A method for accelerating charging of energy storage products based on multi-power interfaces

Abstract

The application discloses a kind of energy storage product charging speed-up methods based on multiple power interfaces, it is related to portable energy storage equipment multiple power interface collaborative charging speed-up technical field, including the following steps: configuration at least two portable energy storage equipment containing EMS system, BMS system, DC / DC converter, DC / AC inverter and MPPT module, each device is additionally provided with the expansion power interface consistent with original interface specification, and built-in edge computing module;In the application, by additionally provided with the expansion power interface consistent with original interface specification, built-in edge computing module and deploy optimized multi-objective dynamic power distribution, EMS-BMS collaborative control and abnormal detection algorithm, construct cross-device data synchronization link, realize inverter cross-device reuse and MPPT module bidirectional charge and discharge, solve the problem of charging speed bottleneck caused by single power interface, hardware resource idling in multiple device collaborative scenario and different power level charging demand adaptability insufficient.

Description

Technical Field

[0001] This invention relates to the field of multi-power interface collaborative charging acceleration technology for portable energy storage devices, specifically a charging acceleration method for energy storage products based on multi-power interfaces. Background Technology

[0002] With the booming development of the outdoor economy, the normalization of emergency power supply needs, and the continuous expansion of mobile operation scenarios, portable energy storage devices have moved from the professional field to the mass consumer market, becoming essential products for home backup power, core camping equipment, and field exploration support. Among them, 3KWH and 2KWH models, as mainstream models that balance capacity and portability, have seen their market penetration rate rise year by year due to their strong adaptability and high cost-effectiveness, covering diverse usage scenarios from daily short trips to long-term field operations. Under this trend, users' core demand for energy storage devices has shifted from simply providing power to quickly replenishing energy. Whether it's rapid response in emergency situations, instant power restoration during camping trips, or efficient power replenishment when multiple devices are linked, charging speed has become a key indicator affecting user experience, and users' need for quick full charge and immediate use is becoming increasingly urgent.

[0003] However, most energy storage products in the industry still use the traditional design architecture of a single power interface. This design directly limits the maximum AC charging power of a single device to a fixed level, with mainstream products generally only reaching 2200W, forming a difficult-to-overcome technical bottleneck. This problem is particularly evident in large-capacity products such as 3kWh, which have larger battery capacities and, due to the limitation of a single power interface, charging cycles often last for several hours. More importantly, in scenarios where multiple energy storage devices are used in tandem, existing designs fail to fully exploit the potential of hardware resources between devices. Each device can only charge independently, and the overall charging capacity cannot be improved by reusing core components such as inverters from other devices, resulting in idle and wasted hardware resources. At the same time, the lack of flexible scalability of a single power interface makes it difficult for energy storage products to adapt to charging needs of different power levels. When facing complex scenarios such as rapid energy replenishment and parallel energy replenishment by multiple devices, the problems of insufficient adaptability and compatibility become increasingly prominent.

[0004] This charging speed bottleneck caused by a single power interface has had multiple negative impacts on user experience and industry development. In emergency power supply scenarios, lengthy charging processes may prevent critical loads such as medical and communication equipment from receiving timely power support, causing missed opportunities for rescue or operations. In recreational scenarios such as outdoor camping, users have to spend a lot of time waiting for devices to charge, which not only shortens activity time but may also affect travel plans due to insufficient battery life. Even in daily energy replenishment scenarios, inefficient charging speeds cannot meet users' fast-paced usage needs, reducing the practicality of the product. From an industry competition perspective, charging speed has become one of the core decision-making factors for users when purchasing energy storage products. The design flaw of a single power interface puts many products at a disadvantage in market competition, restricting companies' market share expansion and hindering the industry's upgrade towards high efficiency and intelligence, becoming a core technical pain point that urgently needs to be addressed in the current energy storage equipment field. In view of this, this paper proposes a method for accelerating the charging of energy storage products based on multiple power interfaces to overcome the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for accelerating the charging of energy storage products based on multiple power interfaces, so as to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, this invention provides a method for accelerating the charging of energy storage products based on multi-power interfaces, comprising the following steps: Step 1) Configure at least two portable energy storage devices, each containing an EMS system, a BMS system, a DC / DC converter, a DC / AC inverter, and an MPPT module. Each device is equipped with an extended power interface that matches the original interface specifications and has a built-in edge computing module. Step 2) After the device starts its self-test, the device status assessment model is run through the edge computing module to normalize the collected battery SOC, cell temperature, power interface contact resistance and MPPT module efficiency parameters to determine whether the device meets the conditions for collaborative charging and mutual charging readiness. Step 3) Connect the mains smart power supply to the original power interface and the extended power interface of the equipment using a branched shielded power cable, establish a cross-device data synchronization link between devices through a communication line, and test the stability of the link; Step 4) Activate the collaborative charging and bidirectional mutual charging modes, call the multi-objective dynamic power allocation algorithm, and calculate the optimal power allocation scheme by combining parameters such as the rated power of the power supply and the device status score, and then issue it for execution. Step 5) During the charging process, the power distribution and mutual charging status are adjusted in real time through the EMS-BMS collaborative control algorithm, and the DC / AC inverter of one device is reused to provide auxiliary power to another device. Step 6) Run the anomaly detection algorithm to monitor voltage, current, and temperature, handle abnormal scenarios in a timely manner, and ensure charging continuity.

[0007] Furthermore, the normalization formula for the equipment condition assessment model in step 2) is: ; in For the first The charging and inter-charging compatibility status rating of the devices. For the first The actual remaining power of the device. This is the lower limit threshold of the safe charging range. The upper limit threshold for the safe charging range, For the first Real-time temperature of the battery cells in the device. To determine the lower limit threshold of the appropriate charging temperature range, To determine the upper limit threshold of the appropriate charging temperature range, For the first The power interface contact resistance of the device. This is the lower limit of the normal operating resistance range for the interface. This represents the upper limit of the resistance range for normal operation of the interface. These are the weighting coefficients for the SOC parameters. This is the weighting coefficient for the cell temperature parameter. These are the weighting coefficients for the interface contact resistance parameters. This represents the efficiency weighting coefficient for the MPPT module. To improve the real-time working efficiency of the MPPT module.

[0008] Furthermore, the core formula for power allocation in step 4) is: ; in Power allocation for 3 kWh equipment Power allocation for 2 kWh equipment For the rated capacity of a 3 kWh device, For the rated capacity of a 2 kWh device, , The scores are for the rechargeability and compatibility of the two devices. This represents the upper limit of the total power for collaborative charging. , The real-time efficiency of the MPPT modules of the two devices are respectively. To improve the overall efficiency of the two devices.

[0009] Furthermore, in step 5), the bidirectional mutual charging mode is completed through bidirectional charging and discharging of the MPPT module, and the mutual charging process does not affect the normal output of the AC load; when only one device needs to work as an inverter, the other device switches to the backup power pack mode to continuously replenish power.

[0010] Further, in step 5), the inverter reuse specifically involves the auxiliary equipment converting the received mains power into a voltage level suitable for the target device's battery via a DC / AC inverter, and then transmitting it to the target device's battery charging circuit through the cross-device power transmission line.

[0011] Furthermore, the abnormal detection scenarios in step 6) include power interface input current sudden change exceeding 30%, battery voltage close to the full charge threshold, MPPT mutual charging efficiency below 90%, and response time of abnormal handling command ≤ 50ms, where the full charge threshold for 3KWH device is 54.7V and the full charge threshold for 2KWH device is 53.5V.

[0012] Furthermore, the algorithms deployed in the edge computing module have been optimized: the multi-objective dynamic power allocation algorithm introduces a reinforcement learning mechanism, using charging speed, battery loss, interface load balancing, and mutual charging efficiency as the reward function; the EMS-BMS collaborative control algorithm adds a grid voltage harmonic detection module; and the cross-device interface compatibility algorithm has the functions of automatic identification of interface parameters and adjustment of MPPT mutual charging adaptation logic.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. Significantly improved charging speed: By adding an extended power interface and building a dual-interface collaborative power supply circuit, the power limit of 2200W for a single interface is broken, and the total collaborative charging power reaches 4400W. The charging time for a 3KWH device is reduced from 1.5 hours to 42 minutes, and the charging time for a 2KWH device is reduced from 1 hour to 28 minutes, achieving more than double the speed.

[0014] 2. Improved hardware resource utilization: DC / AC inverters and MPPT modules are reused across devices. When one device is working in inverter mode, another device can switch to a backup power supply to continuously replenish power, solving the problem of idle hardware resources in multi-device collaborative scenarios and significantly enhancing the backup power attributes of the devices.

[0015] 3. Significantly improved mutual charging efficiency: The MPPT module is used for bidirectional charging and discharging to replace the traditional AC mutual charging, increasing the total mutual charging efficiency from 84% to 92.16%, and the mutual charging process does not affect the normal output of the AC load.

[0016] 4. Charging stability and safety assurance: Through the edge computing module, the device status assessment, multi-target dynamic power allocation and anomaly detection algorithm are implemented. The power allocation error is ≤3%, the voltage and current fluctuation is ≤2%, and the anomaly response time is ≤50ms. This effectively avoids problems such as overcharging, overload, and short circuit. The maximum battery temperature is controlled at 43℃ and the interface temperature is ≤35℃.

[0017] 5. Enhanced compatibility and adaptability: The cross-device interface compatibility algorithm supports automatic identification of interface parameters and adjustment of MPPT inter-charging adaptation logic, which can adapt to energy storage devices of different brands and specifications, and meet the needs of complex scenarios such as rapid energy replenishment and parallel energy replenishment of multiple devices. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a charging speed-up method for energy storage products based on a multi-power interface according to the present invention. Figure 2 A schematic diagram illustrating the independent charging state of a single power interface; Figure 3 This is a schematic diagram of the dual-power interface collaborative charging state of a charging speed-up method for energy storage products based on multi-power interfaces according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figures 1-3 The present invention provides a technical solution: See Figures 1-3 As shown, an embodiment of a method for accelerating the charging of energy storage products based on multi-power interfaces is presented: I. Implementation Environment and Equipment Composition: 1. Energy Storage Equipment: One 3kWh portable energy storage unit and one 2kWh portable energy storage unit. Both units are equipped with an EMS system, a BMS system, a DC / DC converter, a DC / AC inverter, and an MPPT module, with reserved mounting positions for future power expansion interfaces. Figure 2 As shown, the original single power interface of a single device supports a maximum AC input power of 2200W, and can only complete charging independently, and cannot reuse the core components of another device; for example Figure 3 As shown, with the addition of an extended power interface, the two devices can form a collaborative charging loop, enabling cross-device reuse of the inverter and MPPT module. The device has a built-in edge computing module that supports real-time algorithm execution and parallel data processing, with a computation latency of ≤10ms.

[0021] 2. Expansion Components: Each of the two devices is equipped with one AC charging power interface of the same specification as the original interface. The interface integrates a current and voltage sensor with a sampling frequency of 100Hz and an accuracy of ±0.5%. It has functions such as preventing mis-insertion, overload protection, and surge protection. It connects to the internal circuitry and edge computing module of the device through standardized shielded wiring. Figure 3 The newly added power interface structure provides hardware support for collaborative charging and mutual charging.

[0022] 3. Power Supply Equipment: A smart AC power supply conforming to mains power standards, with an output voltage of 220V±5%, a frequency of 50Hz±0.5Hz, a rated power ≥5000W, power redundancy regulation capability, and support for real-time communication with the edge computing module of the energy storage device. The communication protocol is Modbus-TCP, with a transmission latency ≤20ms. Figure 3 Total power requirements for dual-interface collaborative power supply.

[0023] 4. Algorithm support: Deploy multi-objective dynamic power allocation algorithm, EMS-BMS collaborative control algorithm and anomaly detection and adaptive adjustment algorithm, run on the device edge computing module, rely on device status data including voltage, current, temperature, SOC, etc. to achieve millisecond-level decision output, and adapt to the bidirectional charging and discharging control logic of MPPT module.

[0024] II. Specific Implementation Steps: (I) Equipment Status Awareness and Algorithm Initialization 1. Initiate the self-test program of both energy storage devices. The EMS system collects parameters such as battery SOC, individual cell voltage, cell temperature, DC / AC inverter operating status, power interface contact resistance, and MPPT module operating status through built-in sensors at a frequency of 50Hz. The data is then filtered and transmitted to the edge computing module. This step addresses the resource idleness issue caused by independent device operation by proactively checking the status of core components, laying the foundation for cross-device reuse.

[0025] 2. To quantify the current rechargeable status and inter-charging compatibility of devices and eliminate the dimensional differences of parameters across different dimensions, the edge computing module runs a device status assessment model to normalize the collected data. The normalization formula is as follows: ; In the formula: : No. The device is rated on its rechargeability and compatibility with each other. The higher the rating, the more suitable the device is for collaborative charging and mutual charging.

[0026] : No. The actual remaining power of the device is the core status parameter collected in real time.

[0027] The lower limit threshold of the safe charging range is set at 20%. Below this value, the battery activity is insufficient and it is not suitable for high-power charging and mutual charging.

[0028] The upper limit threshold for the safe charging range is set at 80%. If the range exceeds this value, the charging power should be reduced to avoid the risk of overcharging.

[0029] : No. The real-time temperature of the battery cells in the device directly affects the battery charging safety, lifespan, and inter-charging stability.

[0030] The lower limit threshold of the suitable charging temperature range, with a value of 0℃. Below this value, the battery ion migration efficiency is low.

[0031] The upper limit of the suitable charging temperature range is set at 45°C. Above this value, the risk of battery thermal runaway increases.

[0032] : No. The resistance of the power interface contacts of the device reflects the contact status of the interface and affects the power transmission efficiency of collaborative charging and mutual charging.

[0033] The lower limit of the normal operating resistance range of the interface is 0.1Ω. Below this value, the contact condition is ideal.

[0034] : The upper limit of the normal operating resistance range of the interface, with a value of 0.5Ω. A value higher than this indicates that the interface is oxidized or loose.

[0035] The weighting coefficient for the SOC parameter is 0.4, as the state of charge has the greatest impact on the priority of charging and mutual charging.

[0036] The weighting coefficient for the cell temperature parameter is set to 0.3. Temperature is a key constraint factor for charging safety and stable cross-charging.

[0037] The weighting coefficient for the interface contact resistance parameter is 0.15. The interface status affects the power transmission efficiency and stability.

[0038] MPPT module efficiency weighting coefficient, with a value of 0.15, ensures that inter-charging efficiency is included in the status assessment; The value represents the real-time working efficiency of the MPPT module, ranging from 0.9 to 0.98.

[0039] The four weighting coefficients were determined based on fitting data from multiple scenarios to ensure a balanced contribution of each factor to the assessment of charging and mutual charging status.

[0040] 3. When two devices When all values ​​are ≥0.6, the device is considered to be in normal condition, the algorithm initialization is complete, and it enters the collaborative charging and mutual charging ready state; if... When the value is less than 0.6, the system triggers targeted warnings, such as starting the cooling fan when the temperature is too high, prompting maintenance when the interface resistance is abnormal, and performing module self-test when the MPPT efficiency is too low, until the equipment status meets the standard.

[0041] (II) Power Interface Connection and Communication Link Establishment: 1. For example Figure 3 As shown, by using a branched shielded power supply cable, the mains power intelligent power supply is connected to the original power interface of the 3KWH device and the expansion power interface of the 2KWH device respectively, forming a dual-interface collaborative power supply circuit, breaking through... Figure 2 The single interface has power limitations; at the same time, the edge computing modules of the two devices are connected through an RS485 communication line to establish an EMS-BMS cross-device data synchronization link, ensuring real-time interaction of data such as battery status, power allocation commands, and MPPT working parameters, and providing communication support for inter-device charging.

[0042] 2. The edge computing module monitors parameters such as packet loss rate and latency of the communication link in real time through the link quality detection algorithm. When the packet loss rate is ≤1% and the latency is ≤30ms, the link is considered stable. If the threshold is exceeded, it automatically switches to the backup communication channel and starts the data retransmission mechanism to ensure the continuity of collaborative charging and mutual charging control, and solves the problem of insufficient adaptability when multiple devices are linked.

[0043] (III) Dynamic configuration of charging and mutual charging parameters based on algorithms: 1. Activate the multi-interface collaborative charging mode and bidirectional mutual charging mode via the device's local operation panel or mobile APP. The edge computing module calls the multi-target dynamic power allocation algorithm, combined with the rated power of the mains power supply and the device status score. Based on the differences in battery capacity and the efficiency of the MPPT module, the optimal power allocation scheme and mutual charging mode parameters for the two devices are calculated to ensure that the power allocation meets the charging speed-up requirements, complies with the hardware capacity of the devices, and improves mutual charging efficiency.

[0044] 2. The core formula for power allocation is as follows. This formula is based on the principles of capacity matching, state adaptation, and efficiency optimization to achieve a reasonable allocation of total power and maximize mutual charging efficiency: ; In the formula: The allocated power of a 3kWh device is the mains input power that the device receives during the collaborative charging process.

[0045] The power allocated to the 2KWH device, together with the power allocated to the 3KWH device, constitutes the total charging power.

[0046] The rated capacity of a 3KWH device is 3KWH, which is the basis for power distribution.

[0047] The rated capacity of the 2KWH equipment is set to 2KWH, which is used to match the capacity difference of the 3KWH equipment.

[0048] The rechargeability and inter-charging compatibility score of a 3KWH device is calculated by the device status assessment model.

[0049] The scoring of the rechargeability and inter-charging compatibility status of 2KWH devices is consistent with the scoring logic of 3KWH devices.

[0050] The maximum total power for collaborative charging is set at 4400W, determined based on the maximum power limit of 2200W for a single device and the power redundancy of the power supply, ensuring stable power output. Figure 3 A power boost design with dual interfaces.

[0051] Real-time efficiency of the MPPT module for a 3KWH device, with a value ≥96%.

[0052] Real-time efficiency of the MPPT module for a 2KWH device, with a value ≥96%.

[0053] The total charging efficiency of the two devices is calculated based on the single-stage MPPT efficiency, ensuring ≥92.16%, which is much higher than the 84% of traditional AC charging.

[0054] 3. The algorithm sets four constraints simultaneously: the first one... ≤2200W, second item ≤2200W, to avoid exceeding the hardware limit for a single device; Item 3 Ensure that the charging rates of the two devices are balanced; Item 4 ≥90% to ensure mutual charging efficiency meets standards. Finally, the initial power allocation command and mutual charging mode command are output and sent to the EMS system for execution.

[0055] (iv) Algorithm closed-loop control of charging and mutual charging processes: 1. Turn on the mains power supply and the charging switch for the energy storage device. Mains power is input to both devices through the dual interfaces. Figure 3 As shown, the 3KWH device receives power through the original power interface, and the 2KWH device receives power through the extended power interface. The edge computing module collects the power output of the power supply, the input voltage and current of the two devices, the battery SOC, temperature and MPPT operating parameters in real time. The sampling period is 100ms to ensure that the data real-time meets the algorithm adjustment requirements.

[0056] 2. Run the EMS-BMS collaborative control algorithm to dynamically adjust power allocation and mutual charging status based on real-time data, achieving closed-loop optimization: When the temperature of the 3kWh device battery rises to 40℃, it is close to... At that time, the algorithm automatically reduces By allocating weights and transferring a portion of the power to the 2 kWh device, the device status scoring formula is adjusted as follows: ; in The value was adjusted to 0.5, which strengthens safety constraints by increasing the temperature weight and ensures that the temperature is controlled within a safe range.

[0057] When the output power of the mains power supply fluctuates by more than ±10%, the algorithm activates the power buffering mechanism, prioritizing the reduction of power consumption. The allocation ratio ensures the stability of charging 3KWH devices, and the balanced allocation is gradually restored after the fluctuations subside.

[0058] When inter-device charging is required, the algorithm automatically switches to MPPT bidirectional charging and discharging mode, replacing the traditional AC charging method. This avoids the problem of the total charging efficiency being only 84% due to the 92% efficiency of the traditional AC inverter. After the charging command is issued via the mobile APP, the EMS system controls the MPPT modules of the two devices to establish a bidirectional transmission channel. The efficiency of a single-stage MPPT is maintained at 96%, and the total charging efficiency reaches 92.16%, while ensuring that the normal output of the AC load is not affected.

[0059] When only one device is needed for portable inverter operation, the algorithm controls another device to switch to backup power mode. The MPPT module continuously supplies power to the device in inverter operation, making full use of functional modules, greatly improving backup power attributes, and solving the problems of poor coordination between devices and idle resources.

[0060] 3. Simultaneously, the algorithm reuses the DC / AC inverter of the 2kW device as an auxiliary power conversion component for the 3kW device. Through the EMS system, it issues coordinated commands to the 2kW device to convert the received AC power into a voltage level suitable for the 3kW device's battery, and then transmits it to the 3kW device's battery charging circuit via cross-device power transmission lines. This achieves efficient reuse of inverter resources, breaking through... Figure 2 The technical bottleneck is that a single interface cannot reuse components.

[0061] (v) Anomaly detection and adaptive algorithm adjustment: 1. During charging and mutual charging, the edge computing module runs an anomaly detection algorithm to perform threshold judgment and trend analysis on the collected data such as voltage, current, temperature, MPPT efficiency, and interface status, and promptly identify various abnormal scenarios: When a sudden change in the input current of a power interface exceeds 30%, it is determined to be a poor contact or a precursor to a short circuit. The algorithm immediately issues a command to cut off the power supply circuit of that interface, while transferring its allocated power to another interface to ensure that the total power is not interrupted, and triggers an audible and visual alarm.

[0062] When the BMS system detects that the battery voltage is close to the full charge threshold (54.7V for a 3kWh device and 53.5V for a 2kWh device), the algorithm automatically starts the constant voltage charging mode to gradually reduce the charging current and avoid overcharging.

[0063] When the MPPT cross-charging efficiency is detected to be below 90%, the algorithm automatically checks the module status and communication link. If the module parameters are drifting, the operating parameters are dynamically adjusted. If the link is interfering, the communication channel is switched to ensure stable cross-charging efficiency.

[0064] 2. The response time for all abnormal handling commands is ≤50ms, ensuring charging and mutual charging safety and timely equipment protection.

[0065] IV. Algorithm Optimization: (I) Optimization of Multi-Objective Dynamic Power Allocation Algorithm: A reinforcement learning mechanism is introduced, using the fastest charging speed, the least battery loss, the most balanced interface load, and the highest mutual charging efficiency as the reward function. By continuously learning the device state and power allocation effect under different scenarios, the mechanism dynamically optimizes the charging process. , , , The weighting coefficient makes the power distribution scheme more in line with actual working conditions, further improving charging efficiency, battery life and cross-charging stability.

[0066] (II) Robustness Optimization of EMS-BMS Cooperative Control Algorithm: A grid voltage harmonic detection module is added. The algorithm decomposes harmonic components through Fourier transform and adjusts the filter parameters of DC / AC inverters accordingly to reduce the impact of harmonics on charging stability and mutual charging efficiency. At the same time, the anti-interference logic of cross-device communication is optimized, and a data encryption transmission and verification mechanism is adopted to avoid command mis-execution caused by external electromagnetic interference.

[0067] (III) Algorithm optimization for cross-device interface compatibility and interoperability: To address the interface differences among different brands and specifications of energy storage devices, the algorithm adds an automatic interface parameter identification function. By collecting data such as the voltage level and current carrying capacity of the interface, it adaptively adjusts the upper limit of power allocation and transmission protocol. At the same time, it optimizes the MPPT mutual charging adaptation logic, dynamically adjusting the mutual charging current and voltage according to the MPPT module parameters of different devices, thereby improving the universality and adaptability of the solution.

[0068] V. Verification of Implementation Results: 1. Charging speed comparison: such as Figure 2 As shown, when charging via a single power interface, a 3kWh device requires 1.5 hours to charge from 0-100%, and a 2kWh device requires 1 hour. After adopting the optimized collaborative charging scheme based on this algorithm, as shown... Figure 3 As shown, the charging time for a 3KWh device is reduced to 42 minutes, and for a 2KWh device to 28 minutes. The charging speed is more than doubled compared to the single-interface solution, fully achieving the goal of accelerating charging and solving the problem of excessively long charging cycles.

[0069] 2. Comparison of mutual charging efficiency: In the traditional AC mutual charging method, the total mutual charging efficiency of the two devices is 84% ​​because the AC inverter efficiency is only 92%. After adopting the MPPT bidirectional mutual charging mode in this embodiment, the total mutual charging efficiency reaches 92.16%, the energy loss is greatly reduced, and the AC load output is not affected, and the backup power attribute of the device is significantly improved.

[0070] 3. Stability Verification: During charging and mutual charging, the power distribution error is ≤3%, the battery temperature is controlled at a maximum of 43℃, the interface temperature is maintained below 35℃, and there are no abnormalities such as overload, short circuit, or overcharging; the voltage and current fluctuation amplitude is ≤2%, which is far lower than the industry's conventional fluctuation threshold of 5%, and the MPPT mutual charging efficiency fluctuation is ≤1%, verifying the algorithm's role in improving charging stability and mutual charging reliability.

[0071] 4. Resource utilization effect: By reusing inverters and MPPT modules across devices, the problem of idle resources between devices is completely solved, and the collaborative working ability is significantly enhanced. In emergency power supply scenarios, one inverter can provide power while the other continuously replenishes power, ensuring continuous power support for critical loads and solving the problem of insufficient equipment adaptability and collaboration.

[0072] 5. Algorithm response performance: In abnormal scenarios such as poor interface contact, mains power fluctuations, and MPPT efficiency anomalies, the average algorithm response time is 32ms, which is within the safe threshold of 50ms, ensuring the timeliness and effectiveness of anomaly handling.

Claims

1. A method for accelerating the charging of energy storage products based on multi-power interfaces, characterized in that, Includes the following steps: Step 1) Configure at least two portable energy storage devices, each containing an EMS system, a BMS system, a DC / DC converter, a DC / AC inverter, and an MPPT module. Each device is equipped with an extended power interface that matches the original interface specifications and has a built-in edge computing module. Step 2) After the device starts its self-test, the device status assessment model is run through the edge computing module to normalize the collected battery SOC, cell temperature, power interface contact resistance and MPPT module efficiency parameters to determine whether the device meets the conditions for collaborative charging and mutual charging readiness. Step 3) Connect the mains smart power supply to the original power interface and the extended power interface of the equipment using a branched shielded power cable, establish a cross-device data synchronization link between devices through a communication line, and test the stability of the link; Step 4) Activate the collaborative charging and bidirectional mutual charging modes, call the multi-objective dynamic power allocation algorithm, and calculate the optimal power allocation scheme by combining parameters such as the rated power of the power supply and the device status score, and then issue it for execution. Step 5) During the charging process, the power distribution and mutual charging status are adjusted in real time through the EMS-BMS collaborative control algorithm, and the DC / AC inverter of one device is reused to provide auxiliary power to another device. Step 6) Run the anomaly detection algorithm to monitor voltage, current, and temperature, handle abnormal scenarios in a timely manner, and ensure charging continuity.

2. The method for accelerating charging of energy storage products based on multiple power interfaces as described in claim 1, characterized in that: The normalization formula for the equipment condition assessment model in step 2) is: ； in For the first The charging and inter-charging compatibility status rating of the devices. For the first The actual remaining power of the device. This is the lower limit threshold of the safe charging range. The upper limit threshold for the safe charging range, For the first Real-time temperature of the battery cells in the device. To determine the lower limit threshold of the appropriate charging temperature range, To determine the upper limit threshold of the appropriate charging temperature range, For the first The power interface contact resistance of the device. This is the lower limit of the normal operating resistance range for the interface. This represents the upper limit of the resistance range for normal operation of the interface. These are the weighting coefficients for the SOC parameters. This is the weighting coefficient for the cell temperature parameter. These are the weighting coefficients for the interface contact resistance parameters. This represents the efficiency weighting coefficient for the MPPT module. To improve the real-time working efficiency of the MPPT module.

3. The method for accelerating charging of energy storage products based on multiple power interfaces as described in claim 1, characterized in that: The core formula for power allocation in step 4) is: ； in Power allocation for 3 kWh equipment Power allocation for 2 kWh equipment For the rated capacity of a 3 kWh device, For the rated capacity of a 2 kWh device, , The scores are for the rechargeability and compatibility of the two devices. This represents the upper limit of the total power for collaborative charging. , The real-time efficiency of the MPPT modules of the two devices are respectively. To improve the overall efficiency of the two devices.

4. The charging speed-up method for energy storage products based on multi-power interfaces as described in claim 1, characterized in that: In step 5), the bidirectional mutual charging mode is completed through bidirectional charging and discharging of the MPPT module, and the mutual charging process does not affect the normal output of the AC load; when only one device needs to work as an inverter, the other device switches to the backup power pack mode to continuously replenish power.

5. The charging speed-up method for energy storage products based on multi-power interfaces as described in claim 1, characterized in that: In step 5), the inverter reuse specifically involves the auxiliary equipment converting the received mains power into a voltage level suitable for the target device's battery via a DC / AC inverter, and then transmitting it to the target device's battery charging circuit through the cross-device power transmission line.

6. The method for accelerating charging of energy storage products based on multiple power interfaces as described in claim 1, characterized in that: The abnormal detection scenarios in step 6) include power interface input current sudden change exceeding 30%, battery voltage close to the full charge threshold, MPPT mutual charging efficiency below 90%, and response time of abnormal handling command ≤ 50ms. The full charge threshold for 3KWH devices is 54.7V, and the full charge threshold for 2KWH devices is 53.5V.

7. The method for accelerating charging of energy storage products based on multiple power interfaces as described in claim 1, characterized in that: The algorithms deployed in the edge computing module have been optimized: the multi-objective dynamic power allocation algorithm introduces a reinforcement learning mechanism, using charging speed, battery loss, interface load balancing, and mutual charging efficiency as the reward function; the EMS-BMS collaborative control algorithm adds a grid voltage harmonic detection module; and the cross-device interface compatibility algorithm has the functions of automatic identification of interface parameters and adjustment of MPPT mutual charging adaptation logic.

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