Power scheduling optimization method and system for multi-cluster SOC balance of mobile storage and charging system

Abstract

The invention relates to the technical field of power scheduling of a power system, and discloses a power scheduling optimization method and system for multi-cluster SOC equalization of a mobile storage and charging system, and the method comprises the steps: energy storage battery clusters are not connected in parallel, the energy storage battery clusters are connected with a charging gun on a charging pile through DC coupling, and the charging pile employs a flexible power scheduling system; state parameters and charging demand parameters of all the energy storage battery clusters are obtained in real time, the priority score of each energy storage battery cluster is calculated according to the state parameters, and the state parameters comprise the SOC, SOH and temperature of the energy storage battery clusters; and selecting the energy storage battery clusters and putting the energy storage battery clusters into the charging piles by combining the charging demand parameters and the priority scores, and distributing the discharging power to the selected energy storage battery clusters by taking the target that the SOC of the selected energy storage battery clusters is equal after charging is completed as a scheduling weight factor. The SOC of each battery cluster in the mobile storage and charge system can be balanced, the service life of the battery is prolonged, and the system efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology in power systems, and in particular to a power dispatching optimization method and system for multi-cluster SOC balancing in mobile energy storage and charging systems. Background Technology

[0002] Fixed charging pile group charging systems are a conventional type of charging facility, with a typical topology as follows: Figure 1 As shown, this system uses the power grid as an infinite source for power allocation. In existing technologies, power scheduling methods for fixed charging pile group systems are based on the infinite source characteristic of the power grid. The electrical energy of each charging module group comes from the grid, and the discharge capacity is unlimited and infinite; scheduling only considers the rated power. This scheduling method does not consider the discharge energy limitations of each charging module group, nor the scheduling frequency of each charging module group. It only focuses on allocating power according to charging demand and scheduling the number of charging module groups based on charging power demand to meet the demand. It does not need to consider issues such as the power weighting of the total discharge capacity and the dynamic priority of scheduling.

[0003] Traditional fixed charging pile systems can meet certain charging needs, but with the rapid development of the electric vehicle industry, emergency charging and temporary energy replenishment scenarios are becoming increasingly common. Traditional fixed charging pile systems cannot meet the complex and ever-changing charging demands in these scenarios. Mobile charging and energy storage systems, due to their flexibility, are being used more and more widely in these scenarios. A typical topology of a mobile charging and energy storage system is as follows: Figure 2 As shown.

[0004] Currently, the power scheduling methods for mobile charging and energy storage systems are often the same as those for fixed charging pile systems, scheduling charging modules based on charging power demand. However, the power of mobile charging and energy storage systems comes from their own onboard battery clusters, which is a limited energy supply mode. This is fundamentally different from the unlimited capacity of fixed charging pile systems. Using this power scheduling method will cause extreme imbalances in the State of Charge (SOC) of the battery clusters in the mobile charging and energy storage system, leading to problems such as short battery life and low system efficiency. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a power scheduling optimization method and system for balancing the SOC of multiple clusters in a mobile energy storage and charging system, which can balance the SOC of each battery cluster in the mobile energy storage and charging system, extend battery life and improve system efficiency.

[0006] To address the aforementioned technical problems, this invention provides a power scheduling optimization method for multi-cluster SOC equalization in a mobile energy storage and charging system, comprising: The energy storage battery clusters are not connected in parallel. Instead, they are connected to the charging gun on the charging pile via DC coupling. The charging pile uses a flexible power dispatching system. The status parameters and charging demand parameters of all energy storage battery clusters are acquired in real time, and the priority score of each energy storage battery cluster is calculated based on the status parameters, including the SOC, SOH and temperature of the energy storage battery cluster. By combining charging demand parameters and priority scores, energy storage battery clusters are selected for use in charging piles. The goal is to ensure that the selected energy storage battery clusters have the same SOC after charging is completed. Discharge power is then allocated to the selected energy storage battery clusters as a scheduling weight factor.

[0007] Furthermore, the priority score is calculated as follows: PS_i=k SOC ×SOC_i+k SOH ×SOH_i+k T ×(1-|T_i-T opt | / T range ), In the formula, PS_i represents the priority score of the i-th energy storage battery cluster, SOC_i represents the SOC of the i-th energy storage battery cluster, SOH_i represents the SOH of the i-th energy storage battery cluster, T_i represents the temperature of the i-th energy storage battery cluster, and k SOC k SOH k T T represents the weight. opt T represents the optimal temperature. range Indicates a temperature range.

[0008] Furthermore, the step of selecting energy storage battery clusters for charging piles by combining charging demand parameters and priority scores specifically involves: Select the energy storage battery cluster with the highest priority score from the idle battery clusters and put it into the charging pile. Determine whether the charging demand power is greater than the sum of the maximum output power of the selected energy storage battery clusters. If so, return to the step of selecting the energy storage battery cluster with the highest priority score from the idle battery clusters and put it into the charging pile. Continue until the charging demand power is less than or equal to the sum of the maximum output power of the selected energy storage battery clusters or there are no idle energy storage battery clusters. Then, use the goal of equal SOC of the selected energy storage battery clusters after charging as the scheduling weight factor to allocate discharge power to the selected energy storage battery clusters. Otherwise, use the goal of equal SOC of the selected energy storage battery clusters after charging as the scheduling weight factor to allocate discharge power to the selected energy storage battery clusters.

[0009] Furthermore, before allocating discharge power to the selected energy storage battery clusters based on the goal of equal SOC after charging, the system determines whether the number of selected energy storage battery clusters is greater than or equal to 2. If not, one energy storage battery cluster is directly used to discharge according to the charging power requirement. If so, the system allocates discharge power to the selected energy storage battery clusters based on the goal of equal SOC after charging.

[0010] Furthermore, the allocation of discharge power to the selected energy storage battery clusters based on the objective of having the same SOC after charging is used as a scheduling weight factor is specifically as follows: Calculate the required charging capacity based on the charging demand parameters, and calculate the remaining discharge capacity of all selected energy storage battery clusters. The calculated discharge power allocated to the selected energy storage battery cluster is as follows: P_i=w / Q req ×P req , In the formula, P_i represents the discharge power allocated to the selected i-th energy storage battery cluster, w is the scheduling weight factor, which is calculated with the goal of having the same SOC for the selected energy storage battery clusters after charging. Q req P represents the amount of electricity required for charging. req This indicates the power required for charging.

[0011] Furthermore, the method for calculating the scheduling weight factor is as follows: , In the formula, Q_i represents the remaining discharge capacity of the selected i-th energy storage battery cluster, and n represents the number of selected energy storage battery clusters.

[0012] Furthermore, it also includes: The SOC of the energy storage battery clusters in use is monitored in real time. It is determined whether the difference between the SOC of the energy storage battery clusters in use and the SOC of the idle energy storage battery clusters is greater than or equal to the protection threshold. If so, the energy storage battery cluster in use with the lowest priority score is cut off, and the idle energy storage battery cluster with the highest priority score is selected for use. Then, the process returns to the step of allocating discharge power to the selected energy storage battery clusters based on the goal of equal SOC of the selected energy storage battery clusters after charging is completed, using this as the scheduling weight factor.

[0013] Furthermore, it also includes: The SOC of all energy storage battery clusters is monitored in real time. When the SOC of an energy storage battery cluster is less than or equal to the discharge protection threshold, the energy storage battery cluster is prohibited from discharging until the SOC of the energy storage battery cluster reaches the recovery threshold and can be put back into the charging pile.

[0014] Furthermore, it also includes: Real-time monitoring of the number of non-prohibited energy storage battery clusters and the remaining discharge capacity of non-prohibited energy storage battery clusters. If the number of non-prohibited energy storage battery clusters is less than the total number of charging guns or the remaining discharge capacity of non-prohibited energy storage battery clusters is less than or equal to the maximum capacity of a single historical charge, the current charging demand is determined. When determining the current charging demand, if there is no ongoing charging demand, the new charging demand will not be accepted. If there is an ongoing charging demand, an energy storage battery cluster will be selected from all energy storage battery clusters and put into the charging pile to maximize the charging capacity and power required.

[0015] This invention also provides a power scheduling optimization system for multi-cluster SOC balancing in a mobile energy storage and charging system, comprising: A charging station equipped with a charging gun, the charging station using a flexible power scheduling system; The energy storage battery clusters are not connected in parallel but are connected to the charging gun on the charging pile via DC coupling. The priority scoring module acquires the status parameters and charging demand parameters of all energy storage battery clusters in real time, and calculates the priority score of each energy storage battery cluster based on the status parameters, including the SOC, SOH and temperature of the energy storage battery cluster. The power scheduling module selects energy storage battery clusters to be put into the charging pile based on charging demand parameters and priority scores. It allocates discharge power to the selected energy storage battery clusters with the goal of having the same SOC after charging as the scheduling weight factor.

[0016] Compared with the prior art, the above-described technical solution of the present invention has the following advantages: This invention calculates a priority score based on the SOC, SOH, and temperature of the energy storage battery clusters, selects the energy storage battery clusters to be put into the charging pile based on the priority score, and allocates discharge power to the selected energy storage battery clusters with the goal of equal SOC after charging. The priority score and power allocation method are comprehensive and scientific, which can effectively balance the SOC of each battery cluster in the mobile energy storage and charging system, avoid over-discharge of individual energy storage battery clusters, extend battery life, and improve system efficiency. Attached Figure Description

[0017] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 This is an example diagram of the topology of a fixed charging pile group charging system.

[0018] Figure 2 This is an example diagram of the topology of a mobile energy storage and charging system.

[0019] Figure 3 This is a flowchart of a method in a preferred embodiment of the present invention.

[0020] Figure 4 This is a flowchart illustrating the process steps of a method in a preferred embodiment of the present invention.

[0021] Figure 5 This is a flowchart illustrating the process of allocating discharge power to a selected energy storage battery cluster in a preferred embodiment of the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0023] Reference Figure 3 and Figure 4 As shown, this invention discloses a power scheduling optimization method for multi-cluster SOC balancing in a mobile energy storage and charging system, comprising the following steps: S1: Basic Topology: The energy storage battery clusters are not connected in parallel. They are connected to the charging guns on the charging piles via DC coupling. The charging piles use a flexible power dispatch system, such as a full-matrix group charging system. A flexible power dispatch system is an intelligent system in the power system used to regulate electricity demand and optimize resource allocation, achieving dynamic supply and demand balance through power electronics technology and intelligent devices. The full-matrix group charging system is a charging solution integrating intelligent power distribution, multi-protocol compatibility, and flexible dispatch, primarily applied to large-scale fast charging scenarios for new energy vehicles.

[0024] S2: Real-time acquisition of status parameters and charging demand parameters for all energy storage battery clusters. Status parameters include: SOC (State of Charge), SOH (State of Health), maximum discharge power, and temperature. Charging demand parameters include: total battery capacity at the user end, SOC at the user end, and charging power demand (i.e., demand voltage × demand current).

[0025] S3: Calculate the priority score for each energy storage battery cluster based on the state parameters.

[0026] The priority score for energy storage battery clusters is calculated as follows: PS_i=k SOC ×SOC_i+k SOH ×SOH_i+k T ×(1-|T_i-T opt | / T range ), In the formula, PS_i represents the priority score of the i-th energy storage battery cluster, SOC_i represents the SOC of the i-th energy storage battery cluster, SOH_i represents the SOH of the i-th energy storage battery cluster, T_i represents the temperature of the i-th energy storage battery cluster, and k SOC k SOH k T k represents the weight. SOC k SOH k T The value of k is adjusted according to the actual situation. SOC +k SOH +k T =1, in this embodiment k SOC =0.8, k SOH =0.1、k T =0.1; T opt T represents the optimal temperature. range The temperature range is used for normalization; in this embodiment, T... opt =25℃, T range =50℃.

[0027] S4: Select energy storage battery clusters to be put into charging piles based on charging demand parameters and priority scores. When putting them into charging piles, priority is given to energy storage battery clusters with high priority scores that are idle. When removing them from charging piles, priority is given to energy storage battery clusters with the lowest priority scores that are in use.

[0028] S4-1: Select the energy storage battery cluster with the highest priority score from the idle ones and put it into the charging pile.

[0029] S4-2: Determine whether the charging power demand is greater than the sum of the maximum output power of the selected energy storage battery clusters. If so, return to S4-1 until the charging power demand is less than or equal to the sum of the maximum output power of the selected energy storage battery clusters, and then execute S5. Otherwise, execute S5.

[0030] S5: Assign discharge power to the selected energy storage battery clusters based on the goal of having the same SOC after charging is completed.

[0031] S5-1: Determine whether the number of selected energy storage battery clusters is greater than or equal to 2. If not, directly use one energy storage battery cluster to discharge according to the charging power requirement. If yes, execute S5-2. S5-2: As Figure 5 As shown, the selected energy storage battery clusters are allocated discharge power based on the objective of having the same SOC after charging.

[0032] S5-2-1: Calculate the required charging capacity based on charging demand parameters: Q req =Q t×(1-SOC t ), In the formula, Q req Q represents the amount of electricity required for charging. t State of Charge (SOC) indicates the total battery capacity at the user end. t This refers to the SOC on the user side.

[0033] S5-2-2: Calculate the remaining discharge capacity of all selected energy storage battery clusters: Q_i=Q Ini ×SOC_i×SOH_i, In the formula, Q_i represents the remaining discharge capacity of the selected i-th energy storage battery cluster, Q Ini This represents the initial rated capacity of the battery cluster, SOC_i represents the SOC of the selected i-th energy storage battery cluster, and SOH_i represents the SOH of the selected i-th energy storage battery cluster.

[0034] S5-2-3: Calculate the discharge power allocated to the selected energy storage battery cluster as follows: P_i=w / Q req ×P req , In the formula, P_i represents the discharge power allocated to the selected i-th energy storage battery cluster, w is the scheduling weight factor, which is calculated with the goal of having the same SOC for the selected energy storage battery clusters after charging. Q req P represents the amount of electricity required for charging. req This indicates the power required for charging.

[0035] The scheduling weight factor is calculated as follows: , In the formula, n represents the number of selected energy storage battery clusters.

[0036] That is, the discharge power allocated to the selected energy storage battery cluster is: .

[0037] S6: Balance the SOC of the energy storage battery clusters according to the protection threshold. Monitor the SOC of the energy storage battery clusters in use in real time, and determine whether the difference between the SOC of the energy storage battery clusters in use and the SOC of the idle energy storage battery clusters is greater than or equal to the protection threshold. If so, disconnect the energy storage battery cluster in use with the lowest priority score, and select the idle energy storage battery cluster with the highest priority score to be put into use. Return to S5 to execute the step of allocating discharge power to the selected energy storage battery clusters with the goal of equal SOC of the selected energy storage battery clusters after charging as the scheduling weight factor.

[0038] The protection threshold is adjusted in stages based on the highest SOC of the idle energy storage battery cluster. For example, when the highest SOC of the idle energy storage battery cluster is ≥50%, the protection threshold is 20% to reduce the switching frequency; when the highest SOC of the idle energy storage battery cluster is <50%, the protection threshold is 10% to improve the level of equalization.

[0039] S7: Extend battery life by setting discharge protection thresholds and recovery thresholds. The SOC of all energy storage battery clusters is monitored in real time. When the SOC of any energy storage battery cluster is less than or equal to the discharge protection threshold, that cluster is prohibited from discharging until its SOC reaches the recovery threshold, at which point it can be re-engaged in the charging pile. In this embodiment, the discharge protection threshold and recovery threshold are adjusted according to actual conditions; for example, the discharge protection threshold can be set to 5% and the recovery threshold to 8%.

[0040] S8: Improve the user charging experience through guaranteed charging services. Real-time monitoring of the number of non-prohibited energy storage battery clusters and the remaining discharge capacity of non-prohibited energy storage battery clusters. If the number of non-prohibited energy storage battery clusters is less than the total number of charging guns or the remaining discharge capacity of non-prohibited energy storage battery clusters is less than or equal to the maximum discharge capacity of a single historical charge, the current charging demand is determined. When assessing current charging demand, if there is no ongoing charging demand, new charging requests will not be accepted. If there is an ongoing charging demand, a battery cluster will be selected from all energy storage battery clusters and deployed to the charging station to maximize the charging capacity and power required. The guaranteed charging service in the S8 not only improves the user charging experience but also achieves a closed-loop balance of low SOC across multiple battery clusters in the entire mobile charging system.

[0041] By using the scheduling methods of S2 to S8, the SOC difference of multiple energy storage battery clusters in the entire mobile energy storage and charging system can be dynamically balanced within a small range, shortening the time for all energy storage battery clusters to reach the SOC discharge protection value, facilitating timely transportation of mobile energy storage products for energy replenishment, and improving the efficiency of the energy storage system.

[0042] This invention also discloses a power scheduling optimization system for multi-cluster SOC balancing in a mobile energy storage and charging system, comprising: A charging station equipped with a charging gun, the charging station using a flexible power scheduling system; The energy storage battery clusters are not connected in parallel but are connected to the charging gun on the charging pile via DC coupling. The priority scoring module acquires the status parameters and charging demand parameters of all energy storage battery clusters in real time, and calculates the priority score of each energy storage battery cluster based on the status parameters, including the SOC, SOH and temperature of the energy storage battery cluster. The power scheduling module selects energy storage battery clusters to be put into the charging pile based on charging demand parameters and priority scores. It allocates discharge power to the selected energy storage battery clusters with the goal of having the same SOC after charging as the scheduling weight factor.

[0043] Compared with the prior art, the advantages of the present invention are: 1. This invention is based on a mobile energy storage and charging system with limited electrical energy. The power scheduling of the charging piles fully considers coordinated control, using a single product discharge and replenishment cycle as the statistical period. Power allocation is centered on balancing the discharge capacity of each charging module group. This enables proactive dynamic balancing of the State of Charge (SOC) of each energy storage battery cluster, preventing some clusters from being over-discharged, affecting their lifespan and product safety. It also prevents some clusters from prematurely discharging, resulting in fewer usable clusters than charging guns, thus impacting the charging experience. Furthermore, balanced SOC discharge allows for timely replenishment of operational products, improving system utilization efficiency and increasing operational revenue.

[0044] 2. Power allocation among the energy storage battery clusters is weighted based on the goal of ensuring that the SOC of each cluster is equal after each charge, rather than being fixed as an on-demand or equal power allocation method. This power allocation method can effectively reduce the SOC difference among battery clusters in a particular charging station, further balancing the SOC of each battery cluster in the mobile energy storage and charging system.

[0045] 3. Proactive intervention in switching energy storage battery clusters. By comparing the SOC of the energy storage battery clusters in use with those in idle use, when the SOC difference between the energy storage battery cluster with the lowest SOC in use and the energy storage battery cluster with the highest SOC in idle use is greater than or equal to the protection threshold, the energy storage battery cluster with the lowest SOC in use is disconnected and the energy storage battery cluster with the highest SOC in idle use is connected. This can extend the SOC equalization of battery clusters to the entire system, thereby achieving system-level SOC equalization and further equalizing the SOC of each battery cluster in the mobile energy storage and charging system.

[0046] 4. Dynamically isolate low-SOC energy storage battery clusters. When the SOC of a certain energy storage battery cluster reaches the discharge protection threshold, discharge is prohibited to prevent over-discharge of the batteries, further improving system safety and service life.

[0047] To further illustrate the scheduling process of the present invention, this embodiment uses a typical scenario of a mobile energy storage and charging system with five energy storage battery clusters as an example.

[0048] There are 5 energy storage battery clusters (numbered 1-5, namely cluster 1, cluster 2, cluster 3, cluster 4, and cluster 5, which are not connected in parallel), each with a rated capacity of 100kWh. The initial SOC, maximum discharge power, initial SOH, and temperature are shown in Table 1.

[0049] Table 1 Initial Parameters of Energy Storage Battery Cluster

[0050] The charging station has a total of 3 charging guns (gun 1, gun 2, and gun 3), which meets the topology requirement that the number of battery clusters is greater than or equal to the number of charging guns.

[0051] Step 1: Initial charging gun connection (gun 1), scenario: vehicle A is connected to gun 1, charging power requirement is 70kW, total battery capacity is 50kWh, SOC=20%.

[0052] Operation process: The main control system performs priority scoring of energy storage battery clusters and calculates the priority scores of each energy storage battery cluster as follows: priority score of energy storage battery cluster 1 is 81.6, priority score of energy storage battery cluster 2 is 65.89, priority score of energy storage battery cluster 3 is 65.894, priority score of energy storage battery cluster 4 is 57.78, and priority score of energy storage battery cluster 5 is 41.994.

[0053] Priority is given to cluster 1 (maximum discharge capacity 50kW) which has the highest priority rating in the idle area, but 50kW < 70kW, so the power is insufficient.

[0054] Continue to invest in cluster 3 (maximum discharge capacity 50kW), which has the second highest priority score. After stacking, the total power is 50+50=100kW≥70kW, which meets the requirements.

[0055] Since the number of energy storage battery clusters in gun 1 is ≥2 (cluster 1 and cluster 2), the power is allocated based on the target weight of equal SOC of each cluster after a single discharge (i.e., vehicle-side charging): Weighted calculation: Charging power demand Q for vehicle A A =50kWh×(1-20%)=40kWh Initial rated capacity of all battery clusters Q Ini =100kWh Remaining discharge capacity of cluster 1: Q_1 = 90% × 95% × 100 kWh = 85.5 kWh Remaining discharge capacity of cluster 3: Q_3 = 70% × 98% × 100kWh = 68.6kWh When the SOCs of clusters 1 and 3 are equal after discharge, SOC_1 = SOC_3 = (Q_1 + Q_3 - Q_3) req ) / n / Q Ini =(85.5kWh+68.6kWh-40kWh) / 2 / 100kWh≈57.1%.

[0056] Power distribution: Cluster 1: P_1 = (85.5 - (85.5 + 68.6 - 40) / 2) / 40 × 70 ≈ 50 kW Cluster 3: P_3 = (68.6 - (85.5 + 68.6 - 40) / 2) / 40 × 70 ≈ 20 kW, Clusters 1 and 3 did not exceed the maximum discharge power of 50kW, thus meeting the requirements. After allocation, clusters 1 and 3 were in use, while clusters 2, 4, and 5 were idle. The status of each energy storage battery cluster at this time is shown in Table 2.

[0057] Table 2. Status table of each energy storage battery cluster after performing step 1.

[0058] Step 2: Second charging gun connection (gun 2), scenario: vehicle B is connected to gun 2, charging power requirement is 40kW, total battery capacity is 40kWh, SOC=40%.

[0059] Operating procedures: The priority ranking of idle energy storage battery clusters is as follows: Cluster 2 (65.894) > Cluster 4 (57.78) > Cluster 5 (41.994).

[0060] Cluster 2 (maximum discharge capacity 50kW ≥ 40kW) should be prioritized for deployment, without the need to stack other clusters.

[0061] Power allocation: 40kW is allocated directly according to demand. After the allocation is executed, cluster 2 is in use, while clusters 4 and 5 are idle. The status of each energy storage battery cluster at this time is shown in Table 3.

[0062] Table 3. Status table of each energy storage battery cluster after performing step 2.

[0063] Step 3: Third charging gun connection (gun 3), scenario: vehicle C connects to gun 3, charging power requirement is 70kW, total battery capacity is 50kWh, SOC=40%.

[0064] Operating procedures: Priority ranking of idle energy storage battery clusters: Cluster 4 (57.78) > Cluster 5 (41.994).

[0065] Cluster 4 is put into operation (maximum discharge capacity 50kW), but 50kW < 70kW, so the power is insufficient.

[0066] Continue to add cluster 5 (maximum discharge capacity 50kW), and the total power after stacking is 50+50=100kW≥70kW, which meets the demand.

[0067] Since the number of energy storage battery clusters in gun 3 is ≥2 (cluster 4 and cluster 5), the power is allocated based on the target weight of equal SOC of each cluster after a single discharge (i.e., vehicle-side charging): Weighted calculation: Charging power demand Q for vehicle C C =50kWh × (1-40%) = 30kWh Remaining charge in cluster 4: Q_4 = 60% × 97% × 100 kWh = 58.2 kWh Remaining charge in cluster 5: Q_5 = 40% × 99% × 100 kWh = 39.6 kWh When the SOCs of clusters 4 and 5 are equal after discharge, the SOC = (Q_4 + Q_5 - Q_5) C ) / 2×Q Ini =(58.2+39.6-30) / 2 / 100=33.9%.

[0068] Power distribution: Cluster 4: P_4 = (58.2 - (58.2 + 39.6 - 30) / 2) / 30 × 70 ≈ 57 kW Cluster 5: P_5 = (39.6 - (58.2 + 39.6 - 30) / 2) / 30 × 70 ≈ 13 kW, Cluster 4 outputs 57kW, which is greater than 50kW (exceeding the maximum discharge power of 50kW). Therefore, the output is readjusted. Cluster 4 outputs at its maximum capacity of 50kW, and Cluster 5 outputs at 70-50=20kW. Neither output exceeds the maximum discharge power of 50kW, thus meeting the requirements.

[0069] After the allocation is executed, clusters 4 and 5 are in use. The status of each energy storage battery cluster at this time is shown in Table 4.

[0070] Table 4. Status of each energy storage battery cluster after step 3.

[0071] Step 4: Active intervention switching (SOC difference trigger), scenario: after 0.5 hours of charging, the status of each energy storage battery cluster is shown in Table 5.

[0072] Table 5. Status of each energy storage battery cluster after 0.5 hours of charging.

[0073] Operating procedures: Assuming that vehicle B has finished charging, gun 2 is disconnected, and clusters 1 and 3 become idle, the state of each energy storage battery cluster at this time is shown in Table 6.

[0074] Table 6. Status of each energy storage battery cluster after vehicle B's charging is completed.

[0075] The difference between the energy storage battery cluster with the lowest SOC in use (cluster 5 = 31.4%) and the energy storage battery cluster with the highest SOC in idle state (cluster 2 = 44.6%) is 44.6% - 31.4% = 13.2% ≥ 10% (because the SOC of the idle cluster = 44.6% < 50%, the protection threshold is set at 10%), triggering active intervention.

[0076] Cluster 5, which has the lowest SOC in use, is removed, and clusters 2 to 3, which have the highest SOC and are currently idle, are added to the idle clusters. Cluster 5 then becomes idle.

[0077] Gun 3 redistributes power (cluster 4 and cluster 2), assuming that vehicle C's SOC has now risen to 80%; Weight calculation: Vehicle C charging power requirement: Q C =50kWh × (1-80%) = 10kWh Remaining charge in cluster 4: Q_4 = 38.6% × 97% × 100kWh ≈ 38.2kWh Remaining charge in cluster 2: Q_2 = 44.6% × 98% × 100kWh = 43.7kWh When the SOCs of clusters 4 and 2 are equal after discharge, the SOC = (Q_4 + Q_2 - Q_2) / (Q_4 + Q_2 - Q_2) C ) / 2×Q Ini =(38.2+43.7-10) / 2 / 100≈36.0%, Power distribution: Cluster 4: P_4 = (38.2 - (38.2 + 43.7 - 10) / 2) / 10 × 70 ≈ 15 kW Cluster 2: P_2 = (43.7 - (38.2 + 43.7 - 10) / 2) / 10 × 70 ≈ 55 kW, Cluster 2's output of 55kW > 50kW (exceeding the maximum discharge power of 50kW), so adjustments were made. Cluster 2 was set to output at its maximum capacity of 50kW, and Cluster 4 was set to output at 70-50=20kW. Neither exceeded the maximum discharge power of 50kW, thus meeting the requirements. After the allocation was executed, the status of each energy storage battery cluster is shown in Table 7.

[0078] Table 7. Status of each energy storage battery cluster after triggering active intervention.

[0079] Step 5: Health Management (Low SOC Isolation and Recovery), Scenario: Imagine a new round of charging begins.

[0080] Vehicle A is connected to charging gun 1, with a charging power requirement of 100kW, a total battery capacity of 100kWh, and a SOC of 20%.

[0081] Vehicle B is connected to charging gun 2, with a charging power requirement of 30kW, a total battery capacity of 40kWh, and a SOC of 10%.

[0082] Vehicle C is connected to charging gun 3, with a charging power requirement of 60kW, a total battery capacity of 100kWh, and a SOC of 43%.

[0083] Based on the power weighting calculations described above, the status of each energy storage battery cluster after approximately one hour of charging is shown in Table 8.

[0084] Table 8. Status of each energy storage battery cluster after 1 hour of charging.

[0085] Operating procedures: Clusters 4 and 5, with a State of Charge (SOC) of 5%, trigger the venting protection threshold, prohibiting discharge and isolating them from gun 3. If the SOC of clusters 4 and 5 later recovers to 8%, reaching the recovery threshold, they will revert to idle status, awaiting subsequent use.

[0086] After all vehicles have finished charging, the status of each energy storage battery cluster is shown in Table 9.

[0087] Table 9. Status of each energy storage battery cluster after all vehicles have finished charging.

[0088] At this point, the SOC difference of each energy storage battery cluster is ≤10%, close to the discharge protection value (5%), which facilitates unified operation and energy recovery, and achieves the goal of SOC balance among multiple clusters.

[0089] As can be seen from the examples, the present invention can effectively solve the problem of unbalanced SOC of multiple energy storage battery clusters in mobile energy storage and charging systems, avoid over-discharge of some energy storage battery clusters, improve system reliability and battery life, improve system efficiency, and enhance the user charging experience.

[0090] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0091] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0092] 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.

[0093] 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.

[0094] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A power scheduling optimization method for multi-cluster SOC equalization in a mobile energy storage and charging system, characterized in that, include: The energy storage battery clusters are not connected in parallel. Instead, they are connected to the charging gun on the charging pile via DC coupling. The charging pile uses a flexible power dispatching system. The status parameters and charging demand parameters of all energy storage battery clusters are acquired in real time, and the priority score of each energy storage battery cluster is calculated based on the status parameters, including the SOC, SOH and temperature of the energy storage battery cluster. By combining charging demand parameters and priority scores, energy storage battery clusters are selected for use in charging piles. The selected energy storage battery clusters are allocated discharge power based on the goal of having the same SOC after charging.

2. The power scheduling optimization method for multi-cluster SOC equalization in a mobile energy storage and charging system according to claim 1, characterized in that: The priority score is calculated as follows: PS_i=k SOC ×SOC_i+k SOH ×SOH_i+k T ×(1-|T_i-T opt | / T range ), In the formula, PS_i represents the priority score of the i-th energy storage battery cluster, SOC_i represents the SOC of the i-th energy storage battery cluster, SOH_i represents the SOH of the i-th energy storage battery cluster, T_i represents the temperature of the i-th energy storage battery cluster, and k SOC k SOH k T T represents the weight. opt T represents the optimal temperature. range Indicates a temperature range.

3. The power scheduling optimization method for multi-cluster SOC balancing in a mobile energy storage and charging system according to claim 1, characterized in that: The process of selecting energy storage battery clusters for charging pile deployment by combining charging demand parameters and priority scores is as follows: Select the energy storage battery cluster with the highest priority score from the idle battery clusters and put it into the charging pile. Determine whether the charging demand power is greater than the sum of the maximum output power of the selected energy storage battery clusters. If so, return to the step of selecting the energy storage battery cluster with the highest priority score from the idle battery clusters and put it into the charging pile. Continue until the charging demand power is less than or equal to the sum of the maximum output power of the selected energy storage battery clusters or there are no idle energy storage battery clusters. Then, use the goal of equal SOC of the selected energy storage battery clusters after charging as the scheduling weight factor to allocate discharge power to the selected energy storage battery clusters. Otherwise, use the goal of equal SOC of the selected energy storage battery clusters after charging as the scheduling weight factor to allocate discharge power to the selected energy storage battery clusters.

4. The power scheduling optimization method for multi-cluster SOC equalization in a mobile energy storage and charging system according to claim 1, characterized in that: Before allocating discharge power to the selected energy storage battery clusters based on the goal of equal SOC after charging, the system determines whether the number of selected energy storage battery clusters is greater than or equal to 2. If not, it directly uses one energy storage battery cluster to discharge according to the charging power requirement. If so, it allocates discharge power to the selected energy storage battery clusters based on the goal of equal SOC after charging.

5. The power scheduling optimization method for multi-cluster SOC balancing in a mobile energy storage and charging system according to claim 1, characterized in that: The allocation of discharge power to the selected energy storage battery clusters based on the objective of having the same SOC after charging is used as a scheduling weight factor is as follows: Calculate the required charging capacity based on the charging demand parameters, and calculate the remaining discharge capacity of all selected energy storage battery clusters. The calculated discharge power allocated to the selected energy storage battery cluster is as follows: P_i=w / Q req ×P req , In the formula, P_i represents the discharge power allocated to the selected i-th energy storage battery cluster, w is the scheduling weight factor, which is calculated with the goal of having the same SOC for the selected energy storage battery clusters after charging. Q req P represents the amount of electricity required for charging. req This indicates the power required for charging.

6. The power scheduling optimization method for multi-cluster SOC equalization in a mobile energy storage and charging system according to claim 5, characterized in that: The method for calculating the scheduling weight factor is as follows: ， In the formula, Q_i represents the remaining discharge capacity of the selected i-th energy storage battery cluster, and n represents the number of selected energy storage battery clusters.

7. The power scheduling optimization method for multi-cluster SOC equalization in a mobile energy storage and charging system according to claim 1, characterized in that, Also includes: The SOC of the energy storage battery clusters in use is monitored in real time. It is determined whether the difference between the SOC of the energy storage battery clusters in use and the SOC of the idle energy storage battery clusters is greater than or equal to the protection threshold. If so, the energy storage battery cluster in use with the lowest priority score is cut off, and the idle energy storage battery cluster with the highest priority score is selected for use. Then, the process returns to the step of allocating discharge power to the selected energy storage battery clusters based on the goal of equal SOC of the selected energy storage battery clusters after charging is completed, using this as the scheduling weight factor.

8. The power scheduling optimization method for multi-cluster SOC equalization in a mobile energy storage and charging system according to claim 1, characterized in that, Also includes: The SOC of all energy storage battery clusters is monitored in real time. When the SOC of an energy storage battery cluster is less than or equal to the discharge protection threshold, the energy storage battery cluster is prohibited from discharging until the SOC of the energy storage battery cluster reaches the recovery threshold and can be put back into the charging pile.

9. The power scheduling optimization method for multi-cluster SOC balancing in a mobile energy storage and charging system according to claim 8, characterized in that, Also includes: Real-time monitoring of the number of non-prohibited energy storage battery clusters and the remaining discharge capacity of non-prohibited energy storage battery clusters. If the number of non-prohibited energy storage battery clusters is less than the total number of charging guns or the remaining discharge capacity of non-prohibited energy storage battery clusters is less than or equal to the maximum capacity of a single historical charge, the current charging demand is determined. When determining the current charging demand, if there is no ongoing charging demand, the new charging demand will not be accepted. If there is an ongoing charging demand, an energy storage battery cluster will be selected from all energy storage battery clusters and put into the charging pile to maximize the charging capacity and power required.

10. A power scheduling optimization system for multi-cluster SOC balancing in a mobile energy storage and charging system, characterized in that, include: A charging station equipped with a charging gun, the charging station using a flexible power scheduling system; The energy storage battery clusters are not connected in parallel but are connected to the charging gun on the charging pile via DC coupling. The priority scoring module acquires the status parameters and charging demand parameters of all energy storage battery clusters in real time, and calculates the priority score of each energy storage battery cluster based on the status parameters, including the SOC, SOH and temperature of the energy storage battery cluster. The power scheduling module selects energy storage battery clusters to be put into the charging pile based on charging demand parameters and priority scores. It allocates discharge power to the selected energy storage battery clusters with the goal of having the same SOC after charging as the scheduling weight factor.

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