Battery state adaptive v2g power control method and corresponding products

Abstract

This application relates to the field of smart grid technology and provides a V2G power control method based on battery state adaptation. The method includes: establishing a communication connection with the battery management system (BMS) of the connected electric vehicle and acquiring battery state parameters and grid state parameters in real time; dynamically determining a safe operating boundary adapted to the current battery, including at least the SOC safe range, temperature safe range, and maximum allowable charge / discharge current, based on the battery type and state of health (SOH); calculating a theoretical power command to respond to grid regulation demands based on the grid state parameters; comparing the theoretical power command, battery state parameters, and safe operating boundaries, and dynamically generating and outputting an actual power control command limited by the safe operating boundaries based on the comparison results. The technical solution of this application achieves coordinated optimization of electric vehicle battery safety and grid service by dynamically adapting battery state and grid demands in power control.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, and in particular to a V2G power control method and corresponding products based on battery state adaptation. Background Technology

[0002] With the increasing proportion of renewable energy in the power system, the volatility and instability of the power grid are becoming increasingly prominent, creating an urgent need for rapid and flexible resource regulation. Vehicle-to-Grid (V2G) technology, as an emerging distributed energy storage resource utilization method, enables bidirectional energy interaction between electric vehicles and the power grid. It allows electric vehicles to feed power to the grid during peak hours and charge from the grid during off-peak hours, thus providing ancillary services such as peak shaving and frequency regulation, improving the economic efficiency and stability of grid operation. Therefore, achieving rapid and accurate power response of V2G charging stations to grid demand has become crucial for the large-scale application of this technology.

[0003] Existing V2G power control schemes typically focus on demand response on the grid side. A typical implementation involves the V2G charging station's control system receiving power commands from the grid dispatch center or cloud platform, and then using its internal power conversion unit to control the electric vehicle battery to charge or discharge according to these commands. This type of scheme treats the electric vehicle battery as an ideal, infinitely rechargeable, and uniformly characteristic energy storage unit, and its control logic primarily revolves around how to accurately and quickly track external power commands.

[0004] However, the aforementioned existing technical solutions have the following significant drawbacks: they completely ignore the inherent characteristics and real-time state of the electric vehicle battery itself as the energy carrier. The battery's charge and discharge capacity is not constant but strongly depends on various factors such as its current state of charge (SOC), temperature, state of health (SOH), and battery chemistry type (e.g., ternary lithium, lithium iron phosphate). For example, high-power charging and discharging when the battery's SOC is too low or too high will severely damage the battery's lifespan; power interaction when the battery temperature exceeds the safe range may trigger safety accidents such as thermal runaway; different types of batteries also have different maximum charge and discharge currents they can withstand. Because existing solutions do not use these key battery state parameters as core inputs for power control decisions, but only unidirectionally execute grid commands, they may force the battery to operate under unsafe conditions in practical applications. This not only accelerates battery degradation and shortens the electric vehicle's lifespan but also creates serious safety hazards, ultimately hindering the reliable promotion and large-scale application of V2G technology. Summary of the Invention

[0005] This application provides a V2G power control method and corresponding product based on battery state adaptation. By dynamically adapting the battery state and grid demand in power control, it achieves coordinated optimization of electric vehicle battery safety and grid service.

[0006] On the one hand, this application provides a V2G power control method based on battery state adaptation, the method comprising:

[0007] Establish a communication connection with the battery management system (BMS) of the connected electric vehicle and obtain the battery status parameters and grid status parameters in real time;

[0008] Based on the battery type and state of health (SOH), dynamically determine the safe operating boundaries that are compatible with the current battery, including at least the SOC safe range, temperature safe range, and maximum permissible charge and discharge current.

[0009] Based on the state parameters of the power grid, calculate the theoretical power command in response to the power grid regulation demand;

[0010] The theoretical power command, the battery state parameters, and the safe operating boundary are compared, and the actual power control command limited by the safe operating boundary is dynamically generated and output based on the comparison results.

[0011] On the other hand, this application provides a V2G power control device based on battery state adaptation, the device comprising:

[0012] The acquisition module is used to establish a communication connection with the battery management system (BMS) of the connected electric vehicle and acquire the status parameters of the battery and the grid in real time.

[0013] The determination module is used to dynamically determine, based on the battery type and state of health (SOH), at least including the SOC safety range, temperature safety range, and maximum permissible charge / discharge current safety operating boundaries adapted to the current battery.

[0014] The calculation module is used to calculate the theoretical power command in response to the grid regulation demand based on the state parameters of the grid.

[0015] The comparison module is used to compare the theoretical power command, the battery state parameters, and the safe operating boundary, and dynamically generate and output the actual power control command limited by the safe operating boundary based on the comparison result.

[0016] Thirdly, this application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the technical solution of the above-described V2G power control method based on battery state adaptation.

[0017] Fourthly, this application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described V2G power control method based on battery state adaptation.

[0018] As can be seen from the technical solution provided in this application, on the one hand, by establishing a communication connection with the battery management system (BMS) of the connected electric vehicle and obtaining the battery's status parameters in real time, the real-time operating status of the battery can be accurately and dynamically grasped, laying a data foundation for implementing battery status-based control. This allows power control decisions to be based on the battery's actual capabilities and status, rather than a preset, fixed model. On the other hand, based on the specific type and aging degree (SOH) of the connected battery, a set of safe operating boundaries adapted to the specific battery is dynamically and individually generated. This ensures that safety standards vary from vehicle to vehicle, improves the adaptability and accuracy of control, and can provide targeted protection benchmarks for electric vehicle batteries of different models and aging stages. Thirdly, the real-time status and personalized safety boundaries of the battery are integrated into the system. The power output command is coordinated with grid demand commands for decision-making and safety verification. The final power output command is limited by safe operating boundaries. Specifically, when the theoretical grid demand command is within the battery's safe operating capacity, it will be executed. However, if the theoretical command might cause the battery's operating state to exceed its individual safety boundaries, or if the required current exceeds its maximum allowable value, the output command will be dynamically limited within these boundaries. This establishes a dynamic and adaptive balance mechanism between meeting grid regulation needs and ensuring the battery's safe and healthy operation. This fundamentally avoids dangerous conditions such as overcharging, over-discharging, overheating, and overcurrent caused by forcibly responding to grid demands, effectively extending the lifespan of the vehicle battery and significantly improving the safety of the V2G interaction process. This provides key technical support for the large-scale, safe, and reliable application of V2G technology. In summary, the technical solution of this application achieves coordinated optimization of electric vehicle battery safety and grid service by dynamically adapting battery status and grid demand in power control. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the V2G power control method based on battery state adaptation provided in the embodiments of this application;

[0021] Figure 2 This is a schematic diagram of the structure of the V2G power control device based on battery state adaptation provided in the embodiments of this application;

[0022] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] In this specification, adjectives such as "first" and "second" are used only to distinguish one element or action from another, without necessarily requiring or implying any actual such relationship or order. Where circumstances permit, reference to an element or component or step (etc.) should not be construed as being limited to only one of the elements, components, or steps, but may be one or more of the elements, components, or steps, etc.

[0025] For ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn to actual scale.

[0026] Existing vehicle-to-grid (V2G) power control solutions typically focus on demand response on the grid side. A typical implementation involves the V2G charging station's control system receiving power commands from the grid dispatch center or cloud platform, and then controlling the electric vehicle battery to charge or discharge according to these commands via its internal power conversion unit. This type of solution treats the electric vehicle battery as an ideal, infinitely rechargeable, and uniformly characteristic energy storage unit, with its control logic primarily revolving around accurately and quickly tracking external power commands. However, these existing technologies suffer from a significant drawback: they completely ignore the inherent characteristics and real-time state of the electric vehicle battery itself as the energy carrier. The battery's charge / discharge capacity is not constant but strongly depends on various factors such as its current state of charge (SOC), temperature, state of health (SOH), and battery chemistry type (e.g., ternary lithium, lithium iron phosphate). For example, high-power charging and discharging when the battery's state of charge (SOC) is too low or too high can severely damage battery life; power interaction when the battery temperature exceeds the safe range may trigger safety accidents such as thermal runaway; different types of batteries also have different maximum charge and discharge currents they can withstand. Current solutions, because they do not use these critical battery state parameters as core inputs for power control decisions, but only unidirectionally execute grid commands, may force the battery to operate under unsafe conditions in practical applications. This not only accelerates battery degradation and shortens the lifespan of electric vehicles, but also creates serious safety hazards, ultimately hindering the reliable promotion and large-scale application of V2G technology.

[0027] To address the aforementioned problems in the prior art, this application proposes a V2G power control method based on battery state adaptation, the flowchart of which is attached. Figure 1 As shown, the main steps include S101 to S104, which are detailed below:

[0028] Step S101: Establish a communication connection with the battery management system (BMS) of the connected electric vehicle and obtain the battery status parameters and grid status parameters in real time.

[0029] Once an electric vehicle connects to a V2G charging station via a charging gun, the station's main control unit begins attempting to establish communication with the vehicle's Battery Management System (BMS). In one embodiment of this application, establishing a communication connection with the BMS of the connected electric vehicle can involve attempting to establish communication with the BMS via a high-speed Ethernet protocol. If Ethernet communication fails, the system automatically switches to the CAN bus protocol for degraded communication to ensure the reliability of the communication connection. This dual-protocol adaptive negotiation mechanism prioritizes high-speed Ethernet to ensure low-latency transmission of massive amounts of data (such as detailed data of multiple battery cells) and seamlessly degrades to the widely supported CAN bus protocol when the vehicle communication interface is incompatible. This significantly improves the communication compatibility and connection success rate of the V2G charging station for electric vehicles of different brands and models, laying a reliable foundation for subsequent data acquisition.

[0030] Once the communication connection is stable, the main control unit acquires the battery's status parameters in real time through the BMS communication module. These parameters form the data foundation for all subsequent decisions and include at least the battery's state of charge, real-time temperature, maximum allowable charge / discharge current, battery type identification, and health status, etc., which will be explained one by one below:

[0031] State of charge (SOC) refers to the percentage of remaining charge in a battery. Real-time temperature typically refers to the core or average temperature of the battery pack. The maximum permissible charge / discharge current is the instantaneous safe current limit calculated by the BMS based on the current state of the battery. The battery type identifier is used to distinguish between ternary lithium batteries, lithium iron phosphate batteries, and other chemical systems. The state of health (SOH) of a battery is a key indicator characterizing the degree of degradation of the battery's current maximum usable capacity relative to its factory rated capacity. It can be calculated in real time or periodically by the BMS using its built-in estimation algorithm based on the battery's long-term operating history data (e.g., cumulative charge / discharge capacity, internal resistance change curve, open-circuit voltage characteristics, etc.), or calculated by the main control unit based on parameters such as voltage, temperature, and historical cycle count using an algorithm (e.g., capacity decay method).

[0032] Meanwhile, the bidirectional power conversion unit continuously collects the state parameters of the power grid, mainly including the grid frequency, voltage amplitude, etc., to sense the operating status and regulation needs of the power grid.

[0033] After obtaining the above parameters, in order to ensure that control decisions are based on reliable data and to prevent erroneous control due to communication interference or false alarms from the BMS, this application further introduces a data cleaning process, that is, after acquiring the battery's state parameters in real time, Figure 1The example method may also include the following parameter reliability verification steps: performing mutation rate detection and / or reasonable range filtering on the real-time acquired SOC and temperature values; if the detected data mutation rate exceeds a threshold or the value exceeds the physical limit range, the acquired parameter is deemed invalid, and one of the following operations is triggered: enabling the previously valid cached parameter, or outputting an instruction to request the BMS to re-report the data. Specifically, mutation rate detection can calculate the rate of change between the current sampled value and one or more previous historical sampled values. For example, if the SOC jumps by more than 5% within a single control cycle, it is considered an unreasonable mutation, possibly due to communication interference. Reasonable range filtering refers to determining whether the data is within the physically possible range. For example, battery temperatures below -40℃ or above 100℃, and SOC less than 0% or greater than 100%, are all considered invalid data. These parameter reliability verification steps act like a "filter" before the data flows into the control core, effectively filtering out abnormal data noise, greatly improving the robustness and decision reliability of the entire control system, and avoiding drastic fluctuations in power commands or false protection caused by a single erroneous data entry.

[0034] Step S102: Based on the battery type and state of health (SOH), dynamically determine the safe operating boundaries that are compatible with the current battery, including at least the SOC safe range, temperature safe range, and maximum allowable charge / discharge current.

[0035] In existing technologies, V2G charging stations typically use fixed, uniform safety thresholds to constrain all vehicles. This ignores the fundamental differences between different battery chemistry systems (e.g., ternary lithium batteries have high energy density but relatively poor thermal stability, while lithium iron phosphate batteries have good thermal stability but slightly weaker low-temperature performance) and different life stages (new batteries have lower aging levels and can withstand greater stress). Using a fixed threshold either limits the capabilities of high-performance batteries or fails to adequately protect older batteries.

[0036] To address this issue, the dynamic determination of safe operating boundaries—including at least the SOC safety range, temperature safety range, and maximum permissible charge / discharge current—based on the battery type and State of Health (SOH) can be achieved through the following steps S1021 to S1023:

[0037] Step S1021: Obtain or parse the battery type identifier from the BMS of the connected electric vehicle.

[0038] Step S1022: If the battery type identifier indicates a ternary lithium battery, then the maximum allowable charge and discharge current is set to the first current threshold.

[0039] Step S1023: If the battery type identifier indicates a lithium iron phosphate battery, then the maximum allowable charge and discharge current is set to a second current threshold that is different from the first current threshold.

[0040] For example, in one specific embodiment, a first current threshold I_max1 = 1C (where C is the battery capacity) can be preset for ternary lithium batteries, and a second current threshold I_max2 = 1.2C can be preset for lithium iron phosphate batteries. This is because lithium iron phosphate batteries typically have better rate performance and thermal safety, so a relatively lenient current upper limit can be set. This allows for the full exploitation of the power potential of different types of batteries participating in V2G services while ensuring safety, thereby improving resource utilization efficiency.

[0041] To further improve the adaptation accuracy, this application also supports obtaining more refined parameters from the cloud, specifically including: obtaining optimized safety parameters that uniquely correspond to the vehicle model or battery model of the connected electric vehicle from the cloud server through the communication connection established in step S101; using the optimized safety parameters, correcting the first current threshold or the second current threshold preset based on the type identifier to obtain the maximum allowable charge and discharge current of the battery adapted to the connected electric vehicle.

[0042] The cloud can store a vast database of battery parameters, containing detailed test data for batteries of different brands, models, and even production batches, such as more precise DC internal resistance-temperature curves and capacity decay characteristics under different state of equilibrium (SOH). The "optimized safety parameters" can be a correction coefficient or a set of more specific thresholds. For example, for a certain model of high-performance ternary lithium battery, the cloud can issue a corrected current limit of 1.1C instead of the universal 1C. This represents a leap from "protection by type" to "precise protection by vehicle model and even by individual battery."

[0043] Besides current, other dimensions of the safe operating boundary also need to be dynamically determined. The general SOC safe range can be preset to 20%~90%, and the temperature safe range can be preset to -10℃ to 45℃. For aged batteries, the safe window should be narrowed. This is achieved through the treatment of SOH, which is a key step before power matching and will be detailed in the relevant sub-steps of step S104.

[0044] Furthermore, the battery's temperature safety range is not static and is significantly affected by the environment. In hot summers, high ambient temperatures make battery heat dissipation difficult, and the maximum tolerable temperature should be appropriately lowered. In cold winters, low temperatures reduce battery activity, and high-current charging can easily lead to lithium deposition, so the minimum tolerable temperature should be appropriately increased. Therefore, dynamically determining the safe operating boundaries for the current battery, including at least the SOC safety range, temperature safety range, and maximum permissible charge / discharge current, can also include: real-time monitoring of ambient temperature; and dynamic offset compensation of the upper and lower limits of the temperature safety range based on the ambient temperature, where the upper limit is appropriately narrowed in high-temperature environments and the lower limit is appropriately narrowed in low-temperature environments. For example, a compensation function can be set:

[0045] T_max_adj=T_max_default -α*(T_env-T_env_ref) (when T_env> T_env_ref);

[0046] T_min_adj=T_min_default +β*(T_env_ref-T_env) (when T_env< T_env_ref)

[0047] Where T_max_default and T_min_default are the default upper and lower limits, T_env is the ambient temperature, T_env_ref is the reference ambient temperature (e.g., 25℃), and α and β are positive compensation coefficients. This scheme allows the safety boundary to "breathe" with changes in the environment, achieving more intelligent and realistic protection of battery thermal safety.

[0048] Step S103: Calculate the theoretical power command to respond to grid regulation requirements based on the grid state parameters.

[0049] Step S103 embodies the core function of V2G serving the power grid. The main control unit receives the target power command P_cmd and / or Q_cmd from the cloud platform or regional controller via a communication module (e.g., 4G / 5G, Ethernet). This command may be generated to respond to regional regulation needs, participate in peak shaving and valley filling, or track fluctuations in renewable energy output. At this time, the theoretical power command P_ref is directly set as follows:

[0050] P_ref = P_cmd, Q_ref = Q_cmd (if independent reactive power control is supported)

[0051] The above plan will Figure 1 The example method extends from local autonomous control to wide-area collaborative control, enabling it to participate in centralized grid optimization scheduling and achieve more complex ancillary service functions, such as automatic generation control (AGC) tracking and demand-side response.

[0052] Step S104: Compare the theoretical power command in response to grid regulation requirements, the battery state parameters, and the safe operating boundary, and dynamically generate and output the actual power control command limited by the safe operating boundary based on the comparison results.

[0053] Step S104 is the core of this application's battery safety feedforward protection. Existing technologies directly use the P_ref calculated in step S103 as the final command output, while this application adds a "safety arbitration" step. Specifically, as an embodiment of this application, comparing the theoretical power command, battery state parameters, and safe operating boundaries, and dynamically generating and outputting the actual power control command limited by the safe operating boundaries based on the comparison results, can be achieved through steps S1041 to S1045, as detailed below:

[0054] Step S1041: Basic security verification.

[0055] Specifically, dynamically generating and outputting actual power control commands limited by safe operating boundaries based on comparison results can be achieved by: determining whether the battery's real-time SOC is within the safe SOC range to obtain a first judgment result, and determining whether the battery's real-time temperature is within the safe temperature range to obtain a second judgment result; if either the first or second judgment result is negative, then generating and outputting a command to pause power output; if both the first and second judgment results are positive, then entering the power matching process. This scheme serves as the first line of defense to ensure the battery does not operate under extreme conditions. Immediately pausing power output upon detecting a breach is the most direct and effective protection. After pausing, following the generation and output of the command to pause power output, the following recovery monitoring steps are also included: continuously monitoring the abnormal state parameters that caused the output pause; when all abnormal state parameters have recovered to their corresponding safe ranges and remain stable for a preset delay time, automatically restarting the power matching process, generating and outputting a new actual power control command. The purpose of introducing a "delay time" (such as 30 seconds) in the above embodiments is to prevent the power output from frequently starting and stopping when the battery state fluctuates near the boundary, which protects the battery and grid equipment and improves the continuity of user experience.

[0056] Step S1042: Battery aging status processing.

[0057] If a battery passes the basic safety check, it indicates that its State of Charge (SOC) and temperature are within a safe window. However, for batteries with a declining State of Health (SOH), their internal chemical activity decreases and internal resistance increases. Using the maximum permissible charge / discharge current set for new batteries may accelerate aging or even cause localized overheating. Therefore, aging compensation is necessary before entering the power matching process. Before entering the power matching process (which compares the theoretical power command with a current limit value to generate the actual power control command, detailed later), Figure 1The exemplary method may further include a processing step for the battery aging state, that is: obtaining or calculating the current State of Health (SOH) value of the battery; mapping the current SOH value of the battery to a preset current decay coefficient curve to obtain the corresponding real-time decay coefficient K, where 0 < K ≤ 1, and the lower the SOH value, the smaller the K value; multiplying the maximum allowable charge-discharge current I_max by the real-time decay coefficient K to obtain the maximum allowable charge-discharge current I_max_adj after aging compensation for subsequent power matching. It should be noted that the current decay coefficient curve defines the functional relationship between SOH and the current decay coefficient K. This curve can be obtained by fitting battery aging experimental data. A typical mapping relationship can be a piecewise linear function: when SOH ≥ 90%, K = 1.0; when 80% ≤ SOH < 90%, K linearly decreases from 1.0 to 0.9; when SOH < 80%, a steeper descent slope is adopted. For example, K = 0.7 + 0.3 * (SOH / 100) (when SOH < 80%). This method dynamically converts the long-term performance indicator of the battery health into an immediate limit on the instantaneous power capacity, achieving a "gentle" use of the aging battery and effectively delaying its further degradation rate.

[0058] Step S1043: Power matching process.

[0059] Step S1043 is the core step for finally arbitrating between the grid demand and the battery safety capability. As mentioned above, the power matching process is used to compare the theoretical power command with a current limit value to generate an actual power control command, specifically including: converting the theoretical power command into the corresponding desired current I_ref; comparing the absolute value of the desired current I_ref with the maximum allowable charge-discharge current or the maximum allowable charge-discharge current I_max_adj after aging compensation; if |I_ref| ≤ the maximum allowable charge-discharge current I_max or the maximum allowable charge-discharge current I_max_adj after aging compensation, then output the theoretical power command as the actual power control command; if |I_ref| > the maximum allowable charge-discharge current I_max or the maximum allowable charge-discharge current I_max_adj after aging compensation, then output the power value corresponding to I_max or I_max_adj as the actual power control command. In the above embodiments, the current conversion can be achieved through I_ref = P_ref / V_bus, where V_bus is the DC bus voltage and can be obtained from the bidirectional power conversion unit; and the comparison and decision implement a "limiting" control, that is, if the current demanded by the grid is within the battery safety current limit, the grid demand is fully satisfied; if the grid demand exceeds the safety limit, then ensuring the battery safety is prioritized and the maximum safe current is output. This effectively eliminates the possibility of overusing the battery and establishes a clear and reliable priority level between the grid frequency regulation demand and the battery body safety.

[0060] However, comparing only the instantaneous current value may still pose a thermal risk. The battery's temperature rise is not only related to the current but also closely related to the battery's internal resistance. A large current flowing through the internal resistance generates ohmic heat; if this heat cannot be dissipated in time, it will cause the battery temperature to rise. To address this potential risk, this application introduces a more refined internal resistance compensation step. Specifically, comparing the theoretical power command, the battery's state parameters, and the safe operating boundary includes the following internal resistance compensation steps: real-time acquisition or estimation of the battery's current internal resistance R; calculation of the estimated temperature rise ΔT under the current command based on the desired current I_ref and the current internal resistance R, where the desired current I_ref is the current converted from the theoretical power command; if the sum of the estimated temperature rise ΔT and the battery's real-time temperature T exceeds the upper limit of the temperature safety range, the maximum allowable charge / discharge current is further reduced to generate a more conservative safe current limit for power matching.

[0061] The internal resistance R in the above embodiment can be obtained directly by reading from the BMS, or estimated online by applying a small pulse current and measuring the voltage response. The estimated temperature rise ΔT can be calculated using a simplified thermal model, for example, ΔT = (I_ref * I_ref * R * Δt) / (m * C), where Δt is the estimated power duration (e.g., the next control cycle), m is the battery mass, and C is the battery specific heat capacity. To simplify online calculation, a lookup table of the relationship between I_ref * I_ref * R and temperature rise can be pre-established. If T + ΔT > T_max (T_max is a dynamically determined upper limit of safe temperature), it means that although the current I_ref does not immediately exceed the temperature limit, it will quickly lead to future temperature exceedance. At this time, the system will back-calculate a maximum allowable current I_thermal_safe that makes T + ΔT ≤ T_max, and use it to replace the original I_max or I_max_adj for power matching in step S1043. This step elevates the protection action from "post-event response" to "pre-event prevention." By predicting future temperature rises, it avoids the risks that heat accumulation may bring in advance, achieving proactive management of battery thermal safety.

[0062] Step S1044: Power output smoothing processing.

[0063] In the power matching process, if the decision requires limiting the output power to the power value corresponding to I_max (or I_max_adj), and this value differs significantly from the original theoretical command or the current actual output power, a direct step jump to the target power would impact the battery and the power grid. To mitigate this impact, when using the power value corresponding to I_max or I_max_adj as the actual power control command output, a smoothing process is also included: controlling the output power corresponding to the actual power control command so that it gradually transitions from the current output power to the target power value according to a smooth curve within a preset transition time, thereby reducing the impact on the battery and the power grid. The smoothing curve can be an S-curve (Sigmoid function), a polynomial curve, or a linear ramp. For example, a first-order inertial element P_out(s) = P_target / (τs+1) can be used for filtering, where τ is a time constant corresponding to the transition time. In a discrete system, P_out(k) = P_out(k-1) + (P_target - P_out(k-1)) / N, where N is the number of steps required to reach the target. This step ensures the continuity of power changes, avoids abrupt changes in voltage and current, protects the battery and power devices, and makes V2G more "friendly" to the power grid, thus improving power quality.

[0064] Step S1045: Dynamic looping and updating.

[0065] Figure 1 The example method executes in a variable-cycle manner, dynamically adjusting the cycle based on the rate of change in battery state and the intensity of grid demand fluctuations. This includes: automatically shortening the control cycle to a first short cycle (e.g., 50ms) when the battery temperature change rate exceeds a set value or when there are drastic changes in grid frequency deviation; and automatically extending the control cycle to a second long cycle (e.g., 200ms) when the battery state is stable and grid demand is steady, where the second long cycle is longer than the first short cycle. This adaptive cycle mechanism increases the control frequency to enhance protection during rapidly changing, high-risk operating conditions and decreases the frequency to reduce the controller's computational load during stable operating conditions, achieving efficient and intelligent allocation of control resources.

[0066] When V2G stubs are deployed at scale, the optimization of individual stubs needs to be elevated to system-level optimization. Therefore, Figure 1The example method may also include the following collaborative control steps: When multiple V2G piles executing this method are detected running simultaneously in the same power distribution network, the master control units of each V2G pile interact with each other or through the cloud; the battery status and power capabilities of each connected electric vehicle are coordinated, and the power commands of each V2G pile are optimized and allocated under the premise of meeting the overall grid regulation requirements, prioritizing the use of vehicles with better battery status for power support. For example, after receiving the total regulation power P_grid_total requirement, the cloud coordinator collects the available power (i.e., the maximum output / input power under the current safety boundary) and battery SOH reported by each pile. Then, with the goal of minimizing overall battery loss or maximizing the contribution of batteries with higher SOH, the optimization problem is solved, and P_grid_total is decomposed into P_ref_i issued to each pile. This optimizes resource scheduling at the cluster level, minimizing the impact of V2G services on the overall battery health of the fleet while meeting grid requirements, thus improving economic efficiency.

[0067] To achieve self-evolution of control strategies and transparency of services, Figure 1 The example method may also include the following historical data recording and learning steps: recording the sequence of key parameters for each power control process, including the battery's SOC, temperature, SOH, actual output power, and corresponding grid state; based on historical data, using a machine learning model to train and optimize a personalized safe operating boundary prediction model for the battery of the connected electric vehicle, which is used to more accurately and dynamically determine the safe operating boundary in subsequent control. Specifically, battery parameters (SOC, T, SOH) from historical data can be used as input, and the maximum power or current of actual safe operation (without triggering protection) under this condition can be used as the output label to train a regression model (such as a neural network or gradient boosting tree). This model will gradually learn the true capability boundary of the specific battery under complex operating conditions, and its predicted value can be used as a supplement or correction to the rule-based safety boundary set in step S102, enabling the system to learn from experience, and the protection strategy to become increasingly aligned with the actual characteristics of the individual battery.

[0068] Furthermore, to build user trust and provide data support, after each power control cycle, an evaluation report for the battery service is generated and submitted. This report includes at least: the battery's SOC change, average output / input power, and highest / lowest temperature during the service period; and, based on a pre-established battery degradation model, an estimated equivalent cycle life loss due to the battery's State of Health (SOH) caused by the V2G service, which is then fed back to the user or cloud platform. The battery degradation model can be based on empirical formulas, such as ΔSOH_equ=k * (ΔAh_throughput / Capacity_rated), where ΔAh_throughput is the total ampere-hour throughput through the battery during the service, Capacity_rated is the rated capacity, and k is a degradation coefficient related to average SOC, temperature, and current. This step quantifies the "battery cost" of the V2G service, making the service value and degradation transparent, and helping to build a reasonable user incentive and business settlement model.

[0069] From the above appendix Figure 1The example of a battery state-adaptive V2G power control method demonstrates that, on the one hand, by establishing a communication connection with the battery management system (BMS) of the connected electric vehicle and acquiring the battery's state parameters in real time, the real-time operating state of the battery can be accurately and dynamically grasped. This lays the data foundation for implementing battery state-based control, enabling power control decisions to be based on the battery's actual capabilities and state, rather than a preset, fixed model. On the other hand, based on the specific type and aging level (SOH) of the connected battery, a set of safe operating boundaries adapted to that specific battery is dynamically and individually generated. This ensures that safety standards vary from vehicle to vehicle, improving the adaptability and accuracy of control, and providing targeted protection benchmarks for electric vehicle batteries of different models and aging stages. Thirdly, the real-time state and personalized... The safety boundary and grid demand commands are coordinated in decision-making and safety verification. The final power output command is limited by the safety operating boundary. That is, when the theoretical grid demand command is within the battery's safe operating capacity, it will be executed. Once the theoretical command may cause the battery's operating state to exceed its individual safety boundary, or the required current exceeds its maximum allowable value, the output command will be dynamically limited within the safety boundary. This establishes a dynamic and adaptive balance mechanism between meeting grid regulation needs and ensuring the safe and healthy operation of the battery. It can fundamentally avoid dangerous conditions such as overcharging, over-discharging, over-temperature, and overcurrent caused by forcibly responding to grid demands, effectively extending the service life of the vehicle battery, significantly improving the safety of the V2G interaction process, and providing key technical support for the large-scale, safe, and reliable application of V2G technology. In summary, the technical solution of this application achieves coordinated optimization of electric vehicle battery safety and grid service by dynamically adapting battery state and grid demand in power control.

[0070] Please see the appendix Figure 2 This application provides a V2G power control device based on battery state adaptation. The device may include an acquisition module 201, a determination module 202, a calculation module 203, and a comparison module 204, as detailed below:

[0071] The acquisition module 201 is used to establish a communication connection with the battery management system (BMS) of the connected electric vehicle and acquire the status parameters of the battery and the grid in real time.

[0072] The determination module 202 is used to dynamically determine, based on the battery type and state of health (SOH), at least including the SOC safety range, temperature safety range, and maximum permissible charge and discharge current safety operating boundaries adapted to the current battery.

[0073] Calculation module 203 is used to calculate the theoretical power command in response to grid regulation demand based on grid state parameters;

[0074] The comparison module 204 is used to compare the theoretical power command in response to the grid regulation demand, the battery state parameters and the safe operating boundary, and dynamically generate and output the actual power control command limited by the safe operating boundary based on the comparison result.

[0075] From the above appendix Figure 2 As illustrated by the example of a battery state-adaptive V2G power control device, on the one hand, by establishing a communication connection with the battery management system (BMS) of the connected electric vehicle and acquiring the battery's state parameters in real time, it can accurately and dynamically grasp the real-time operating state of the battery. This lays the data foundation for implementing battery state-based control, enabling power control decisions to be based on the battery's actual capabilities and state, rather than a preset, fixed model. On the other hand, based on the specific type and aging level (SOH) of the connected battery, a set of safe operating boundaries adapted to that specific battery is dynamically and individually generated. This ensures that safety standards vary from vehicle to vehicle, improving the adaptability and accuracy of control, and providing targeted protection benchmarks for electric vehicle batteries of different models and aging stages. Thirdly, it integrates the battery's real-time state and personalized... The safety boundary and grid demand commands are coordinated in decision-making and safety verification. The final power output command is limited by the safety operating boundary. That is, when the theoretical grid demand command is within the battery's safe operating capacity, it will be executed. Once the theoretical command may cause the battery's operating state to exceed its individual safety boundary, or the required current exceeds its maximum allowable value, the output command will be dynamically limited within the safety boundary. This establishes a dynamic and adaptive balance mechanism between meeting grid regulation needs and ensuring the safe and healthy operation of the battery. It can fundamentally avoid dangerous conditions such as overcharging, over-discharging, over-temperature, and overcurrent caused by forcibly responding to grid demands, effectively extending the service life of the vehicle battery, significantly improving the safety of the V2G interaction process, and providing key technical support for the large-scale, safe, and reliable application of V2G technology. In summary, the technical solution of this application achieves coordinated optimization of electric vehicle battery safety and grid service by dynamically adapting battery state and grid demand in power control.

[0076] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 3 As shown, the electronic device 3 in this embodiment mainly includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a program for a V2G power control method based on battery state adaptation. When the processor 30 executes the computer program 32, it implements the steps in the above-described embodiment of the V2G power control method based on battery state adaptation, for example... Figure 1Steps S101 to S104 are shown. Alternatively, when processor 30 executes computer program 32, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of the acquisition module 201, determination module 202, calculation module 203, and comparison module 204 are shown.

[0077] For example, the computer program 32 of the battery state-adaptive V2G power control method mainly includes: establishing a communication connection with the battery management system (BMS) of the connected electric vehicle and acquiring the battery state parameters and grid state parameters in real time; dynamically determining, based on the battery type and state of health (SOH), a safe operating boundary that is adapted to the current battery, including at least the SOC safe range, temperature safe range, and maximum allowable charge / discharge current; calculating the theoretical power command in response to grid regulation requirements based on the grid state parameters; comparing the theoretical power command in response to grid regulation requirements, the battery state parameters, and the safe operating boundary, and dynamically generating and outputting the actual power control command limited by the safe operating boundary based on the comparison result. The computer program 32 can be divided into one or more modules / units, one or more of which are stored in the memory 31 and executed by the processor 30 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 32 in the electronic device 3. For example, computer program 32 can be divided into the functions of acquisition module 201, determination module 202, calculation module 203, and comparison module 204 (a module in the virtual device). The specific functions of each module are as follows: Teaming module 201 is used for multiple vehicle terminals to form a team and play the accompaniment audio of the same song independently on their local machines; Acquisition module 201 is used to establish a communication connection with the battery management system (BMS) of the connected electric vehicle and acquire the battery status parameters and grid status parameters in real time; Determination module 202 is used to dynamically determine the safe operating boundary, including at least the SOC safe range, temperature safe range, and maximum allowable charge and discharge current, that is compatible with the current battery based on the battery type and state of health (SOH); Calculation module 203 is used to calculate the theoretical power command in response to grid regulation requirements based on the grid status parameters; Comparison module 204 is used to compare the theoretical power command in response to grid regulation requirements, the battery status parameters, and the safe operating boundary, and dynamically generate and output the actual power control command limited by the safe operating boundary based on the comparison results.

[0078] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0079] The processor 30 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0080] The memory 31 can be an internal storage unit of the electronic device 3, such as a hard disk or RAM. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 31 can include both internal and external storage units of the electronic device 3. The memory 31 is used to store computer programs and other programs and data required by the electronic device. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0081] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed. That is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above-described device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0082] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0083] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0084] In the embodiments provided in this application, it should be understood that the disclosed apparatus / device and method can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0085] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0086] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0087] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program for the battery state adaptive V2G power control method can be stored in a storage medium. When executed by a processor, this computer program can implement the steps of the various method embodiments described above, namely: establishing a communication connection with the battery management system (BMS) of the connected electric vehicle and acquiring the battery state parameters and grid state parameters in real time; dynamically determining, based on the battery type and state of health (SOH), a safe operating boundary that is adapted to the current battery, including at least the SOC safety range, temperature safety range, and maximum allowable charge / discharge current; calculating the theoretical power command in response to grid regulation requirements based on the grid state parameters; comparing the theoretical power command in response to grid regulation requirements, the battery state parameters, and the safe operating boundary, and dynamically generating and outputting the actual power control command limited by the safe operating boundary based on the comparison results. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the contents of storage media can be appropriately added to or removed according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, storage media may not include electrical carrier signals and telecommunication signals.

[0088] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application. The specific embodiments described above further illustrate the purpose, technical solutions, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the protection scope of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A V2G power control method based on battery state adaptation, characterized in that, The method includes: Establish a communication connection with the battery management system (BMS) of the connected electric vehicle and obtain the battery status parameters and grid status parameters in real time; Based on the battery type and state of health (SOH), dynamically determine the safe operating boundaries that are compatible with the current battery, including at least the SOC safe range, temperature safe range, and maximum permissible charge and discharge current. Based on the state parameters of the power grid, calculate the theoretical power command in response to the power grid regulation demand; The theoretical power command, the battery state parameters, and the safe operating boundary are compared, and the actual power control command limited by the safe operating boundary is dynamically generated and output based on the comparison results.

2. The V2G power control method based on battery state adaptation according to claim 1, characterized in that, The dynamic determination of safe operating boundaries for adapting to the current battery, including at least the SOC safe range, temperature safe range, and maximum permissible charge / discharge current, also includes: Real-time monitoring of ambient temperature; Based on the ambient temperature, the upper and lower limits of the temperature safety range are dynamically offset and compensated, wherein the upper limit is appropriately narrowed in high-temperature environments and the lower limit is appropriately narrowed in low-temperature environments.

3. The V2G power control method based on battery state adaptation according to claim 1, characterized in that, The method is executed cyclically with a variable period, which is dynamically adjusted according to the rate of change of battery state and the intensity of grid demand fluctuations, including: When the battery temperature change rate is detected to exceed the set value, or the power grid frequency deviation changes drastically, the control cycle is automatically shortened to the first shortest cycle. When the battery status is stable and the grid demand is stable, the control cycle is automatically extended to the second longest cycle, which is longer than the first shortest cycle.

4. The V2G power control method based on battery state adaptation according to claim 1, characterized in that, The comparison of the theoretical power command, the battery state parameters, and the safe operating boundary further includes the following internal resistance compensation step: The current internal resistance R of the battery can be acquired or estimated in real time. Based on the expected current I_ref and the current internal resistance R, calculate the estimated temperature rise ΔT of the battery under the current command. The expected current I_ref is the current converted from the theoretical power command. If the sum of the estimated temperature rise ΔT and the real-time battery temperature T exceeds the upper limit of the temperature safety range, the maximum allowable charge and discharge current will be further reduced to generate a more conservative safe current limit for power matching.

5. The V2G power control method based on battery state adaptation according to claim 1, characterized in that, The method also includes the following historical data recording and learning steps: Record the sequence of key parameters for each power control process, including battery SOC, temperature, SOH, actual output power and corresponding grid status; Based on historical data, a personalized safety operation boundary prediction model for the battery of the connected electric vehicle is trained and optimized using a machine learning model, which is used to more accurately and dynamically determine the safety operation boundary in subsequent control.

6. The V2G power control method based on battery state adaptation according to claim 1, characterized in that, The method further includes the following collaborative control steps: When multiple V2G piles implementing this method are detected to be running simultaneously in the same power distribution network, the main control units of each V2G pile can exchange information with each other or through the cloud. By coordinating the battery status and power capacity of all connected electric vehicles, and under the premise of meeting the overall grid regulation requirements, the power instructions of each V2G charging station are optimized and the vehicles with better battery status are given priority for power support.

7. The V2G power control method based on battery state adaptation according to claim 1, characterized in that, After each power control cycle, an evaluation report for the battery service is generated and submitted, which includes at least the following: Battery SOC change, average output / input power, and highest / lowest temperature during this service period; Based on a pre-established battery loss model, the equivalent cycle life loss caused by this V2G service to the battery health status (SOH) is estimated and fed back to the user or cloud platform.

8. A V2G power control device based on battery state adaptation, characterized in that, The system includes: The acquisition module is used to establish a communication connection with the battery management system (BMS) of the connected electric vehicle and acquire the status parameters of the battery and the grid in real time. The determination module is used to dynamically determine, based on the battery type and state of health (SOH), at least including the SOC safety range, temperature safety range, and maximum permissible charge / discharge current safety operating boundaries adapted to the current battery. The calculation module is used to calculate the theoretical power command in response to the grid regulation demand based on the state parameters of the grid. The comparison module is used to compare the theoretical power command, the battery state parameters, and the safe operating boundary, and dynamically generate and output the actual power control command limited by the safe operating boundary based on the comparison result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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