Electric vehicle cluster collaborative scheduling method and system based on internal and external characteristics of battery, storage medium and equipment

Abstract

The invention discloses an electric vehicle cluster collaborative scheduling method and system based on internal and external characteristics of a battery, a storage medium and equipment. The method comprises the following steps: acquiring electric vehicle battery parameters, environment temperature data and user schedulable time periods; constructing a discrete evolution equation for calculating the real-time battery health state according to the charging and discharging power in the battery parameters of the electric vehicle and the environment temperature; according to the real-time battery health state and the environment temperature, the actually allowable maximum charging and discharging power and the actually available capacity of each electric vehicle battery are dynamically determined; constructing an optimization model aiming at smoothing the power grid load and minimizing the battery life loss, and solving the optimization model to generate a charging and discharging scheduling plan; executing the scheduling plan, and dynamically updating the battery state according to the real-time operation feedback data; by considering the health state of the battery, smooth dispatching of the power grid and battery life loss minimization can be realized at the same time.

Description

Technical Field

[0001] This invention relates to the integration of smart grids and electric vehicles, and in particular to a method, system, storage medium, and device for coordinated scheduling of electric vehicle clusters based on the internal and external characteristics of batteries. Background Technology

[0002] With the rapid development of renewable energy and electric vehicles, utilizing electric vehicle energy storage for grid dispatch (i.e., vehicle-grid interaction) has become an important means to improve grid flexibility. Existing dispatch methods typically treat electric vehicles as power units with fixed external characteristics, optimizing them with the goal of smoothing loads and reducing grid losses to improve grid economy. Their constraints generally only include the state-of-charge boundary, maximum charging and discharging power, and grid connection time window.

[0003] However, these methods generally overlook the fundamental impact of dynamic changes in battery internal characteristics (especially State of Health (SOH) and temperature) on its real-time dispatchability. The actual maximum power and available capacity of a battery decrease significantly with SOH decay and temperature fluctuations. Traditional static constraints cannot reflect this time-varying process, which may cause dispatch commands to exceed the battery's current actual safe operating boundaries.

[0004] More importantly, existing models often separate grid objectives from battery health management, failing to quantify and factor in battery life degradation in scheduling decisions. This open-loop scheduling model, which prioritizes the grid over the battery, easily leads to accelerated battery aging through frequent charging and discharging, not only damaging user asset lifespan but also reducing the overall economic efficiency and sustainability of the vehicle-grid interaction system in the long run. Therefore, there is an urgent need for a dynamic scheduling method that can couple the internal and external characteristics of the battery and synergistically optimize grid operation and battery health. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a method, system, storage medium, and device for collaborative scheduling of electric vehicle clusters based on internal and external battery characteristics, which takes into account battery health status, achieves smooth grid scheduling, and minimizes battery life loss.

[0006] Technical solution: The electric vehicle cluster cooperative scheduling method based on internal and external battery characteristics described in this invention includes the following processes:

[0007] Acquire electric vehicle battery parameters, ambient temperature data, and user-scheduled time periods;

[0008] Discrete evolution equations for calculating real-time battery health status are constructed based on charging and discharging power and ambient temperature data from electric vehicle battery parameters.

[0009] Based on real-time battery health status and ambient temperature data, the actual maximum allowable charging and discharging power of each electric vehicle battery is dynamically determined; the actual available capacity of each electric vehicle battery is dynamically determined based on real-time battery health status; an optimization model is constructed with the objectives of smoothing grid load and minimizing battery life loss, using discrete evolution equations, the actual maximum allowable charging and discharging power of the electric vehicle battery, the actual available capacity, and the user's schedulable time period as constraints for the optimization model; and the optimization model is solved to generate a charging and discharging scheduling plan.

[0010] The scheduling plan is executed, and the battery status is dynamically updated based on real-time operational feedback data.

[0011] By constructing a discrete evolution equation for battery health status, the battery health status is transformed from a static variable into a dynamic variable (real-time battery health status) adjusted according to actual charging and discharging conditions and ambient temperature. Then, based on the real-time battery health status, the actual allowable maximum charging and discharging power and actual usable capacity of the battery are determined. This transforms these two variables from static to dynamic. Constraints for the optimization model are constructed based on these variables, simultaneously achieving the goals of grid load smoothing and minimizing battery life loss. This systematically solves the problem of the disconnect between grid objectives and battery health in traditional scheduling. Furthermore, after executing the corresponding scheduling plan based on the optimization model's solution results, the battery status is dynamically updated based on operational feedback data, ensuring that battery-related variables are continuously updated and preventing excessive battery life loss during scheduling. The entire method enables the scheduling system not only to respond to grid smoothing needs but also to proactively perceive and manage the long-term health status of battery assets.

[0012] In summary, this method transforms battery health status from a static and ignored parameter into a core dynamic variable that runs through the entire scheduling decision-making process by using battery health status as a dynamic decision-making variable and constructing a two-way dynamic coupling model of battery internal and external characteristics. This achieves deep synergy between grid economic scheduling and battery life management, significantly improving the sustainability of energy utilization and the level of system intelligence.

[0013] Preferably, the discrete evolution equation is expressed as follows:

[0014] ,

[0015] in, Let represent the battery health status of the i-th electric vehicle at time t. is the aging rate coefficient related to the battery chemistry system; is a function of the temperature acceleration factor. Let t be the temperature during time period t. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively. Let be the nominal capacity of the battery of the i-th electric vehicle;

[0016] in, The expression is

[0017] ,

[0018] in, Pre-exponential factor, The activation energy of the aging reaction. is the gas constant.

[0019] The equation introduces a charge / discharge power term. This study quantitatively characterizes the direct impact of scheduling behavior (charging and discharging) on ​​battery aging, enabling the accelerated aging caused by high-power charging and discharging to be accurately quantified and factored into optimization costs. Secondly, a temperature acceleration factor function is introduced. This scientifically reflects the catalytic or inhibitory effect of ambient temperature on the electrochemical aging rate, enabling the model to accurately predict lifespan degradation under different climatic conditions. The equation transforms the long-term, slow degradation process of batteries, accumulated over months and years, into an "instantaneous aging rate" synchronized with minute- and hourly scheduling decisions, thus successfully embedding long-term lifespan management issues into short-term operational optimization.

[0020] Preferably, the actual maximum allowable charging and discharging power of each electric vehicle battery is dynamically determined by the following formula.

[0021] ,

[0022] in, and These are the actual maximum allowable charging power and maximum discharging power of the battery of the i-th electric vehicle during time period t, respectively. and These are the initial maximum allowable charging power and the initial maximum allowable discharging power of the battery of the i-th electric vehicle, respectively. The derating factor is affected by temperature. The derating factor is affected by the battery's health status. This is the preset reference temperature.

[0023] The above formula uses the temperature effect term. Achieve dynamic power derating. Under extreme high or low temperatures, this coefficient automatically decreases, forcibly lowering the charge / discharge power boundary and effectively avoiding safety risks such as overheating, lithium plating, and even thermal runaway caused by a sharp increase in battery internal resistance or insufficient heat dissipation. This is achieved through the SOH (State of Health) effect. This system reflects the power capacity degradation caused by battery aging in real time. For a battery with a low State of Health (SOH), its allowable power boundary automatically shrinks, ensuring that any scheduling instructions generated by the optimization model do not exceed the battery's current physical capacity, preventing the issuance of invalid or dangerous instructions. This formula generates a unique, time-varying power boundary for each vehicle at each time period, replacing the traditional coarse-grained management mode with fixed boundaries. This allows scheduling optimization to finely match the real-time status of each battery, significantly improving the accuracy of cluster scheduling and resource utilization efficiency.

[0024] Preferably, the actual usable capacity of each electric vehicle battery is dynamically determined using the following formula.

[0025] ,

[0026] in, Let be the actual usable capacity of the battery of the i-th electric vehicle during time period t.

[0027] The above formula breaks with the traditional scheduling model's assumption that the battery's nominal capacity is a fixed value, establishing a correlation between State of Health (SOH) and actual usable energy. As the battery ages (SOH decreases), its actual total energy that can be stored and released changes. By proportionally reducing the capacity, this formula can more accurately reflect the current available capacity of the battery. It avoids the problems of infeasibility of planning (such as requiring the release of non-existent power) or damage to the battery due to prolonged overcharging (exceeding the current battery capacity limit) caused by scheduling power according to the capacity when the battery is partially aged. Thus, it ensures the physical feasibility of the scheduling strategy and the health of the battery at the energy level.

[0028] Preferably, the objective function of the optimization model is:

[0029] ,

[0030] in, This represents the total load of the power grid during time period t. The average value of the grid base load during the dispatching cycle. This is the battery life loss factor. Let T represent the battery's charge / discharge efficiency, and T and N represent the scheduling period and the number of electric vehicles, respectively.

[0031] The objective function is subject to the following constraints: power balance constraint, charge-discharge mutual exclusion constraint, dynamic power boundary constraint, battery dynamic capacity constraint, battery power dynamic continuity constraint, discrete evolution equation constraint, and grid connection time constraint.

[0032] The first term of the objective function minimizes the variance of the total grid load, directly serving the core requirement of "peak shaving and valley filling" to achieve grid smoothing and improve grid operational stability; the second term introduces the cost of battery lifespan degradation, through coefficients... By applying weighted averages, the long-term implicit cost of battery aging is made explicit. This allows the optimization model to automatically balance short-term grid benefits with long-term battery deterioration costs during the solution process. The seven types of constraints listed together constitute a rigorous and realistic decision space. They not only include basic rules such as grid power balance and physical mutual exclusion of charging and discharging, but more importantly, they integrate dynamic boundaries (dynamic power boundary constraints and battery dynamic capacity constraints) and dynamic evolution laws (discrete evolution equation constraints). This makes the optimization model no longer a static mathematical programming problem, but a dynamic system optimization model that reflects the time-varying nature of battery state and the chain effects of decision-making. This generates a sustainable dispatch strategy that satisfies both current grid demand and battery health.

[0033] The electric vehicle cluster collaborative scheduling system based on internal and external battery characteristics of the present invention includes:

[0034] Data acquisition module: used to acquire electric vehicle battery parameters, ambient temperature data, and user-scheduling time periods;

[0035] Dynamic feedback module: used to construct discrete evolution equations for calculating real-time battery health status based on charging and discharging power and ambient temperature data in electric vehicle battery parameters;

[0036] Dynamic mapping module: used to dynamically determine the actual maximum allowable charge and discharge power of each electric vehicle battery based on real-time battery health status and ambient temperature data; and to dynamically determine the actual usable capacity of each electric vehicle battery based on real-time battery health status.

[0037] The optimization solution module is used to construct an optimization model with the objectives of smoothing grid load and minimizing battery life loss. It uses the discrete evolution equation, the actual maximum allowable charging and discharging power of electric vehicle batteries, the actual available capacity, and the user's scheduleable time period as constraints for the optimization model; and solves the optimization model to generate a charging and discharging scheduling plan.

[0038] Closed-loop control module: used to execute the scheduling plan and dynamically update the battery status based on real-time operation feedback data, repeating the above process.

[0039] Preferably, the discrete evolution equation is expressed as follows:

[0040] ,

[0041] in, Let represent the battery health status of the i-th electric vehicle at time t. The aging rate coefficient is related to the battery chemistry system. The temperature acceleration factor function, Let t be the temperature during time period t. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively. Let be the nominal capacity of the battery of the i-th electric vehicle;

[0042] in, The expression is

[0043] ,

[0044] in, Pre-exponential factor, The activation energy of the aging reaction. is the gas constant.

[0045] Preferably, the actual maximum allowable charging and discharging power of each electric vehicle battery is dynamically determined by the following formula.

[0046] ,

[0047] in, and These are the actual maximum allowable charging power and maximum discharging power of the battery of the i-th electric vehicle during time period t, respectively. and These are the initial maximum allowable charging power and the initial maximum allowable discharging power of the battery of the i-th electric vehicle, respectively. The derating factor is affected by temperature. The derating factor is affected by the battery's health status. Preset reference temperature; Let represent the battery health status of the i-th electric vehicle at time t. Let t be the temperature during time period t.

[0048] Preferably, the actual usable capacity of each electric vehicle battery is dynamically determined using the following formula.

[0049] ,

[0050] in, Let be the actual usable capacity of the battery of the i-th electric vehicle during time period t.

[0051] Preferably, the objective function of the optimization model is:

[0052] ,

[0053] in, This represents the total load of the power grid during time period t. The average value of the grid base load during the dispatching cycle. This is the battery life loss factor. Let T represent the battery's charge / discharge efficiency, and T and N represent the scheduling period and the number of electric vehicles, respectively. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively.

[0054] The objective function has at least one of the following constraints: power balance constraint, charge-discharge mutual exclusion constraint, dynamic power boundary constraint, battery dynamic capacity constraint, battery power dynamic continuity constraint, discrete evolution equation constraint, and grid connection time constraint.

[0055] Preferably, the expression for the power balance constraint is:

[0056] ,

[0057] in, Let be the net charging and discharging power of the i-th electric vehicle during time period t. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively.

[0058] The expression for the charge-discharge mutual exclusion constraint is:

[0059] ,

[0060] The expression for dynamic power boundary constraints is:

[0061] ,

[0062] in, Let be the access state variable of the i-th electric vehicle in time period t. When the electric vehicle accesses... When electric vehicles are not connected , and These are the actual maximum allowable charging power and maximum discharging power of the battery of the i-th electric vehicle during time period t, respectively.

[0063] The expression for the battery dynamic capacity constraint is as follows:

[0064] ,

[0065] in, and These are the upper and lower limits of state of charge, respectively, set according to the battery safety life strategy. Let be the battery energy value of the i-th electric vehicle during time period t;

[0066] The expression for the dynamic continuity constraint of battery charge is:

[0067] ,

[0068] in, This is the scheduling time step.

[0069] Preferably, an optimization model based on Benders decomposition is used to solve the optimization model; the power grid basic load data includes predicted or measured load curves, and the electric vehicle battery parameters include the battery's nominal capacity, initial charge and discharge power boundaries, and charge and discharge efficiency.

[0070] The computer-readable storage medium of the present invention stores one or more programs, including one or more programs comprising instructions that, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0071] The device of the present invention includes one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0072] Beneficial effects: By transforming battery health status from a static variable to a dynamic variable through discrete evolution equations, the actual allowable maximum charge / discharge power and actual usable capacity of the battery are dynamically determined. This allows the construction of an optimization model aimed at simultaneously achieving grid load smoothing and minimizing battery life loss. This systematically solves the problem of the disconnect between grid objectives and battery health in traditional scheduling. Furthermore, the battery status is dynamically updated based on the execution status of the scheduling plan, ensuring that excessive battery life loss does not occur during scheduling. The entire method enables the scheduling system not only to respond to grid smoothing needs but also to proactively perceive and manage the long-term health status of battery assets. Attached Figure Description

[0073] Figure 1 This is a flowchart illustrating the process of the method of the present invention;

[0074] Figure 2 This is a battery timescale coupling diagram. Detailed Implementation

[0075] like Figure 1 As shown, the electric vehicle cluster collaborative scheduling method based on internal and external battery characteristics described in this invention includes the following processes:

[0076] Acquire electric vehicle battery parameters, ambient temperature data, and user-scheduling time periods.

[0077] Power grid base load data Pbase (t) can be a predicted (future) or measured (real-time) load curve; electric vehicle battery parameters include nominal capacity E cap Initial charge / discharge power boundary P max0 and P min0 The charging and discharging efficiency η; the ambient temperature is a measured or predicted value; the user's schedulable time period is organized into a schedulable time period matrix W, which is used to identify the grid connection availability of each vehicle in each time period.

[0078] Discrete evolution equations for calculating real-time battery health status are constructed based on the charging and discharging power and ambient temperature parameters of electric vehicle batteries.

[0079] The discrete evolution equation is expressed as follows:

[0080] ,

[0081] in, Let represent the battery health status of the i-th electric vehicle at time t. The aging rate coefficient is related to the battery chemistry system. The temperature acceleration factor function, Let be the temperature during time period t. hour The value is greater than 1. Preset reference temperature ( =25℃), the specific value can be set according to the actual situation, such as 50℃; and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively. Let be the nominal capacity of the battery of the i-th electric vehicle.

[0082] in, The expression is

[0083] ,

[0084] in, Pre-exponential factor, The activation energy of the aging reaction. is the gas constant.

[0085] Based on real-time battery health status and ambient temperature, the actual maximum allowable charge and discharge power of each electric vehicle battery is dynamically determined; based on real-time battery health status, the actual usable capacity of each electric vehicle battery is dynamically determined.

[0086] The upper and lower limits of the actual charging and discharging power of each electric vehicle battery are dynamically determined by the following formula.

[0087] ,

[0088] in, and These are the actual maximum allowable charging power and maximum discharging power of the battery of the i-th electric vehicle during time period t, respectively. and These are the initial maximum allowable charging power and the initial maximum allowable discharging power of the battery of the i-th electric vehicle, respectively. The derating factor is the temperature-dependent factor, representing the temperature deviation from the reference temperature. Limitations on power output capability; The derating factor is the factor that affects battery health status, reflecting the limiting effect of battery health degradation on actual chargeable and dischargeable power.

[0089] The actual usable capacity of each electric vehicle battery is dynamically determined by the following formula.

[0090] ,

[0091] in, Let be the actual available capacity of the battery of the i-th electric vehicle during time period t. It replaces the fixed nominal capacity used in the traditional model with the actual available capacity.

[0092] An optimization model is constructed with the objectives of smoothing grid load and minimizing battery life loss, and the model is solved to generate a charge and discharge scheduling plan.

[0093] The objective function of the optimization model is

[0094] ,

[0095] in, This represents the total load of the power grid during time period t. The average value of the grid base load within the dispatching cycle (i.e., within 24 hours). This is the battery life loss factor. The charging and discharging efficiency of the battery is given by T and N, which are the scheduling period (T=24h in this embodiment) and the number of electric vehicles (N=7 in this embodiment), respectively.

[0096] The objective function has the following constraints: power balance constraint, charge / discharge mutual exclusion constraint, dynamic power boundary constraint, battery dynamic capacity constraint, battery power dynamic continuity constraint, discrete evolution equation constraint, and grid connection time constraint (i.e., constraint on user-schedulable time periods). The expression for the power balance constraint is as follows:

[0097] ,

[0098] in, Let be the net charging and discharging power of the i-th electric vehicle during time period t;

[0099] The expression for the charge-discharge mutual exclusion constraint is:

[0100] ,

[0101] The expression for dynamic power boundary constraints is:

[0102] ,

[0103] in, Let be the access state variable of the i-th electric vehicle in time period t. When the electric vehicle accesses... When electric vehicles are not connected Dynamic power boundary constraints can ensure that the charging and discharging power does not exceed the actual operating range of the battery.

[0104] The expression for the battery dynamic capacity constraint is as follows:

[0105] ,in, and These are the upper and lower limits of state of charge, respectively, set according to the battery safety life strategy. Let be the battery energy value of the i-th electric vehicle during time period t.

[0106] The expression for the dynamic continuity constraint of battery charge is:

[0107] ,

[0108] in, This is the scheduling time step.

[0109] The optimization model can be solved using an optimization algorithm based on Benders decomposition, with a time cap and an optimal gap threshold set. The optimization results include the optimal charging and discharging power plan for each electric vehicle within future scheduling cycles. and its corresponding SOH prediction trajectory SOH opt (t).

[0110] Execute the aforementioned scheduling plan, note that it is only applicable to... Electric vehicles that are connected to the power grid and are in a dispatchable state will only be subject to charging and discharging power commands issued by the dispatch center; and the battery status will be dynamically updated based on real-time operation feedback data, repeating the above process.

[0111] To better illustrate this method, the following example will be used for further explanation:

[0112] This paper uses a typical application scenario of a regional aggregator managing a cluster of 1,000 electric vehicles to participate in the daily peak-shaving service of the power grid. This implementation adopts a hierarchical control architecture, comprising two levels: centralized optimization control in the cloud and distributed execution at the vehicle terminals.

[0113] During the system initialization and data acquisition phase, the aggregator's cloud control center initiates a multi-source data acquisition and preprocessing process. Specifically, this includes: establishing a data connection with the power grid dispatch center's Energy Management System (EMS) via the IEC61850 protocol to obtain the predicted base load curve for the next 24 hours with a time resolution of 15 minutes. This data includes base load electricity consumption, renewable energy generation forecasts, and network topology information; and communicating with the charging pile management system via the OCPP protocol to obtain the static parameters of registered vehicles, including the nominal battery pack capacity (ranging from 40kWh to 100kWh), maximum allowable charging power (7kW for AC piles, 50kW for DC piles), maximum allowable discharge power (limited to 5-50kW depending on the vehicle model), charge / discharge efficiency (between 0.93-0.97), and SOC operating limits (usually set to 0.2). -0.9); Based on vehicle historical travel records and user driving habits, an improved LSTM time series prediction algorithm is used to generate the schedulable time window for each vehicle in the next 24 hours with a time granularity of 15 minutes, forming a 1000×96-dimensional vehicle availability state matrix; high-precision temperature forecast data for the next 24 hours is obtained through the meteorological data API interface, with the time resolution of the temperature data consistent with the scheduling period and the spatial resolution reaching the district / county level; based on the recent operation data and historical health status records uploaded by the vehicle BMS, the Kalman filter algorithm is used for state estimation, and the initial SOH value of each vehicle is initialized. For newly connected vehicles lacking historical data, a conservative estimate is made using the industry typical initial value of 0.98.

[0114] In the battery state dynamic modeling stage, a battery state assessment system considering the coupling of multiple factors is established. The dynamic evolution model is based on the battery electrochemical aging mechanism and adopts an aging rate model modified by the Arrhenius equation. This model comprehensively considers the combined effects of charge / discharge power and ambient temperature on the battery aging rate. The power influence factor adopts the square root characteristic to accurately reflect the accelerated effect of high current operation on lifespan. The dynamic mapping model adopts a multi-parameter derating algorithm based on experimental data. This algorithm is constructed based on battery electrochemical characteristic test data. The temperature influence coefficient is determined according to the temperature-power characteristic curves of different types of batteries (such as lithium iron phosphate or ternary lithium), and the SOH decay coefficient is obtained by fitting battery cycle aging test data. The model calculates the maximum allowable charge / discharge power of the battery in real time under the current temperature and health state. The temperature influence considers the linear derating characteristics within the operating range of -10℃ to 50℃, and the SOH influence considers the power decay characteristics within the health state range of 0.7 to 1.0. At the same time, the actual usable capacity is dynamically calculated based on the current battery health state, and a linear mapping relationship between SOH and capacity is established.

[0115] In the multi-objective optimization model construction phase, the system establishes a collaborative optimization framework considering both grid operation objectives and battery life constraints. The optimization objectives consist of two parts: the primary objective is to minimize grid load variance by smoothing the load curve through adjusting the charging and discharging power of the electric vehicle cluster; the secondary objective is to minimize battery life loss costs by quantifying the impact of different charging and discharging strategies on battery aging, transforming the long-term lifespan problem into real-time optimization costs. By adjusting the weighting coefficients, an optimal balance can be achieved between grid peak-shaving effects and battery life extension. These weighting coefficients are dynamically adjusted based on real-time electricity prices, peak-shaving compensation standards, and battery replacement costs. The optimization problem needs to satisfy multiple constraints, including power balance constraints to ensure correct charging and discharging power calculation; mutual exclusion constraints for charging and discharging operations implemented by introducing 0-1 integer variables; dynamic power operation boundary constraints to ensure that power commands do not exceed the real-time available battery capacity; SOC range limits based on actual available capacity to ensure energy dispatch feasibility; dynamic continuity constraints to maintain energy conservation; and SOH evolution constraints to ensure accurate health state prediction.

[0116] In the optimization and solution phase, a large-scale optimization algorithm based on Benders decomposition is employed. The original problem is decomposed into a main problem and multiple sub-problems. The main problem addresses the power grid-level optimization objective, while the sub-problems address vehicle-level constraints. Global optimization is achieved through coordination variables. The solution process fully utilizes the parallel computing capabilities of the high-performance computing platform, employing a heuristic initialization strategy to generate initial feasible solutions, and combining branch-and-bound and interior-point methods for hybrid solution. A solution process monitoring mechanism is established to evaluate the solution progress and solution quality in real time. A solution time cap of 2 hours and an optimal gap threshold of 0.5% are set to ensure that an optimized solution meeting engineering application requirements is obtained within the predetermined timeframe.

[0117] During the scheduling instruction execution and closed-loop control phases, a multi-timescale optimized scheduling strategy is implemented. A complete 96-segment scheduling plan is generated during the day-ahead phase, and a rolling optimization mechanism is employed during real-time operation, collecting actual operating data every 15 minutes and performing re-optimization every hour. A comprehensive state monitoring system is established, collecting vehicle operating data in real time via 4G / 5G communication networks, including parameters such as actual charging and discharging power, SOC changes, and battery temperature. Through data assimilation technology, an extended Kalman filter algorithm is used to improve state prediction accuracy.

[0118] This embodiment differs from existing technologies that treat battery health status as a static parameter. It considers the cumulative impact of scheduling behavior on battery aging, enabling the optimization process to simultaneously balance short-term grid operating demands with long-term battery life degradation. This avoids excessive battery wear caused by pursuing scheduling performance, improving the life-cycle economy and sustainability of the scheduling strategy. A bidirectional coupling feedback mechanism between the battery's internal state and external scheduling commands is constructed. This mechanism first establishes a feedback model of charging and discharging behavior on battery health status to quantify the impact of scheduling commands on battery aging, and feeds this impact back to the optimizer as a cost item, thus forming a closed-loop optimization structure. Furthermore, addressing the grid's minute / hourly rapid response requirements for scheduling and the slow monthly / yearly degradation process of battery health, it models the instantaneous aging rate, transforming the long-term battery life degradation problem into a real-time aging cost function that can be embedded in the short-term scheduling model. This allows the optimization model to achieve a multi-objective trade-off between grid operating benefits and battery life loss at each scheduling moment. For details, please refer to [link to relevant documentation]. Figure 2 This reveals the core coupling mechanism of the model in this invention: unifying the hourly decision-making of grid dispatch, the minute-level dynamics of battery temperature response, and the monthly evolution of battery health degradation within a closed-loop optimization framework. High-power charging and discharging triggered by dispatch commands cause battery temperature fluctuations on a minute-level scale, thus accelerating battery aging in real time; while the accumulated aging effects (such as capacity degradation) in turn constrain the power and energy boundaries of future dispatch cycles. This bidirectional dynamic coupling spanning tens of thousands of times the time scale is the key to achieving the core objective of "ensuring long-term battery health while meeting short-term grid demands."

[0119] Meanwhile, the mechanism also dynamically determines the maximum allowable charge and discharge power based on real-time battery health status and temperature parameters to ensure that scheduling commands are within the actual operating boundaries of the battery. Finally, a dynamically constrained optimization model is established, whose constraints are adaptively adjusted during the optimization process: the scheduling decision at the current moment will affect the battery state at the next moment, thereby changing the constraint boundary of the subsequent optimization problem. This method forces the optimizer to consider the long-term chain effect of the decision, thereby generating a sustainable strategy that not only meets the current grid demand but also maintains the future scheduling capability of the battery. This marks the evolution of vehicle-grid interactive scheduling methods from static optimization to dynamic system optimization.

[0120] The electric vehicle cluster collaborative scheduling system based on internal and external battery characteristics of the present invention includes:

[0121] Data acquisition module: used to acquire electric vehicle battery parameters, ambient temperature data, and user-scheduling time periods;

[0122] Dynamic feedback module: used to construct discrete evolution equations for calculating real-time battery health status based on charging and discharging power and ambient temperature data in electric vehicle battery parameters;

[0123] Dynamic mapping module: used to dynamically determine the actual maximum allowable charge and discharge power of each electric vehicle battery based on real-time battery health status and ambient temperature data; and to dynamically determine the actual usable capacity of each electric vehicle battery based on real-time battery health status.

[0124] The optimization solution module is used to construct an optimization model with the objectives of smoothing grid load and minimizing battery life loss. It uses the discrete evolution equation, the actual maximum allowable charging and discharging power of electric vehicle batteries, the actual available capacity, and the user's scheduleable time period as constraints for the optimization model; and solves the optimization model to generate a charging and discharging scheduling plan.

[0125] Closed-loop control module: used to execute the scheduling plan and dynamically update the battery status based on real-time operation feedback data.

[0126] The electric vehicle battery parameters in the data acquisition module include the battery's nominal capacity, initial charge / discharge power boundaries, and charge / discharge efficiency.

[0127] The discrete evolution equation expression in the dynamic feedback module is as follows:

[0128] ,

[0129] in, Let represent the battery health status of the i-th electric vehicle at time t. The aging rate coefficient is related to the battery chemistry system. The temperature acceleration factor function, Let t be the temperature during time period t. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively. Let be the nominal capacity of the battery of the i-th electric vehicle;

[0130] in, The expression is

[0131] ,

[0132] in, Pre-exponential factor, The activation energy of the aging reaction. is the gas constant.

[0133] The maximum allowable charge and discharge power of each electric vehicle battery in the dynamic mapping module is dynamically determined by the following formula.

[0134] ,

[0135] in, and These are the actual maximum allowable charging power and maximum discharging power of the battery of the i-th electric vehicle during time period t, respectively. and These are the initial maximum allowable charging power and the initial maximum allowable discharging power of the battery of the i-th electric vehicle, respectively. The derating factor is affected by temperature. The derating factor is affected by the battery's health status. Preset reference temperature; Let represent the battery health status of the i-th electric vehicle at time t. Let t be the temperature during time period t.

[0136] The actual usable capacity of each electric vehicle battery in the dynamic mapping module is dynamically determined by the following formula.

[0137] ,

[0138] in, Let be the actual usable capacity of the battery of the i-th electric vehicle during time period t.

[0139] The objective function of the optimization model described in the optimization solution module is:

[0140] ,

[0141] in, This represents the total load of the power grid during time period t. The average value of the grid base load during the dispatching cycle. This is the battery life loss factor. Let T represent the battery's charge / discharge efficiency, and T and N represent the scheduling period and the number of electric vehicles, respectively. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively.

[0142] The objective function has at least one of the following constraints: power balance constraint, charge-discharge mutual exclusion constraint, dynamic power boundary constraint, battery dynamic capacity constraint, battery power dynamic continuity constraint, discrete evolution equation constraint, and grid connection time constraint.

[0143] The expression for the power balance constraint is:

[0144] ,

[0145] in, Let be the net charging and discharging power of the i-th electric vehicle during time period t. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively.

[0146] The expression for the charge-discharge mutual exclusion constraint is:

[0147] ,

[0148] The expression for dynamic power boundary constraints is:

[0149] ,

[0150] in, Let be the access state variable of the i-th electric vehicle in time period t. When the electric vehicle accesses... When electric vehicles are not connected , and These are the actual maximum allowable charging power and maximum discharging power of the battery of the i-th electric vehicle during time period t, respectively.

[0151] The expression for the battery dynamic capacity constraint is as follows:

[0152] ,

[0153] in, and These are the upper and lower limits of state of charge, respectively, set according to the battery safety life strategy. Let be the battery energy value of the i-th electric vehicle during time period t;

[0154] The expression for the dynamic continuity constraint of battery charge is:

[0155] ,

[0156] in, This is the scheduling time step.

[0157] The optimization model is solved using an optimization algorithm based on Benders decomposition.

[0158] The computer-readable storage medium of the present invention stores one or more programs, including one or more programs comprising instructions that, when executed by a computing device, cause the computing device to perform the method described above.

[0159] The device of the present invention includes one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0160] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0161] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0162] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0163] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0164] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0165] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for coordinated scheduling of a cluster of electric vehicles based on internal and external characteristics of the batteries, characterized in that, Includes the following processes: Acquire electric vehicle battery parameters, ambient temperature data, and user-scheduled time periods; Discrete evolution equations for calculating real-time battery health status are constructed based on charging and discharging power and ambient temperature data from electric vehicle battery parameters. Based on real-time battery health status and ambient temperature data, the actual maximum allowable charging and discharging power of each electric vehicle battery is dynamically determined. The actual usable capacity of each electric vehicle's battery is dynamically determined based on the real-time battery health status. An optimization model is constructed with the objectives of smoothing grid load and minimizing battery life loss. The discrete evolution equation, the actual maximum allowable charging and discharging power of electric vehicle batteries, the actual available capacity, and the user's scheduleable time period are used as constraints for the optimization model. The optimization model is then solved to generate a charge / discharge scheduling plan. The scheduling plan is executed, and the battery status is dynamically updated based on real-time operational feedback data.

2. The method of claim 1, wherein, The expression for the discrete evolution equation is: ， wherein, S i is the state of health of the battery of the i-th electric vehicle at time period t, S i is the state of health of the battery of the i-th electric vehicle at time period t, S i is the state of health of the battery of the i-th electric vehicle at time period t, S i is the state of health of the battery of the i-th electric vehicle at time period t, and S i is the state of health of the battery of the i-th electric vehicle at time period t, S i is the state of health of the battery of the i-th electric vehicle at time period t, wherein the expression for ， wherein is the pre-exponential factor, is the activation energy of the aging reaction, is the gas constant.

3. The method of claim 1, wherein: The actual maximum allowable charging and discharging power of each electric vehicle battery is dynamically determined by the following formula. ， wherein, and Pmax, i, t, actual and Pmin, i, t, actual are the actual allowed maximum charge power and the maximum discharge power of the i-th electric vehicle battery at the time period t, respectively; and Pmax, i, t, initial and Pmin, i, t, initial are the initial allowed maximum charge power and the initial allowed maximum discharge power of the i-th electric vehicle battery, respectively; is a temperature impact derating factor, is a state of health impact derating factor, is a preset reference temperature; is the state of health of the i-th electric vehicle at the time period t; is the temperature at the time period t.

4. The method of claim 1, wherein: The actual usable capacity of each electric vehicle battery is dynamically determined by the following formula. ， wherein, is the actual available capacity of the battery of the ith electric vehicle at time period t.

5. The method according to claim 1, characterized in that: The objective function of the optimization model is: ， in, This represents the total load of the power grid during time period t. The average value of the grid base load during the dispatching cycle. This is the battery life loss factor. Let T represent the battery's charge / discharge efficiency, and T and N represent the scheduling period and the number of electric vehicles, respectively. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively. The objective function has at least one of the following constraints: power balance constraint, charge-discharge mutual exclusion constraint, dynamic power boundary constraint, battery dynamic capacity constraint, battery power dynamic continuity constraint, discrete evolution equation constraint, and grid connection time constraint.

6. The method according to claim 5, characterized in that: The expression for the power balance constraint is: ， in, Let be the net charging and discharging power of the i-th electric vehicle during time period t. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively. The expression for the charge-discharge mutual exclusion constraint is: ， The expression for dynamic power boundary constraints is: ， in, Let be the access state variable of the i-th electric vehicle in time period t. When the electric vehicle accesses... When electric vehicles are not connected , and These are the actual maximum allowable charging power and maximum discharging power of the battery of the i-th electric vehicle during time period t, respectively. The expression for the battery dynamic capacity constraint is as follows: ， in, and These are the upper and lower limits of state of charge, respectively, set according to the battery safety life strategy. Let be the battery energy value of the i-th electric vehicle during time period t; The expression for the dynamic continuity constraint of battery charge is: ， in, This is the scheduling time step.

7. The method according to claim 1, characterized in that: The optimization model is solved using an optimization algorithm based on Benders decomposition; the electric vehicle battery parameters include the battery's nominal capacity, initial charge / discharge power boundaries, and charge / discharge efficiency.

8. A collaborative scheduling system for electric vehicle clusters based on internal and external battery characteristics, characterized in that, include: Data acquisition module: used to acquire electric vehicle battery parameters, ambient temperature data, and user-scheduling time periods; Dynamic feedback module: used to construct discrete evolution equations for calculating real-time battery health status based on charging and discharging power and ambient temperature data in electric vehicle battery parameters; Dynamic mapping module: used to dynamically determine the actual maximum allowable charge and discharge power of each electric vehicle battery based on real-time battery health status and ambient temperature data; and to dynamically determine the actual usable capacity of each electric vehicle battery based on real-time battery health status. The optimization solution module is used to construct an optimization model with the objectives of smoothing grid load and minimizing battery life loss. The discrete evolution equation, the actual maximum allowable charging and discharging power of electric vehicle batteries, the actual available capacity, and the user's scheduleable time period are used as constraints for the optimization model. The optimization model is then solved to generate a charge / discharge scheduling plan. Closed-loop control module: used to execute the scheduling plan and dynamically update the battery status based on real-time operation feedback data, repeating the above process.

9. The system according to claim 8, characterized in that, The expression for the discrete evolution equation is: ， in, Let represent the battery health status of the i-th electric vehicle at time t. The aging rate coefficient is related to the battery chemistry system. The temperature acceleration factor function, Let t be the temperature during time period t. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively. Let be the nominal capacity of the battery of the i-th electric vehicle; in, The expression is ， in, Pre-exponential factor, The activation energy of the aging reaction. is the gas constant.

10. The system according to claim 8, characterized in that: The actual maximum allowable charging and discharging power of each electric vehicle battery is dynamically determined by the following formula. ， in, and These are the actual maximum allowable charging power and maximum discharging power of the battery of the i-th electric vehicle during time period t, respectively. and These are the initial maximum allowable charging power and the initial maximum allowable discharging power of the battery of the i-th electric vehicle, respectively. The derating factor is affected by temperature. The derating factor is affected by the battery's health status. Preset reference temperature; Let represent the battery health status of the i-th electric vehicle at time t. Let t be the temperature during time period t.

11. The system according to claim 8, characterized in that: The actual usable capacity of each electric vehicle battery is dynamically determined by the following formula. ， in, Let be the actual usable capacity of the battery of the i-th electric vehicle during time period t.

12. The system according to claim 8, characterized in that: The objective function of the optimization model is: ， in, This represents the total load of the power grid during time period t. The average value of the grid base load during the dispatching cycle. This is the battery life loss factor. Let T represent the battery's charge / discharge efficiency, and T and N represent the scheduling period and the number of electric vehicles, respectively. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively. The objective function has at least one of the following constraints: power balance constraint, charge-discharge mutual exclusion constraint, dynamic power boundary constraint, battery dynamic capacity constraint, battery power dynamic continuity constraint, discrete evolution equation constraint, and grid connection time constraint.

13. The system according to claim 12, characterized in that: The expression for the power balance constraint is: ， in, Let be the net charging and discharging power of the i-th electric vehicle during time period t. and Let be the charging and discharging power of the i-th electric vehicle during time period t, respectively. The expression for the charge-discharge mutual exclusion constraint is: ， The expression for dynamic power boundary constraints is: ， in, Let be the access state variable of the i-th electric vehicle in time period t. When the electric vehicle accesses... When electric vehicles are not connected , and These are the actual maximum allowable charging power and maximum discharging power of the battery of the i-th electric vehicle during time period t, respectively. The expression for the battery dynamic capacity constraint is as follows: ， in, and These are the upper and lower limits of state of charge, respectively, set according to the battery safety life strategy. Let be the battery energy value of the i-th electric vehicle during time period t; The expression for the dynamic continuity constraint of battery charge is: ， in, This is the scheduling time step.

14. The method according to claim 8, characterized in that: The optimization model is solved using an optimization algorithm based on Benders decomposition; the electric vehicle battery parameters include the battery's nominal capacity, initial charge / discharge power boundaries, and charge / discharge efficiency.

15. A computer-readable storage medium for storing one or more programs, characterized in that: The program includes one or more instructions that, when executed by a computing device, cause the computing device to perform any of the methods according to claims 1 to 7.

16. A device, characterized in that... It includes one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods according to claims 1 to 7.

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