Efficiency optimization and health balance-based charging module control method and device

Abstract

The application discloses a charging module control method and equipment based on efficiency optimization and health balance, which is used to solve the problems of the existing charging control, such as the superficial health management, the coarse efficiency optimization granularity, the passive and blind scheduling, the uneven module aging, and the too frequent switching. The method obtains charging data of a charging terminal, predicts power demand of the charging terminal in a next period, obtains operation data of each charging module, judges the health degree of each charging module based on a health degradation quantitative model, determines the switching number of the charging module required by the charging terminal in the next period according to the predicted power demand, and selects the charging module to be switched in the next period according to the switching number in combination with the health degree, calculates a health degree dispersion coefficient according to the health degree of all online charging modules, and determines to execute an efficiency optimization strategy or a health balance strategy according to the health degree dispersion coefficient.

Description

Technical Field

[0001] This application relates to the field of charging control, and in particular to a charging module control method and device based on efficiency optimization and health balance. Background Technology

[0002] With the rapid development of the new energy vehicle industry, sales and ownership of new energy vehicles continue to climb, and private cars, commercial vehicles, and logistics vehicles are becoming fully electrified. Existing low-power slow charging equipment can no longer meet the large-scale charging demand. In response, the industry generally adopts the method of connecting multiple standard power modules in parallel to increase the total output power and build a high-power charging system to meet the large-scale charging demand.

[0003] In multi-module parallel chargers and charging piles, the operating efficiency, module lifespan, and equipment reliability of the charging modules directly determine the operating revenue of the charging station, the grid load pressure, and the user charging experience. These are key issues of concern in current multi-module parallel charging.

[0004] The current power scheduling of multi-module parallel charging has problems such as superficial health management, coarse efficiency optimization, passive and blind scheduling, uneven module aging, and excessively frequent switching, making it impossible to achieve full-cycle, refined, and global coordinated control of efficiency and lifespan. Summary of the Invention

[0005] This application provides a charging module control method and device based on efficiency optimization and health balancing, which solves the problems of superficial health management, coarse efficiency optimization, passive and blind scheduling, uneven module aging, and excessively frequent switching in existing charging control systems.

[0006] This application provides a charging module control method based on efficiency optimization and health balancing, comprising: Acquire charging data from the charging terminal and predict the power demand of the charging terminal in the next cycle; Obtain the operating data of each charging module and determine the health status of each charging module based on the health degradation quantification model; Based on the predicted power demand, determine the number of charging modules required for the next cycle of the charging terminal, and in conjunction with the health status, select the charging modules to be switched in the next cycle according to the number of modules to be switched. Calculate the health dispersion coefficient based on the health status of all online charging modules; Based on the health dispersion coefficient, determine the execution efficiency optimization strategy or the health balance strategy.

[0007] In one example, the charging data includes the state of charge; After acquiring the charging data of the charging terminal, the method further includes: The charging stage is determined based on the state of charge of the charging terminal; the charging stage includes a constant current stage, a constant voltage stage, and a float charging stage. The corresponding cycle duration is determined based on the charging stage; wherein the cycle duration of the float charging stage is longer than the cycle duration of the constant current stage, and the cycle duration of the constant current stage is longer than the cycle duration of the constant voltage stage.

[0008] In one example, the runtime data includes cumulative runtime, cumulative inefficient runtime, cumulative number of switching operations, single runtime, and single alarm count; The health assessment of each charging module based on the health degradation quantification model includes: Based on the cumulative runtime, cumulative inefficient runtime, and cumulative switching count, the health score of each charging module is calculated using a health degradation quantification model. The health level of each charging module is determined based on the health value, or based on the single running time and the number of single alarms; the health level includes at least one of healthy, concerned, deteriorated, or faulty.

[0009] In one example, the health degradation quantification model is represented as:

[0010] in, Let be the real-time health value of the i-th charging module. Here, α represents the initial health value of the charging module, β represents the degradation coefficient due to runtime, β represents the degradation coefficient due to inefficient operation, and γ represents the degradation coefficient due to switching shock. For cumulative runtime, Nswitch represents the cumulative number of inefficient runtimes.

[0011] In one example, determining the number of charging modules required for the next cycle of the charging terminal based on the predicted power demand, and selecting the charging modules to be switched in the next cycle according to the number of modules required based on the health status, includes: Based on the preset optimal efficiency range, and according to the predicted power demand and the rated power of the charging module, the range of the number of charging modules required for the next cycle of the charging terminal is determined. For each candidate switching quantity within the specified switching quantity range, multiple candidate schemes are constructed based on the health status of each charging module; each candidate scheme corresponds one-to-one with a candidate switching quantity. Based on the average efficiency, average health, and number of new switching operations of the charging modules in the candidate schemes, calculate the comprehensive evaluation value corresponding to each candidate scheme. Based on the comprehensive evaluation value, the candidate scheme is determined as the charging module to be switched in the next cycle.

[0012] In one example, calculating the health dispersion coefficient based on the health status of all online charging modules includes: Calculate the standard deviation and average health of all online charging modules; The coefficient of variation of health status is calculated based on the ratio of the standard deviation of health status to the average health status.

[0013] In one example, determining the execution efficiency optimization strategy or the health balancing strategy based on the health dispersion coefficient includes: When the health degree dispersion coefficient is greater than the preset dispersion threshold, a health balancing strategy is executed to adjust the priority of switching on the charging modules with the highest and / or lowest health degrees. When the health degree dispersion coefficient is less than or equal to a preset dispersion threshold, an efficiency optimization strategy is executed, and charging modules with a conversion efficiency less than the minimum efficiency threshold are cut off based on the conversion efficiency of the online charging modules of each charging terminal.

[0014] In one example, after the execution efficiency optimization strategy, the method further includes: Based on the power requirements of other terminals, the charging module that has been removed from the current terminal is preferentially allocated to terminals that meet the preset requirements standards.

[0015] In one example, after selecting the charging module to be switched in the next cycle according to the number of switches, the method further includes: Determine the current capacity of the online charging module; When the predicted power demand exceeds a first preset ratio of the capacity, a pre-wake-up operation is performed on the switched charging module; When the predicted power demand is lower than a second preset ratio of the capacity, a pre-sleep operation is performed on the switched charging module.

[0016] This application provides a charging module control device based on efficiency optimization and health balancing, comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to implement the charging module control method based on efficiency optimization and health balancing as described above.

[0017] This application provides a charging module control method and device based on efficiency optimization and health balancing, which can achieve the following beneficial effects: Based on charging data from charging terminals, different charging stages are identified, and future power demand, peak power, and duration are predicted in advance, achieving an upgrade from passive response to proactive prediction. Furthermore, the scheduling cycle is adaptively adjusted, closely combined with the status of the charging terminals to achieve a balance between response speed and switching frequency.

[0018] By incorporating key multi-dimensional degradation factors into coupled modeling, a multi-level health classification is constructed to quantify health status, enabling refined health management and addressing the pain points of superficial and unquantifiable health management. This fundamentally reduces the probability of sudden failures and extends the lifespan of charging modules. A dual-dimensional reverse priority scheduling approach, employing wake-up and hibernation, ensures that healthy modules are used more frequently and degraded modules are used less frequently, achieving a natural balance.

[0019] Furthermore, a health dispersion coefficient is introduced as a balance trigger condition to achieve simultaneous control over health balance and efficiency optimization, balance on demand, and quantitative triggering, avoiding blind scheduling and frequent switching.

[0020] By setting the optimal efficiency range, the effective operation of the charging module is ensured, ineffective switching is reduced, and precise cross-terminal allocation is enabled to solve the problem of low load and low efficiency, thereby improving the power utilization rate and service capability of the station. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. The accompanying drawings described herein are used to provide a further understanding of this application and constitute a part of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the accompanying drawings: Figure 1 A flowchart illustrating a charging module control method based on efficiency optimization and health balancing, provided for an embodiment of this application; Figure 2 A flowchart of another charging module control method based on efficiency optimization and health balancing provided in an embodiment of this application; Figure 3 A diagram illustrating the architecture of a charging module control system based on efficiency optimization and health balancing, provided for an embodiment of this application. Figure 4 This is a schematic diagram of the structure of a charging module control device based on efficiency optimization and health balancing, provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] A charging system (such as a charging station) contains a large number of charging modules that can simultaneously supply power to multiple charging terminals (such as electric vehicles). Based on the power requirements of different charging terminals, a corresponding number or status of charging modules can be scheduled to each terminal. Different charging modules have different health states, including current performance, degree of aging, accumulated damage, and risk of failure. To improve the overall operational lifespan of the charging system and extend its operating time, the health status of different charging modules needs to be measured, and modules in different health states need to be scheduled accordingly to extend their lifespan as much as possible. However, at the same time, to improve user experience, the efficiency of the charging modules must also be ensured to minimize charging time and improve charging efficiency.

[0024] Existing charging control methods suffer from several drawbacks. First, they only rotate start and stop based on runtime, failing to consider factors such as operating conditions and switching impacts, resulting in superficial health management and an inability to achieve refined management throughout the entire lifecycle. Second, the granularity of single-module efficiency optimization is coarse, passively responding to real-time power, leading to long-term inefficient operation during low-load phases and frequent module switching. Third, power sharing only achieves hardware path switching, making it difficult to balance efficiency and lifespan. Fourth, charging module scheduling often involves starting and stopping based on address sequence, which can easily lead to some charging modules being heavily loaded for extended periods while others are idle for long periods, resulting in uneven aging, high overall failure rate, and short lifespan.

[0025] Based on this, this application proposes a charging module control method based on efficiency optimization and health balance, so as to achieve the dual goals of improving overall efficiency and balancing module life.

[0026] Figure 1 The flowchart of the charging module control method based on efficiency optimization and health balancing provided in the embodiments of this application specifically includes the following steps: S101: Obtain charging data from the charging terminal and predict the power demand of the charging terminal in the next cycle.

[0027] In this embodiment, by setting a period, the status of the charging terminal is detected in each period to determine whether the charging module corresponding to the charging terminal needs to be adjusted. This allows for flexible adjustments based on the needs of the charging terminal and the current status of each charging module, enabling free scheduling of each charging module to the appropriate charging terminal.

[0028] The cycle duration needs to be set reasonably. If the cycle is too long, it may not be able to keep up with power surges, resulting in poor efficiency of the charging module. If the cycle is too short, it will increase useless extra calculations and may also cause the charging module to have frequent switching problems, resulting in impact losses and accelerated aging.

[0029] In one embodiment, the cycle duration can be varied and adjusted according to the state of the charging terminal. The charging data of the charging terminal may include its state of charge (SOC), which represents different levels of charge. Specifically, the charging stage is determined based on the SOC, and the corresponding cycle duration is determined based on the charging stage.

[0030] The charging stage can be divided into constant current stage, constant voltage stage, and float charging stage according to the state of charge.

[0031] The constant current phase is the main charging phase, characterized by lower current levels. During this phase, charging power is high, output is stable, and fluctuations are minimal; this phase also lasts the longest. Because the power remains essentially constant during this phase, high-frequency detection and switching are unnecessary, allowing for a longer scheduling cycle. This significantly reduces the number of start-stop cycles of the charging module, minimizing switching shock damage and protecting components. Simultaneously, the charging module can operate stably at high efficiency for extended periods, resulting in high energy efficiency.

[0032] The constant voltage stage is when the battery is nearly fully charged. During this stage, the voltage remains constant, the current continuously decreases, the power gradually decays and changes rapidly, and the load is in a dynamic decline state. Because the power is constantly decreasing, it is necessary to monitor frequently and quickly adjust the charging modules to prevent excess charging modules from entering the light-load, inefficient range. Therefore, a short cycle can be set for this stage to ensure power matching accuracy and maintain overall efficiency.

[0033] The float charging stage is the final stage of charging, characterized by extremely low power and very slow changes. Only a small current is used to maintain the battery at full charge, and the load is basically stable. Since the overall load tends to be stable, frequent scheduling is unnecessary, and the longest possible cycle can be used to minimize ineffective switching, reduce the impact on modules that have already entered a low-load state, and delay aging.

[0034] That is, the float charging stage has the longest cycle time, which is longer than the constant current stage, while the constant pressure stage has the shortest cycle time, which is shorter than the constant current stage.

[0035] By setting different scheduling cycles according to the charging stage, invalid switching can be reduced, losses can be decreased, and the charging module can be kept running in a stable efficiency range to avoid inefficient operation and make the operation more stable. The optimal balance between response speed and switching frequency can be achieved, effectively improving the robustness of the system.

[0036] In one possible implementation, the constant current stage can be 0%~80% of the SOC with a period of 10 minutes, the constant voltage stage can be 80%~95% of the SOC with a period of 2 minutes, and the float charge stage can be 95%~100% of the SOC with a period of 15 minutes.

[0037] In one embodiment, charging data includes the state of charge (SOC) of the charging terminal, charging voltage, and current. A load charging prediction model can be constructed to determine the charging stage of the charging terminal based on its SOC, thereby obtaining the voltage, current, and power variation patterns of the charging terminal in the corresponding stage.

[0038] Specifically, in the constant current stage, the current is constant and the voltage increases approximately linearly with the state of charge, so the corresponding power demand can be predicted; in the constant voltage stage, the voltage is constant and the current decays over time, so the corresponding power demand can be predicted; in the float charging stage, the current approaches 0, the voltage is basically stable, and the power hardly changes.

[0039] S102: Obtain the operating data of each charging module and determine the health status of each charging module based on the health degradation quantification model.

[0040] During the operation of each charging module, its health can be measured through operational data. Health is a quantitative indicator of the charging module's operating status, including its aging degree, cumulative damage, and remaining reliable operating capacity, and can be used as a standard for charging module scheduling.

[0041] In one embodiment, the operational data may include cumulative runtime, cumulative inefficient runtime, cumulative switching count, single runtime, and single alarm count. Specifically, cumulative runtime represents the total runtime of the charging module since it was put into operation; cumulative inefficient runtime represents the total duration of inefficient operation since the charging module was put into operation; cumulative switching count represents the total number of switching operations since the charging module was put into operation; single runtime represents the runtime of the charging module during a specific power supply process since its startup; and single alarm count represents the number of alarms generated by the charging module during a specific power supply process since its startup.

[0042] The operating efficiency of the charging module can be determined by the ratio between the output power and the rated power, and can also be regarded as the load state of the module. Setting an optimal efficiency range can be regarded as the best operating efficiency of the module, and setting a minimum operating efficiency threshold can be regarded as inefficient operation when the operating efficiency is lower than the minimum operating efficiency threshold.

[0043] Switching includes connecting and disconnecting. Connecting refers to waking up a dormant charging module, connecting it to the DC bus, and enabling it to participate in power output; disconnecting refers to shutting down a working charging module, disconnecting it from the bus, and putting it into a dormant state.

[0044] Alarms indicate abnormal conditions during the operation of the charging module, including electrical issues such as overvoltage, undervoltage, overcurrent, output short circuit, input phase loss, abnormal bus voltage, and uneven current flow; temperature-related issues such as excessive internal temperature of the module, poor heat dissipation, and fan failure; and communication-related issues such as interruption of communication between the module and the main controller, signal loss, and abnormal data.

[0045] In one embodiment, the health of each charging module can be determined based on a health degradation quantification model. Specifically, the health value of each charging module can be calculated based on the cumulative runtime, cumulative inefficient runtime, and cumulative switching count, using the health degradation quantification model. Alternatively, the health level of each charging module can be determined based on the health value, or on the runtime of a single operation or the number of alarms in a single operation. The health level includes at least one of the following: healthy, concerning, degraded, or faulty. Incorporating inefficient operating damage and switching impact damage into the quantification model can accurately reflect the impact of actual operating conditions on the lifespan of the charging module. Furthermore, by measuring the health of the charging module using both quantified values ​​and graded levels, with quantified values ​​as the primary criterion and graded levels as a correction criterion, the accuracy of judging the health status of the charging module can be enhanced.

[0046] In one possible implementation, the health degradation quantification model can be expressed as Equation 1: Formula 1 in, Let be the real-time health value of the i-th charging module, with a value range of [0,1]. This is the initial health value of the charging module. A brand new charging module has an initial health value of 1. α This is the runtime degradation factor. β The inefficient operation degradation coefficient, γ The impact degradation coefficient is the coefficient of the cutting process. For cumulative runtime, To accumulate inefficient runtime, Nswitch This represents the cumulative number of switching operations. The degradation coefficients for runtime, inefficient operation, and switching impact are derived from life test data and field operation data statistics.

[0047] It can be concluded that the health value of the charging module is negatively correlated with the cumulative running time, the cumulative inefficient running time, and the cumulative number of switching operations. The longer the cumulative running time, the lower the health value; the longer the cumulative inefficient running time, the lower the health value; and the more cumulative switching operations, the lower the health value.

[0048] In one possible implementation, when determining the health level of the charging module, it can be determined that the health level of the charging module is negatively correlated with the single running time and the single alarm count. The longer the single running time, the lower the health level; the more single alarm counts, the lower the health level.

[0049] Specifically, the health levels are categorized into Healthy, Attention, Deteriorated, and Faulty. When the duration of a single run is less than or equal to a first duration threshold, the charging module's health level is determined to be Healthy; when the duration is greater than the first duration threshold but less than or equal to a second duration threshold, the charging module's health level is determined to be Attention; when the duration is greater than the second duration threshold, the charging module's health level is determined to be Deteriorated. Similarly, when the number of alarms in a single run is less than or equal to a first alarm count threshold, the charging module's health level is determined to be Healthy; when the number of alarms in a single run is greater than the first alarm count threshold but less than or equal to a second alarm count threshold, the charging module's health level is determined to be Attention; when the number of alarms in a single run is greater than the second alarm count threshold, the charging module's health level is determined to be Deteriorated. When the charging module is damaged or unable to function, its health level is determined to be Faulty. There is an "OR" relationship between the duration of a single run and the number of alarms in a single run; satisfying either condition is sufficient to determine the charging module's health level. Among them, the first duration threshold is less than the second duration threshold, and the first count threshold is less than the second count threshold. For example, the first duration threshold is 1 hour, the second duration threshold is 2 hours, the first count threshold is 10 times, and the second count threshold is 20 times.

[0050] Alternatively, health levels can be determined based on the magnitude of the health value. For example, when the health value is greater than or equal to the first health threshold, the charging module's health level is determined to be healthy; when the health value is less than the first health threshold but greater than or equal to the second health threshold, the charging module's health level is determined to be "concerned"; when the health value is less than the second health threshold but greater than or equal to the third health threshold, the charging module's health level is determined to be "deteriorated"; and when the health value is less than the third health threshold, the charging module's health level is determined to be "fault warning". The first, second, and third health thresholds decrease sequentially, for example, the first health threshold is 0.8, the second health threshold is 0.5, and the third health threshold is 0.3.

[0051] In one embodiment, a latching mechanism can be used to prevent frequent jumps in health levels. When it is determined that the health level will change, a pre-defined latching interval can be used to determine if the change exceeds the latching interval before confirming that the health level has indeed changed. This creates hysteresis, avoiding jitter and frequent jumps.

[0052] For example, if the first health threshold is 0.8, then 0.75~0.85 is set as the latching range. Only if the change in the health value exceeds this range is the health level considered to have changed.

[0053] S103: Based on the predicted power demand, determine the number of charging modules required for the next cycle of the charging terminal, and in conjunction with the health status, select the charging modules to be switched in the next cycle according to the number of modules to be switched.

[0054] The power demand of a charging terminal determines the number of charging modules needed to power it. If the power demand increases in the next cycle, new charging modules need to be added to meet the needs of the charging terminal; if the power demand decreases in the next cycle, some charging modules can be switched out to improve module operating efficiency.

[0055] In one embodiment, an optimal efficiency range can be set for the charging module. The optimal efficiency range represents the best ratio between the actual output power of the charging module and its rated power. Generally speaking, when the charging module operates within its optimal efficiency range, its load condition is reasonable, the loss in power conversion is minimal, and the heat generation is reasonable. Therefore, the power conversion efficiency of the charging module is also at its optimal state.

[0056] In addition, the efficiency curve in the boundary region changes rapidly, and the control margin is small, so some redundancy can be reserved. For example, the optimal efficiency range is set to 30%~80%, but in actual execution, a 10% redundancy is reserved, with 40%~70% as the benchmark.

[0057] In one embodiment, the steps of calculating the number of charging modules to be switched on and determining the specific charging modules that need to be switched on include the following four steps: First, based on the predicted power demand of the terminal, in order for the charging module to operate in the preset optimal efficiency range, the output power range of the charging module can be determined according to the rated power of a single charging module, and the corresponding range of the number of charging modules to be switched can be calculated based on the predicted power demand and the output power range of a single charging module.

[0058] Specifically, the formula for calculating the range of cutting quantities can be expressed as Formula 2: Formula 2 in, For the predicted power demand, This represents the upper limit of the optimal efficiency range. This represents the lower limit of the optimal efficiency range. This refers to the rated power of the charging module.

[0059] Second, there are multiple candidate switching quantities within the switching quantity range. In order to determine which quantity to use in the end, multiple candidate schemes can be formed for each candidate switching quantity within the switching quantity range based on the health status of each charging module; each candidate scheme corresponds one-to-one with the candidate switching quantity.

[0060] The charging modules can be sorted based on their health level and health value. A higher health level results in a higher ranking; for modules with the same health level, a higher health value results in a higher ranking. Based on the candidate switching quantity and health ranking, charging modules with the same number of candidate switching options are selected to form a candidate scheme. For example, if the candidate switching quantity is 5, the top 5 charging modules with the highest health values ​​are selected to form the candidate scheme.

[0061] Third, based on the average efficiency, average health, and number of new switching operations of the charging modules in the candidate schemes, calculate the comprehensive evaluation value corresponding to each candidate scheme.

[0062] The comprehensive evaluation value is an integrated quantitative score calculated by combining multiple indicators such as the health status, load conditions, degree of degradation, and operating characteristics of the charging module. It can be used to evaluate the value of candidate solutions. Generally speaking, the higher the average efficiency, the better; the higher the average health, the better; and the fewer the number of new switching operations, the better.

[0063] The comprehensive evaluation value can be calculated using the following formula three: Score(N) = W e E(N)+W h H(N)-W s S(N) Formula 3 in, Score(N) For comprehensive evaluation, E(N) For average efficiency, H(N) For average health, S(N) To increase the number of throws, W e For efficiency coefficient, W h For health index, W s This is to add a cutting coefficient.

[0064] Fourth, based on the comprehensive evaluation value, the candidate schemes are determined as the charging modules to be switched in the next cycle.

[0065] Under the condition of satisfying the efficiency range constraint, the candidate scheme with the highest efficiency, the best health status and the fewest switching times can be comprehensively selected, and the charging module in the scheme can be used as the charging module corresponding to the charging terminal in the next cycle.

[0066] Ultimately, the optimal number of charging modules determined in this step must satisfy the following formula: Formula 4 in, This represents the average output power of the charging module. This is the predicted power demand (which is also the total power demand of the system). To determine the optimal number of cuts. This represents the lower limit of the optimal efficiency range. This represents the upper limit of the optimal efficiency range.

[0067] S104: Calculate the health dispersion coefficient based on the health status of all online charging modules.

[0068] An online charging module indicates a charging module that is currently activated, connected to the DC bus, and in a energized operating state, participating in power output. In contrast, an offline module indicates a module that is in standby mode, not connected to the output circuit, and not supplying power to external devices.

[0069] The health dispersion coefficient quantifies the difference in health status between charging modules, determining whether the aging and deterioration of each module is balanced. The balance of deterioration among charging modules directly impacts the overall lifespan of the charging station. Therefore, the health dispersion coefficient of the online charging modules must not be too large during charging station operation.

[0070] In one embodiment, the standard deviation and average health status of all online charging modules are calculated; and the coefficient of variation of health status is calculated based on the ratio of the standard deviation and the average health status.

[0071] Specifically, this can be expressed using the following formula five: Cd=σ / μ Formula 5 in, Cd The coefficient of variation for health status. σ The standard deviation of the health of the online module. μ This represents the average health status of the online modules.

[0072] S105: Determine the execution efficiency optimization strategy or the health balance strategy based on the health dispersion coefficient.

[0073] When the health dispersion coefficient is small, it indicates that the overall health of the charging station is good and the health of each charging module is relatively balanced. At this time, the focus can be on optimizing efficiency and improving the efficiency of the charging station. When the health dispersion coefficient is too large, it indicates that the overall health of the charging station is unbalanced, with charging modules with poor health being used too frequently and charging modules with good health being underutilized. At this time, the focus should be on balancing the health of the charging modules.

[0074] In one embodiment, a health balancing strategy can be implemented when the health dispersion coefficient is greater than the preset discrete threshold. This adjusts the priority of switching charging modules with the highest and / or lowest health, thereby increasing the utilization of high-health charging modules and reducing the utilization of low-health charging modules. When the health dispersion coefficient is less than or equal to the preset discrete threshold, an efficiency optimization strategy is implemented. This monitors the operating status of the charging modules corresponding to each charging terminal and, based on the conversion efficiency of the online charging modules of each charging terminal, cuts off charging modules with efficiency below the minimum efficiency threshold to improve efficiency. This enables quantitative triggering and on-demand balancing, avoiding ineffective scheduling and reducing switching wear.

[0075] The "highest" and "lowest" health ratings do not refer to a single charging module, but rather to multiple charging modules with relatively high and low health ratings. The specific range can be set as needed, such as the top 10% and bottom 10% of charging modules in the online module health rating range. The minimum efficiency threshold represents the lowest limit of the charging module's conversion efficiency (i.e., energy conversion efficiency). Below this threshold, it indicates that the charging module's conversion efficiency is too low, energy loss is too high, and it is not conducive to improving overall efficiency.

[0076] In one embodiment, after disconnecting inefficient charging modules, charging modules can be preferentially allocated to terminals that meet preset demand standards based on the power requirements of other terminals. Such cross-terminal dynamic scheduling is beneficial for achieving optimal global efficiency at the site level. The preset demand standards may include charging terminals that are in a constant current phase, have stable power, and have high demand.

[0077] In one embodiment, when determining whether to switch charging modules on or off, a predictive operation can be performed. Specifically, the total rated power of the currently online charging modules is determined as the current capacity. When the predicted power demand exceeds a first preset proportion of the current capacity, it indicates that the power margin of the existing online modules is insufficient and the load is about to approach full capacity. At this time, a pre-wake-up operation is performed to wake up the charging modules to be put into use in the current cycle, ready for use in the next cycle. This can avoid module overload and deviation from the optimal efficiency range caused by instantaneous power fluctuations, while also preventing impact damage caused by emergency switching.

[0078] When the predicted power demand is lower than a second preset percentage of the current capacity, it indicates that the existing online modules have excessive power margin, which may cause the charging modules to enter a light-load condition. In this case, a pre-sleep operation is performed, putting the charging modules to be cut off into sleep mode in the current cycle to improve efficiency. The first preset percentage can be 90%, and the second preset percentage can be 80%.

[0079] In one embodiment, the priority of the waking-up charging modules is ranked from high to low based on their health level, and from high to low based on the health value of the same level; the priority of the sleeping charging modules is ranked from low to high based on their health level, and from low to high based on the health value of the same level.

[0080] In this embodiment, based on charging data from the charging terminal, different charging stages are identified, and future power demands, peak power, and duration are predicted in advance, achieving an upgrade from passive response to proactive prediction. Furthermore, the scheduling cycle is adaptively adjusted, closely combined with the charging terminal's status to achieve a balance between response speed and switching frequency.

[0081] By incorporating key multi-dimensional degradation factors into coupled modeling, a multi-level health classification is constructed to quantify health status, enabling refined health management and addressing the pain points of superficial and unquantifiable health management. This fundamentally reduces the probability of sudden failures and extends the lifespan of charging modules. A dual-dimensional reverse priority scheduling approach, employing wake-up and hibernation, ensures that healthy modules are used more frequently and degraded modules are used less frequently, achieving a natural balance.

[0082] Furthermore, a health dispersion coefficient is introduced as a balance trigger condition to achieve simultaneous control over health balance and efficiency optimization, balance on demand, and quantitative triggering, avoiding blind scheduling and frequent switching.

[0083] By setting the optimal efficiency range, the effective operation of the charging module is ensured, ineffective switching is reduced, and precise cross-terminal allocation is enabled to solve the problem of low load and low efficiency, thereby improving the power utilization rate and service capability of the station.

[0084] Corresponding to the above methods, Figure 2 A flowchart of another charging module control method based on efficiency optimization and health balancing provided in this application embodiment. Figure 2 Includes the following steps: 1. System Initialization Configure the charging module's rated power, efficiency-power curve, optimal efficiency range of 30%–80%, minimum efficiency threshold, health grading threshold, degradation coefficient, and scheduling cycle; initialize the module's activation status, operating data, initial health value, and priority sorting.

[0085] 2. Load characteristic identification and demand forecasting The system collects the SOC, charging voltage, and current of the charging terminal in real time, and divides them into three stages: constant current (0%–80% SOC), constant voltage (80%–95% SOC), and float charging (95%–100% SOC). It then predicts the power demand curve, peak value, and duration for the next cycle.

[0086] 3. Health Status Update and Dual-Dimensional Priority Ranking The health status value and health status level are updated according to the health deterioration quantification model, and are divided into healthy level (≥0.8), attention level (0.5≤Hi<0.8), deterioration level (0.3≤Hi<0.5), and fault warning level (<0.3). Wake-up priority: Healthy level → Attention level → Degraded level (emergency only) → Fault level (disable wake-up); Hibernation priority: Fault level → Deterioration level → Attention level → Healthy level.

[0087] 4. Pre-casting and efficiency range matching Calculate the optimal number of switches based on the predicted power demand, so that the average power falls within the optimal efficiency range and a 10% redundancy is reserved. If the predicted power demand exceeds 90% of the current capacity, it will be pre-wake up; if it is below 80%, it will be pre-sleep, thus matching the optimal operating condition in advance.

[0088] 5. Terminal-level efficiency optimization and cross-terminal scheduling The module efficiency of each charging terminal is iterated, and the module is cut off if it is below the threshold or is predicted to be out of the optimal range. The cut-out modules are preferentially allocated to the constant current stage and power-stable redundant terminals to ensure that all module groups operate in the high-efficiency range.

[0089] 6. Lifetime Balanced Closed-Loop and Adaptive Scheduling Periodically update the cumulative runtime, inefficient runtime, and number of deployments, and refresh the health status. If the health dispersion coefficient exceeds the threshold, life balance scheduling is initiated, adjusting the priority of modules participating in scheduling and balancing the degradation rate. The scheduling cycle is automatically adjusted according to the proportion of load phases, balancing responsiveness and reliability.

[0090] 7. Interface Display and Maintenance Operations The "Charging Module Status" page can be accessed during standby, startup, and charging, displaying information for all modules in a paginated manner. Each row has a "Reset" button, which can clear the number of alarms and the cumulative duration; the "Return" button in the lower right corner returns to the previous screen, and the refresh rate is synchronized with the telemetry and telecontrol frames.

[0091] Reset supports manual clearing of alarm records; for modules whose performance has been confirmed to have been restored after maintenance, the administrator can perform a life record reconstruction operation, and at the same time reset the cumulative runtime, inefficient runtime, number of switching and initial health parameters, and re-establish health assessment records.

[0092] Corresponding to the above-mentioned charging module control method based on efficiency optimization and health balancing, this application also provides a corresponding charging module control system based on efficiency optimization and health balancing, as shown in the specific system architecture diagram below. Figure 3 As shown.

[0093] The system includes: an integrated charging management board, power module groups, matrix switch units, a human-machine interface, and a charging operation and maintenance platform. The human-machine interface displays module address, status, voltage, current, power, alarm count, runtime, and health status in real time, and supports manual reset of alarms and cumulative duration. The hardware architecture of the matrix switch unit supports dynamic power scheduling across terminals, charging piles, and charging stacks.

[0094] Based on the same inventive concept, embodiments of this application also provide corresponding charging module control devices based on efficiency optimization and health balancing, such as... Figure 4 As shown.

[0095] Figure 4 The schematic diagram of the charging module control device structure based on efficiency optimization and health balancing provided in the embodiments of this application specifically includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to implement the above-described method. It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0096] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0097] The devices and methods provided in this application are one-to-one correspondences. Therefore, the devices also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices will not be repeated here.

[0098] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using dedicated hardware combined with computer instructions. The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0099] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0100] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A charging module control method based on efficiency optimization and health balancing, characterized in that, include: Acquire charging data from the charging terminal and predict the power demand of the charging terminal in the next cycle; Obtain the operating data of each charging module and determine the health status of each charging module based on the health degradation quantification model; Based on the predicted power demand, determine the number of charging modules required for the next cycle of the charging terminal, and in conjunction with the health status, select the charging modules to be switched in the next cycle according to the number of modules to be switched. Calculate the health dispersion coefficient based on the health status of all online charging modules; Based on the health dispersion coefficient, determine the execution efficiency optimization strategy or the health balance strategy; The operational data includes cumulative runtime, cumulative inefficient runtime, cumulative number of switching operations, single runtime, and single alarm count; The method of determining the health of each charging module based on the health degradation quantification model includes: calculating the health value of each charging module based on the cumulative running time, cumulative inefficient running time, and cumulative switching times; determining the health level of each charging module based on the health value, or based on the single running time and single alarm count; the health level includes at least one of healthy, concerned, degraded, and faulty. The quantitative model for health deterioration is expressed as follows: in, Let be the real-time health value of the i-th charging module. This represents the initial health value of the charging module. α This is the runtime degradation factor. β The inefficient operation degradation coefficient, γ The impact degradation coefficient is the coefficient of the cutting process. For cumulative runtime, To accumulate inefficient runtime, Nswitch This represents the cumulative number of throws / cuts. The step of determining the execution efficiency optimization strategy or health balancing strategy based on the health degree dispersion coefficient includes: when the health degree dispersion coefficient is greater than a preset dispersion threshold, executing the health balancing strategy to adjust the priority of switching the charging modules with the highest and / or lowest health degrees; when the health degree dispersion coefficient is less than or equal to the preset dispersion threshold, executing the efficiency optimization strategy to cut off the charging modules with lower efficiency thresholds based on the conversion efficiency of the online charging modules of each charging terminal.

2. The charging module control method based on efficiency optimization and health balancing according to claim 1, characterized in that, The charging data includes the state of charge. After acquiring the charging data of the charging terminal, the method further includes: The charging stage is determined based on the state of charge of the charging terminal; the charging stage includes a constant current stage, a constant voltage stage, and a float charging stage. The corresponding cycle duration is determined based on the charging stage; wherein the cycle duration of the float charging stage is longer than the cycle duration of the constant current stage, and the cycle duration of the constant current stage is longer than the cycle duration of the constant voltage stage.

3. The charging module control method based on efficiency optimization and health balancing according to claim 1, characterized in that, The step of determining the number of charging modules required for the next cycle of the charging terminal based on the predicted power demand, and selecting the charging modules to be switched in the next cycle according to the number of modules to be switched in, in conjunction with the health status, includes: Based on the preset optimal efficiency range, and according to the predicted power demand and the rated power of the charging module, the range of the number of charging modules required for the next cycle of the charging terminal is determined. For each candidate switching quantity within the specified switching quantity range, multiple candidate schemes are constructed based on the health status of each charging module; each candidate scheme corresponds one-to-one with a candidate switching quantity. Based on the average efficiency, average health, and number of new switching operations of the charging modules in the candidate schemes, calculate the comprehensive evaluation value corresponding to each candidate scheme. Based on the comprehensive evaluation value, the candidate scheme is determined as the charging module to be switched in the next cycle.

4. The charging module control method based on efficiency optimization and health balancing according to claim 1, characterized in that, The step of calculating the health dispersion coefficient based on the health status of all online charging modules includes: Calculate the standard deviation and average health of all online charging modules; The coefficient of variation of health status is calculated based on the ratio of the standard deviation of health status to the average health status.

5. The charging module control method based on efficiency optimization and health balancing according to claim 1, characterized in that, Following the execution efficiency optimization strategy, the method further includes: Based on the power requirements of other terminals, the charging module that has been removed from the current terminal is preferentially allocated to terminals that meet the preset requirements standards.

6. The charging module control method based on efficiency optimization and health balancing according to claim 1, characterized in that, After selecting the charging module to be switched in the next cycle according to the number of switches, the method further includes: Determine the current capacity of the online charging module; When the predicted power demand exceeds a first preset ratio of the capacity, a pre-wake-up operation is performed on the switched charging module; When the predicted power demand is lower than a second preset ratio of the capacity, a pre-sleep operation is performed on the switched charging module.

7. A charging module control device based on efficiency optimization and health balance, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to implement the charging module control method based on efficiency optimization and health balancing as described in any one of claims 1 to 6.

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