Vehicle-mounted direct-current two-way charging and discharging control method for electric vehicle

Abstract

The invention relates to the technical field of electric vehicle power devices and energy management, in particular to an electric vehicle-mounted direct-current two-way charging and discharging control method which comprises the steps that 1, multi-source parameters are collected in real time, and scenes are recognized; step 2, battery stress adaptive modeling: calculating a dynamic stress factor reflecting the ion migration rate in the battery based on the electrolyte temperature gradient value and the single internal resistance change value; determining the maximum allowable current change slope according to the dynamic stress factor and the battery health state; step 3, scene differentiation charging and discharging control; 4, cooperative execution and safety protection: uploading the power plan of the vehicle to a cloud end, receiving a cooperative correction factor, and calculating final execution power in combination with a temperature attenuation coefficient; and controlling the charging and discharging process according to the final execution power and the current change slope, and starting balance control when the voltage difference of the single batteries exceeds the limit. Scene adaptability, battery protection and multi-vehicle cooperative control in the charging and discharging process of the electric vehicle are systematically optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle power devices and energy management, and in particular to a method for controlling on-board direct current bidirectional charging and discharging of an electric vehicle. Background Art

[0002] With the rapid growth of electric vehicles, their potential as distributed mobile energy storage units is becoming increasingly prominent. Bidirectional charging and discharging systems based on V2G (vehicle-to-grid) technology can achieve functions such as grid peak regulation, home power supply, and emergency support, and have become a core technology direction in the field of new energy vehicles. However, existing bidirectional charging and discharging control methods have three inherent drawbacks:

[0003] Insufficient scenario adaptability: Traditional control strategies use fixed parameter models and fail to differentiate between the differentiated needs of scenarios such as grid frequency regulation (requiring millisecond-level response), home power supply (requiring kilowatt-level stable power output), and emergency support (requiring high-rate instantaneous discharge). For example, grid frequency regulation requires rapid power adjustment, while home power supply requires long-term voltage stability. Existing technologies cannot dynamically switch control logic, resulting in batteries frequently experiencing mismatched current stress.

[0004] The conflict between battery life and grid demand: High-frequency grid dispatch accelerates battery aging. Existing technologies rely on fixed current threshold limits (e.g., constant-current charging with a fixed cutoff voltage at a nominal value), failing to account for changes in internal characteristics (such as decreased ion diffusion rate and increased internal resistance) caused by battery state of health (SOH) degradation. Statistics show that in grid peak-shaving scenarios, traditional control strategies result in an average annual battery capacity degradation rate of up to 12%, far exceeding the 7% under single-charge mode.

[0005] Grid risks caused by the lack of multi-vehicle coordination: When multiple vehicles in a region charge and discharge simultaneously, the superposition of power may cause local resonance in the grid (such as 5-10kHz high-frequency oscillations caused by abnormal impedance matrix eigenvalues). However, existing technologies lack a power fluctuation prediction mechanism for vehicle-cloud collaboration and rely only on local voltage fluctuation cycle detection, which cannot suppress regional grid disturbances in advance.

[0006] Therefore, there is an urgent need for an on-board DC bidirectional charging and discharging control method for electric vehicles to solve the above problems. Summary of the Invention

[0007] Based on the above objectives, the present invention provides a method for controlling onboard DC bidirectional charging and discharging of an electric vehicle, comprising the following steps:

[0008] Step 1: Real-time acquisition of multi-source parameters and scene recognition:

[0009] Collect grid frequency deviation, voltage fluctuation period, battery cell internal resistance change, electrolyte temperature gradient and battery health status;

[0010] When the grid frequency deviation exceeds the dynamic frequency threshold and is within the peak-shaving range of electricity prices, it is identified as a grid frequency regulation scenario;

[0011] When the vehicle is located within the residential geofence and the charging pile output is in constant power mode, it is identified as a home power supply scenario;

[0012] When the grid voltage fluctuation period is shorter than the critical collapse period and the battery discharge rate exceeds the safety threshold, it is identified as an emergency support scenario;

[0013] Step 2: Battery stress adaptive modeling:

[0014] Based on the electrolyte temperature gradient and the change in the internal resistance of the cell, the dynamic stress factor reflecting the ion migration rate inside the battery is calculated;

[0015] Determine the maximum allowable current change slope based on the dynamic stress factor and battery health status;

[0016] Step 3: Scenario-specific charge and discharge control:

[0017] In the grid frequency regulation scenario, the grid frequency deviation value is converted into a power command value, and the current change slope is limited according to the proportion of the dynamic stress factor;

[0018] In home power supply scenarios, the charging cut-off voltage is adjusted based on the battery health status, and the power maintenance time is calculated;

[0019] In the emergency support scenario, the instantaneous discharge rate is limited according to the ion migration rate benchmark value, and a forced interruption time is generated;

[0020] Step 4: Collaborative execution and security protection:

[0021] Upload the vehicle's power plan to the cloud and receive the coordinated correction factor, and calculate the final execution power based on the temperature attenuation coefficient;

[0022] The charge and discharge process is controlled according to the final execution power and current change slope. When the battery cell voltage difference exceeds the limit, the balancing control is started.

[0023] Preferably, the method for determining the dynamic frequency threshold, the critical crash period, and the safety threshold in step 1 includes:

[0024] The dynamic frequency threshold is generated by analyzing the relationship between the frequency fluctuation amplitude in historical grid frequency modulation events and the stress change rate that the battery can withstand. Specifically, the standard deviation distribution of grid frequency fluctuations is statistically analyzed, and the frequency value corresponding to the intersection of the standard deviation distribution and the battery stress safety boundary curve is selected as the threshold reference. Then, linear compensation is made based on the current battery health status.

[0025] The critical collapse period is determined based on the relationship between the grid voltage drop rate and the maximum discharge capacity of the battery. Specifically, an inverse function model is established between the grid voltage drop rate and the instantaneous discharge rate of the battery. The shortest allowable response time to maintain grid stability is inferred from this inverse function model and used as the critical collapse period.

[0026] The safety threshold is obtained by fitting the discharge thermal runaway critical point curve of batteries with different aging degrees. Specifically, the minimum discharge rate that causes thermal runaway under various health states is measured in the battery aging experiment, and the safety threshold curve is generated by segmented interpolation according to the percentage of health state.

[0027] Preferably, the dynamic stress factor in step 2 is calculated by:

[0028] Establish the temperature gradient influence term: multiply the difference between the electrolyte temperature gradient value and the reference temperature gradient by the temperature sensitivity coefficient of the ion migration rate, which is determined by the slope of the temperature-ionic conductivity relationship curve in the battery electrochemical characteristics test;

[0029] Establish the internal resistance attenuation effect term: multiply the ratio of the cell internal resistance change to the initial internal resistance by the retardation coefficient of the internal resistance on ion diffusion. The retardation coefficient is calculated by converting the voltage drop change rate caused by the increase in internal resistance during the battery pulse test.

[0030] Dynamic stress factor = reference ion migration rate × (1 + temperature gradient effect term and internal resistance attenuation effect term).

[0031] Preferably, the method for adjusting the charging cut-off voltage in the home power supply scenario in step 3 includes:

[0032] Voltage compensation calculation: The voltage compensation amount is obtained based on the battery health status percentage in a preset voltage compensation mapping table. The voltage compensation mapping table is established by the correspondence between capacity decay and full charge voltage drop during aging battery cycle testing;

[0033] Charging cut-off voltage = nominal cut-off voltage - voltage compensation amount × health state attenuation coefficient, where the health state attenuation coefficient is calculated by the ratio of the current health state to the rated health state;

[0034] Power maintenance time = historical average power consumption time of household load × health status percentage × temperature attenuation factor. The temperature attenuation factor is obtained by looking up the temperature difference between the maximum temperature and the optimal operating temperature in the battery pack.

[0035] Preferably, the maximum allowable current change slope in step 2 is determined by:

[0036] Generation of aging voltage compensation: During the battery life cycle test, the maximum current change rate attenuation curve that the battery can withstand under different health states is calibrated. The current health state is input into the curve and interpolated to obtain the aging voltage compensation;

[0037] Stress safety margin calculation: The ratio of the dynamic stress factor to the rated stress factor is multiplied by the battery design safety factor, which is determined by the electrochemical safety margin data provided by the battery manufacturer;

[0038] Maximum allowable current change slope = reference current change slope × aging voltage compensation amount × stress safety margin.

[0039] Preferably, the temperature attenuation coefficient in step 4 is determined by:

[0040] Temperature impact weight calculation: The temperature difference between the highest and lowest temperature points in the battery pack is used to find the weight base on the temperature gradient-life decay relationship curve. This is then multiplied by the temperature sensitivity coefficient corresponding to the current health state to obtain the temperature impact weight. This sensitivity coefficient is determined by the proportional relationship between the temperature gradient and the capacity decay rate in the accelerated aging experiment.

[0041] Dynamic adjustment of the reference temperature value: The reference temperature value is updated monthly based on seasonal change data and the median of the battery's historical operating temperature;

[0042] Temperature attenuation coefficient = 1-(highest temperature point value-dynamic reference temperature value) × temperature influence weight.

[0043] Preferably, the generation of the synergy correction factor in step 4 includes:

[0044] Establishing a regional grid impedance model in the cloud: Based on the real-time geographic locations of all online vehicles, the grid node admittance matrix is ​​constructed, and the resonant frequency points are extracted through matrix eigenvalue decomposition.

[0045] Power fluctuation prediction: Map the planned power curve of each vehicle to the grid node according to its geographical location and calculate the theoretical fluctuation amplitude at the resonant frequency point;

[0046] Correction factor optimization: With the goal of suppressing the theoretical fluctuation amplitude of the resonant frequency point, a distributed optimization algorithm is used to reversely solve the power adjustment ratio of each vehicle and use it as a collaborative correction factor.

[0047] Preferably, when the battery cell voltage difference exceeds the limit in step 4, starting the balancing control specifically includes:

[0048] Gradient slope suppression strategy: When the voltage difference value exceeds the threshold for the first time, the current change slope is reduced to 50% of the original value; if it continues for more than a set time and still does not recover, it is further reduced to 30% and a static interval is inserted;

[0049] Static interval calculation: The difference between the voltage difference value and the threshold value is multiplied by the recovery time coefficient determined by the battery polarization characteristics. This coefficient is obtained by calibrating the voltage self-recovery rate in the battery relaxation test.

[0050] Active balancing trigger: The charge transfer circuit between battery cells is activated during the static interval. The transfer amount is determined by the voltage difference value and the cell capacity difference.

[0051] Preferably, controlling the charge and discharge process in step 4 specifically includes:

[0052] Dynamic adjustment of switching frequency: When the voltage difference between battery cells increases, the IGBT switching frequency is increased in proportion to the voltage difference change rate; when the difference decreases, the switching frequency is reduced according to the exponential decay law;

[0053] Dead time compensation: Dynamically adjust the dead time of the complementary switching signal based on the corresponding relationship between the power device junction temperature and the drive voltage. This corresponding relationship is established through device thermal characteristic test data.

[0054] Preferably, the method for identifying the peak-shaving interval of electricity prices in step 1 includes:

[0055] Dynamic electricity price threshold generation: Obtain the day-ahead electricity price curve published by the power grid company, calculate the moving average of the curve within a preset time window, and use 1.2 times the moving average as the peak-shaving benchmark threshold;

[0056] Peak shaving interval determination: When the real-time electricity price continuously exceeds the peak shaving benchmark threshold and the duration reaches the minimum peak shaving duration, it is determined to enter the peak shaving interval. The minimum peak shaving duration is obtained from historical frequency regulation event statistics.

[0057] Beneficial effects of the present invention:

[0058] 1. The scenario recognition and differentiated charge-discharge control method proposed in this invention can collect multiple parameters such as grid frequency, voltage fluctuation, and battery internal resistance in real time, accurately identify different scenarios such as grid frequency regulation, home power supply, and emergency support, and dynamically adjust the control logic. For example, in the grid frequency regulation scenario, the frequency deviation is converted into a power command, and the current change slope is limited according to the dynamic stress factor to achieve a rapid response in milliseconds; in the home power supply scenario, the charging cut-off voltage is adjusted to achieve a smooth output of kilowatt-level power; in the emergency support scenario, the instantaneous discharge rate is limited to meet the high-rate instantaneous discharge requirements, thereby effectively avoiding the battery from being subjected to mismatched current stress and improving system adaptability.

[0059] 2. This invention uses adaptive battery stress modeling, combining electrolyte temperature gradients and cell internal resistance changes to calculate dynamic stress factors and adjust charge and discharge parameters based on the battery's health status. For example, the maximum allowable current slope is calculated based on the dynamic stress factor to protect battery life. By adjusting the charge cutoff voltage and power hold time to adapt to the battery's health status, the impact of frequent grid dispatch on battery life is significantly reduced, thereby reducing the battery capacity decay rate.

[0060] 3. This invention achieves coordinated control among multiple electric vehicles by establishing a cloud-based regional grid impedance model and a power fluctuation prediction mechanism. When multiple vehicles are charging and discharging simultaneously, the system calculates the theoretical fluctuation amplitude at the resonant frequency and uses a distributed optimization algorithm to determine the coordinated correction factor. This adjusts the power output of each vehicle to prevent local grid resonance, effectively suppressing regional grid disturbances and improving grid stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0062] Figure 1 is a flow chart of the steps of the method of the present invention;

[0063] Figure 2 This is a flow chart of the steps for generating the synergy correction factor in step 4 of the method of the present invention;

[0064] Figure 3 This is a flow chart of the steps for starting balancing control when the battery cell voltage difference exceeds the limit in step 4 of the method of the present invention. DETAILED DESCRIPTION

[0065] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0066] See Figure 1-Figure 3, an embodiment of the present invention provides a method for controlling DC bidirectional charging and discharging of electric vehicles. In step 1, real-time collection of multi-source parameters and scene recognition are first performed. Through comprehensive analysis of the grid frequency deviation value, voltage fluctuation period, battery cell internal resistance change value, electrolyte temperature gradient value and battery health status, accurate identification of different application scenarios is achieved. For example, when the grid frequency deviation value exceeds a specific threshold and is in the electricity price peak regulation range, the system identifies the scenario as grid frequency regulation; when the vehicle is located in a residential area and the charging pile is operating in constant power mode, it is identified as home power supply; and when the voltage fluctuates violently and the discharge rate is high, it is identified as emergency support. Accurate scene recognition lays the foundation for subsequent charging and discharging control.

[0067] In step 2, adaptive battery stress modeling is performed. This step calculates a dynamic stress factor that reflects the ion migration rate within the battery and determines the maximum slope of the current change based on the battery's health status to ensure that the charging and discharging process does not cause excessive damage to the battery. By dynamically adjusting the current slope, this method effectively protects the battery, extends its service life, and resolves the conflict between battery life and grid demand.

[0068] In step 3, scenario-based charge and discharge control is implemented based on the identified scenarios. In grid frequency regulation scenarios, frequency deviations are converted into power commands, and the current slope is limited by combining stress factors to rapidly respond to grid demand. In home power supply scenarios, the charge cutoff voltage and power hold time are adjusted to ensure stable power output. In emergency support scenarios, battery and device safety is ensured by limiting the discharge rate and setting forced interruption times, providing reliable emergency power support when needed.

[0069] Finally, in step 4, coordinated execution and safety protection are implemented. The power plan is uploaded and the coordinated correction factor is received. The final execution power is optimized based on the temperature attenuation coefficient, ensuring the effectiveness of multi-vehicle coordinated operation. By controlling the balanced charging and discharging process, excessive battery cell voltage variations are prevented, thereby improving the safety and stability of system operation.

[0070] The present invention systematically optimizes scenario adaptability, battery protection, and multi-vehicle collaborative control during the charging and discharging process of electric vehicles, effectively solving the problems existing in the prior art.

[0071] In one possible implementation, first, in the process of determining the dynamic frequency threshold, by analyzing previous grid frequency modulation events, the correspondence between the frequency fluctuation amplitude and the stress change rate that the battery can withstand is studied in detail. In the specific implementation, the standard deviation distribution of the grid frequency fluctuation is first statistically analyzed, which is an effective means to quantify the characteristics of the grid frequency fluctuation. Then, the frequency value in the distribution that intersects with the battery stress safety boundary curve is identified and used as the threshold reference. On this basis, linear compensation is further performed in combination with the current health status of the battery to cope with changes in the battery's tolerance under different health states. This method can not only provide a more accurate threshold setting, but also dynamically adapt to the impact of grid frequency modulation changes on battery stress.

[0072] Secondly, determining the critical collapse period depends on the relationship between the grid voltage drop rate and the maximum discharge capacity of the battery. By establishing an inverse function model, we can clearly express the impact of the grid voltage drop rate on the instantaneous battery discharge rate. Using this model, we can infer the minimum allowable response time to maintain grid stability, namely the critical collapse period. This parameter is particularly important in emergency support scenarios because it provides a safe time window for the battery to respond quickly in emergencies, ensuring grid stability.

[0073] Finally, the safety threshold is derived from a study of the critical points of thermal runaway during discharge in batteries with varying degrees of aging. During battery aging experiments, the minimum discharge rates under various healthy conditions are measured and collected to extract information on the critical points that trigger thermal runaway. By interpolating this data in percentage segments, a safety threshold curve is generated. This curve provides an important safety reference for charge and discharge strategies, ensuring safety and reliability under varying battery aging conditions.

[0074] This precise parameter determination method significantly improves the system's responsiveness, flexibility, and safety during charge and discharge control. Through in-depth analysis of historical data and aging processes, this invention provides reliable parameter support under complex and changing grid and battery conditions, ensuring the safe and efficient operation of electric vehicles.

[0075] In one possible implementation, first, for the establishment of the temperature gradient influence term, the temperature gradient value of the electrolyte inside the battery is measured and compared with the reference temperature gradient to identify the difference. Temperature has a significant effect on the ion migration rate in the battery, and this sensitivity is quantified by the slope of the temperature-ionic conductivity relationship curve obtained in the electrochemical properties test. This slope shows the trend of change in ionic conductivity when the temperature changes, and is an important parameter for determining the temperature effect. In practical applications, when calculating the temperature gradient influence term, the temperature gradient difference needs to be multiplied by the above-mentioned sensitivity coefficient to quantify the influence of the temperature factor on the ion migration rate.

[0076] Secondly, establishing the influence term of internal resistance decay is also essential. Changes in the internal resistance of a single cell have a direct impact on battery performance and health, especially after long-term use. By measuring the rate of change of the internal resistance and comparing it with the initial internal resistance, the ratio is obtained. On this basis, the rate of change of the voltage drop caused by the increase in internal resistance is revealed through battery pulse testing, and the retardation coefficient of the internal resistance on ion diffusion is calculated. This coefficient plays a key role in measuring the impact of internal resistance changes on battery performance.

[0077] Ultimately, the calculation of the dynamic stress factor integrates the two aforementioned influencing terms. Using the baseline ion migration rate as a foundation, by introducing the effects of temperature gradient and internal resistance decay, a dynamic assessment of the battery state under different operating conditions is achieved. The formula is expressed as: The dynamic stress factor equals the baseline ion migration rate multiplied by (1 plus the temperature gradient effect and the internal resistance decay effect). This calculation method is simple and efficient in practice.

[0078] By considering battery performance under varying temperatures and internal resistance conditions, the dynamic stress factor assessment method designed in this paper helps better understand and monitor the battery's true operating state and potential risks. This not only improves the accuracy of electric vehicle charging and discharging control strategies, but also provides strong support for battery life management and safe operation.

[0079] In one possible implementation, the calculation of voltage compensation takes into account the battery's state of health, a key factor affecting its performance. This invention constructs a voltage compensation mapping table that correlates battery state of health percentages with corresponding voltage compensation amounts. This mapping table relies on extensive cycle test data from aged batteries, which documents the relationship between battery capacity decay and full-charge voltage drop. This allows for predicting changes in voltage compensation under different states of health, enabling appropriate adjustments to the charge cutoff voltage.

[0080] Next, the charge cutoff voltage is adjusted by subtracting the voltage compensation amount multiplied by the health state attenuation coefficient from the nominal cutoff voltage. The health state attenuation coefficient is determined by the ratio of the current battery health state to its rated health state. This adjustment mechanism allows the charge cutoff voltage to dynamically respond to changes in battery health state, avoiding potential safety hazards caused by overcharging and effectively extending the battery life.

[0081] In addition, the calculation of power maintenance time introduces the linkage between the historical average power consumption time of household loads and the battery health status and temperature factor. Specifically, the power maintenance time is calculated by multiplying the historical average power consumption time, the health status percentage and the temperature attenuation factor. In particular, the temperature attenuation factor is obtained by looking up the temperature difference table, which lists the impact of the temperature difference between the maximum temperature and the optimal operating temperature in the battery pack on battery performance. This step ensures that the battery can continue to stably power the home in different temperature scenarios. Whether in extremely low or high temperature environments, this calculation method can ensure that the battery's charging and discharging efficiency is maintained at the best state.

[0082] By using this method, the present invention not only enables precise management of the battery's charge cutoff voltage in home power supply scenarios, but also efficiently utilizes the battery's limited energy storage resources. By considering the impact of health and temperature on battery performance, this method effectively improves battery safety and service life, further tapping into the battery's potential and promoting the intelligent development of home energy management.

[0083] In one possible implementation, the method first addresses performance changes over the battery's lifecycle by determining an aging voltage compensation amount. Specifically, through extensive full-lifecycle testing, a decay curve is established to show the maximum current change rate that the battery can withstand under different health states. This decay curve describes the gradual decline in the battery's tolerance as it ages. This data is used to calibrate the aging voltage compensation amount for the battery at any given health state. During operation, the system interpolates the decay curve based on the current battery health state to calculate the corresponding aging voltage compensation amount to reflect the battery's current maximum safe current capability.

[0084] Then, the method mentions calculating the stress safety margin. This process needs to take into account the stresses that the battery is subjected to during actual use. These stresses include the impact caused by current changes and the chemical reactions inside the battery, which will affect the life and safety of the battery. The stress safety margin is calculated by the ratio of the dynamic stress factor to the rated stress factor, and then multiplied by a design safety factor. This design safety factor is usually provided by the battery manufacturer and is based on electrochemical safety boundary data derived from a large amount of experimental data, thereby ensuring the safety of the battery under maximum load.

[0085] The final maximum allowable current slope is calculated as: the baseline current slope multiplied by the aging voltage compensation and the stress safety margin. By combining these factors, the battery ensures effective output without exceeding safety limits under all operating conditions. Optimizing the current slope allows the battery to more intelligently adjust to changing external load demands while maintaining safe operation.

[0086] This approach has several beneficial effects. First, it extends battery life by properly controlling the rate of current change, reducing unnecessary battery wear. Second, it enhances battery operation safety by design-in a way that mitigates potential risks associated with sudden current changes. Finally, it improves the battery's adaptability across diverse usage scenarios, enabling stable operation in diverse scenarios, including home charging, vehicle operation, and even extreme climates. This intelligent current regulation strategy contributes to more efficient and sustainable battery management in electric vehicles.

[0087] In one possible implementation, first, in the process of determining the temperature attenuation coefficient, calculating the temperature impact weight is a key step. A battery pack usually has multiple temperature points, and the temperature difference between the highest temperature point and the lowest temperature point reflects the temperature gradient difference inside the battery. By using the temperature gradient and battery life attenuation relationship curve, the weight base corresponding to the difference can be found. This weight base reflects the potential impact of different temperature differences on battery life. On this basis, it is necessary to multiply it by the temperature sensitivity coefficient obtained according to the current health status. This coefficient is usually measured in an accelerated aging experiment, and it is derived from the proportional relationship between the capacity attenuation rate of the battery under different temperature gradients. This step ensures that the temperature impact weight not only takes into account the current temperature difference, but also combines the sensitivity of the current degradation state of the battery.

[0088] Secondly, the dynamic adjustment of the baseline temperature value fully considers seasonal variations and the battery's historical operating temperature conditions. Monthly adjustments are made based on seasonal variations in actual usage scenarios. Combined with analysis of past temperature data, the baseline temperature value is updated using the median temperature. This reflects the actual temperature characteristics experienced by the battery during long-term operation, making the temperature management strategy more timely and reliable.

[0089] The temperature attenuation coefficient is calculated as: 1 - (maximum temperature point value - dynamic reference temperature value) × temperature impact weight. By combining current operating conditions with long-term temperature trends, the negative impact of high temperatures on battery performance can be effectively limited.

[0090] The beneficial effects of this method are as follows: First, by optimizing temperature management, it effectively reduces damage to the battery caused by high temperatures, provides a milder operating environment for the battery, and avoids electrochemical side reactions exacerbated by high temperatures. Furthermore, through dynamic adjustment and weight calculation, it is possible to maintain continuous stability in battery performance under different environmental conditions. Ultimately, this refined temperature control strategy not only improves battery safety and extends service life, but also enhances the overall energy efficiency and adaptability of electric vehicles, enabling more reliable battery system operation in both tropical and cold regions.

[0091] In one possible implementation, a regional grid impedance model established in the cloud serves as the foundation for this purpose. The first step is to construct the grid's node admittance matrix based on the real-time geographic locations of all online vehicles. This matrix characterizes the grid's electrical characteristics. Using eigenvalue decomposition techniques, it accurately extracts resonant frequencies associated with the grid structure. Identifying these resonant frequencies is crucial for subsequent power management, as they are often potential sources of power oscillation and instability.

[0092] Furthermore, predicting power fluctuations is essential. By mapping the planned power curve of each EV to the corresponding grid node based on its geographic location, a holistic analysis is performed. This allows the theoretical power fluctuation amplitude that could occur at the grid's resonant frequency to be calculated. This information is crucial for implementing effective oscillation suppression measures.

[0093] Finally, the coordination correction factor is optimized using a distributed optimization algorithm. The goal is to suppress power fluctuations at the grid's resonant points. This algorithm determines the optimal power adjustment ratio for each vehicle and, through a reverse calculation, achieves collective coordination at the fleet level. These calculated ratios serve as the coordination correction factor, guiding each vehicle in adjusting its power output to minimize adverse impacts on the grid.

[0094] By dynamically adjusting the charging and discharging behavior of electric vehicles, instability in the power grid can be effectively suppressed, preventing power loss and equipment damage caused by resonance. Furthermore, this approach improves the overall efficiency of coordinated operation between the electric vehicle fleet and the power grid, reduces operating costs, and achieves higher levels of power quality management in the deep integration of new energy and smart grids. This optimization approach fully leverages the advantages of electric vehicles while also ensuring the sustainable operation of the power grid.

[0095] In one possible implementation, first, the gradient slope suppression strategy is activated when the voltage difference between the battery cells exceeds the set threshold for the first time. At this time, the current change rate is reduced to 50% of the original value by regulating the current change slope. The purpose of this operation is to alleviate the rapid charging and discharging fluctuations of the battery cells, thereby preventing excessive current fluctuations from burdening the internal structure of the battery or accelerating battery aging. If the voltage difference continues to exceed the set time and has not returned to normal, the current slope is further reduced to 30%, and a static interval is inserted. In this process, the gradual reduction in the current change rate can effectively suppress the expansion of the difference between battery cells and avoid damage to the battery due to excessive charging and discharging.

[0096] Secondly, the calculation of the rest interval is crucial. This interval is calculated based on the difference between the cell voltage variance and the threshold, and multiplied by a recovery time coefficient determined by the cell polarization characteristics. This coefficient is derived from the voltage self-recovery rate calibration during battery relaxation testing and accurately predicts the battery's voltage recovery rate during rest. This allows precise control of the rest time, allowing the battery sufficient time for voltage recovery, thereby effectively reducing cell voltage variance.

[0097] Finally, active balancing is an important supplementary process. During the rest interval, the inter-cell charge transfer circuit is activated to adjust the charge imbalance between the cells. The amount of charge transferred between cells is determined by the voltage and capacity differences between the cells. During this process, the battery's charge is evenly distributed across the cells, achieving balanced voltage across the cells.

[0098] The beneficial effect of this balancing control method is that it not only effectively reduces voltage differences between battery cells, but also prevents problems such as overheating and excessive charging and discharging caused by excessive voltage differences, thereby extending the battery life. Through this intelligent balancing strategy, the battery can maintain good performance and stability during the charging and discharging process, reducing the risk of rapid battery degradation, while improving the overall operating efficiency and safety of electric vehicles.

[0099] In one possible implementation, the switching frequency is first dynamically adjusted based on changes in battery cell voltage differences. When the voltage differences between battery cells increase, it indicates a growing imbalance between the cells. At this point, the frequency of charge and discharge regulation needs to be increased to quickly adjust the battery voltage differences. Therefore, increasing the IGBT switching frequency in proportion to the rate of change of the voltage differences can improve the response speed of current regulation, allowing the voltage differences between the cells to be controlled more quickly. When the battery cell voltage differences decrease, the battery is close to equilibrium. Excessively high switching frequencies can lead to unnecessary energy loss. Therefore, reducing the switching frequency according to the exponential decay law can reduce energy loss while avoiding excessive impact on the battery. This dynamic adjustment ensures the efficiency of the charge and discharge process while avoiding the negative effects of excessive charge and discharge on the battery.

[0100] Secondly, dead time compensation is designed to optimize the control performance of power devices, ensuring that the power switches switch at the optimal time during battery charging and discharging, avoiding efficiency losses and thermal effects caused by excessively long or short dead times. The dead time of the complementary switching signals is dynamically adjusted based on the relationship between the power device's junction temperature and the drive voltage. Specifically, the device's junction temperature affects its switching characteristics, and the switching performance of power devices varies at different temperatures. By testing the device's thermal characteristics and establishing a relationship between junction temperature and dead time, the dead time can be precisely adjusted based on real-time temperature changes, ensuring efficient operation of the power device under all operating conditions. This reduces energy loss caused by inappropriate dead time and minimizes thermal effects, thereby improving overall system efficiency and stability.

[0101] By dynamically adjusting the switching frequency and dead time, the battery's charge and discharge processes can be more precisely controlled, effectively suppressing variations in battery cell voltages and minimizing battery losses during the charge and discharge process. This improves power conversion efficiency and avoids excessive thermal loads caused by frequent switching or inappropriate dead time. This control method optimizes the charge and discharge efficiency of electric vehicle batteries, extending battery life and ensuring stable operation under various operating conditions.

[0102] In one possible implementation, first, the generation of the dynamic electricity price threshold is based on the day-ahead electricity price curve published by the power grid company. By obtaining the electricity price curve published by the power grid company, the fluctuation of electricity prices in different time periods can be reflected. In order to make the electricity price judgment more accurate and avoid misjudgment due to sudden fluctuations, the moving average of the curve within the preset time window is calculated. This approach can smooth the changing trend of electricity prices, eliminate the interference of short-term fluctuations, and make the judgment of electricity prices more stable. Then, 1.2 times the calculated moving average value is used as the peak-shaving benchmark threshold, which means that if the electricity price continues to exceed the benchmark threshold, it can be determined to have entered the peak-shaving interval. The purpose of this approach is to ensure that when the electricity price reaches a certain high level, the peak-shaving period can be identified in a timely manner to facilitate the reasonable arrangement of charging time.

[0103] Furthermore, when the real-time electricity price continuously exceeds the peak-shaving benchmark threshold and the duration reaches the minimum peak-shaving duration, the peak-shaving interval is determined. The minimum peak-shaving duration is derived from statistical data of historical frequency regulation events and represents the shortest duration of sustained high electricity prices during actual peak-shaving. This approach avoids unnecessary peak-shaving decisions caused by short-term price fluctuations. The peak-shaving interval is only considered in place when electricity prices remain high for an extended period of time, effectively mitigating the impact of short-term price fluctuations on charging strategies.

[0104] By accurately identifying peak-shaving intervals for electricity prices, charging burdens can be reduced during periods of higher electricity prices, while charging can be concentrated during periods of lower prices, effectively reducing charging costs and improving the economic efficiency of EV charging. Furthermore, the generation of dynamic electricity price thresholds and the determination of peak-shaving intervals can avoid misjudgments caused by excessive price fluctuations, making charging strategies more scientific and reasonable and contributing to more intelligent EV charging management. Furthermore, by statistically analyzing historical frequency modulation event data to determine the minimum peak-shaving duration, the system's adaptability to long-term electricity price trends can be enhanced, improving the accuracy of peak-shaving determinations and further optimizing the charging process.

[0105] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0106] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for controlling DC bidirectional charging and discharging of an electric vehicle, characterized in that: The following steps are involved: Step 1: Real-time acquisition of multi-source parameters and scene recognition: Collect grid frequency deviation, voltage fluctuation period, battery cell internal resistance change, electrolyte temperature gradient and battery health status; When the grid frequency deviation exceeds the dynamic frequency threshold and is within the peak-shaving range of electricity prices, it is identified as a grid frequency regulation scenario; When the vehicle is located within the residential geofence and the charging pile output is in constant power mode, it is identified as a home power supply scenario; When the grid voltage fluctuation period is shorter than the critical collapse period and the battery discharge rate exceeds the safety threshold, it is identified as an emergency support scenario; Step 2: Battery stress adaptive modeling: Based on the electrolyte temperature gradient and the change in the internal resistance of the cell, the dynamic stress factor reflecting the internal ion migration rate of the battery is calculated; Determine the maximum allowable current change slope based on the dynamic stress factor and battery health status; Step 3: Scenario-specific charge and discharge control: In the grid frequency regulation scenario, the grid frequency deviation value is converted into a power command value, and the current change slope is limited according to the proportion of the dynamic stress factor; In home power supply scenarios, the charging cut-off voltage is adjusted based on the battery health status, and the power maintenance time is calculated; In the emergency support scenario, the instantaneous discharge rate is limited according to the ion migration rate benchmark value, and a forced interruption time is generated; Step 4: Collaborative execution and security protection: Upload the vehicle's power plan to the cloud and receive the coordinated correction factor, and calculate the final execution power based on the temperature attenuation coefficient; The charge and discharge process is controlled according to the final execution power and current change slope. When the battery cell voltage difference exceeds the limit, the balancing control is started.

2. The electric vehicle onboard DC bidirectional charging and discharging control method according to claim 1, characterized in that: The method for determining the dynamic frequency threshold, the critical crash period, and the safety threshold in step 1 includes: The dynamic frequency threshold is generated by analyzing the relationship between the frequency fluctuation amplitude in historical grid frequency modulation events and the stress change rate that the battery can withstand. Specifically, the standard deviation distribution of grid frequency fluctuations is statistically analyzed, and the frequency value corresponding to the intersection of the standard deviation distribution and the battery stress safety boundary curve is selected as the threshold reference. Then, linear compensation is made based on the current battery health status. The critical collapse period is determined based on the relationship between the grid voltage drop rate and the maximum discharge capacity of the battery. Specifically, an inverse function model is established between the grid voltage drop rate and the instantaneous discharge rate of the battery. The shortest allowable response time to maintain grid stability is inferred from this inverse function model and used as the critical collapse period. The safety threshold is obtained by fitting the discharge thermal runaway critical point curve of batteries with different aging degrees. Specifically, the minimum discharge rate that causes thermal runaway under various health states is measured in the battery aging experiment, and the safety threshold curve is generated by segmented interpolation according to the percentage of health state.

3. The electric vehicle onboard DC bidirectional charging and discharging control method according to claim 1, characterized in that: The dynamic stress factor in step 2 is calculated as follows: Establish the temperature gradient influence term: multiply the difference between the electrolyte temperature gradient value and the reference temperature gradient by the temperature sensitivity coefficient of the ion migration rate, which is determined by the slope of the temperature-ionic conductivity relationship curve in the battery electrochemical characteristics test; Establish the internal resistance attenuation effect term: multiply the ratio of the cell internal resistance change to the initial internal resistance by the retardation coefficient of the internal resistance on ion diffusion. The retardation coefficient is calculated by converting the voltage drop change rate caused by the increase in internal resistance during the battery pulse test. Dynamic stress factor = reference ion migration rate × (1 + temperature gradient effect + internal resistance attenuation effect).

4. The electric vehicle onboard DC bidirectional charging and discharging control method according to claim 1, characterized in that: In step 3, the method for adjusting the charging cut-off voltage and calculating the power maintenance time in the home power supply scenario includes: Voltage compensation calculation: The voltage compensation amount is obtained based on the battery health status percentage in a preset voltage compensation mapping table. The voltage compensation mapping table is established by the correspondence between capacity decay and full charge voltage drop during aging battery cycle testing; Charging cut-off voltage = nominal cut-off voltage - voltage compensation amount × health state attenuation coefficient, where the health state attenuation coefficient is calculated by the ratio of the current health state to the rated health state; Power maintenance time = historical average power consumption time of household load × health status percentage × temperature attenuation factor. The temperature attenuation factor is obtained by looking up the temperature difference between the maximum temperature and the optimal operating temperature in the battery pack.

5. The electric vehicle onboard DC bidirectional charging and discharging control method according to claim 1, characterized in that: The maximum allowable current change slope in step 2 is determined by: Generation of aging voltage compensation: During the battery life cycle test, the maximum current change rate attenuation curve that the battery can withstand under different health states is calibrated. The current health state is input into the curve and interpolated to obtain the aging voltage compensation; Stress safety margin calculation: The ratio of the dynamic stress factor to the rated stress factor is multiplied by the battery design safety factor, which is determined by the electrochemical safety margin data provided by the battery manufacturer; Maximum allowable current change slope = reference current change slope × aging voltage compensation amount × stress safety margin.

6. The electric vehicle onboard DC bidirectional charging and discharging control method according to claim 1, characterized in that: The temperature attenuation coefficient in step 4 is determined as follows: Temperature impact weight calculation: The temperature difference between the highest and lowest temperature points in the battery pack is used to find the weight base on the temperature gradient-life decay relationship curve. This is then multiplied by the temperature sensitivity coefficient corresponding to the current health state to obtain the temperature impact weight. This sensitivity coefficient is determined by the proportional relationship between the temperature gradient and the capacity decay rate in the accelerated aging experiment. Dynamic adjustment of the reference temperature value: The reference temperature value is updated monthly based on seasonal change data and the median of the battery's historical operating temperature; Temperature attenuation coefficient = 1-(highest temperature point value-dynamic reference temperature value) × temperature influence weight.

7. The electric vehicle onboard DC bidirectional charging and discharging control method according to claim 1, characterized in that: The generation of the synergy correction factor in step 4 includes: Establishing a regional grid impedance model in the cloud: Based on the real-time geographic locations of all online vehicles, the grid node admittance matrix is ​​constructed, and the resonant frequency points are extracted through matrix eigenvalue decomposition. Power fluctuation prediction: Map the planned power curve of each vehicle to the grid node according to its geographical location and calculate the theoretical fluctuation amplitude at the resonant frequency point; Correction factor optimization: With the goal of suppressing the theoretical fluctuation amplitude of the resonant frequency point, a distributed optimization algorithm is used to reversely solve the power adjustment ratio of each vehicle and use it as a collaborative correction factor.

8. The electric vehicle onboard DC bidirectional charging and discharging control method according to claim 1, characterized in that: When the battery cell voltage difference exceeds the limit in step 4, starting the balancing control specifically includes: Gradient slope suppression strategy: When the voltage difference value exceeds the threshold for the first time, the current change slope is reduced to 50% of the original value; if it continues for more than a set time and still does not recover, it is further reduced to 30% and a static interval is inserted; Static interval calculation: The difference between the voltage difference value and the threshold value is multiplied by the recovery time coefficient determined by the battery polarization characteristics. This coefficient is obtained by calibrating the voltage self-recovery rate in the battery relaxation test. Active balancing trigger: The charge transfer circuit between battery cells is activated during the static interval. The transfer amount is determined by the voltage difference value and the cell capacity difference.

9. The electric vehicle onboard DC bidirectional charging and discharging control method according to claim 1, characterized in that: Controlling the charge and discharge process in step 4 specifically includes: Dynamic adjustment of switching frequency: When the voltage difference between battery cells increases, the IGBT switching frequency is increased in proportion to the voltage difference change rate; when the difference decreases, the switching frequency is reduced according to the exponential decay law; Dead time compensation: Dynamically adjust the dead time of the complementary switching signal based on the corresponding relationship between the power device junction temperature and the drive voltage. This corresponding relationship is established through device thermal characteristic test data.

10. The electric vehicle onboard DC bidirectional charging and discharging control method according to claim 1, characterized in that: The method for identifying the peak-shaving interval of electricity prices in step 1 includes: Dynamic electricity price threshold generation: Obtain the day-ahead electricity price curve published by the power grid company, calculate the moving average of the curve within a preset time window, and use 1.2 times the moving average as the peak-shaving benchmark threshold; Peak shaving interval determination: When the real-time electricity price continuously exceeds the peak shaving benchmark threshold and the duration reaches the minimum peak shaving duration, it is determined to enter the peak shaving interval. The minimum peak shaving duration is obtained from historical frequency regulation event statistics.

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