Cascade battery energy storage system based on water-cooling heat dissipation and comprehensive environment monitoring

Abstract

The application relates to the technical field of battery energy storage, in particular to a cascade battery energy storage system based on water-cooling heat dissipation and comprehensive environment monitoring, which comprises a cascade battery energy storage module, a water-cooling heat dissipation module, a comprehensive environment monitoring module, a power management module and an energy management module. The application ensures efficient and stable operation of the energy storage system, scientifically and effectively uses the retired power battery, and is more energy-saving and environment-friendly.

Description

Technical Field

[0001] This invention relates to the field of battery energy storage technology, and in particular to a cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring. Background Technology

[0002] With the rapid development of the electric vehicle industry, a large number of power batteries are being retired due to capacity decay. These retired batteries still possess a certain energy storage capacity and have secondary utilization value. However, due to issues such as large differences in state of health (SOH), poor consistency, and varying degrees of aging, directly using retired batteries in energy storage systems will pose challenges in terms of safety and lifespan.

[0003] Traditional energy storage systems often utilize brand-new batteries, resulting in high costs and underutilization of retired battery resources. Furthermore, existing cascaded battery energy storage systems generally suffer from the following problems:

[0004] 1) Lack of effective screening and reorganization mechanism: Most systems do not scientifically sort retired batteries, resulting in large capacity differences between modules and affecting overall performance.

[0005] 2) Insufficient thermal management capabilities: Battery operating temperature has a significant impact on its lifespan and safety, but traditional air-cooling methods are difficult to meet the temperature control requirements of high-density energy storage systems.

[0006] 3) Weak environmental monitoring capabilities: There is a lack of effective perception of external environmental factors such as vibration, temperature and humidity, and an inability to respond to abnormal situations in a timely manner, which poses safety hazards.

[0007] 4) Inefficient power scheduling strategy: The lack of dynamic optimization of charge and discharge sequence based on SOC / SOH results in uneven battery wear and shortened lifespan. The absence of a mechanism for assessing the full life-cycle cost of batteries makes it impossible to scientifically determine the timing of rebuilding or recycling. Summary of the Invention

[0008] This invention provides a cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring, which solves the main problems existing in the existing cascaded battery energy storage system, such as the lack of an effective screening and recombination mechanism, insufficient thermal management capabilities and weak environmental monitoring, and significantly improves the overall performance and safety of the system while reducing costs.

[0009] To achieve the objective of this invention, the technical solution adopted is: a cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring, characterized in that: it includes a cascaded battery energy storage module, a water cooling module, a comprehensive environmental monitoring module, a power management module, and an energy management module;

[0010] Secondary battery energy storage module: retired power batteries are classified into battery modules according to their health status and capacity consistency through screening and capacity grading. The BMS monitors the voltage, temperature, current, SOC and SOH of the battery modules in real time.

[0011] The water-cooled heat dissipation module receives temperature data from the BMS and uses a PID controller to dynamically adjust the coolant flow rate and radiator power to maintain the optimal operating temperature of the battery module.

[0012] The integrated environmental monitoring module monitors the surrounding environmental conditions and provides the power management module with vibration over-limit signals, temperature and humidity data, and smoke concentration data.

[0013] The power management module receives SOC / SOH data from the secondary battery energy storage module, determines the output ratio, and optimizes the charging and discharging sequence; it switches charging and discharging modes according to the scheduling instructions of the energy management module; and it limits the discharge rate when the environmental monitoring module triggers a vibration alarm.

[0014] The energy management module receives SOC / SOH data from the cascaded battery energy storage module, constructs a multi-objective decision matrix based on the improved VIKOR algorithm, and dynamically selects the optimal operating scenario; combined with the LCC model, it guides the battery module reconfiguration or wet recycling.

[0015] As an optimized solution of the present invention, the tiered battery energy storage module includes a battery module and a BMS. Retired batteries are screened by electrochemical impedance spectroscopy, and battery cells with capacity deviations exceeding ±5% of the initial nominal value or internal resistance exceeding 150% of the initial value are eliminated. The remaining retired batteries are graded according to SOH using an improved DBSCAN, and then reassembled into energy storage modules with consistent capacity through series and parallel connections.

[0016] As an optimization scheme of the present invention, a suitable neighborhood radius ε and minimum number of points MinPts are selected through the K-distance graph, and the improved DBSCAN algorithm is run using the selected ε and MinPts parameters.

[0017] As an optimized solution of the present invention, the water-cooled heat dissipation module includes a variable frequency centrifugal pump, a plate heat exchanger, a PID controller, a liquid storage expansion tank, and an emergency cooling device. The variable frequency centrifugal pump dynamically adjusts the flow rate of the coolant according to the PID instructions provided by the PID controller. The plate heat exchanger transfers the heat transferred from the battery module to the coolant to the external environment. The PID controller controls the speed of the variable frequency centrifugal pump and the power of the radiator fan based on the temperature data of the BMS. The liquid storage expansion tank is used to accommodate the coolant with volume changes and to maintain the pressure stability inside the coolant system. The emergency cooling device is used to quickly reduce the temperature of the battery module.

[0018] As an optimized solution of the present invention, the integrated environmental monitoring module includes a vibration monitoring sensor, a temperature and humidity monitoring sensor, and a smoke sensor. The vibration monitoring sensor detects abnormal vibration amplitude and generates a vibration over-limit signal. The temperature and humidity monitoring sensor is used to monitor ambient temperature and humidity data. The smoke sensor monitors the concentration of smoke particles in the air.

[0019] As an optimized solution of the present invention, the power management module includes a controller and a PCS unit. The controller sends a control signal to the PCS to adjust the charging and discharging mode according to the scheduling instructions of the power management module. The power management module then adjusts the charging and discharging power according to the temperature and humidity data.

[0020] As an optimized solution of the present invention, the power management module receives the SOC / SOH data from the secondary battery energy storage module, determines the output ratio, and optimizes the charging and discharging sequence as follows:

[0021] The power management module estimates the state of charge (SOC) through Kalman filtering and calculates the SOC based on the capacity decay rate. It defines the priority formula: Priority = α·SOC + β·SOH, where α and β are weighting coefficients. Based on the calculated priority, the module sorts the battery modules and formulates the optimal charging and discharging strategy.

[0022] As an optimized solution of the present invention, the total output power P required by the system at present is determined according to the load demand and the scheduling instructions of the energy management module. total The initial power of each battery module is P. i =P total / N, where N is the number of battery modules. According to the priority list, for the top k% of battery modules in Priority, their output power can be set to Pi′=P. i ×(1+γ), and for the battery modules ranked in the bottom (100-k)%, the output power is Pi′=P i ×(1-δ), where γ and δ are adjustment coefficients.

[0023] As an optimized solution of the present invention, the energy management module constructs a multi-objective decision matrix based on the improved VIKOR algorithm and dynamically selects the optimal operating scenario as follows:

[0024] 1) For each running scenario a i and each indicator f j Collect the actual values ​​under the current state to form an n×m decision matrix D:

[0025]

[0026] 2) Cost-related indicators are: Where: n is the number of scenes, r ijf represents the performance of the i-th scenario under the j-th metric. j (a i ) represents the i-th scene a i The actual measured value of the j-th cost index;

[0027] 3) Determine the ideal solution and negative ideal solution

[0028] 4) Calculate the group utility value S i And individual regret value R i :

[0029] in,

[0030]

[0031] Comprehensive ranking index Q i for:

[0032]

[0033] θ is the decision preference coefficient;

[0034] 5) Press Q i Sort by value, select the optimal running scenario, and sort all running scenarios by Q. i The values ​​are sorted from smallest to largest, and the optimal running scenario is finally output:

[0035] Where: a opt This is the optimal operating scenario.

[0036] This invention has the following positive effects: 1) This invention uses electrochemical impedance spectroscopy (EIS) testing and an improved DBSCAN algorithm for scientific sorting and recombination, eliminating battery cells with large performance deviations, and classifying and recombinizing the remaining batteries according to their SOH values. This not only improves the consistency of battery modules but also extends their service life, ensuring the efficient and stable operation of the energy storage system, and making scientific and effective use of retired power batteries, which is more energy-efficient and environmentally friendly.

[0037] 2) This invention utilizes a PID controller to dynamically adjust the coolant flow rate and radiator power of a water-cooled heat dissipation module. This precise temperature control measure greatly reduces safety hazards caused by overheating and also improves the overall lifespan of the energy storage system.

[0038] 3) The integrated environmental monitoring module of this invention can monitor external environmental factors such as vibration, temperature and humidity in real time, and promptly feed them back to the power management module to adjust the charging and discharging strategy or take emergency measures, effectively preventing potential risks caused by environmental changes and further enhancing the safety of the system.

[0039] 4) The power management module of this invention uses Kalman filtering to estimate the State of Charge (SOC) and combines it with the capacity decay rate to calculate the output ratio and determine the optimal charging and discharging sequence. Simultaneously, the energy management module, guided by the LCC model, can also rationally schedule regeneration or recycling based on the battery state, thereby reducing costs while extending battery life.

[0040] 5) This invention constructs a multi-objective decision matrix based on an improved VIKOR algorithm, enabling dynamic selection of the optimal operating scenario. This method can find an equilibrium point in complex multi-objective environments, quickly responding to changes in grid demand or environmental conditions, ensuring the energy storage system is always in a highly efficient operating state. Furthermore, combined with the economic assessment provided by the LCC model, the entire system becomes more intelligent, flexible, and cost-effective. Attached Figure Description

[0041] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0042] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0043] like Figure 1 As shown, the present invention discloses a cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring, including a cascaded battery energy storage module 1, a water cooling module 2, a comprehensive environmental monitoring module 3, a power management module 4, and an energy management module 5.

[0044] Retired power batteries are classified into battery modules based on their health status and capacity consistency through screening and capacity grading. The Battery Management System (BMS) monitors the voltage, temperature, current, state of charge (SOC), and state of equilibrium (SOH) of the battery modules in real time. Voltage is used to determine SOC, identify aging, detect imbalances, and prevent overcharging / over-discharging. Current is used to calculate SOC, control charge and discharge rates, assess thermal risks, and support power dispatching.

[0045] The cascaded battery energy storage module 1 includes battery modules and a BMS. Retired batteries are screened using electrochemical impedance spectroscopy (EIS). Small-amplitude sinusoidal voltage (or current) excitation is applied to the batteries within a set frequency range (e.g., 10 MHz to 100 kHz). The battery's response to these excitation signals is measured and recorded. This includes the measured impedance modulus and phase angle as a function of frequency. A Nyquist plot is plotted based on the collected data, showing the relationship between the battery's complex impedance and frequency. Characteristic points in the Nyquist plot are analyzed; for example, the semicircle diameter in the high-frequency region reflects changes in charge transfer resistance, and the slanted lines in the low-frequency region are related to the diffusion process. By comparing the EIS data of different batteries, those exhibiting significant internal resistance growth or marked degradation are identified, determining which retired batteries are suitable for use as battery modules and which should be replaced or scrapped.

[0046] Battery cells with capacity deviations exceeding ±5% of the initial nominal value or internal resistance exceeding 150% of the initial value are removed; the remaining retired batteries are classified according to SOH using an improved DBSCAN and reassembled into energy storage modules with consistent capacity through series and parallel connections.

[0047] The modified DBSCAN is used to classify remaining retired batteries according to their state of harm (SOH) as follows:

[0048] 1) Obtain SOH data for battery modules that exclude battery cells with capacity deviation exceeding ±5% of the initial nominal value or battery cells with internal resistance exceeding 150% of the initial value;

[0049] 2) Select an appropriate neighborhood radius ε and minimum number of points MinPts using the K-distance graph.

[0050] Run the improved DBSCAN algorithm with the selected ε and MinPts parameters, |Nε(p)| ≥ MinPts, N ε (p) represents the set of points (including p itself) within a circle centered at p and with radius ε. If the above conditions are met, p is considered a core object, and clusters are expanded from each core object until no more directly reachable points can be found. If there is at least one path consisting of core objects connecting two core objects, they belong to the same cluster, and are ranked according to the SOH value of the cluster center.

[0051] The specific steps for improving the DBSCAN algorithm are as follows:

[0052] Input: Dataset D, neighborhood radius ε, minimum number of points MinPts.

[0053] Initialization: All battery cells are marked as "unvisited"; cluster number C = 0.

[0054] For each unvisited point p ∈ D: Mark p as "visited", calculate N ε (p). If |N ε (p)| < MinPts, mark p as a noise point; otherwise: Create a new cluster C = C + 1

[0055] , Add p to the current cluster, initialize the seed set Seeds = N ε (p) \ {p}. For each point q ∈ Seeds in the seed set: If q is not visited: Mark it as "visited", calculate N ε (q). If |Nε(q)| ≥ MinPts, expand the seed set: Seeds = Seeds ∪ N ε (q)

[0056] Connect the core objects to form the final clusters. If there is a path composed of core objects connecting two core objects, they belong to the same cluster. Finally, output several clusters C1, C2,..., Ck, where each cluster represents a class of battery cells with similar SOH values.

[0057] For each cluster Ci, calculate its SOH mean μi as the central value of the cluster. Sort the clusters in descending order according to the cluster center μi. According to the actual application requirements, divide the clusters into multiple grades according to the SOH level, including Grade A: SOH ≥ 80%; Grade B: 70% ≤ SOH < 80%; Grade C: 60% ≤ SOH < 70%.

[0058] Improved DBSCAN determines appropriate ε and MinPts through the K-distance graph, reduces the dependence on manual parameter tuning, better distinguishes abnormal battery cells, avoids misclassification, and ensures a tight internal structure and high consistency of the clusters through the connectivity judgment of core objects. By improving the DBSCAN algorithm, assisting parameter selection through the K-distance graph and optimizing the core object expansion strategy, it can effectively perform automatic hierarchical clustering based on SOH for the selected retired power batteries, significantly improving the consistency and reliability of the battery modules.

[0059] The water-cooled heat dissipation module 2 receives temperature data from the BMS and uses a PID controller to dynamically adjust the coolant flow rate and radiator power to maintain the optimal operating temperature of the battery module. The water-cooled heat dissipation module 2 includes a variable frequency centrifugal pump, a plate heat exchanger, a PID controller, a coolant expansion tank, and an emergency cooling device. The variable frequency centrifugal pump dynamically adjusts the coolant flow rate according to PID commands provided by the PID controller. The plate heat exchanger transfers heat from the battery module to the coolant to the external environment. The PID controller controls the speed of the variable frequency centrifugal pump and the power of the radiator fan based on the temperature data from the BMS. The coolant expansion tank accommodates changes in coolant volume and maintains stable pressure within the coolant system. The emergency cooling device rapidly reduces the temperature of the battery module. The variable frequency centrifugal pump receives PWM signals from the PID controller and adjusts the motor speed accordingly, thereby changing the pumping flow rate. The plate heat exchanger efficiently transfers the heat carried by the coolant to the external environment; it is composed of multiple layers of thin metal plates, forming coolant channels and heat dissipation medium channels. The PID controller receives real-time temperature data (such as the average or maximum value of multiple temperature measurement points) from the battery management system (BMS). Output control signals are sent to the variable frequency centrifugal pump (to adjust frequency / speed). Output control signals are also sent to the radiator (to adjust power or speed). The liquid storage expansion tank is equipped with a diaphragm or air bladder to buffer volume changes. An emergency cooling device automatically activates emergency cooling measures when localized overheating of the battery module or cooling system failure is detected. The emergency cooling device can be a spray-type cooling system: spraying flame-retardant coolant or inert gas onto the battery module.

[0060] The integrated environmental monitoring module 3 monitors the surrounding environmental conditions and provides the power management module 4 with vibration over-limit signals, temperature and humidity data, and smoke concentration data. The integrated environmental monitoring module 3 includes a vibration monitoring sensor, a temperature and humidity monitoring sensor, and a smoke sensor. The vibration monitoring sensor detects abnormal vibration amplitude and generates a vibration over-limit signal. The temperature and humidity monitoring sensor is used to monitor ambient temperature and humidity data, and the smoke sensor monitors the concentration of smoke particles in the air.

[0061] The power management module 4 receives the SOC / SOH data from the secondary battery energy storage module 1, determines the output ratio, and optimizes the charging and discharging sequence; it switches the charging and discharging modes according to the scheduling instructions of the energy management module 5; it limits the discharge rate when the environmental monitoring module 3 triggers a vibration alarm; and it prioritizes cutting off the power supply to non-critical loads and starting the ventilation system when the smoke concentration exceeds the limit.

[0062] The logic for determining the output ratio includes the SOC grading strategy and the SOH compensation mechanism.

[0063] SOC tiered strategy:

[0064] ≥80%: Allows for 100% rated power discharge.

[0065] 50%-80%: Linear adjustment of output ratio (each 1% decrease in SOC reduces power by 1.2%).

[0066] <50%: Charging mode is allowed only, output ratio = 0.

[0067] SOH compensation mechanism: When SOH < 90%, the maximum output ratio is corrected according to (SOH / 90%).

[0068] Power management module 4 receives SOC / SOH data from secondary battery energy storage module 1, determines the output ratio, and optimizes the charging and discharging sequence as follows:

[0069] Power management module 4 estimates SOC through Kalman filtering and calculates SOC based on capacity decay rate; it defines the priority formula: Priority=α·SOC+β·SOH, where α and β are weighting coefficients, and sorts each battery module based on the calculated priority to formulate the optimal charging and discharging strategy.

[0070] Charge / discharge sequence optimization:

[0071] Prioritize discharging battery packs with higher SOC (>60%) and better SOH (>85%).

[0072] During charging, priority is given to battery packs with the lowest SOC (<30%), and a two-stage constant current-constant voltage strategy is adopted.

[0073] Charging / discharging mode switching, the corresponding mode for the energy management module 5 command:

[0074] Peak shaving and valley filling mode: Discharge when SOC > 70%, charge when SOC < 30%;

[0075] Emergency power supply mode: Forced discharge to SOC ≥ 10%;

[0076] Maintenance mode: Allows only 0.1C trickle charging.

[0077] Vibration alarm response strategy:

[0078] Level 1 vibration (2-4 mm / s) 2 ): Discharge rate is limited to 0.8C.

[0079] Second-order vibration (>4mm / s) 2 ): Switch to charging mode, charging rate ≤ 0.5.

[0080] Continuous vibration for more than 10 seconds: triggers emergency shutdown.

[0081] Temperature and humidity linkage rule: When the temperature is >45℃ or the humidity is >80%, the discharge rate of all modes will be reduced by an additional 30%.

[0082] The power management module 4 includes a controller and a PCS unit. The controller sends control signals to the PCS to adjust the charging and discharging mode according to the scheduling instructions of the energy management module 5. The power management module 4 then adjusts the charging and discharging power according to temperature and humidity data. The PCS unit receives the controller signals from the power management module 4 and switches the charging and discharging mode (such as peak shaving and valley filling or frequency modulation mode). The PCS unit dynamically adjusts the charging and discharging power according to temperature and humidity data, for example, reducing the power in high-temperature environments to prevent the battery from overheating.

[0083] Based on the load demand and the scheduling instructions from the energy management module 5, determine the total output power P currently required by the system. total The initial power of each battery module is P. i =P total / N, where N is the number of battery modules. According to the priority list, for the top k% of battery modules in Priority, their output power can be set to Pi′=P. i ×(1+γ), and for the battery modules ranked in the bottom (100-k)%, the output power is Pi′=P i ×(1-δ), where γ and δ are adjustment coefficients.

[0084] The energy management module 5 receives the SOC / SOH data from the secondary battery energy storage module 1, constructs a multi-objective decision matrix based on the improved VIKOR algorithm, and dynamically selects the optimal operating scenario; combined with the LCC model, it guides the battery module recombination or wet recycling.

[0085] Energy management module 5, based on an improved VIKOR algorithm, constructs a multi-objective decision matrix and dynamically selects the optimal operating scenario, specifically:

[0086] (1) For each running scenario a i and each indicator f j Collect the actual values ​​under the current state to form an n×m decision matrix D:

[0087]

[0088] (2) Cost-related indicators are: Where: n is the number of scenarios, which includes three operating modes: peak shaving and valley filling, frequency regulation service, and backup power supply. Therefore, n = 3, r ij f represents the performance of the i-th scenario under the j-th metric. j (a i ) represents the i-th scene a i The actual measured value of the j-th cost indicator. j (a i It includes SOH decay rate (reflecting the impact of different operating scenarios on battery life) and SOC balance index (assessing the SOC difference between battery modules).

[0089] (3) Determine the positive ideal solution and negative ideal solution

[0090] (4) Calculate the group utility value S i And individual regret value R i :

[0091] in,

[0092]

[0093] Comprehensive ranking index Q i for:

[0094]

[0095] Where: θ is the decision preference coefficient, and: S+=min(S i ),S-=max((S i ), R+=min(R i ), R-=max(R i ).

[0096] Press Q i Sort by value, select the optimal running scenario, and sort all running scenarios by Q. i Sort the values ​​in ascending order and finally output the optimal running scenario a. opt .

[0097]

[0098] The LCC (Life Cycle Cost) model is used to evaluate the total cost of a battery module from its initial use to its eventual disposal. This includes: initial investment cost C1, operation and maintenance cost C2, energy efficiency loss cost C3, and residual value C4. The total cost formula is: LCC = C1 + C2 + C3 - C4. Decision-making includes: if the LCC of a battery module is close to the critical value, refactoring is recommended; if the state of equilibrium (SOH) is <60% and the LCC exceeds the threshold, wet recycling is recommended; if there is still a high SOH but poor consistency, re-grading and refactoring are recommended.

[0099] Dynamic Scenario Selection: By constructing a multi-objective decision matrix, this module can dynamically evaluate the performance of each scenario under different indicators based on real-time collected data (including performance of three operating modes: peak shaving and valley filling, frequency regulation service, and backup power). The comprehensive ranking index Q is calculated using an improved VIKOR algorithm. i It can effectively identify the optimal operating scenario a optThis dynamic selection mechanism ensures that the battery energy storage system can maintain efficient operation under different operating conditions.

[0100] The energy management module 5 achieves dynamic scenario selection through the improved VIKOR algorithm and guides the reorganization or recycling decisions of battery modules in combination with the LCC model, which significantly improves the operating efficiency, economic benefits and safety of the cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring.

[0101] The improved VIKOR algorithm is particularly well-suited for solving complex multi-objective decision-making problems. In cascaded battery energy storage systems, multiple conflicting objectives such as cost, efficiency, safety, and state of health (SOH) need to be considered simultaneously. The VIKOR algorithm can effectively find a compromise solution that balances the contradictions between these objectives, thereby selecting the optimal operating scenario. This algorithm allows for the dynamic construction and updating of the decision matrix based on real-time collected data (such as performance under different operating modes), enabling the energy management module 5 to respond quickly and adjust its strategies according to the actual operating conditions of the system. This is especially important for responding to rapidly changing grid demands or environmental conditions.

[0102] Combining the LCC model, this module not only considers the initial investment cost C1, operation and maintenance cost C2, and energy efficiency loss cost C3, but also incorporates the residual value of recycling C4, comprehensively assessing the entire lifecycle cost of the battery module. This helps to make more informed investment and operational decisions, such as recommending refactoring when the LCC of a battery module is close to the critical value, or suggesting that modules with SOH less than 60% and LCC exceeding the threshold should enter the wet recycling process, thereby maximizing cost-effectiveness.

[0103] For battery modules that still have a high State of Health (SOH) but poor consistency, it is recommended to reclassify and reorganize them. This strategy effectively extends the lifespan of these battery modules and reduces the overall cost of ownership.

[0104] The application of water-cooling and integrated environmental monitoring technologies not only improves the thermal stability of battery energy storage systems and reduces safety hazards caused by overheating, but also provides support for accurate environmental data collection, which is crucial for accurately assessing battery performance and lifespan.

[0105] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring, characterized in that: It includes a cascade battery energy storage module (1), a water-cooled heat dissipation module (2), a comprehensive environmental monitoring module (3), a power management module (4), and an energy management module (5); Cascaded battery energy storage module (1): retired power batteries are classified into battery modules according to their health status and capacity consistency through screening and capacity division. The BMS monitors the voltage, temperature, current, SOC and SOH of the battery modules in real time. The water-cooled heat dissipation module (2) receives temperature data from the BMS and uses a PID controller to dynamically adjust the coolant flow rate and radiator power to maintain the optimal operating temperature of the battery module. The integrated environmental monitoring module (3) monitors the surrounding environmental conditions and provides the power management module (4) with vibration over-limit signals, temperature and humidity data and smoke concentration data; The power management module (4) receives the SOC / SOH data from the cascade battery energy storage module (1), sets the output ratio, and optimizes the charging and discharging sequence; it switches the charging and discharging mode according to the scheduling instructions of the energy management module (5); and it limits the discharge rate when the integrated environmental monitoring module (3) triggers a vibration alarm. The energy management module (5) receives the SOC / SOH data from the cascade battery energy storage module (1), constructs a multi-objective decision matrix based on the improved VIKOR algorithm, and dynamically selects the optimal operating scenario; combined with the LCC model, it guides the battery module reorganization or wet recycling. The water-cooled heat dissipation module (2) includes a variable frequency centrifugal pump, a plate heat exchanger, a PID controller, a liquid storage expansion tank, and an emergency cooling device. The variable frequency centrifugal pump dynamically adjusts the flow rate of the coolant according to the PID instructions provided by the PID controller. The plate heat exchanger transfers the heat transferred from the battery module to the coolant to the external environment. The PID controller controls the speed of the variable frequency centrifugal pump and the power of the radiator fan based on the temperature data of the BMS. The liquid storage expansion tank is used to contain the coolant with volume changes and to maintain the pressure inside the coolant system. The emergency cooling device is used to quickly reduce the temperature of the battery module.

2. The cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring according to claim 1, characterized in that: The tiered battery energy storage module (1) includes a battery module and a BMS. Retired batteries are screened by electrochemical impedance spectroscopy, and battery cells with capacity deviation exceeding ±5% of the initial nominal value or internal resistance exceeding 150% of the initial value are eliminated. The remaining retired batteries are classified according to SOH using an improved DBSCAN and reassembled into energy storage modules with consistent capacity through series and parallel connections.

3. The cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring according to claim 2, characterized in that: By selecting an appropriate neighborhood radius ε and a minimum number of points MinPts using the K-distance graph, the improved DBSCAN algorithm is run using the selected ε and MinPts parameters.

4. The cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring according to claim 3, characterized in that: The integrated environmental monitoring module (3) includes a vibration monitoring sensor, a temperature and humidity monitoring sensor and a smoke sensor. The vibration monitoring sensor detects abnormal vibration amplitude and generates a vibration over-limit signal. The temperature and humidity monitoring sensor is used to monitor ambient temperature and humidity data. The smoke sensor monitors the concentration of smoke particles in the air.

5. The cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring according to claim 4, characterized in that: The power management module (4) includes a controller and a PCS unit. The controller sends a control signal to the PCS to adjust the charging and discharging mode according to the scheduling instructions of the energy management module (5). The power management module (4) adjusts the charging and discharging power according to the temperature and humidity data.

6. The cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring according to claim 5, characterized in that: The power management module (4) receives the SOC / SOH data from the secondary battery energy storage module (1), determines the output ratio, and optimizes the charging and discharging sequence as follows: The power management module (4) estimates the SOC through Kalman filtering and calculates the SOC based on the capacity decay rate; it defines the priority formula: Priority=α•SOC+β•SOH, where α and β are weighting coefficients, and sorts each battery module based on the calculated priority to formulate the optimal charging and discharging strategy.

7. The cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring according to claim 6, characterized in that: Based on the load demand and the scheduling instructions from the energy management module (5), determine the total output power P currently required by the system. total The initial power of each battery module is P. i =P total / N, where N is the number of battery modules. According to the priority list, for the top k% of battery modules in Priority, their output power is set to Pi′=P. i ×(1+γ), and for the battery modules ranked in the bottom (100-k)%, the output power is Pi′=P i ×(1-δ), where γ and δ are adjustment coefficients.

8. The cascaded battery energy storage system based on water cooling and comprehensive environmental monitoring according to claim 7, characterized in that: The energy management module (5) receives the SOC / SOH data from the secondary battery energy storage module (1), and constructs a multi-objective decision matrix based on the improved VIKOR algorithm to dynamically select the optimal operating scenario. Specifically: 1) For each running scenario a i and each indicator f j Collect the actual values ​​under the current state to form an n×m decision matrix D: 2) Cost-related indicators are: Where: n is the number of scenes, r ij f represents the performance of the i-th scenario under the j-th metric. j (a i ) indicates the first i Scenario a i In the j The actual measured value on a cost-related indicator; 3) Determine the ideal solution and negative ideal solution , , ; 4) Calculate the group utility value S i And individual regret value R i : ,in, ； Comprehensive ranking index Q i for: ； Where θ is the decision preference coefficient. S i Group utility value S + =min( S i ), S - =max( S i ), R + =min( R i ), R - =max( R i ); 5) Press Q i Sort by value, select the optimal running scenario, and sort all running scenarios by Q. i The values ​​are sorted from smallest to largest, and the optimal running scenario is finally output: a opt = Where: a opt This is the optimal operating scenario.

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