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

Abstract

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

Description

Technical Field

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

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

[0003] Traditional energy storage systems are mostly built with new batteries, which are costly and do not fully utilize retired battery resources. At the same time, existing second-life battery energy storage systems generally have 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, affecting overall performance.

[0005] 2) Insufficient thermal management capabilities: The operating temperature of the battery has a significant impact on its life 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: Lack of effective perception of external environmental factors such as vibration, temperature and humidity, inability to respond to abnormal situations in a timely manner, posing a safety hazard.

[0007] 4) Extensive power scheduling strategies: Failure to dynamically optimize the charging and discharging sequence based on SOC / SOH results in uneven battery wear and shortened battery life. Lack of a cost assessment mechanism for the entire battery lifecycle prevents scientific judgment on the timing of reassembly or recycling. Summary of the Invention

[0008] The present invention provides a second-life battery energy storage system based on water cooling and comprehensive environmental monitoring. It solves the main problems existing in existing second-life battery energy storage systems, such as the lack of an effective screening and recombination mechanism, insufficient thermal management capabilities, and weak environmental monitoring. It also significantly improves the overall performance and safety of the system and reduces costs.

[0009] To achieve the purpose of the present invention, the technical solution adopted is: a second-life battery energy storage system based on water cooling and comprehensive environmental monitoring, characterized by comprising a second-life battery energy storage module, a water cooling module, a comprehensive environmental monitoring module, a power management module, and an energy management module;

[0010] Second-life battery energy storage module: Retired power batteries are sorted and classified into battery modules based on 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-cooling 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 ambient conditions and provides the power management module with vibration limit signals, temperature and humidity data, and smoke concentration data;

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

[0014] The energy management module receives the SOC / SOH data of the cascade 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 reorganization or wet recycling.

[0015] As an optimization solution of the present invention, the cascade battery energy storage module includes a battery module and a BMS. Retired batteries are screened through electrochemical impedance spectroscopy testing to eliminate battery cells with capacity deviations exceeding ±5% of the initial nominal value or battery cells with internal resistance higher than 150% of the initial value. The remaining retired batteries are graded according to SOH using an improved DBSCAN and reorganized into energy storage modules with consistent capacity through series and parallel connection.

[0016] As an optimization solution 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-cooling 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 instruction 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 coolant with changing volume and maintain the pressure inside the coolant system stable. 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 comprehensive 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 out-of-limit signal. The temperature and humidity monitoring sensor is used to monitor environmental temperature and humidity data. The smoke sensor monitors the concentration of smoke particles in the air.

[0019] As an optimization 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 charge and discharge mode according to the scheduling instructions of the energy management module, and the power management module adjusts the charge and discharge power according to the temperature and humidity data.

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

[0021] The power management module estimates the SOC through Kalman filtering and calculates the SOC based on the capacity decay rate; defines the priority formula: Priority = α·SOC + β·SOH, where α and β are weight coefficients. Based on the calculated priority, each battery module is sorted to formulate the optimal charging and discharging strategy.

[0022] As an optimization solution of the present invention, the total output power P currently required by the system 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 battery modules ranked in the top k% by priority, the output power can be set to Pi′=P i ×(1+γ), and for the battery modules ranked after (100-k)%, the output power is Pi′=P i ×(1-δ), where γ and δ are adjustment coefficients.

[0023] As an optimization 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 value in the current state and form an n×m decision matrix D:

[0025]

[0026] 2) Cost indicators are: Where: n is the number of scenes, r ijis the performance of the i-th scenario under the j-th indicator, f j (a i ) represents the i-th scene a i The actual measurement value of the jth cost-type indicator;

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

[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 best running scenario, and sort all running scenarios by Q i The values ​​are sorted from small to large, and the optimal running scenario is finally output:

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

[0036] The present invention has positive effects: 1) Through electrochemical impedance spectroscopy (EIS) testing and an improved DBSCAN algorithm, the present invention scientifically sorts and reorganizes cells with large performance deviations, eliminating cells with large performance deviations and classifying and reorganizing the remaining cells according to their SOH values. This not only improves the consistency of the battery modules but also extends their service life, ensuring the efficient and stable operation of the energy storage system and scientifically and effectively utilizing retired power batteries, which is more energy-efficient and environmentally friendly.

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

[0038] 3) The integrated environmental monitoring module of the present invention can monitor external environmental factors such as vibration, temperature and humidity in real time, and promptly provide feedback 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 system security;

[0039] 4) The power management module of the present invention uses a Kalman filter to estimate the SOC and calculates the output ratio based on the capacity decay rate to determine the optimal charge and discharge sequence. Furthermore, the energy management module, guided by the LCC model, can also rationally schedule reassembly or recycling based on battery status, 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 a balance point in complex multi-objective environments, rapidly respond to changes in grid demand or environmental conditions, and ensure that the energy storage system remains in a highly efficient state. Furthermore, combined with the economic evaluation provided by the LCC model, the entire system becomes more intelligent, flexible, and cost-effective. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0042] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0043] like Figure 1 As shown, the present invention discloses a second-life battery energy storage system based on water cooling and comprehensive environmental monitoring, including a second-life 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] Through screening and capacity grading, retired power batteries are classified into battery modules based on health and capacity consistency. The BMS monitors the battery module's voltage, temperature, current, SOC, and SOH in real time. Voltage is used to determine SOC, identify aging, detect imbalances, and prevent overcharge / overdischarge. Current is used to calculate SOC, control charge and discharge rates, assess thermal risks, and support power scheduling.

[0045] The second-life battery energy storage module 1 includes a battery module and a BMS. Retired batteries are screened by electrochemical impedance spectroscopy (EIS) testing, and a small-amplitude sinusoidal voltage (or current) excitation is applied to the battery within a set frequency range (such as 10mHz to 100kHz). The battery's response to these excitation signals is measured and recorded. This includes the relationship between the measured impedance modulus and phase angle as a function of frequency. Based on the collected data, a Nyquist plot is drawn, which shows the relationship between the complex impedance of the battery and the frequency. The characteristic points in the Nyquist plot are analyzed, such as the semicircle diameter in the high-frequency region reflects the change in charge transfer resistance, and the oblique line part in the low-frequency region is related to the diffusion process. Compare the EIS data of different batteries, identify those battery cells that show a large internal resistance increase or obvious degradation, and determine which retired batteries are suitable as battery modules and which should be replaced or scrapped.

[0046] Battery cells with capacity deviation exceeding ±5% of the initial nominal value or battery cells with internal resistance higher than 150% of the initial value are eliminated; the remaining retired batteries are graded according to SOH using an improved DBSCAN and reorganized into energy storage modules with consistent capacity through series and parallel connection.

[0047] The improved DBSCAN is used to classify the remaining retired batteries according to SOH as follows:

[0048] 1) Obtain the SOH data of the battery module, excluding battery cells with a capacity deviation exceeding ±5% of the initial nominal value or battery cells with an internal resistance higher than 150% of the initial value;

[0049] 2) Select the appropriate neighborhood radius ε and minimum number of points MinPts through 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 within a circle centered at p and with a radius of ε (including p itself). 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. Two core objects belong to the same cluster if there is at least one path connecting them consisting of core objects. Clusters are then ranked according to the SOH values ​​of the cluster centers.

[0051] The specific steps to improve the DBSCAN algorithm are:

[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” and calculate N ε (p), if |N ε (p)∣ <MinPts,将p标记为噪声点,否则:创建新簇C=C+1

[0055] , add p to the current cluster and initialize the seed set Seeds = N ε (p)\{p}, for each point q∈Seeds in the seed set: if q has not been visited: mark it as "visited", calculate N ε (q), if |Nε(q)|≥MinPts, expand the seed set: Seeds=Seeds∪N ε (q)

[0056] Add q to the current cluster C. Connect the core objects to form the final cluster. If there is a path between two core objects consisting of core objects, they belong to the same cluster. Finally, several clusters C1, C2, ..., Ck are output, each cluster representing a type of battery cell with similar SOH values.

[0057] For each cluster Ci, calculate its mean SOH value μi and use it as the cluster center value. Sort clusters from high to low based on their center μi. Based on actual application requirements, clusters are divided into multiple levels based on SOH, including Level A: SOH ≥ 80%; Level B: 70% ≤ SOH < 80%; and Level C: 60% ≤ SOH < 70%.

[0058] The improved DBSCAN algorithm uses a K-distance graph to determine the appropriate ε and MinPts, reducing reliance on manual parameter adjustment, better distinguishing abnormal battery cells, and avoiding misclassification. It also ensures a tight internal cluster structure and high consistency by determining the connectivity of core objects. The improved DBSCAN algorithm uses a K-distance graph to assist in parameter selection and optimize the core object expansion strategy. This allows for effective automatic, hierarchical clustering of screened retired power batteries based on SOH, significantly improving the consistency and reliability of battery modules.

[0059] The water-cooling module 2 receives temperature data from the battery management system (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. It comprises 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 coolant flow rate based on PID commands provided by the PID controller. The plate heat exchanger transfers 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 temperature data from the BMS. The liquid storage expansion tank accommodates the changing volume of coolant and maintains a stable pressure within the coolant system. The emergency cooling device rapidly reduces the battery module temperature. The variable-frequency centrifugal pump receives a PWM signal from the PID controller and adjusts the motor speed according to the command, thereby varying the pumping flow rate. The plate heat exchanger efficiently transfers heat carried by the coolant to the external environment. It is constructed from multiple layers of metal plates, forming channels for the coolant and the heat dissipation medium. 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). Outputs control signals to the variable-frequency centrifugal pump (to adjust frequency / speed). Outputs control signals to the radiator (to adjust power or speed). A diaphragm or air bag is installed inside the liquid storage expansion tank to buffer volume changes. The emergency cooling device automatically initiates emergency cooling measures when it detects local overheating of the battery module or cooling system failure. The emergency cooling device can be a spray-type cooling device: it sprays flame-retardant coolant or inert gas onto the battery module.

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

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

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

[0063] SOC classification strategy:

[0064] ≥80%: 100% rated power discharge is allowed.

[0065] 50%-80%: Linearly adjust the output ratio (power decreases by 1.2% for every 1% decrease in SOC).

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

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

[0068] The power management module 4 receives the SOC / SOH data of the cascade battery energy storage module 1, formulates the output ratio, and optimizes the charging and discharging sequence as follows:

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

[0070] Optimization of charge and discharge sequence:

[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%), using a two-stage constant current-constant voltage strategy.

[0073] Charging and discharging mode switching, energy management module 5 instructions corresponding mode:

[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: Only 0.1C trickle charge is allowed.

[0077] Vibration alarm response strategy:

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

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

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

[0081] Temperature and humidity linkage rules: When the temperature is >45°C or the humidity is >80%, the discharge rate in all modes is 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 charge and discharge modes based on the scheduling instructions from the energy management module 5. The power management module 4 then adjusts the charge and discharge power based on temperature and humidity data. The PCS unit receives controller signals from the power management module 4 and switches the charge and discharge modes (such as peak shaving or frequency modulation). The PCS unit dynamically adjusts the charge and discharge power based on temperature and humidity data, for example, reducing power in high-temperature environments to prevent battery overheating.

[0083] According to the load demand and the scheduling instructions of the energy management module 5, the total output power P currently required by the system is determined. 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 battery modules ranked in the top k% by priority, the output power can be set to Pi′=P i ×(1+γ), and for the battery modules ranked after (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 of 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; it also guides the battery module reorganization or wet recycling in combination with the LCC model.

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

[0086] (1) For each running scenario a i And each indicator f j , collect the actual value in the current state and form an n×m decision matrix D:

[0087]

[0088] (2) Cost 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. So n=3, r ij is the performance of the i-th scenario under the j-th indicator, f j (a i ) represents the i-th scene a i The actual measurement value of the jth cost-type indicator. j (a i ) includes SOH decay rate (reflecting the impact of different operating scenarios on battery life) and SOC balance index (evaluating the SOC difference between battery modules).

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

[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, where: 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 best running scenario, and sort all running scenarios by Q i The values ​​are sorted from small to large, and the optimal running scenario a is finally output. opt .

[0097]

[0098] The LCC (Life Cycle Cost) model is used to assess the costs of a battery module from its inception to its end-of-life. This includes initial investment cost (C1), operating and maintenance costs (C2), energy efficiency loss costs (C3), and salvage value (C4). The total cost formula is: LCC = C1 + C2 + C3 - C4. Decisions include: If a battery module's LCC approaches a critical value, reassembly is recommended; if the SOH is <60% and the LCC exceeds the threshold, wet recycling is recommended; and if the SOH remains high but consistency is poor, reclassification and reassembly are recommended.

[0099] Dynamic scenario selection: By building 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 the performance of three operating modes: peak shaving, frequency regulation service, and backup power supply). The comprehensive ranking index Q is calculated using the improved VIKOR algorithm. i , 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] Energy management module 5 implements dynamic scenario selection through an improved VIKOR algorithm and combines it with the LCC model to guide battery module reorganization or recycling decisions, significantly improving the operating efficiency, economic benefits, and safety of the cascade battery energy storage system based on water cooling and comprehensive environmental monitoring.

[0101] The improved VIKOR algorithm is particularly suitable for solving complex multi-objective decision-making problems. In a cascade battery energy storage system, multiple conflicting objectives such as cost, efficiency, safety and battery state of health (SOH) need to be considered simultaneously. The VIKOR algorithm can effectively find a compromise solution to balance the contradictions between these objectives and thus select the optimal operating scenario. The algorithm allows the decision matrix to be dynamically constructed and updated based on real-time collected data (such as performance under different operating modes), which enables the energy management module 5 to respond quickly and adjust strategies according to the actual operating conditions of the system. This is particularly important for responding to rapidly changing grid demands or environmental conditions.

[0102] Incorporating the LCC model, this module not only considers initial investment cost (C1), operating and maintenance costs (C2), and energy efficiency loss costs (C3), but also incorporates salvage value (C4) to comprehensively assess the entire lifecycle cost of a battery module. This helps make more informed investment and operational decisions, such as recommending restructuring when a battery module's LCC approaches a critical value, or recommending wet recycling for modules with a SOH less than 60% and an LCC exceeding the threshold, thereby maximizing cost-effectiveness.

[0103] For those battery modules whose state of health (SOH) is still high but with poor consistency, re-grading and reorganization are recommended. This strategy effectively extends the service life of these battery modules and reduces the overall cost of ownership.

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

[0105] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only 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 in the scope of protection of the present invention.

Claims

1. A second-life battery energy storage system based on water cooling and integrated environmental monitoring, characterized by: It includes a secondary battery energy storage module (1), a water cooling module (2), a comprehensive environment monitoring module (3), a power management module (4) and an energy management module (5); Second-life battery energy storage module (1): Through screening and capacity classification, retired power batteries are classified into battery modules according to health status and capacity consistency. The BMS monitors the voltage, temperature, current, SOC and SOH of the battery modules in real time. The water cooling 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 environment 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 of the cascade battery energy storage module (1), formulates the output ratio, and optimizes the charging and discharging sequence; switches the charging and discharging mode according to the scheduling instructions of the energy management module (5); and limits the discharge rate when the environmental monitoring module (3) triggers the vibration alarm; The energy management module (5) receives the SOC / SOH data of the cascade battery energy storage module (1), constructs a multi-objective decision matrix based on the improved VIKOR algorithm, and dynamically selects the optimal operation scenario; and combines the LCC model to guide the battery module reorganization or wet recycling.

2. The second-life battery energy storage system based on water cooling and integrated environmental monitoring according to claim 1 is characterized by: The second-life battery energy storage module (1) includes a battery module and a BMS. Retired batteries are screened by electrochemical impedance spectroscopy testing to remove battery cells with a capacity deviation exceeding ±5% of the initial nominal value or a battery cell with an internal resistance higher than 150% of the initial value. The remaining retired batteries are graded according to SOH using an improved DBSCAN and reassembled into energy storage modules with consistent capacity through series and parallel connection.

3. The second-life battery energy storage system based on water cooling and integrated environmental monitoring according to claim 2 is characterized by: The appropriate 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.

4. The second-life battery energy storage system based on water cooling and integrated environmental monitoring according to claim 3 is characterized by: The water-cooling 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 a PID instruction 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 a changing volume and maintain the pressure inside the coolant system stable. The emergency cooling device is used to quickly reduce the temperature of the battery module.

5. The second-life battery energy storage system based on water cooling and integrated environmental monitoring according to claim 4 is characterized in that: The integrated environment monitoring module (3) comprises 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 environmental temperature and humidity data. The smoke sensor monitors the concentration of smoke particles in the air.

6. The second-life battery energy storage system based on water cooling and integrated environmental monitoring according to claim 5 is characterized by: The power management module (4) includes a controller and a PCS unit. The controller sends a control signal to the PCS to adjust the charge and discharge mode according to the scheduling instruction of the energy management module (5). The power management module (4) adjusts the charge and discharge power according to the temperature and humidity data.

7. The second-life battery energy storage system based on water cooling and integrated environmental monitoring according to claim 6 is characterized by: The power management module (4) receives the SOC / SOH data of the cascade battery energy storage module (1), formulates 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; defines the priority formula: Priority = α·SOC + β·SOH, where α and β are weight coefficients, and sorts each battery module based on the calculated priority to formulate the optimal charge and discharge strategy.

8. The second-life battery energy storage system based on water cooling and integrated environmental monitoring according to claim 7 is characterized by: According to the load demand and the scheduling instructions of the energy management module (5), the total output power P currently required by the system is determined 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 battery modules ranked in the top k% by priority, the output power can be set to Pi′=P i ×(1+γ), and for the battery modules ranked after (100-k)%, the output power is Pi′=P i ×(1-δ), where γ and δ are adjustment coefficients.

9. The second-life battery energy storage system based on water cooling and integrated environmental monitoring according to claim 8 is characterized by: The energy management module (5) receives the SOC / SOH data of 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 as follows: 1) For each running scenario a i And each indicator f j , collect the actual value in the current state and form an n×m decision matrix D: 2) Cost indicators are: Where: n is the number of scenes, r ij is the performance of the i-th scenario under the j-th indicator, f j (a i ) represents the i-th scene a i The actual measurement value of the jth cost-type indicator; 3) Determine the positive ideal solution and negative ideal solutions 4) Calculate the group utility value S i and individual regret value R i : in, Comprehensive ranking index Q i for: θ is the decision preference coefficient; 5) Press Q i Sort by value, select the best running scenario, and sort all running scenarios by Q i The values ​​are sorted from small to large, and the optimal running scenario is finally output: Among them: a opt This is the optimal operating scenario.

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