipment, then pulse width modulation technology is used to adjust the battery energy distribution to obtain the initial energy distribution scheme of the battery.

[0014] S107. Based on the initial energy distribution scheme of the battery, calculate the current density requirements of each electrical device and initially divide the activation area. If the current density requirements exceed the preset threshold, adjust the switching frequency of the activation area to obtain the optimized switching frequency distribution of the activation area. Based on the optimized switching frequency distribution of the activation area, dynamically adjust the power output of the battery management system to generate the real-time energy distribution scheme of the battery.

[0015] As a preferred technical solution, step S101, calculating the initial flow direction of the electrolyte inside the battery when the vehicle is parked, includes: generating a parking confirmation signal if the acceleration value is within a threshold; calculating the gravitational acceleration influence vector using a vector decomposition method based on the triaxial acceleration values; calculating the liquid tilt angle using a Kalman filter algorithm based on the gravitational acceleration influence vector and the triaxial angular velocity values; and simulating the initial flow direction of the battery electrolyte using the Navier-Stokes equations based on the liquid tilt angle and the battery electrolyte density and viscosity.

[0016] As a preferred technical solution, in step S102, obtaining the flow velocity field of the electrolyte using a fluid dynamics simulation method includes: generating a mesh model of the battery electrolyte using the finite volume method based on the initial flow direction and the geometry of the battery cavity; and calculating the flow velocity distribution of the battery electrolyte using the Navier-Stokes equations based on the mesh model to obtain the flow velocity field of the battery electrolyte.

[0017] As a preferred technical solution, in step S102, the calculation of the concentration gradient distribution of the electrolyte based on the flow velocity field includes: if the maximum flow velocity exceeds a preset threshold, the k-ε turbulence model is used to calculate the turbulence intensity distribution of the battery electrolyte; based on the flow velocity field, turbulence intensity distribution, and battery electrolyte density, the concentration gradient is calculated using the convection-diffusion equation to obtain the concentration gradient distribution of the electrolyte.

[0018] As a preferred technical solution, step S102 further includes: calculating the standard deviation of the concentration gradient; if the standard deviation of the concentration gradient exceeds a preset threshold, then optimizing the flow field of the electrolyte by adjusting the flow rate at the inlet of the battery electrolyte.

[0019] As a preferred technical solution, in step S103, the process of dividing the battery electrode region and optimizing the electrode activation ratio of each region includes: dividing the battery electrode region using a k-means clustering algorithm; calculating the deposition thickness of each electrode region; and if the deposition thickness exceeds a preset threshold, optimizing the electrode activation ratio of each region using a genetic algorithm.

[0020] As a preferred technical solution, step S104, calculating the electrochemical reaction rate distribution of the battery includes: calculating the electrochemical reaction rate of the battery using the Butler-Volmer equation and calculating the electrochemical reaction rate distribution of the battery using the finite element analysis method; calculating the electrode temperature distribution using the heat conduction equation based on the electrochemical reaction rate distribution and the heat capacity of the electrode material; analyzing the dynamic changes of the electrode temperature distribution using numerical simulation method in conjunction with the electrode heat conduction performance; if the electrode temperature change rate exceeds a preset threshold, adjusting the current density distribution through an optimization algorithm and recalculating the electrochemical reaction rate distribution.

[0021] As a preferred technical solution, in step S106, the step of adjusting the battery energy distribution using pulse width modulation (PWM) technology to obtain the battery energy distribution scheme includes: calculating the electrode working efficiency based on the electrode activation ratio of each region in step 103; obtaining the deposition thickness of each electrode region; using a support vector machine (SVM) algorithm to classify and predict the battery energy output based on the electrode working efficiency and the deposition thickness of each electrode region to obtain a preliminary energy distribution scheme; calculating the output power of the preliminary energy distribution scheme; if the output power of the preliminary energy distribution scheme meets the real-time power demand of the electrical equipment, then generating a control signal using PWM technology to adjust the battery energy distribution ratio to obtain an optimized energy distribution scheme; adjusting the battery output power in real time according to the optimized energy distribution scheme; using a proportional-integral-derivative (PID) control algorithm to dynamically correct the output power fluctuation to obtain a stable output power; and continuously monitoring the electrode activation ratio and surface deposition state using a dynamic adjustment mechanism to update the energy distribution scheme.

[0022] As a preferred technical solution, the energy distribution optimization method for the parking battery further includes the following steps: acquiring terrain data and temperature data collected by external sensors; calculating the complexity of the parking scenario and the trend of complexity change of the parking scenario based on the terrain data, temperature data, the gravitational acceleration influence vector and liquid tilt angle in step S101; and correcting the electrode activation ratio of each region and updating the initial energy distribution scheme by using a Kalman filter algorithm based on the trend of complexity change of the parking scenario and the turbulence intensity distribution in step S103.

[0023] This application also provides an energy distribution optimization system for a parking battery. The system includes: an internal state monitoring and prediction module, which acquires the acceleration and angular velocity values ​​of the vehicle's three axes in real time and calculates the initial flow direction of the electrolyte inside the battery when the vehicle is parked; based on the initial flow direction and the battery cavity geometry, it uses a fluid dynamics simulation method to obtain the electrolyte velocity field and calculates the electrolyte concentration gradient distribution according to the velocity field; and an electrode control and power evaluation module, which, when the concentration gradient exceeds a preset threshold in the concentration gradient distribution, divides the battery electrode regions and optimizes the electrode activation ratio in each region, calculating the battery current density distribution; calculates the battery electrochemical reaction rate distribution based on the current density distribution and electrode material characteristic parameters; and calculates the battery's equivalent circuit model based on the electrochemical reaction rate distribution and the battery's internal impedance. The system includes: a real-time output power; a power demand matching and determination module, which acquires the real-time power demand of the vehicle's electrical equipment and determines whether the real-time output power of the battery meets the real-time power demand of the electrical equipment; an energy allocation strategy formulation module, which, when the real-time output power of the battery meets the real-time power demand of the electrical equipment, uses pulse width modulation technology to adjust the battery energy allocation to obtain an initial energy allocation scheme for the battery; and calculates the current density demand of each electrical equipment according to the initial energy allocation scheme and initially divides the activation area, adjusting the switching frequency of the activation area when the current density demand exceeds a preset threshold to obtain an optimized switching frequency distribution of the activation area; and a dynamic energy output execution module, which dynamically adjusts the energy output of the battery management system according to the optimized switching frequency distribution of the activation area, and generates and executes the real-time energy allocation scheme of the battery.

[0024] Compared with the prior art, the beneficial effects of this application are as follows:

[0025] This application deeply integrates real-time vehicle environmental perception with the internal physical processes of the battery to achieve dynamic energy optimization. Through a closed-loop control mechanism, this application ensures precise matching between the battery's real-time output power and the demands of the electrical equipment, reducing power fluctuations; optimizes electrolyte distribution and current density to avoid localized overheating and deposition problems, thus reducing battery aging rates; and dynamically adjusts the energy distribution scheme to reduce ineffective energy consumption. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the steps of the energy distribution optimization method for parking batteries in this application. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in specific embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0028] like Figure 1 As shown, this application provides a method for optimizing energy distribution of a parking battery, the method comprising the following steps:

[0029] S101, real-time acquisition of acceleration and angular velocity values ​​of the vehicle's three axes, and calculation of the initial flow direction of electrolyte inside the battery when the vehicle is parked;

[0030] S102, based on the initial flow direction and battery cavity geometry, uses a fluid dynamics simulation method to obtain the electrolyte velocity field, and calculates the electrolyte concentration gradient distribution based on the velocity field;

[0031] S103, If the concentration gradient in the concentration gradient distribution exceeds the preset threshold, the battery electrode region is divided, the activation ratio of the electrodes in each region is optimized, and the current density distribution of the battery is calculated.

[0032] S104. Based on the current density distribution and electrode material characteristic parameters, calculate the electrochemical reaction rate distribution of the battery; based on the electrochemical reaction rate distribution and the battery's internal impedance, use an equivalent circuit model to calculate the battery's real-time output power.

[0033] S105, obtain the real-time power demand of the vehicle's electrical equipment, and determine whether the real-time output power of the battery meets the real-time power demand of the electrical equipment.

[0034] S106 If the real-time output power of the battery meets the real-time power demand of the electrical equipment, then pulse width modulation technology is used to adjust the battery energy distribution to obtain the initial energy distribution scheme of the battery.

[0035] S107. Based on the initial energy distribution scheme of the battery, calculate the current density requirements of each electrical device and initially divide the activation area. If the current density requirements exceed the preset threshold, adjust the switching frequency of the activation area to obtain the optimized switching frequency distribution of the activation area. Based on the optimized switching frequency distribution of the activation area, dynamically adjust the power output of the battery management system to generate the real-time energy distribution scheme of the battery.

[0036] This application deeply integrates real-time vehicle environmental perception with the internal physical processes of the battery to achieve dynamic energy optimization. Through a closed-loop control mechanism, this application ensures precise matching between the battery's real-time output power and the demands of the electrical equipment, reducing power fluctuations; optimizes electrolyte distribution and current density to avoid localized overheating and deposition problems, thus reducing battery aging rates; and dynamically adjusts the energy distribution scheme to reduce ineffective energy consumption.

[0037] Furthermore, in step S101, calculating the initial flow direction of the electrolyte inside the battery when the vehicle is parked includes: generating a parking confirmation signal if the acceleration value is within a threshold; calculating the gravitational acceleration influence vector using a vector decomposition method based on the triaxial acceleration values; calculating the liquid tilt angle using a Kalman filter algorithm based on the gravitational acceleration influence vector and the triaxial angular velocity values; and simulating the initial flow direction of the battery electrolyte using the Navier-Stokes equations based on the liquid tilt angle and the battery electrolyte density and viscosity. In this application, the vehicle's triaxial acceleration values ​​are acquired using a triaxial accelerometer, and the triaxial angular velocity values ​​are acquired using a gyroscope. The accelerometer and gyroscope data are read via the vehicle's CAN bus at a sampling rate of 100Hz. For example, the triaxial accelerometer outputs ax = 0.2 m / s², ay = 0.1 m / s², and az = 9.8 m / s², while the gyroscope angular velocity outputs ωx = 0.01 rad / s, ωy = 0.02 rad / s, and ωz = 0.005 rad / s. In another embodiment, the acceleration and angular velocity values ​​of the vehicle's three axes are acquired using an integrated 6-axis motion processing unit (MPU-6050), and the data is transmitted from the integrated 6-axis motion processing unit to an embedded processor (such as an STM32 chip) via the I2C protocol.

[0038] In this application, the effect of gravitational acceleration is determined through vector decomposition. The dominant component indicates that gravity is predominant along the Z-axis. A Kalman filter algorithm is used to fuse acceleration and angular velocity data, with filter parameters Q=0.01 and R=0.1. The state vector is iteratively updated to obtain the smoothed attitude angle. The liquid tilt angle when the vehicle is parked is calculated based on the acceleration component.

[0039] Pitch angle: ;

[0040] Roll angle: ;

[0041] Substitute the data to get , .

[0042] The initial flow direction of the electrolyte inside the battery is determined by the liquid tilt angle and the gravitational component. Assuming the electrolyte is a liquid LiPF6 solution with a density of... viscosity The flow direction can be estimated using a simplified model based on the Navier-Stokes equations: Among them, the gravitational component Through acceleration components and pitch angle and roll angle calculate.

[0043] Ignoring turbulence effects, the liquid along... Initial velocity in direction: .

[0044] Furthermore, in step S102, obtaining the velocity field of the electrolyte using a fluid dynamics simulation method includes: generating a mesh model of the battery electrolyte using the finite volume method based on the initial flow direction and the geometry of the battery cavity; and calculating the velocity distribution of the battery electrolyte using the Navier-Stokes equations based on the mesh model to obtain the velocity field of the battery electrolyte.

[0045] In this application, a three-dimensional battery cavity model is established using computational fluid dynamics (CFD) software (such as ANSYS Fluent). For example, the battery cavity is rectangular, 0.1 m long, 0.05 m wide, and 0.02 m high, with an initial electrolyte flow velocity of 0.01 m / s, flowing in along the positive x-axis. The simulation employs the finite volume method for discretization, with a mesh size of approximately 1 million tetrahedral elements, a time step of 0.001 s, and 1000 iterations to ensure convergence.

[0046] Specifically, the Navier-Stokes equations are: in, For density, For the velocity vector, For pressure, For dynamic viscosity, This is the acceleration due to gravity.

[0047] In this application, the flow field results of the battery electrolyte show that the flow velocity is relatively high near the inlet, reaching 0.012 m / s, the flow velocity in the central region of the cavity decreases to 0.008 m / s, and the flow velocity near the wall is close to 0.002 m / s due to the boundary layer effect.

[0048] Furthermore, in step S102, calculating the concentration gradient distribution of the electrolyte based on the velocity field includes: if the maximum velocity exceeds a preset threshold, then using the k-ε turbulence model to calculate the turbulence intensity distribution of the battery electrolyte; and calculating the concentration gradient using the convection-diffusion equation based on the electrolyte's velocity field, turbulence intensity distribution, and battery electrolyte density to obtain the electrolyte's concentration gradient distribution.

[0049] In this application, the analysis of turbulence intensity distribution shows that the turbulence intensity near the inlet is 5%, and drops to 2% in the central region, indicating that the turbulence is mainly concentrated at the inlet.

[0050] Specifically, the convection-diffusion equation is: ;

[0051] Where c is the electrolyte concentration and D is the diffusion coefficient.

[0052] In this application, the initial electrolyte concentration is 1 mol / L, and the diffusion coefficient is 1×10⁻⁻⁻⁻⁶. 9 m² / s. Concentration gradient results show that the concentration gradient near the inlet reaches 100 mol / m² / s. 4 The central region decreased to 20 mol / m 4 .

[0053] Furthermore, step S102 also includes: calculating the standard deviation of the concentration gradient; if the standard deviation of the concentration gradient exceeds a preset threshold, then optimizing the flow field of the electrolyte by adjusting the flow rate at the battery electrolyte inlet.

[0054] The standard deviation of the concentration gradient is calculated to quantify the uneven distribution of electrolyte deposition areas. In this application, the standard deviation of the concentration gradient is 0.15 mol / L, indicating that more electrolyte is deposited near the inlet and less is deposited further away. Considering battery performance requirements, the inlet flow rate is adjusted to 0.015 m / s to reduce the concentration gradient and minimize deposition unevenness.

[0055] Furthermore, in step S103, the battery electrode region is divided and the activation ratio of each region is optimized by: using the k-means clustering algorithm to divide the battery electrode region; calculating the deposition thickness of each electrode region; and if the deposition thickness exceeds a preset threshold, optimizing the activation ratio of each region's electrode by using a genetic algorithm.

[0056] In this application, the k-means clustering algorithm is used to divide the electrode into four regions. The center point of each region is calculated based on the concentration distribution, and the number of iterations is set to 10 to ensure that the division accuracy error is less than 5%. The deposition thickness of each region is analyzed through the battery management system (BMS). For example, if the thickness deviation of a certain region reaches 0.2 mm, exceeding the threshold of 0.1 mm, the optimization process is triggered. The electrode activation ratio is optimized through a genetic algorithm, with a population size of 50, a crossover probability of 0.8, a mutation probability of 0.01, and the objective function being to minimize the current density deviation, with the constraint that the total current remains constant at 1000 mA. After 20 iterations, the calculated activation ratios are 30% for region 1, 25% for region 2, 20% for region 3, and 25% for region 4, and the current density distribution deviation is reduced from the initial 15% to within 5%. After optimization, the BMS adjusts the energization ratio of the electrodes in each region through the controller, monitors the feedback data in real time, and verifies whether the concentration gradient and deposition thickness deviation have recovered to within the threshold. If the deviation still exceeds the standard, the system will automatically enter the next round of optimization, correlate with the battery health status (SOH) data, and adjust the upper limit of the number of cycles to 100 to avoid over-optimization leading to increased energy consumption.

[0057] Furthermore, in step S104, calculating the electrochemical reaction rate distribution of the battery includes: calculating the electrochemical reaction rate of the battery using the Butler-Volmer equation and calculating the electrochemical reaction rate distribution of the battery using the finite element analysis method; calculating the electrode temperature distribution using the heat conduction equation based on the electrochemical reaction rate distribution and the heat capacity of the electrode material; analyzing the dynamic changes of the electrode temperature distribution using numerical simulation method in conjunction with the electrode heat conduction performance; if the electrode temperature change rate exceeds a preset threshold, adjusting the current density distribution through an optimization algorithm and recalculating the electrochemical reaction rate distribution.

[0058] In this application, the battery electrode material is graphite with a conductivity of 1000 S / m and an electrode surface area of ​​0.01 m². The initial current density distribution is obtained by finite element method simulation and ranges from 100 to 500 A / m².

[0059] Specifically, the Butler-Volmer equation is as follows: ;in, Indicates current density, For exchange current density, and The anode and cathode transfer coefficients, It is Faraday's constant. For overpotential, The gas constant is This refers to absolute temperature.

[0060] In this application, the Faraday constant is... The current density is 96485 C / mol. For example, with a current density of 300 A / m² and an electron transfer number of 2, the electrochemical reaction rate of the battery is r = 300 / (2×96485) = 0.00155 mol / (m²•s). The overall electrochemical reaction rate distribution of the battery is obtained by integrating the current density distribution.

[0061] Specifically, the heat conduction equation is: ;in, For material density, For heat capacity, Thermal conductivity, As a heat source for the reaction, For temperature, For time.

[0062] In this application, the graphite electrode has a thermal conductivity of 2 W / (m•K), a material density of 2200 kg / m³, and a specific heat capacity of 710 J / (kg•K). For example, if i = 300 A / m² and σ = 1000 S / m, then... The value is 90 W / m³. The temperature distribution is solved using the finite difference method. Assuming an ambient temperature of 25°C and a boundary heat dissipation coefficient of 10 W / (m²•K), the simulated temperature at the electrode center is approximately 28.5°C, and at the edge it is approximately 26.2°C.

[0063] In step S104, the real-time output power of the battery is calculated using an equivalent circuit model. The equivalent circuit model is as follows: .

[0064] The voltage V is estimated using the Nernst equation, and the total current I is obtained by integrating the current density. For example, with a standard potential of 1.2 V, a change in reactant concentration causing a potential decrease of 0.1 V results in a net voltage of 1.1 V. I = 300 × 0.01 = 3 A, and the power P = 1.1 × 3 = 3.3 W. Analysis shows that increasing current density leads to an increase in reaction rate and Joule heating, which in turn affects temperature distribution and output power. Electrode design needs to be optimized to balance performance.

[0065] In step S105, for example, the real-time power requirements of the air conditioning and auxiliary systems are obtained from the vehicle control unit, which can be done by reading data via the vehicle CAN bus protocol. Assume the current operating power of the air conditioning system is 2.5 kW, and the power of the auxiliary systems (such as interior lighting and audio) is 0.3 kW, for a total requirement of 2.8 kW. The reading process uses the OBD-II interface, collecting data once per second to ensure real-time performance. Data parsing uses the CAN frame parsing algorithm to extract the frame with ID 0x123, and the power value is decoded from the 3rd byte, with units of 0.1 kW / bit. Next, the electrode temperature distribution is obtained. Assume that data from 10 temperature sensors in the battery pack are collected through the battery management system (BMS), with a temperature range of 25°C to 40°C and an average temperature of 32.5°C. The temperature distribution is used to generate a continuous distribution curve through an interpolation algorithm (such as linear interpolation), with the formula T(x) = T_i + (T_{i+1} - T_i) * (x - x_i) / (x_{i+1} - x_i), where x is the electrode position. Based on the temperature distribution, the real-time output power capability of the battery is calculated using the formula P_max = P_nominal * f(T), where P_nominal is the nominal power (e.g., 50 kW), and f(T) is the temperature influence factor. Assuming f(T) = 1 - 0.005 * (T - 25), when T = 32.5°C, f(32.5) = 0.9625, P_max = 50 * 0.9625 = 48.125 kW. To determine if the power demand is met, P_max is compared with the demand power of 2.8 kW, and it is found that 48.125 kW > 2.8 kW, thus meeting the demand. The analysis process records the uniformity of temperature distribution. If the maximum temperature difference exceeds 15°C, an alarm is triggered, and the thermal management module of the BMS is invoked to reduce power output to prevent overheating.

[0066] Furthermore, in step S106, adjusting the battery energy distribution using pulse width modulation (PWM) technology to obtain the battery energy distribution scheme includes: calculating the electrode working efficiency based on the electrode activation ratio of each region in step 103; obtaining the deposition thickness of each electrode region; using a support vector machine (SVM) algorithm to classify and predict the battery energy output based on the electrode working efficiency and the deposition thickness of each electrode region to obtain a preliminary energy distribution scheme; calculating the output power of the preliminary energy distribution scheme; if the output power of the preliminary energy distribution scheme meets the real-time power demand of the electrical equipment, then generating a control signal through PWM technology to adjust the battery energy distribution ratio to obtain an optimized energy distribution scheme; adjusting the battery output power in real time according to the optimized energy distribution scheme; using a proportional-integral-derivative (PID) control algorithm to dynamically correct the output power fluctuation to obtain a stable output power; and continuously monitoring the electrode activation ratio and surface deposition state using a dynamic adjustment mechanism to update the energy distribution scheme.

[0067] For example, through electrochemical impedance spectroscopy analysis, assuming that 80% of the electrodes are in an activated state, the remaining 20% ​​are not fully activated due to polarization effects. The activation rate can be improved through pulse width modulation (PWM). The PWM technology is controlled at a frequency of 10kHz, with an initial duty cycle of 0.6. The calculated effective output power P_eff = P × duty cycle = 960 × 0.6 = 576W, which is lower than the requirement, necessitating dynamic adjustment of the duty cycle. Cyclic voltammetry is used for detection. Assuming a deposit coverage rate of 5%, which affects output efficiency, a short-duration high-frequency pulse (15kHz, duty cycle 0.8) is needed to remove the deposit, restoring 2% efficiency and increasing the power to 979.2W. With a target power of 1000W as a constraint, the PWM duty cycle is iteratively adjusted in increments of 0.05, calculating P_eff each time, until P_eff ≥ 1000W. After three iterations, the duty cycle reached 0.65, and P_eff = 960 × 0.65 = 624W, which was still insufficient. After removing the deposits, the power increased to 1010W, meeting the requirements. The final energy distribution scheme is: PWM frequency 10kHz, duty cycle 0.65, with periodic application of 15kHz high-frequency pulses to remove deposits and ensure stable output.

[0068] For example, in step S107, the BMS optimizes the power allocation based on real-time load data of the electrical equipment (e.g., equipment A requires 100W, equipment B requires 50W, and equipment C requires 30W), using a linear programming algorithm, with a total power limit of 160W. The algorithm formula is: Where Pi is the allocated power and Di is the required power, with the constraint ΣPi ≤ 160W. The calculation results show that device A is allocated 95W, device B is allocated 45W, device C is allocated 20W, and the remaining power is reserved. Next, the switching frequency of the activation area is dynamically adjusted by monitoring the battery state of charge (SOC). When the SOC drops from 80% to 70%, the switching frequency increases from 1Hz to 2Hz to reduce the risk of local overheating. The current density distribution is analyzed using the finite element method, assuming the battery surface is divided into 100 grid cells, with an initial current density of 0.5A / cm². Based on temperature feedback (e.g., areas with temperatures > 40°C), an iterative formula is used. Dynamic adjustment, where ΔT represents temperature deviation. Analysis shows that after adjusting the current density, the battery temperature variance decreased from 5°C to 2°C, and stability improved by 60%.

[0069] Furthermore, the energy distribution optimization method for parking batteries also includes the following steps: acquiring terrain data and temperature data collected by external sensors; calculating the complexity of the parking scenario and the trend of complexity change of the parking scenario based on the terrain data, temperature data, the gravitational acceleration influence vector and liquid tilt angle in step S101; and correcting the electrode activation ratio of each region and updating the initial energy distribution scheme by using a Kalman filter algorithm based on the trend of complexity change of the parking scenario and the turbulence intensity distribution in step S103.

[0070] For example, a LiDAR scanner is used to scan the parking area to acquire terrain point cloud data. Assuming a point cloud density of 1000 points per square meter and a scanning range of 50 meters × 50 meters, 2.5 million terrain height data points are generated. A point cloud processing algorithm (such as RANSAC) is used to fit the ground plane and calculate the slope. Assuming the slope range is 0° to 15°, slopes greater than 10° are marked as complex terrain. Simultaneously, an infrared thermometer collects the ambient temperature, assuming a temperature range of -10°C to 40°C. Temperatures below 0°C or above 35°C may affect sensor accuracy and require data correction. When assessing the complexity of the parking scenario, a comprehensive evaluation model is constructed: terrain slope weight 0.4, temperature influence weight 0.3, tilt angle weight 0.2, and gravity force weight 0.1. Assuming a slope of 10° scores 8, a temperature of 25°C scores 5, an inclination angle of 4.8° scores 6, and a force of 1710N scores 4, the total complexity score is 0.4×8 + 0.3×5 + 0.2×6 + 0.1×4 = 6.5 (out of 10). Through time series analysis, the score changes are recorded for 10 consecutive minutes. If the score continues to rise (e.g., from 6.5 to 7.0), the complexity is considered to be trending upwards.

[0071] For example, the current turbulent velocity is 0.5 m / s with a standard deviation of 0.1 m / s. The Kalman filter algorithm initializes the state vector, including the electrode activation ratio (initially set to 0.6) and the power allocation coefficient (initially set to 0.4). The state transition matrix is ​​an identity matrix, the observation matrix is ​​linearly mapped from the sensor data, and the noise covariance matrix Q is set to 0.01 and R to 0.05. The algorithm iterative process is as follows: In the prediction step, the state estimate for the next time step is calculated, with the predicted value for the electrode activation ratio being 0.62 and the predicted value for the power allocation coefficient being 0.41. In the update step, combined with the new observation data, the scene complexity change rate is 0.02, the turbulent velocity change rate is 0.03, the Kalman gain is calculated to be 0.45, and the electrode activation ratio is updated to 0.63 and the power allocation coefficient to 0.42.

[0072] Battery operational stability was assessed: the stability metric was defined as voltage fluctuation rate, with a target below 0.05V. The current voltage fluctuation rate was 0.07V. Through optimization of the electrode activation ratio (0.63) and energy allocation coefficient (0.42), simulations showed the voltage fluctuation rate decreased to 0.04V, meeting the target. Optimized parameters were sent to the battery management unit in real-time via the control system, adjusting the electrode activation ratio to 63% of the total and allocating 42% of energy to the main battery pack to ensure improved stability. The logic chain was as follows: sensor data drove Kalman filtering to dynamically adjust parameters, and the optimization results were fed back to the battery control system to reduce voltage fluctuation rate and improve operational stability. If the turbulence suddenly increased to 0.8 m / s, the algorithm would iterate again, predicting the electrode activation ratio would rise to 0.65 and the energy allocation coefficient would be adjusted to 0.43 to maintain stability.

[0073] This application also provides an energy distribution optimization system for a parking battery, which includes: an internal state monitoring and prediction module, an electrode regulation and power assessment module, a power demand matching determination module, an energy distribution strategy formulation module, and a dynamic energy output execution module.

[0074] The internal state monitoring and prediction module acquires the acceleration and angular velocity values ​​of the vehicle's three axes in real time and calculates the initial flow direction of the electrolyte inside the battery when the vehicle is parked. Based on the initial flow direction and the battery cavity geometry, the internal state monitoring and prediction module uses fluid dynamics simulation methods to obtain the electrolyte velocity field and calculates the electrolyte concentration gradient distribution based on the velocity field.

[0075] When the concentration gradient exceeds a preset threshold in the concentration gradient distribution, the electrode regulation and power assessment module divides the battery electrode regions and optimizes the activation ratio of each region, calculating the current density distribution of the battery. Based on the current density distribution and electrode material characteristic parameters, the module calculates the electrochemical reaction rate distribution of the battery. Finally, based on the electrochemical reaction rate distribution and the battery's internal impedance, the module uses an equivalent circuit model to calculate the battery's real-time output power.

[0076] The power demand matching and determination module obtains the real-time power demand of the vehicle's electrical equipment and determines whether the real-time output power of the battery meets the real-time power demand of the electrical equipment.

[0077] When the real-time output power of the battery meets the real-time power demand of the electrical equipment, the power allocation strategy formulation module uses pulse width modulation technology to adjust the battery energy allocation and obtain the initial energy allocation scheme of the battery. Based on the initial energy allocation scheme, the power allocation strategy formulation module calculates the current density demand of each electrical equipment and initially divides the activation area. When the current density demand exceeds the preset threshold, the switching frequency of the activation area is adjusted to obtain the optimized switching frequency distribution of the activation area.

[0078] The dynamic power output execution module dynamically adjusts the power output of the battery management system based on the optimized switching frequency distribution of the activated region, and generates and executes the real-time energy distribution scheme of the battery.

[0079] It should be noted that the terms "first," "second," and similar terms used in this application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, "a" or "one," and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. "A plurality" or "several" indicates at least two. Unless otherwise stated, terms such as "front," "back," "left," "right," "lower," and / or "upper" are for illustrative purposes only and are not limited to a location or spatial orientation. Terms such as "comprising" or "including" indicate that the elements or objects preceding "comprising" encompass the elements or objects listed following "comprising" or "including" and their equivalents, and do not exclude other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0080] The singular forms “a,” “the,” and “the” used in this application specification and appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0081] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for optimizing energy distribution of a parking battery, characterized in that, The method includes the following steps: S101, real-time acquisition of acceleration and angular velocity values ​​of the vehicle's three axes, and calculation of the initial flow direction of electrolyte inside the battery when the vehicle is parked; S102, based on the initial flow direction and battery cavity geometry, uses a fluid dynamics simulation method to obtain the electrolyte velocity field, and calculates the electrolyte concentration gradient distribution based on the velocity field; S103, If the concentration gradient in the concentration gradient distribution exceeds the preset threshold, the battery electrode region is divided, the activation ratio of the electrodes in each region is optimized, and the current density distribution of the battery is calculated. S104. Based on the current density distribution and electrode material characteristic parameters, calculate the electrochemical reaction rate distribution of the battery; based on the electrochemical reaction rate distribution and the battery's internal impedance, use an equivalent circuit model to calculate the battery's real-time output power. S105, obtain the real-time power demand of the vehicle's electrical equipment, and determine whether the real-time output power of the battery meets the real-time power demand of the electrical equipment. S106 If the real-time output power of the battery meets the real-time power demand of the electrical equipment, then pulse width modulation technology is used to adjust the battery energy distribution to obtain the initial energy distribution scheme of the battery. S107. Based on the initial energy distribution scheme of the battery, calculate the current density requirements of each electrical device and initially divide the activation area. If the current density requirements exceed the preset threshold, adjust the switching frequency of the activation area to obtain the optimized switching frequency distribution of the activation area. Based on the optimized switching frequency distribution of the activation area, dynamically adjust the power output of the battery management system to generate the real-time energy distribution scheme of the battery.

2. The energy distribution optimization method for parking batteries according to claim 1, characterized in that, In step S101, calculating the initial flow direction of the electrolyte inside the battery when the vehicle is parked includes: generating a parking confirmation signal if the acceleration value is within a threshold; calculating the gravitational acceleration influence vector using a vector decomposition method based on the acceleration values ​​of the three axes; calculating the liquid tilt angle using a Kalman filter algorithm based on the gravitational acceleration influence vector and the angular velocity values ​​of the three axes; and simulating the initial flow direction of the battery electrolyte using the Navier-Stokes equations based on the liquid tilt angle and the battery electrolyte density and viscosity.

3. The energy distribution optimization method for parking batteries according to claim 2, characterized in that, In step S102, obtaining the flow velocity field of the electrolyte using a fluid dynamics simulation method includes: generating a mesh model of the battery electrolyte using the finite volume method based on the initial flow direction and the geometry of the battery cavity; and calculating the flow velocity distribution of the battery electrolyte using the Navier-Stokes equations based on the mesh model to obtain the flow velocity field of the battery electrolyte.

4. The energy distribution optimization method for parking batteries according to claim 3, characterized in that, In step S102, the calculation of the concentration gradient distribution of the electrolyte based on the flow velocity field includes: if the maximum flow velocity exceeds a preset threshold, the k-ε turbulence model is used to calculate the turbulence intensity distribution of the battery electrolyte; based on the flow velocity field, turbulence intensity distribution, and battery electrolyte density, the concentration gradient is calculated using the convection-diffusion equation to obtain the concentration gradient distribution of the electrolyte.

5. The energy distribution optimization method for parking batteries according to claim 4, characterized in that, Step S102 further includes: calculating the standard deviation of the concentration gradient; if the standard deviation of the concentration gradient exceeds a preset threshold, then optimizing the flow field of the electrolyte by adjusting the flow rate at the inlet of the battery electrolyte.

6. The energy distribution optimization method for parking batteries according to claim 1, characterized in that, In step S103, the process of dividing the battery electrode region and optimizing the activation ratio of each region includes: dividing the battery electrode region using a k-means clustering algorithm; calculating the deposition thickness of each electrode region; and if the deposition thickness exceeds a preset threshold, optimizing the activation ratio of each region using a genetic algorithm.

7. The energy distribution optimization method for parking batteries according to claim 1, characterized in that, In step S104, calculating the electrochemical reaction rate distribution of the battery includes: calculating the electrochemical reaction rate of the battery using the Butler-Volmer equation and calculating the electrochemical reaction rate distribution of the battery using the finite element analysis method; calculating the electrode temperature distribution using the heat conduction equation based on the electrochemical reaction rate distribution and the heat capacity of the electrode material; analyzing the dynamic changes of the electrode temperature distribution using numerical simulation method in conjunction with the electrode heat conduction performance; if the electrode temperature change rate exceeds a preset threshold, adjusting the current density distribution through an optimization algorithm and recalculating the electrochemical reaction rate distribution.

8. The energy distribution optimization method for parking batteries according to claim 1, characterized in that, In step S106, the step of adjusting the battery energy distribution using pulse width modulation (PWM) technology to obtain the battery energy distribution scheme includes: calculating the electrode working efficiency based on the electrode activation ratio of each region in step 103; obtaining the deposition thickness of each electrode region; using a support vector machine (SVM) algorithm to classify and predict the battery energy output based on the electrode working efficiency and the deposition thickness of each electrode region to obtain a preliminary energy distribution scheme; calculating the output power of the preliminary energy distribution scheme; if the output power of the preliminary energy distribution scheme meets the real-time power demand of the electrical equipment, then generating a control signal using PWM technology to adjust the battery energy distribution ratio to obtain an optimized energy distribution scheme; adjusting the battery output power in real time according to the optimized energy distribution scheme; using a proportional-integral-derivative (PID) control algorithm to dynamically correct the output power fluctuation to obtain a stable output power; and continuously monitoring the electrode activation ratio and surface deposition state using a dynamic adjustment mechanism to update the energy distribution scheme.

9. The energy distribution optimization method for parking batteries according to claim 4, characterized in that, The energy distribution optimization method for the parking battery further includes the following steps: acquiring terrain data and temperature data collected by external sensors; calculating the complexity of the parking scenario and the trend of complexity change of the parking scenario based on the terrain data, temperature data, the gravitational acceleration influence vector and liquid tilt angle in step S101; and correcting the electrode activation ratio of each region and updating the initial energy distribution scheme by using a Kalman filter algorithm based on the trend of complexity change of the parking scenario and the turbulence intensity distribution in step S103.

10. An energy distribution optimization system for parking batteries, characterized in that, The energy distribution optimization system for the parking battery includes: An internal state monitoring and prediction module acquires the acceleration and angular velocity values ​​of the vehicle's three axes in real time, calculates the initial flow direction of the electrolyte inside the battery when the vehicle is parked, and uses a fluid dynamics simulation method to obtain the electrolyte's velocity field based on the initial flow direction and the battery cavity geometry, and calculates the electrolyte's concentration gradient distribution based on the velocity field. The electrode control and power evaluation module divides the battery electrode regions and optimizes the activation ratio of each region when the concentration gradient exceeds a preset threshold in the concentration gradient distribution, and calculates the current density distribution of the battery; based on the current density distribution and electrode material characteristic parameters, it calculates the electrochemical reaction rate distribution of the battery; and based on the electrochemical reaction rate distribution and the battery internal impedance, it uses an equivalent circuit model to calculate the real-time output power of the battery. The power demand matching and determination module obtains the real-time power demand of the vehicle's electrical equipment and determines whether the real-time output power of the battery meets the real-time power demand of the electrical equipment. The power allocation strategy formulation module adjusts the battery energy allocation using pulse width modulation technology when the real-time output power of the battery meets the real-time power demand of the electrical equipment to obtain an initial energy allocation scheme for the battery; and calculates the current density demand of each electrical equipment according to the initial energy allocation scheme and initially divides the activation area. When the current density demand exceeds a preset threshold, the switching frequency of the activation area is adjusted to obtain an optimized switching frequency distribution of the activation area. The dynamic power output execution module dynamically adjusts the power output of the battery management system based on the switching frequency distribution of the optimized activation region, and generates and executes a real-time energy distribution scheme for the battery.

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