A Dynamic Power Simulation Method for Electric Vehicle Charging Based on BMS Vehicle-Charging Pile Coordination

Abstract

This invention discloses a dynamic power simulation method for electric vehicle charging based on BMS vehicle-charging pile coordination, belonging to the field of electric vehicle charging simulation and power control technology. The method includes: when an electric vehicle connects to a charging pile, reading vehicle parameters and creating a corresponding battery management system instance; allocating initial charging capacity to the charging pile and triggering a power calculation process; querying the per-unit power value based on the vehicle's preferred charging characteristic curve type and current state of charge, and calculating the required power of the battery management system in conjunction with the vehicle's maximum supported power; comparing the required power with the allocated capacity of the charging pile and taking the smaller value as the actual charging power; updating the state of charge based on the actual charging power and time step; after each time step, reallocating capacity according to the vehicle status in the station, and determining whether the vehicle has left the station to terminate charging.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging simulation and power control technology, and particularly relates to a dynamic power simulation method for electric vehicle charging based on BMS vehicle-charging station coordination. Background Technology

[0002] With the rapid popularization of DC fast charging technology, DC charging piles have become a core component of public charging infrastructure. In charging simulation and scheduling optimization research, accurately modeling the charging power control process of electric vehicles is an important condition for building simulation environments and carrying out refined optimization research.

[0003] Current modeling methods for charging power control have the following characteristics: Most studies assume that charging power is entirely controlled by the charging station, and that the charging station can arbitrarily set and adjust its output power, without considering the dominant role of the vehicle's battery management system in power regulation. Many simulation studies use a fixed power output assumption, ignoring the dynamic changes in power with the battery's state of charge (SOC) during charging. While some studies have introduced the influence of SOC on charging power, they have not established a dynamic negotiation mechanism between the vehicle's battery management system and the charging station, failing to reflect the true bilateral constraint characteristics. They still treat the charging station as the control entity and lack power negotiation modeling under a battery management system-dominated scenario.

[0004] Existing technologies suffer from the following defects and shortcomings: Current models generally assume that charging piles arbitrarily control power output, leading to discrepancies with actual engineering implementation. The mainstream approach treats the electric vehicle load as a constant-power component, neglecting the impact of voltage dependence and state-of-charge (SOC) changes on actual charging power, making it difficult to provide accurate information for charging system analysis. DC fast charging is typically controlled by the vehicle's battery management system (BMS), but existing unilateral control models fail to reflect the physical nature of this bilateral constraint. Different manufacturers' BMS algorithms may differ, and existing models lack a unified modeling framework for various charging characteristic curve types, failing to reflect the aliasing effects of different manufacturers' BMS algorithms in real-world fleets. Existing methods typically employ an open-loop approach, calculating the charging power timing sequence based on initial parameters in a single step, failing to reflect the real-time feedback adjustment of power based on SOC changes during charging. Since charging power and SOC are negatively correlated, and the charging trajectory is inherently non-linear, the open-loop static assumption introduces systematic errors during the high SOC phase, causing simulation errors to accumulate over time. Summary of the Invention

[0005] This invention proposes a dynamic power simulation method for electric vehicle charging based on BMS vehicle-charging station coordination, comprising the following steps: When an electric vehicle connects to a charging station, the vehicle parameters are read, and a corresponding Battery Management System (BMS) instance is created based on the vehicle parameters. Allocate initial charging capacity to the BMS instance; Using a fixed time step as the unit, perform the following sub-steps for each time step until the vehicle leaves the station: Calculate the BMS power requirement of the BMS instance based on the current battery state of charge (SOC) and vehicle parameters. The BMS power demand is compared with the current charging pile capacity, and the smaller of the two values ​​is taken as the actual charging power at the current time step. Based on the actual charging power and the time step, update the battery state of charge (SOC) of the BMS instance; At the end of the current time step, the charging pile allocation capacity of the BMS instance is recalculated and updated according to the preset capacity allocation strategy. Based on the updated charging pile allocation capacity and the updated battery state of charge (SOC), the demand power calculation and bilateral constraint power determination steps are re-executed to obtain the actual charging power for the next time step.

[0006] Furthermore, the vehicle parameters include at least the battery capacity, the vehicle's maximum supported power, the initial state of charge (SOC) of the battery at the time of connection, and the charging characteristic curve type preferred by the BMS.

[0007] Furthermore, the power demand calculation step includes: Using the current battery state of charge (SOC) as an index, the corresponding power per-unit value is obtained by querying according to the charging characteristic curve type preferred by the BMS. The BMS power requirement is obtained by multiplying the vehicle's maximum supported power by the power per unit value.

[0008] Furthermore, the preferred charging characteristic curve type of the BMS includes at least one of the following: Multiple constant power curves, in which the per-unit power value takes different constant values ​​in multiple preset SOC threshold ranges, and decreases in a stepwise manner as SOC increases; The rise-plateau-decrease curve shows that the per-unit power value rises in the low SOC range, maintains its peak value in the medium SOC range, and continuously decays in the high SOC range. An approximate constant power curve, in which the per-unit power value remains approximately constant at a high power value in the low SOC region, and decays exponentially in the high SOC region.

[0009] Furthermore, the capacity allocation strategy includes: Equal distribution strategy: Distribute the total available capacity of the charging station equally among all vehicles currently charging at the station; Alternatively, a SOC-weighted priority allocation strategy can be adopted: a weighted weight is calculated based on the SOC value of each vehicle currently at the station, where vehicles with lower SOC values ​​receive higher weights, and the total available capacity of the charging station is allocated to each vehicle based on the weights, so that vehicles with low SOC values ​​receive higher charging pile allocation capacity.

[0010] Furthermore, after updating the charging pile allocation capacity of the BMS instance and obtaining the actual charging power for the next time step, a departure determination step is also included: Determine whether the current vehicle's cumulative charging time has reached its preset departure time. If the target is reached, the charging process of the vehicle is terminated and its charging pile resources are released, and the vehicle exits the time-step simulation loop. If the target is not reached, the actual charging power of the next time step is used as the actual charging power of the next time step, and the cycle of the next time step continues.

[0011] Furthermore, the specific steps for updating the battery state of charge (SOC) of the BMS instance include: Calculate the product of the actual charging power and the time step to obtain the charging energy acquired by the battery within this time step; Divide the charging energy by the battery capacity in the vehicle parameters to obtain the SOC increment; The updated battery state of charge (SOC) is added to the SOC increment to obtain the updated SOC, which is then used as the input for calculating the required power in the next time step.

[0012] Furthermore, in the process of determining the bilateral constraint power, when the power demanded by the BMS is greater than the allocated capacity of the charging pile, it is determined that the actual charging power at the current time step is limited by the charging pile side; when the power demanded by the BMS is less than the allocated capacity of the charging pile, it is determined that the actual charging power at the current time step is limited by the BMS side.

[0013] Furthermore, the multiple preset SOC threshold intervals of the multi-segment constant power curve include at least: an initial charging limit region, a full-power charging region, and multiple progressively decreasing current regions; The power per-unit value corresponding to the initial charging restriction zone is lower than the power per-unit value of the full-power charging zone; the power per-unit value of the multiple progressively decreasing current zones decreases progressively as the SOC increases.

[0014] The beneficial effects of this invention are as follows: This invention breaks through the traditional assumption of unilateral control of charging piles and establishes a bilateral constraint mechanism between the vehicle's BMS power demand and the charging pile's allocated capacity. The actual charging power is taken as the smaller value between the BMS power demand and the charging pile's allocated capacity, truly restoring the physical essence of the dynamic transfer of charging control power along with the equipment's capacity boundary. Compared with existing unilateral control models, this method can more accurately calculate the actual charging power at each time step in the supercharging pile scenario, where the rated power of the charging pile is usually higher than the single vehicle's BMS demand.

[0015] This invention establishes a unified modeling framework using three types of charging characteristic curves—multi-segment constant power, rise-plateau-attenuation, and approximately constant power—as examples. The charging characteristic type is incorporated as a vehicle attribute parameter into the BMS instance initialization, enabling an explicit expression of the differences in BMS algorithms among different manufacturers. The BMS queries the per-unit power value of the corresponding charging characteristic curve based on the current SOC, and then calculates the required power in conjunction with the vehicle's maximum supported power, thus completing the differentiated BMS algorithm modeling.

[0016] This invention uses a fixed time step as the basic control unit, and executes a complete iterative process of power calculation, simulation execution, SOC update, capacity reallocation, and power recalculation sequentially within each time step. Compared with open-loop prediction methods, this time-step feedback mechanism ensures that the power calculation at each time step is based on the latest SOC value corrected by simulation, thereby guaranteeing the physical consistency of the simulation results. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0018] Figure 1 This is a flowchart of the dynamic power control process for charging based on BMS vehicle-charging station coordination. Figure 2 Charging characteristic curves for three BMS types; Figure 3 The charging power profiles for each vehicle are shown below; (a) is the charging power profile of vehicle A, with a power of 150kW and a battery capacity of 82.5kWh; (b) is the charging power profile of vehicle B, with a power of 250kW and a battery capacity of 78.4kWh; and (c) is the charging power profile of vehicle C, with a power of 100kW and a battery capacity of 58.0kWh. Figure 4 The SOC change trajectory diagram for each vehicle; Figure 5 The diagram shows the power composition stacking and capacity utilization curves at the station level. Figure 6 A timing diagram showing the allocation of charging control rights; Figure 7A graph showing the cumulative charging energy of each vehicle. Detailed Implementation

[0019] The invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0020] Example 1 like Figure 1 The diagram shown is a flowchart of the dynamic power control for charging based on BMS vehicle-charging station coordination, including: When an electric vehicle arrives at and connects to a charging station, the system first reads the vehicle's complete parameters from the charging demand queue, including: battery capacity. (kWh), maximum charging power supported by the vehicle (kW), initial value of battery state of charge (SOC) at the time of connection (Per unit value, range [0,1]), the type of charging characteristic curve preferred by the BMS, such as one of the following: multi-segment constant power MCC, rising-plateau-decay Decay, or approximate constant power CC-CV.

[0021] After completing the parameter reading, the system immediately enters the BMS instance creation phase. The system creates the corresponding EVBMS instance based on the above parameters and completes internal state initialization, setting the current battery state of charge (SOC) to the initial value at the time of access. .

[0022] After the BMS instance is created, it needs to be allocated initial charging capacity to initiate the power calculation process. The charging pile provides the newly connected BMS instance with the initially allocated charging capacity. This triggers the initial charging power calculation process.

[0023] After capacity allocation is completed, the core charging power calculation method begins execution. This method is a crucial step in achieving vehicle-pile collaborative simulation. The charging power calculation method is a core function of the EVBMS class. It comprehensively considers the charging characteristic curve type, the current battery state of charge (SOC), and the vehicle-pile bilateral constraints to calculate the actual charging power.

[0024] Step 1: Calculate the power requirement of the Battery Management System (BMS) First, the power demand of the BMS is calculated. Based on the charging characteristic curve type preferred by the BMS, the corresponding per-unit power value is queried using the current SOC as index s. Combined with the vehicle's maximum supported power The required power of the BMS can then be obtained. : ; The query process can be completed by fitting classic curves, etc. The following provides the specific definitions, parameter setting basis, and physical meaning of three types of exemplary charging characteristic curves. It should be noted that these parameter values ​​are only simulation examples; in actual applications, they can be flexibly adjusted based on publicly available charging curve data for specific vehicle models.

[0025] (1) Multi-segment constant power curve (MCC): ; The above-mentioned multi-segment constant power The resulting multi-segment constant power curve (MCC) corresponds to the stepped current-limiting charging strategy commonly used in lithium iron phosphate battery vehicles. The relationship between the open-circuit voltage and the state of charge (SOC) of a lithium iron phosphate battery is extremely flat between 20% and 80%, with voltage changes of only about 40 millivolts. This makes it difficult for the battery management system to accurately estimate the SOC solely based on the voltage signal. Therefore, battery management systems using lithium iron phosphate batteries typically reduce the charging current stepwise at a preset SOC threshold, forming a stepped power curve. The specific meanings of the parameters in each segment are as follows: When SOC < 10% (per unit value 0.70), the battery is in the initial charging stage. The battery management system performs contactor closure detection and battery pack preheating procedures, conservatively limiting the power to 70% of the rated value. 10%≤SOC<40% (per unit value 1.00): At this time, the lithium iron phosphate cell is in the low internal resistance plateau region, the polarization voltage is small, and full power charging is allowed; 40%≤SOC<60% (per unit value 0.80): This is the first stage of current reduction, corresponding to the start of polarization growth in the middle section of the lithium iron phosphate cell. The battery management system actively reduces the charging current to control the temperature rise. 60%≤SOC<80% (per unit value 0.55): This is the second stage of current reduction, where the polarization voltage increases significantly, and the charging current is further reduced to avoid the risk of lithium plating. 80%≤SOC<90% (per unit value 0.30): This is the third stage of current reduction, where the cell voltage gradually approaches the charging cutoff voltage (3.65 volts for a single lithium iron phosphate cell), and the battery management system significantly limits the current. 90%≤SOC<100% (per unit value 0.15): This is within the trickle protection zone, where power balancing and top charging are completed with extremely low power.

[0026] The above segmented parameters refer to the DC fast charging test charging curve of a certain vehicle equipped with an 82.5 kWh lithium iron phosphate blade battery, which was publicly reported. The maximum charging power of this vehicle is about 150 kW. It maintains a peak power of about 150 kW in the range of 10% to 40% SOC. After 40%, the power begins to decrease in stages. After 80%, the power drops to about 45 kW, which shows the typical stepped current limiting characteristics of lithium iron phosphate batteries.

[0027] (2) Decay curve: ; This curve corresponds to the typical power output of ternary lithium (NMC or NCA) battery vehicles under high-power fast charging scenarios. The characteristics of ternary lithium batteries are as follows: The open-circuit voltage versus state of charge (SOC) curve exhibits good monotonicity, allowing the battery management system to adjust the charging current in real time based on the voltage. The power curve shows a continuous change rather than a stepped jump. The meanings of the three parameters in the curve are as follows: Rising stage (SOC < 8%): Power increases linearly, with the initial power being approximately 50% of the peak value. This stage corresponds to the battery pack preheating process—ternary lithium cells have high internal resistance at low temperatures or low SOC, and the battery management system limits the initial charging current to prevent lithium plating on the negative electrode; once the battery pack reaches a suitable temperature through the heat generated during charging, the system gradually releases the power limit. Platform segment (8%≤SOC<25%): Power maintains peak value, corresponding to the ternary lithium battery cell being in the low polarization range, the cell voltage is much lower than the cutoff voltage, and the battery management system allows the maximum charging current to pass; Attenuation stage (SOC ≥ 25%): Power decays approximately exponentially. As SOC increases, the cell voltage gradually approaches the upper limit cutoff voltage (4.2 volts for ternary lithium cells), and the battery management system must reduce the charging current to maintain the voltage within limits. An attenuation coefficient of 3.5 is used, based on publicly available measured data: a certain vehicle equipped with a 78.4 kWh NCA battery has a peak charging power of approximately 250 kW. At 25% SOC, the power is approximately 250 kW; at 50% SOC, it is approximately 150 kW (approximately 60% peak); and at 80% SOC, it is approximately 55 kW (approximately 22% peak). This attenuation rate matches the exponential coefficient of 3.5. The formula uses a maximum value function to ensure that a minimum trickle power of 8% is maintained even in the extremely high SOC range.

[0028] The above fitting data comes from publicly published charging test analysis reports and community fleet charging log statistics.

[0029] (3) Approximate constant power curve (CC-CV): ; Approximate constant power The resulting approximate constant power curve (CC-CV) corresponds to a vehicle model employing the classic constant current-constant voltage charging strategy. In the constant current phase, the battery management system maintains a constant charging current, and since the battery terminal voltage rises slowly with the state of charge (SOC), the charging power is approximately constant. Once the cell voltage reaches the cutoff voltage, the system transitions to the constant voltage phase, where constant voltage charging is maintained, and the current decays exponentially with time.

[0030] Constant current range (CC range: SOC < 80%, per-unit value 0.95): Approximate constant power output. The per-unit value is 0.95 instead of 1.0 to account for cable resistance loss and the temperature control margin of the battery management system. The constant current to constant voltage conversion point is set at 80% SOC, based on the typical charging characteristics of ternary lithium cells: under a 4.2V cutoff voltage and normal temperature conditions, constant current charging can typically be maintained in the 75% to 85% SOC range; Constant voltage range (CV range: SOC ≥ 80%): Power decreases exponentially, with a decay coefficient of 5.0, resulting in power decreasing to approximately 57% of its peak value at 90% SOC and approximately 43% of its peak value at 95% SOC. This is largely consistent with publicly available test data for low-to-medium power DC fast charging vehicles. The formula uses a maximum value function. Ensure that the trickle power remains at a minimum of 5% even in the extremely high SOC range.

[0031] The above parameters are based on DC fast charging test data from a vehicle equipped with a 58 kWh ternary lithium battery, which has a maximum charging power of approximately 100 kW. Here, 's' represents the index of the current battery state of charge (SOC), ranging from 0 to 1. When 's' equals 1, the battery management system requests zero power. The charging characteristic curves for the three BMS are shown below. Figure 2 As shown.

[0032] Step 2: After obtaining the BMS power requirement, it is also necessary to combine the allocated capacity of the charging pile side to perform bilateral constraint values ​​in order to determine the actual execution power.

[0033] BMS power requirements Capacity allocated with charging stations The smaller value is taken as the actual charging power at the current time step. Output: ; This constraint reflects the pattern of charging control: when At this time, the charging power is dominated by the vehicle's BMS; when At that time, the charging power is limited by the rated parameters of the charging pile.

[0034] Once the actual charging power is determined, the system will update the vehicle's battery state of charge accordingly, preparing for the simulation at the next time step.

[0035] Based on the actual charging power at the current time step and time step Calculate the charging energy acquired by the battery within the current time step, update the SOC, and obtain the new battery state of charge. : ; in, This represents the total battery capacity.

[0036] Updated This will serve as the input for calculating the charging power in the next time step, forming a state feedback.

[0037] After the SOC update is completed, the charging pile needs to perform a capacity reallocation process based on the changes in the vehicle status within the station, in order to respond to the charging results of the current time step and adjust the allocation strategy for the next time step.

[0038] At the end of each time step and after the SOC update is completed, the charging pile executes the capacity reallocation process: Step 1: Trigger capacity reallocation The charging station management module can reissue capacity allocation instructions to all charging piles of vehicles at the station according to external policies. The newly allocated capacity is recorded as follows: .

[0039] The charging station management module can be flexibly configured with different capacity allocation strategies. Two typical strategy examples are given below: Strategy 1: Equal Distribution Strategy (Default Strategy) Assume the total available capacity of the charging station is The current number of active charging vehicles at the station is Therefore, the charging station capacity allocated to each vehicle is: ; When a vehicle arrives at the station ( (Add) or leave station ( (Reducing capacity) triggers a capacity redistribution, with all vehicles at the station receiving a new, evenly distributed capacity. This strategy is simple to implement and suitable for public charging station scenarios with undifferentiated service needs.

[0040] Strategy 2: SOC Weighted Priority Allocation Strategy To improve charging efficiency, a weighted allocation can be performed based on the current State of Charge (SOC) of each vehicle—vehicles with lower SOCs can be allocated a larger capacity share. Let the first vehicle... The vehicle's current SOC is Its allocation weight for: ; No. The allocated capacity of the vehicle for: ; The weights of all vehicles j at the station are: , The total number of vehicles actively charging at the station at the current time step.

[0041] This strategy prioritizes vehicles with low SOC (State of Charge) for higher charging power, which helps to shorten the average waiting time and is suitable for operational charging stations or fleet management scenarios.

[0042] Step 2: Update the condition judgment The system performs the following condition checks on each vehicle's BMS instance: If this is the initial capacity setting, then update, making ; If the additional update capacity conditions are met, such as a new instruction being issued by the upper-level control policy, then update; Otherwise, keep the original capacity settings unchanged. Maintain the value from the previous time step.

[0043] Step 3: Recalculate charging power Regardless of whether the capacity is updated, the system is based on the current... and effective Re-execute the charging power calculation method in Section 4.3 to obtain the actual charging power at the new time step. Proceed to the next simulation time step.

[0044] After completing the redistribution of charging capacity and recalculation of power, the system needs to determine whether each vehicle in the station should be terminated before it leaves the station.

[0045] After each time step is completed, the charging station management module checks whether each vehicle in the station has reached its departure time. : ; in d represents the vehicle's arrival time, and d represents the dwell time. If the condition is met, the charging process for the vehicle is terminated, and the charging station resources it occupies are released; if the condition is not met, the aforementioned control process continues. The control of the entire charging process continues until the electric vehicle leaves the charging station.

[0046] Example 2 To verify the feasibility of the above method, a complete simplified simulation example is given below.

[0047] A public DC charging station is equipped with a station-level transformer capacity. It comes with 3 charging terminals. Capacity allocation uses an equal-sharing strategy, that is... Simulation time step Minutes. Three different types of electric vehicles arrive at the charging station sequentially, with parameter settings shown in Table 1 below:

[0048] The vehicle parameters are based on the publicly available specifications of the three typical models mentioned above. Vehicle A corresponds to the LFP battery model (82.5kWh / 150kW), Vehicle B corresponds to the NCA battery model (78.4kWh / 250kW), and Vehicle C corresponds to the medium-capacity NMC model (58.0kWh / 100kW).

[0049] The simulation was performed using the aforementioned method, and the charging status of each vehicle at each time step is shown in the table below. The constraint side in the table indicates which side the actual charging power at the current time step is limited by: If... This is pile side constraint, if This is a BMS-side constraint.

[0050] Table 2 shows the charging process of vehicle A (MCC curve, LFP 82.5kWh / 150kW), Table 3 shows the charging process of vehicle B (Decay curve, NCA 78.4kWh / 250kW), and Table 4 shows the charging process of vehicle C (CC-CV curve, NMC 58.0kWh / 100kW).

[0051]

[0052]

[0053] Charging power profiles for each vehicle are shown below. Figure 3 As shown, (a) is the charging power profile of vehicle A, with a power of 150kW and a battery capacity of 82.5kWh; (b) is the charging power profile of vehicle B, with a power of 250kW and a battery capacity of 78.4kWh; (c) is the charging power profile of vehicle C, with a power of 100kW and a battery capacity of 58.0kWh; the SOC change trajectory diagrams for each vehicle are shown below. Figure 4 As shown.

[0054] The simulation results can be analyzed to yield the following points: (1) Bilateral constraint mechanism and transfer of control All three vehicles exhibited a dynamic transfer of charging control from the charging station to the BMS during the charging process: Vehicle A (MCC): Steps 0-4 (SOC 15%-55%): The BMS requested power (120-150kW) is greater than the pile-side allocated capacity (100kW), resulting in pile-side constraint; Step 5 (SOC 65.5%): Due to stepped current reduction, the BMS reduces the requested power to 82.5kW, and control is transferred to the BMS side; Step 6 (30 minutes): Vehicle C arrives at the station, and the allocated capacity is reduced to 66.7kW, resulting in pile-side constraint again (P_BMS=82.5kW>P_charger=66.7kW); From Step 7 onwards, the BMS further reduces the power to 45kW, and the BMS remains dominant throughout; Vehicle B (Decay): Steps 0-3 (SOC 10%-42%), the BMS requests power as high as 138-250kW, but the charging pile only provides 100kW, which is severely limited by the charging pile – reflecting a typical scenario where the BMS supports super-fast charging but the charging pile capacity is insufficient; Step 4 (SOC 52.5%), the BMS power decreases to 95.4kW, which is lower than the charging pile's 100kW, and control is transferred to the BMS side; Vehicle C (CC-CV): Steps 6-12 (SOC 25%-82.5%): During the CC phase, the BMS request of 95kW is consistently higher than the charging pile's 66.7kW, constrained by the charging pile. Step 13 (SOC 92.0%): CV decay causes the BMS request to drop to 52kW, and control is transferred to the BMS side. The charging control allocation timing diagram is as follows. Figure 6 As shown.

[0055] (2) Comparison of charging trajectories of differentiated BMS curves Under the same pile-side capacity conditions, the three curves produced significantly different charging trajectories: the MCC curve exhibited a stepped power reduction; the Decay curve showed power truncation at the pile side in the low SOC stage, exhibiting a clipped top shape, and continuous exponential decay in the high SOC stage; the CC-CV curve showed a constant value truncation shape in the CC segment, and rapid decay in the CV segment. The differences in charging time and power trajectory caused by different BMS strategies provide a refined simulation basis for the optimization of charging scheduling for hybrid vehicle fleets.

[0056] (3) Station-level power utilization analysis The total station-level power and capacity utilization rate at each time step are shown in Table 5 below. The station-level power composition stacking diagram and capacity utilization rate curve are shown below. Figure 5 As shown.

[0057]

[0058] The changes in station utilization rate reveal the following: In the early stages of charging (0-15 minutes), both vehicles are at high power, and the station capacity is fully utilized (100%). As the State of Charge (SOC) of each vehicle increases and the BMS gradually reduces power, the utilization rate continues to decline. After vehicle C joins at the 30-minute mark, the utilization rate briefly rebounds to 92.7%. Subsequently, as all three vehicles complete charging, the utilization rate approaches zero. This trend reveals the load characteristics of charging station operation: due to the power decay related to the BMS's SOC, a large amount of station capacity remains idle in the later stages of charging, providing optimization space for upper-level orderly charging control strategies (such as off-peak scheduling and guiding new vehicles into the station).

[0059] (4) Cumulative charging energy The cumulative charging energy curves for each vehicle are shown below. Figure 7 As shown. Example

[0060] Typical application scenarios This simulation method can serve as a standardized verification environment and engineering implementation tool for ordered charging control strategies. Specific application scenarios include: By configuring different vehicle arrival time distributions (such as Poisson processes and actual trip chain data), BMS charging characteristic curve type distributions (such as the ratio of LFP models to NMC models), and charging station capacity constraints, diverse standard test scenario sets can be constructed. Researchers can compare and evaluate the performance indicators of different charging scheduling algorithms (such as first-come-first-served, SOC priority, price response, etc.) within a unified simulation framework, including average vehicle charging completion rate, station-level capacity utilization, and user waiting time distribution.

[0061] The structure of this simulation method is naturally adapted to the reinforcement learning framework: the state space can be defined as the current SOC vector and remaining dwell time of each vehicle at the station, the action space is the capacity allocation vector of each charging pile, and the reward function can be flexibly defined according to the optimization objective (such as maximizing charging completion rate, minimizing grid peak-valley difference, weighted user satisfaction, etc.). Since the SOC update adopts a feedback mechanism, the state transition at each step is based on physically consistent simulation calculation results, avoiding the spurious optimization problem that may occur in open-loop simulations where the strategy is effective in simulation but infeasible in reality.

[0062] In distribution network planning and operation analysis, it is necessary to accurately predict the time-series load curves of charging stations. This method, through differentiated BMS modeling and SOC updates, can generate charging load time-series data that is closer to reality than the constant power assumption, providing more reliable input for distribution network transformer capacity planning, line load forecasting, and other tasks.

[0063] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for simulating dynamic power of electric vehicle charging based on BMS vehicle-charging station coordination, characterized in that, Includes the following steps: When an electric vehicle connects to a charging station, the vehicle parameters are read, and a corresponding Battery Management System (BMS) instance is created based on the vehicle parameters. Allocate initial charging capacity to the BMS instance; Using a fixed time step as the unit, perform the following sub-steps for each time step until the vehicle leaves the station: Calculate the BMS power requirement of the BMS instance based on the current battery state of charge (SOC) and vehicle parameters. The BMS power demand is compared with the current charging pile capacity, and the smaller of the two values ​​is taken as the actual charging power at the current time step. Based on the actual charging power and the time step, update the battery state of charge (SOC) of the BMS instance; At the end of the current time step, the charging pile allocation capacity of the BMS instance is recalculated and updated according to the preset capacity allocation strategy. Based on the updated charging pile allocation capacity and the updated battery state of charge (SOC), the demand power calculation and bilateral constraint power determination steps are re-executed to obtain the actual charging power for the next time step.

2. The electric vehicle charging dynamic power simulation method based on BMS vehicle-charging pile coordination according to claim 1, characterized in that, The vehicle parameters include at least the battery capacity, the vehicle's maximum supported power, the initial state of charge (SOC) of the battery at the time of connection, and the charging characteristic curve type preferred by the BMS.

3. The electric vehicle charging dynamic power simulation method based on BMS vehicle-charging pile coordination according to claim 2, characterized in that, The power demand calculation steps include: Using the current battery state of charge (SOC) as an index, the corresponding power per-unit value is obtained by querying according to the charging characteristic curve type preferred by the BMS. The BMS power requirement is obtained by multiplying the vehicle's maximum supported power by the power per unit value.

4. The electric vehicle charging dynamic power simulation method based on BMS vehicle-charging pile coordination according to claim 3, characterized in that, The preferred charging characteristic curve type of the BMS includes at least one of the following: Multiple constant power curves, in which the per-unit power value takes different constant values ​​in multiple preset SOC threshold ranges, and decreases in a stepwise manner as SOC increases; The rise-plateau-decrease curve shows that the per-unit power value rises in the low SOC range, maintains its peak value in the medium SOC range, and continuously decays in the high SOC range. An approximate constant power curve, in which the per-unit power value remains approximately constant at a high power value in the low SOC region, and decays exponentially in the high SOC region.

5. The electric vehicle charging dynamic power simulation method based on BMS vehicle-charging pile coordination according to claim 1, characterized in that, The capacity allocation strategy includes: Equal distribution strategy: Distribute the total available capacity of the charging station equally among all vehicles currently charging at the station; Alternatively, a SOC-weighted priority allocation strategy can be adopted: a weighted weight is calculated based on the SOC value of each vehicle currently at the station, where vehicles with lower SOC values ​​receive higher weights, and the total available capacity of the charging station is allocated to each vehicle based on the weights, so that vehicles with low SOC values ​​receive higher charging pile allocation capacity.

6. The electric vehicle charging dynamic power simulation method based on BMS vehicle-charging pile coordination according to claim 1, characterized in that, After obtaining the actual charging power at the next time step, the process also includes a departure determination step: Determine whether the current vehicle's cumulative charging time has reached its preset departure time. If the target is reached, the charging process of the vehicle is terminated and its charging pile resources are released, and the vehicle exits the time-step simulation loop. If the target is not reached, the actual charging power of the next time step is used as the actual charging power of the next time step, and the cycle of the next time step continues.

7. The electric vehicle charging dynamic power simulation method based on BMS vehicle-charging pile coordination according to claim 1, characterized in that, The specific steps for updating the battery state of charge (SOC) of a BMS instance include: Calculate the product of the actual charging power and the time step to obtain the charging energy acquired by the battery within this time step; Divide the charging energy by the battery capacity in the vehicle parameters to obtain the SOC increment; The updated battery state of charge (SOC) is added to the SOC increment to obtain the updated SOC, which is then used as the input for calculating the required power in the next time step.

8. The method for simulating dynamic power of electric vehicle charging based on BMS vehicle-charging pile coordination according to claim 1, characterized in that, During the determination of bilaterally constrained power, when the power demanded by the BMS is greater than the allocated capacity of the charging pile, it is determined that the actual charging power at the current time step is constrained by the charging pile side; when the power demanded by the BMS is less than the allocated capacity of the charging pile, it is determined that the actual charging power at the current time step is constrained by the BMS side.

9. The electric vehicle charging dynamic power simulation method based on BMS vehicle-charging pile coordination according to claim 4, characterized in that, The multiple preset SOC threshold intervals of the multi-segment constant power curve include at least: an initial charging limit region, a full-power charging region, and multiple progressively decreasing current regions; The power per-unit value corresponding to the initial charging restriction zone is lower than the power per-unit value of the full-power charging zone; the power per-unit value of the multiple progressively decreasing current zones decreases progressively as the SOC increases.

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