V2G battery charging and discharging optimization method and system based on anode potential real-time feedback

Abstract

The invention discloses a V2G battery charging and discharging optimization method and system based on anode potential real-time feedback, and relates to the technical field of vehicle battery management. Comprising the steps that 1, initial parameters of a vehicle-mounted battery are pre-stored in an edge controller of an edge end based on a cloud edge architecture, 2, after V2G service is started, real-time data of the battery are continuously collected at high frequency through a vehicle-mounted BMS and transmitted to the edge controller, the collected real-time data comprise terminal voltage U, current I, temperature T and an estimated SOC value, and the edge controller is connected with the vehicle-mounted BMS. 3, the edge controller carries out battery internal health stress state estimation, and 4, on-line optimization is carried out based on an internal state estimation value, feedback data is obtained, and an execution strategy is continuously adjusted.

Description

TECHNICAL FIELD

[0001] The application discloses a V2G battery charging and discharging optimization method and system based on real-time feedback of anode potential and relates to the technical field of vehicle battery management. BACKGROUND

[0002] With the popularization of electric vehicles (EV), as a kind of large-scale and distributed energy storage resource, the vehicle-to-grid (V2G) technology has become a key direction for the integration of energy and transportation by participating in grid regulation. However, the promotion of V2G faces a core contradiction: the balance between grid dispatching requirements and battery life decay.

[0003] The main function of the battery management system (BMS) is to ensure that the battery works within a safe range, and its life management strategy is relatively extensive. The aging mechanism of the battery: the aging of the power battery is mainly caused by internal electrochemical side reactions, such as lithium metal deposition, which is the main cause of the irreversible and rapid capacity decay of the battery. When the potential of the negative electrode (anode) of the battery is lower than 0V with respect to the lithium metal reference electrode, thermodynamic unstable lithium deposition occurs. In the prior art, the V2G battery life protection based on fixed constraint rules sets a fixed working boundary in the BMS or cloud controller, limits the battery working boundary, and fails to fully exploit the potential of V2G, resulting in resource waste. Moreover, the protection strategy is static and cannot perceive the real internal health stress of the battery. With the aging of the battery, the risk of lithium deposition is higher. It is impossible to dynamically exploit the potential of the battery under the premise of ensuring absolute safety. SUMMARY

[0004] The application provides a V2G battery charging and discharging optimization method and system based on real-time feedback of anode potential to overcome the extensive and lagging nature of the fixed rule strategy in the existing V2G battery management and realize a charging and discharging control method that can be actively and finely managed in real time from the electrochemical mechanism level.

[0005] The specific scheme provided by the application is as follows:

[0006] The application provides a V2G battery charging and discharging optimization method based on real-time feedback of anode potential, which comprises the following steps:

[0007] Step 1: Based on the cloud edge architecture, the initial parameters of the vehicle-mounted battery are pre-stored in the edge controller at the edge, and the initial parameters include the initial parameters of the SPM, the initial parameters of the ECM and the safety threshold,

[0008] Step 2: After starting the V2G service, the real-time data of the battery are continuously collected at a high frequency by the vehicle-mounted BMS and transmitted to the edge controller, and the collected real-time data include the terminal voltage U, the current I, the temperature T and the estimated SOC value,

[0009] Step 3: The edge controller performs internal health stress state estimation of the battery:

[0010] Perform ECM online parameter identification: dynamically update R0 and R1 parameters in ECM according to real-time U, I data, for analyzing the instantaneous ohmic voltage drop and polarization state of the battery,

[0011] Perform SPM state analysis: input the updated R0 and R1 parameters as boundary conditions into SPM, use the state equation of SPM to describe the diffusion of lithium ions within the electrode particles, compare the end voltage estimated by ECM with the actual end voltage measured by BMS through Kalman filter, and correct the internal state of SPM,

[0012] After filtering and correction, output the internal state estimation value:

[0013] Anode potential Φ_anode: the sum of the negative electrode equilibrium potential and overpotential calculated by SPM, which is a key constraint condition for control,

[0014] Solid phase surface lithium ion concentration C_s_surf(cathode), C_s_surf(anode): used to determine whether the electrode is close to the lithium precipitation or lithium extraction limit;

[0015] Step 4: Based on the internal state estimation value, perform online optimization, obtain feedback data, and continuously adjust the execution strategy.

[0016] Further, the initial parameters of SPM in step 1 of the V2G battery charging and discharging optimization method based on real-time feedback of anode potential include positive and negative electrode material diffusion coefficient D_s and reaction rate constant k, and the safety threshold includes anode potential safety margin Φ_safe, which is set to a value slightly higher than 0V.

[0017] Further, in step 4 of the V2G battery charging and discharging optimization method based on real-time feedback of anode potential, the internal state estimation value is used to calculate the aging rate, according to the electrochemical aging model, the instantaneous aging rate J is a function of anode potential and temperature, the formula is:

[0018] J = k * exp(-Ea / RT) * f(Φ_anode)

[0019] Where f(Φ_anode) increases sharply when Φ_anode < 0, which is used to determine the optimal power window: aiming to minimize the instantaneous aging rate J, combining the current SOC and T, dynamically calculate a safe optimal charging and discharging power window [P_min_opt, P_max_opt], at low temperature or low SOC, automatically calculate a narrower and more conservative charging and discharging power window to prevent the anode potential from being too low.

[0020] Further, in step 4 of the V2G battery charging and discharging optimization method based on real-time feedback of anode potential, the scheduling power demand P_ref of the power grid is received, and P_ref is compared with the charging and discharging power window:

[0021] If P_ref is within the window, P_ref is preferentially executed, and the power curve is smoothed to avoid sharp fluctuations,

[0022] If P_ref exceeds the window, the power output is limited according to P_max_opt or P_min_opt, and the actual available adjustment capacity is fed back to the power grid, and the generated safety power instruction P_order is issued to the BMS for execution.

[0023] The application also provides a V2G battery charging and discharging optimization system based on real-time feedback of anode potential, characterized by an edge controller of an edge terminal, integrated in a smart bidirectional charging pile or a vehicle-mounted gateway,

[0024] Based on the cloud edge architecture, the initial parameters of the vehicle-mounted battery are pre-stored in the edge controller, including the initial parameters of the SPM, the initial parameters of the ECM, and the safety threshold,

[0025] After starting the V2G service, the vehicle-mounted BMS continuously collects real-time data of the battery at a high frequency and transmits them to the edge controller, and the collected real-time data includes terminal voltage U, current I, temperature T, and estimated SOC value,

[0026] The edge controller estimates the internal health stress state of the battery:

[0027] ECM online parameter identification is performed: R0 and R1 parameters in the ECM are dynamically updated according to real-time U and I data, which are used to analyze the instantaneous ohmic drop and polarization state of the battery,

[0028] SPM state analysis is performed: the updated R0 and R1 parameters are input into the SPM as boundary conditions, the state equation of the SPM is used to describe the diffusion of lithium ions in the electrode particles, and the Kalman filter is used to compare the terminal voltage estimated by the ECM with the actual terminal voltage measured by the BMS, and the internal state of the SPM is corrected,

[0029] After filtering and correction, the internal state estimation value is output:

[0030] Anode potential Φ_anode: the sum of the negative electrode equilibrium potential and overpotential calculated by the SPM, which is a key constraint condition for control,

[0031] Solid phase surface lithium ion concentration C_s_surf(cathode), C_s_surf(anode): used to judge whether the electrode is close to the lithium precipitation or lithium extraction limit;

[0032] Based on the internal state estimation value, online optimization is performed, feedback data is obtained, and the execution strategy is continuously adjusted.

[0033] Further, the initial parameters of the SPM pre-stored in the edge controller in the V2G battery charging and discharging optimization system based on real-time feedback of anode potential include positive and negative electrode material diffusion coefficient D_s and reaction rate constant k, and the safety threshold includes anode potential safety margin Φ_safe, which is set to a value slightly higher than 0V.

[0034] Further, in the V2G battery charging and discharging optimization system based on real-time feedback of anode potential, the edge controller uses internal state estimation value to calculate aging rate, according to the electrochemical aging model, the instantaneous aging rate J is a function of anode potential and temperature, the formula is:

[0035] J=k*exp(-Ea / RT)*f(Φ_anode)

[0036] Wherein when Φ_anode<0, f(Φ_anode) increases sharply, and the optimal power window is determined: taking minimizing the instantaneous aging rate J as the target, combining the current SOC and T, a safe optimal charging and discharging power window [P_min_opt, P_max_opt] is dynamically calculated, and at low temperature or low SOC, a narrower and more conservative charging and discharging power window is automatically calculated to prevent the anode potential from being too low.

[0037] Further, in the V2G battery charging and discharging optimization system based on real-time feedback of anode potential, the edge controller receives the dispatching power demand P_ref of the power grid, compares P_ref with the charging and discharging power window:

[0038] If P_ref is within the window, P_ref is preferentially executed, and the power curve is smoothed to avoid sharp fluctuations,

[0039] If P_ref exceeds the window, limit power output according to P_max_opt or P_min_opt, and feedback the actual available adjustment capacity to the power grid, and generate a safe power instruction P_order to be issued to the BMS for execution.

[0040] The beneficial effects of the present application are:

[0041] The battery life is significantly prolonged: compared with the fixed rule strategy based on macroscopic experience, the root of inhibiting internal harmful electrochemical side reactions lithium deposition is controlled, the capacity attenuation rate of the battery is significantly reduced under the premise of providing the same power grid service, and the service life of the battery is effectively prolonged.

[0042] Control precision and strong adaptability: the strategy can be adjusted according to the real-time state of the battery, realizing personalized and refined protection. It avoids excessive conservatism in the state of new batteries and provides more sufficient protection in harsh working conditions.

[0043] Improve V2G economy and participation: through technical means, the battery health is effectively guaranteed, and the biggest concern of users participating in V2G is eliminated. At the same time, since the strategy can more accurately tap the potential of the battery, it can respond to larger power demand of the power grid under the premise of safety, improving the V2G service capability and potential income of the single vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 is a schematic diagram of the method flow of the present application.

[0045] Figure 2 is a schematic diagram of the application architecture of the present application. DETAILED DESCRIPTION

[0046] The present application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it, but the embodiments are not limiting the present application.

[0047] Example 1

[0048] The present application provides a V2G battery charging and discharging optimization method based on real-time feedback of anode potential, comprising:

[0049] Step 1: Based on the cloud edge architecture, prestore the initial parameters of the vehicle-mounted battery in the edge controller at the edge, the initial parameters including the initial parameters of SPM such as positive and negative electrode material diffusion coefficient D_s and reaction rate constant k, the initial parameters of ECM and safety threshold such as anode potential safety margin Φ_safe, set to a value slightly higher than 0V, and can also include parameters of electrochemical aging model such as activation energy Ea, preposition factor, etc.

[0050] Step 2: After starting the V2G service, use the vehicle-mounted BMS to continuously collect real-time data of the battery at a high frequency such as 10Hz and transmit it to the edge controller, the collected real-time data including terminal voltage U, current I, temperature T and estimated SOC value, these data are sent to the edge controller through CAN bus or Ethernet.

[0051] Step 3: The edge controller estimates the internal health stress state of the battery:

[0052] ECM online parameter identification is performed: according to the real-time U, I data, update the R0 and R1 parameters in the ECM for analyzing the instantaneous ohmic voltage drop and polarization state of the battery,

[0053] SPM state analysis is performed: The updated R0 and R1 parameters are used as boundary conditions and input into the SPM. The SPM state equation is used to describe the diffusion of lithium ions within the electrode particles. A Kalman filter is used to compare the terminal voltage estimated by the ECM with the actual terminal voltage measured by the BMS to correct the internal state of the SPM.

[0054] After filtering and correction, the estimated internal state value is output:

[0055] Anode potential Φ_anode: The sum of the negative electrode equilibrium potential and overpotential calculated by SPM, a key constraint used for control.

[0056] The lithium ion concentration on the solid surface, C_s_surf(cathode) and C_s_surf(anode), is used to determine whether the electrode is close to the lithium deposition or delithiation limit.

[0057] Step 4: Based on the internal state estimates, perform online optimization, obtain feedback data, and continuously adjust the execution strategy.

[0058] The aging rate is calculated using internal state estimates. According to the electrochemical aging model, the instantaneous aging rate J is a function of the anodic potential and temperature, as shown in the formula:

[0059] J = k * exp(-Ea / RT) * f(Φ_anode)

[0060] When Φ_anode < 0, f(Φ_anode) increases sharply and is used to determine the optimal power window: with the goal of minimizing the instantaneous aging rate J, combined with the current SOC and T, a safe optimal charge and discharge power window [P_min_opt, P_max_opt] is dynamically calculated. At low temperature or low SOC, a narrower and more conservative charge and discharge power window is automatically calculated to prevent the anode potential from being too low.

[0061] Receive the grid's dispatched power demand P_ref and compare P_ref with the charging / discharging power window:

[0062] If P_ref is within the window, P_ref is executed first, while the power curve is smoothed to avoid drastic fluctuations.

[0063] If P_ref exceeds the window, power output is limited according to P_max_opt or P_min_opt, and the actual available regulation capacity is fed back to the power grid. The generated safe power command P_order is sent to the BMS for execution.

[0064] The above steps are continuously executed in a V2G service session, for example, several times per second, forming a closed-loop feedback process, so that the control strategy can respond to any changes in the internal state of the battery in real time, achieving true adaptive optimization.

[0065] Embodiment 2

[0066] The application also provides a V2G battery charging and discharging optimization system based on real-time feedback of anode potential, characterized by including an edge controller at the edge end, integrated in a smart bidirectional charging pile or a vehicle-mounted gateway,

[0067] Based on the cloud-edge architecture, the initial parameters of the vehicle-mounted battery are pre-stored in the edge controller, including the initial parameters of the SPM, the initial parameters of the ECM and the safety threshold,

[0068] After starting the V2G service, the vehicle-mounted BMS continuously collects real-time data of the battery at a high frequency and transmits them to the edge controller, and the collected real-time data includes terminal voltage U, current I, temperature T and estimated SOC value,

[0069] The edge controller estimates the internal health stress state of the battery:

[0070] ECM online parameter identification is performed: the R0 and R1 parameters in the ECM are dynamically updated according to the real-time U and I data, which are used to analyze the instantaneous ohmic voltage drop and polarization state of the battery,

[0071] SPM state analysis is performed: the updated R0 and R1 parameters are input into the SPM as boundary conditions, the state equation of the SPM is used to describe the diffusion of lithium ions in the electrode particles, and the Kalman filter is used to compare the terminal voltage estimated by the ECM with the actual terminal voltage measured by the BMS, and the internal state of the SPM is corrected,

[0072] After filtering and correction, the internal state estimation value is output:

[0073] Anode potential Φ_anode: the sum of the negative electrode equilibrium potential and overpotential calculated by the SPM, which is a key constraint condition for control,

[0074] Solid-phase surface lithium ion concentration C_s_surf(cathode), C_s_surf(anode): used to judge whether the electrode is close to the lithium precipitation or lithium extraction limit;

[0075] Based on the internal state estimation value, online optimization is performed, feedback data is obtained, and the execution strategy is continuously adjusted.

[0076] The information interaction and execution process between the modules in the above system, and other contents, are based on the same concept as the method embodiments of the application, and the specific contents can be referred to the description in the method embodiments of the application, which will not be repeated here.

[0077] Likewise, the system of the present application is beneficial in that:

[0078] Battery life extension effect is remarkable: from the root of inhibiting internal harmful electrochemical side reactions lithium deposition, compared with the fixed rule strategy based on macro experience, it can significantly reduce the capacity decay rate of the battery under the premise of providing the same power grid service, effectively prolong the service life of the battery.

[0079] Control precision and strong adaptability: the strategy can be adaptively adjusted according to the real-time state of the battery, realizing personalized and refined protection. Both over-conservatism in new battery state and more sufficient protection in harsh working conditions are avoided.

[0080] Improve V2G economy and participation: through technical means, the battery health is effectively guaranteed, and the biggest concern of users participating in V2G is dispelled. At the same time, since the strategy can more accurately tap the potential of the battery, it can respond to larger power grid power demand under the premise of safety in some working conditions, improving the V2G service capability and potential income of a single vehicle.

[0081] The system architecture of the present application in application can refer to the attached Figure 2 , mainly including three parts: Cloud scheduling platform: responsible for the macro level V2G resource aggregation, receives the power grid advanced scheduling instruction such as regional total power regulation target, and decomposes it into specific power instruction P_ref for a single or a group of V2G terminals. The cloud does not participate in real-time control, only performs strategic distribution and result summary.

[0082] Cloud scheduling platform: responsible for the macro level V2G resource aggregation, receives the power grid advanced scheduling instruction such as regional total power regulation target, and decomposes it into specific power instruction P_ref for a single or a group of V2G terminals. The cloud does not participate in real-time control, only performs strategic distribution and result summary.

[0083] Edge controller, which can be integrated into an intelligent bidirectional charging pile or a vehicle-mounted gateway, has strong computing power.

[0084] The edge controller can include a data interface module: responsible for communication with the vehicle-mounted BMS and the cloud platform, receiving real-time data U, I, T, SOC uploaded by the BMS and P_ref issued by the cloud.

[0085] A battery internal state observer module can also be included: this module runs a hybrid model of first-order RC equivalent circuit model (ECM) and simplified single particle model (SPM). ECM is used for fast online parameter identification such as R0, R1; SPM is used to describe internal electrochemical processes. Through Kalman filtering algorithm, the observer outputs high-precision estimates of internal health stress parameters, mainly including anode potential Φ_anode and solid-phase surface lithium ion concentration C_s_surf.

[0086] A dynamic optimizer can also be included: this module receives internal parameters from the state observer and P_ref from the cloud platform. It has an electrochemical aging model built-in to minimize the instantaneous aging rate, and calculates the current optimal charge-discharge power window [P_min_opt, P_max_opt] in real time, and generates the final safety power instruction P_order.

[0087] An instruction issuing module can also be included: issuing P_order to the on-board BMS or directly controlling the charge-discharge equipment to execute.

[0088] The on-board end includes the battery pack, BMS and V2G charge-discharge device. The BMS is responsible for collecting basic data and uploading, while receiving and executing P_order issued by the edge controller.

[0089] It should be noted that not all steps and modules in the above processes and system structures are necessary, and some steps or modules can be omitted according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in the above embodiments can be a physical structure or a logical structure, i.e. some modules can be implemented by the same physical entity, or some modules can be implemented by multiple physical entities, or some modules can be implemented by some components in multiple independent devices.

[0090] The above-described embodiments are only preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Any equivalent replacement or transformation made by those skilled in the art based on the present application is within the protection scope of the present application. The protection scope of the present application is subject to the claims.

Claims

1. A V2G battery charge / discharge optimization method based on real-time anode potential feedback, characterized in that: include: Step 1: Based on the cloud-edge architecture, pre-store the initial parameters of the vehicle battery in the edge controller at the edge. The initial parameters include the initial parameters of SPM, the initial parameters of ECM, and the safety threshold. Step 2: After enabling V2G service, the onboard BMS continuously collects real-time battery data at high frequency and transmits it to the edge controller. The collected real-time data includes terminal voltage U, current I, temperature T, and estimated SOC value. Step 3: The edge controller estimates the internal health stress state of the battery. Perform online ECM parameter identification: Dynamically update the R0 and R1 parameters in the ECM based on real-time U and I data to analyze the instantaneous ohmic voltage drop and polarization state of the battery. SPM state analysis is performed: The updated R0 and R1 parameters are used as boundary conditions and input into the SPM. The SPM state equation is used to describe the diffusion of lithium ions within the electrode particles. A Kalman filter is used to compare the terminal voltage estimated by the ECM with the actual terminal voltage measured by the BMS to correct the internal state of the SPM. After filtering and correction, the estimated internal state value is output: Anode potential Φ_anode: The sum of the negative electrode equilibrium potential and overpotential calculated by SPM, a key constraint used for control. Solid surface lithium ion concentration C_s_surf(cathode), C_s_surf(anode): used to determine whether the electrode is close to the lithium deposition or delithiation limit; Step 4: Based on the internal state estimates, perform online optimization, obtain feedback data, and continuously adjust the execution strategy.

2. The V2G battery charge / discharge optimization method based on real-time anode potential feedback according to claim 1, characterized in that: In step 1, the initial parameters of SPM include the diffusion coefficients D_s of the positive and negative electrode materials and the reaction rate constant k. The safety threshold includes the anode potential safety margin Φ_safe, which is set to a value slightly higher than 0V.

3. The V2G battery charge / discharge optimization method based on real-time anode potential feedback according to claim 1, characterized in that: In step 4, the aging rate is calculated using the estimated internal state values. According to the electrochemical aging model, the instantaneous aging rate J is a function of the anodic potential and temperature, as shown in the formula: J = k * exp(-Ea / RT) * f(Φ_anode) When Φ_anode < 0, f(Φ_anode) increases sharply and is used to determine the optimal power window: with the goal of minimizing the instantaneous aging rate J, combined with the current SOC and T, a safe optimal charge and discharge power window [P_min_opt, P_max_opt] is dynamically calculated. At low temperature or low SOC, a narrower and more conservative charge and discharge power window is automatically calculated to prevent the anode potential from being too low.

4. A V2G battery charging and discharging optimization method based on real-time anode potential feedback according to claim 1 or 3, characterized in that in step 4, the grid's dispatch power demand P_ref is received, and P_ref is compared with the charging and discharging power window: If P_ref is within the window, P_ref is executed first, while the power curve is smoothed to avoid drastic fluctuations. If P_ref exceeds the window, power output is limited according to P_max_opt or P_min_opt, and the actual available regulation capacity is fed back to the power grid. The generated safe power command P_order is sent to the BMS for execution.

5. A V2G battery charge / discharge optimization system based on real-time anode potential feedback, characterized in that... This includes edge controllers at the edge, integrated into smart bidirectional charging piles or vehicle gateways. Based on a cloud-edge architecture, the initial parameters of the vehicle battery are pre-stored in the edge controller. These initial parameters include the initial parameters of the SPM, the initial parameters of the ECM, and safety thresholds. After enabling V2G service, the onboard BMS continuously collects real-time battery data at high frequency and transmits it to the edge controller. The collected real-time data includes terminal voltage U, current I, temperature T, and estimated SOC value. The edge controller estimates the internal health stress state of the battery: Perform online ECM parameter identification: Dynamically update the R0 and R1 parameters in the ECM based on real-time U and I data to analyze the instantaneous ohmic voltage drop and polarization state of the battery. SPM state analysis is performed: The updated R0 and R1 parameters are used as boundary conditions and input into the SPM. The SPM state equation is used to describe the diffusion of lithium ions within the electrode particles. A Kalman filter is used to compare the terminal voltage estimated by the ECM with the actual terminal voltage measured by the BMS to correct the internal state of the SPM. After filtering and correction, the estimated internal state value is output: Anode potential Φ_anode: The sum of the negative electrode equilibrium potential and overpotential calculated by SPM, a key constraint used for control. Solid surface lithium ion concentration C_s_surf(cathode), C_s_surf(anode): used to determine whether the electrode is close to the lithium deposition or delithiation limit; Based on internal state estimates, online optimization is performed, feedback data is obtained, and the execution strategy is continuously adjusted.

6. The V2G battery charge / discharge optimization system based on real-time anode potential feedback according to claim 5, characterized in that... The initial parameters of SPM pre-stored in the edge controller include the diffusion coefficients D_s of the positive and negative electrode materials and the reaction rate constant k. The safety threshold includes the anode potential safety margin Φ_safe, which is set to a value slightly higher than 0V.

7. The V2G battery charge / discharge optimization system based on real-time anode potential feedback according to claim 5, characterized in that: The edge controller uses internal state estimates to calculate the aging rate. According to the electrochemical aging model, the instantaneous aging rate J is a function of the anode potential and temperature, as shown in the formula: J = k * exp(-Ea / RT) * f(Φ_anode) When Φ_anode < 0, f(Φ_anode) increases sharply and is used to determine the optimal power window: with the goal of minimizing the instantaneous aging rate J, combined with the current SOC and T, a safe optimal charge and discharge power window [P_min_opt, P_max_opt] is dynamically calculated. At low temperature or low SOC, a narrower and more conservative charge and discharge power window is automatically calculated to prevent the anode potential from being too low.

8. A V2G battery charging and discharging optimization method based on real-time anode potential feedback according to claim 5 or 7, characterized in that the edge controller receives the grid's dispatch power demand P_ref and compares P_ref with the charging and discharging power window: If P_ref is within the window, P_ref is executed first, while the power curve is smoothed to avoid drastic fluctuations. If P_ref exceeds the window, power output is limited according to P_max_opt or P_min_opt, and the actual available regulation capacity is fed back to the power grid. The generated safe power command P_order is sent to the BMS for execution.

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