Method and device for determining detector layout in electrochemical energy storage system

Abstract

The present application proposes a method and device for determining the layout of detectors in an electrochemical energy storage system, which relates to the field of electrochemical energy storage monitoring technology, wherein the method includes: obtaining the installation point of the detector and the position of the battery cell; determining the coverage variables of each installation point for each battery cell when the detector is installed based on the relationship between the installation point and the position of the battery cell; optimizing the installation decision variable with the goal of reducing the sum of the total installation cost and the total detection effect according to the preset constraints, and obtaining the target value of the installation decision variable; setting the detector at the corresponding installation point according to the target value of the installation decision variable. By optimizing the installation decision variable with the goal of reducing the sum of the total installation cost and the total detection effect and obtaining the target value of the installation decision variable, the position of the detector is optimized and the monitoring efficiency of the electrochemical energy storage system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of electrochemical energy storage monitoring, and in particular to a method and device for determining a detector layout in an electrochemical energy storage system. Background Art

[0002] The rapid development of electrochemical energy storage (EES) systems, particularly breakthroughs in technologies such as lithium-ion batteries, has become the cornerstone of modern applications such as electric vehicles, renewable energy storage, and portable electronic devices. These systems are crucial in addressing the intermittent nature of renewable energy and improving energy efficiency in various fields. However, the widespread use and operational reliability of these systems require advanced monitoring solutions to ensure safety, efficiency, and long-term reliability.

[0003] Currently, electrochemical energy storage systems are monitored using sensors. The sensor locations are determined using heuristic methods or based on static configurations, without considering the dynamic characteristics of battery operation. This results in low battery monitoring efficiency and high overall sensor installation costs. Summary of the Invention

[0004] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the first objective of this application is to propose a method for determining detector layout in an electrochemical energy storage system.

[0006] The second object of this application is to provide a device.

[0007] The third objective of this application is to provide an electronic device.

[0008] The fourth object of this application is to provide a computer-readable storage medium.

[0009] A fifth object of this application is to provide a computer program product.

[0010] To achieve the above objectives, the first embodiment of the present application provides a method for determining a detector layout in an electrochemical energy storage system, comprising:

[0011] Obtain the detector's installation location and the battery unit's position;

[0012] Determining, based on the relationship between the installation points and the positions of the battery cells, coverage variables of each battery cell at each installation point when the detector is installed;

[0013] Determining the total installation cost and the total detection effect based on the cost information, fault information and detection efficiency information of the detector;

[0014] According to preset constraints, optimizing the installation decision variables with the goal of reducing the sum of the total installation cost and the total detection effect, and obtaining the target value of the installation decision variables;

[0015] The detector is set at the corresponding installation point according to the target value of the installation decision variable.

[0016] Optionally, the installation point of the detector is i, the battery unit is j, and the coverage variable is y ij , where when y ij When the value of is 1, it indicates that the detection range of the detector at the installation point i can cover the battery cell j; when y ij When the value of is 0, it indicates that the detection range of the detector at installation point i cannot cover battery cell j.

[0017] Optionally, the detector cost information includes the installation cost C of the detector at position i. i , the maintenance cost O of the detector at position i i The fault information includes the fault probability F of the detector at position i i , the probability E that the detector at position i successfully detects battery cell j ij , the probability R that the detector at position i successfully responds to the failure of battery cell j in scenario s ijs , the failure probability P of battery cell j j The detection efficiency information includes the detection efficiency D of the detector at position i for battery cell j ij .

[0018] Optionally, the total installation cost is formulated as:

[0019] Where M is the total number of positions i, x i For the installation decision variable at position i, when x i The value is 1, indicating that the detector is installed at position i; when x i The value of is 1, indicating that the detector is not installed at position i;

[0020] The total detection effect is formulated as follows:

[0021]

[0022] Among them, W s is the weight of scenario s, S is the total number of scenarios, z ijs is the response priority of the detector at position i to battery cell j in scenario s, N is the total number of battery cells, λ is the balancing factor, and Z max,ijs is the upper limit of the detection effect of the detector at position i on battery cell j in scenario s.

[0023] Optionally, the constraints include:

[0024] Coverage constraints:

[0025] Budget constraints: Among them, B is the total budget;

[0026] Constraints on detector installation and coverage: Where I is the set of installation points, and J is the set of battery units;

[0027] Detector failure probability constraints: Wherein, Pmin is the lower limit of the detector failure probability;

[0028] Monitoring efficiency constraints: Wherein, Emin is the lower limit of the monitoring efficiency;

[0029] Response success rate constraints:

[0030] Among them, R min is the lower bound on the probability of successfully responding to a battery cell failure;

[0031] Among them, d ij is the distance between the detector at position i and battery cell j, β is the adjustment parameter;

[0032] False alarm rate constraint: F i ≤F max , where F i is the false alarm rate of the detector at position i, F max is the upper limit of the false alarm rate.

[0033] Optionally, optimizing the installation decision variable with the goal of reducing the sum of the total installation cost and the total detection effect, and obtaining the target value of the installation decision variable, includes:

[0034] Adjusting the values ​​of the installation decision variables corresponding to each installation point according to the optimization algorithm to reduce the sum of the total installation cost and the total detection effect;

[0035] Obtain a target value of the installation decision variable corresponding to the solution that minimizes the sum of the total installation cost and the total detection effect.

[0036] Optionally, the step of placing the detector at a corresponding installation point according to the target value of the installation decision variable includes:

[0037] The detector is installed at an installation point where the value of the installation decision variable is 1.

[0038] To achieve the above-mentioned objectives, a second embodiment of the present application provides a device for determining a detector layout in an electrochemical energy storage system, comprising:

[0039] A position determination module is used to obtain the installation point of the detector and the position of the battery unit;

[0040] a coverage variable determination module, configured to determine, based on the relationship between the installation point and the position of the battery cell, the coverage variable of each installation point for each battery cell when the detector is installed;

[0041] A calculation module, configured to determine a total installation cost and a total detection effect based on cost information, fault information, and detection efficiency information of the detector;

[0042] An optimization module is configured to optimize the installation decision variables according to preset constraints with the goal of reducing the sum of the total installation cost and the total detection effect, and obtain a target value of the installation decision variable;

[0043] The detector setting module is used to set the detector at the corresponding installation point according to the target value of the installation decision variable.

[0044] To achieve the above-mentioned purpose, a third embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0045] The memory stores computer-executable instructions;

[0046] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0047] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0048] To achieve the above-mentioned objectives, the fifth embodiment of the present application proposes a computer program product, which implements any one of the methods in the first aspect when executed by a processor.

[0049] The method, device, electronic device and storage medium for determining the layout of detectors in an electrochemical energy storage system provided in the present application optimize the position of the detectors by reducing the sum of the total installation cost and the total detection effect as the target optimization installation decision variable, and obtain the target value of the installation decision variable, thereby improving the monitoring efficiency of the electrochemical energy storage system.

[0050] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0052] Figure 1 A flow chart of a method for determining a detector layout in an electrochemical energy storage system provided in an embodiment of the present application;

[0053] Figure 2 This is a structural schematic diagram of a detector layout determination device in an electrochemical energy storage system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0055] The rapid development of electrochemical energy storage (EES) systems, particularly breakthroughs in technologies such as lithium-ion batteries, has become the cornerstone of modern applications such as electric vehicles, renewable energy storage, and portable electronic devices. These systems are crucial in addressing the intermittent nature of renewable energy and improving energy efficiency in various fields. However, the widespread use and operational reliability of these systems require advanced monitoring solutions to ensure safety, efficiency, and long-term reliability.

[0056] Electrochemical energy storage systems are inherently complex and susceptible to various fault conditions, which can lead to reduced efficiency, shortened lifetime, or even hazardous situations such as fire or explosion. Effective monitoring through strategically placed detectors is crucial for timely detection and response to faults. However, deploying such detectors presents numerous challenges, including optimizing their placement to cover all potential fault points while minimizing associated costs and remaining within operational constraints.

[0057] The evolution of electrochemical energy storage systems, particularly in areas requiring high reliability, such as renewable energy integration and e-mobility, requires innovative approaches to system monitoring and fault detection. EES systems, such as lithium-ion technology, have played a significant role in energy storage solutions. However, they also present unique challenges, such as thermal runaway, electrode degradation, and electrolyte instability, which can lead to efficiency loss or catastrophic failure. These challenges highlight the importance of effective system monitoring that can predict and mitigate faults before they escalate.

[0058] Traditional monitoring techniques in EES systems focus primarily on voltage, current, and temperature measurements. While these methods provide essential data, they often fail to detect more subtle signs of degradation before they lead to failure. More advanced techniques, such as impedance spectroscopy and thermal imaging, have been introduced to provide more detailed diagnostic information. However, these techniques can be invasive or expensive, limiting their widespread adoption. The placement of sensors or detectors in EES systems often employs heuristics or is based on static configurations, failing to account for the dynamic nature of battery operation. Optimization models for sensor placement, typically using methods such as genetic algorithms, linear programming, or particle swarm optimization, improve performance by balancing coverage and cost. These models prioritize high-risk areas but often ignore operational variables and the probabilistic nature of failures.

[0059] To address this issue, the present invention provides a method for determining the layout of detectors in an electrochemical energy storage system. Figure 1 This is a flow chart of a method for determining detector layout in an electrochemical energy storage system provided in an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0060] Step 101, obtaining the installation point of the detector and the position of the battery unit;

[0061] Step 102, determining the coverage variables of each battery cell at each installation point when the detector is installed based on the relationship between the installation point and the position of the battery cell;

[0062] Step 103, determining the total installation cost and the total detection effect based on the cost information, fault information and detection efficiency information of the detector;

[0063] Step 104: Optimize the installation decision variables according to preset constraints with the goal of reducing the sum of the total installation cost and the total detection effect, and obtain target values ​​of the installation decision variables;

[0064] Step 105: Install the detector at the corresponding installation point according to the target value of the installation decision variable.

[0065] In this example, probabilistic risk assessment is combined with an optimization framework to better address the uncertainty of battery failures. These methods dynamically optimize the placement and configuration of sensors by considering various failure scenarios and operating conditions. The use of scenario analysis allows for more adaptive monitoring strategies that are more resilient to potential operational disturbances. The integration of machine learning and artificial intelligence with EES monitoring systems represents a promising area for predictive maintenance. These technologies can learn from historical data to predict future failures, enabling proactive management of EES systems. In addition, the development of more sophisticated models, the integration of real-time data, and continuous learning can significantly improve the accuracy and effectiveness of monitoring systems.

[0066] Current detector placement methods primarily focus on static configurations that do not consider different operating conditions or the probabilistic nature of potential failures. There is an urgent need for a dynamic optimization model that considers not only cost and coverage, but also detector reliability and adaptability to different failure scenarios. This study aims to fill this gap by developing an optimization model that integrates cost, detection reliability, and responsiveness, providing a comprehensive detector deployment approach based on various operational scenarios.

[0067] In this embodiment, the electrochemical energy storage system includes multiple battery cells (also called battery cells). These battery cells are distributed according to a certain pattern. Common arrangements include:

[0068] 1. I-shaped layout: Early electric vehicle battery packs often adopted an I-shaped layout, with the battery cells arranged longitudinally in the center of the vehicle chassis, similar to the letter "I." This layout utilizes existing vehicle space. For example, when converting a fuel vehicle to an electric vehicle, the battery can be installed in the original engine compartment or trunk.

[0069] 2. T-shaped layout: This layout is often used when the battery pack needs to fit within specific vehicle space constraints. For example, in extended-range electric vehicles, the battery pack is designed in a T shape to accommodate the engine and fuel tank. This layout utilizes the center tunnel under the vehicle and the space under the rear seats, improving space utilization.

[0070] 3. U-shaped or L-shaped layout: This is designed to further increase battery capacity by expanding the size or shape of the battery pack to maximize the space under the vehicle. This layout increases the volume and weight of the battery pack, thereby improving the vehicle's range.

[0071] 4. Skateboard Layout: The skateboard layout (also known as flat-bed or integrated) is a common design in modern electric vehicles. The battery pack is designed as part of the vehicle chassis, with the battery cells laid flat under the vehicle floor. This layout helps lower the vehicle's center of gravity, improving vehicle handling and stability while maximizing battery pack size, increasing energy density and range.

[0072] 5. Matrix or honeycomb layout: A matrix or honeycomb layout arranges battery cells in a regular array or honeycomb pattern within the battery pack, facilitating management and cooling while also improving structural rigidity and safety. This layout is often coupled with an efficient thermal management system to ensure consistent battery cell temperatures and mitigate the risk of thermal runaway.

[0073] 6. Modular layout: Modular layout combines battery cells into independently replaceable modules, which facilitates production and maintenance. At the same time, the capacity and shape of the battery pack can be flexibly adjusted to suit different vehicle models and needs.

[0074] The electric vehicle's electronic control system uses a battery management system (BMS) to control battery operation, ensuring safe, efficient, and reliable operation. It monitors physical parameters such as battery voltage, current, and temperature to assess the battery's state of health and remaining charge (SOC). It regulates the voltage and state of charge between individual cells in the battery pack to prevent overcharging or over-discharging of individual cells, thereby extending the overall life of the battery pack. It controls battery temperature to ensure that the battery operates within the optimal temperature range, avoiding overheating or overcooling that can affect battery performance and life. It manages the battery's charge and discharge processes, including controlling charge current and voltage, to prevent overcharging or deep discharge, ensuring safe operation. By detecting faults in the battery system in real time, such as short circuits, open circuits, and overheating, it issues timely alarms and takes necessary protective measures, such as disconnecting the circuit.

[0075] Detectors are used to monitor parameters such as voltage, current, and temperature of each battery cell in the battery and provide early warning of faults. Due to the size of the battery and the cost of installing detectors, it is impossible to install a corresponding detector for every battery cell, so each detector needs to monitor multiple battery cells.

[0076] In order to reduce the installation cost of the detector as much as possible and improve the detection efficiency of the detector, this embodiment designs an optimization model, sets multiple constraints, optimizes the parameters in the model under the constraints of the constraints, and determines the optimal setting method. The detector is set at the corresponding installation point according to the target value of the installation decision variable.

[0077] Optionally, the installation point of the detector is i, the battery unit is j, and the coverage variable is y ij , where when y ij When the value of is 1, it indicates that the detection range of the detector at the installation point i can cover the battery cell j; when y ij When the value of is 0, it indicates that the detection range of the detector at installation point i cannot cover battery cell j.

[0078] In this embodiment, a plurality of installation points are preset in the electrochemical energy storage system, and these installation points are screened to obtain the optimal installation point for installing the detector.

[0079] Optionally, the detector cost information includes the installation cost C of the detector at position i. i , the maintenance cost O of the detector at position i i The fault information includes the fault probability F of the detector at position i i , the probability E that the detector at position i successfully detects battery cell j ij , the probability R that the detector at position i successfully responds to the failure of battery cell j in scenario s ijs , the failure probability P of battery cell j j The detection efficiency information includes the detection efficiency D of the detector at position i for battery cell j ij .

[0080] In this embodiment, the electrochemical energy storage system has multiple operating scenarios. The overall environmental conditions of the system in each operating scenario are quite different, which will cause the detector's success rate in detecting battery cell failures to change. Therefore, the probability R of the detector at position i successfully responding to the failure of battery cell j in scenario s is preset. ijs .

[0081] Optionally, the total installation cost is formulated as:

[0082] Where M is the total number of positions i, x i For the installation decision variable at position i, when x i The value is 1, indicating that the detector is installed at position i; when x i The value of is 1, indicating that the detector is not installed at position i;

[0083] The total detection effect is formulated as follows:

[0084]

[0085] Among them, W s is the weight of scenario s, S is the total number of scenarios, z ijsis the response priority of the detector at position i to battery cell j in scenario s, N is the total number of battery cells, λ is the balancing factor, and Z max,ijs is the upper limit of the detection effect of the detector at position i on battery cell j in scenario s.

[0086] In this embodiment, the installation decision variable is used to determine whether to install a detector at each installation point. Considering the cost of installing the detector and the subsequent dimensional cost of the detector throughout its life cycle, the total installation cost is The physical meaning of this part is that the economic costs of installing the detector and maintaining the operation are taken into account, with the goal of minimizing these costs.

[0087] Includes consideration of detection and response effectiveness, including It represents the sum of the response priorities of battery cells in different scenarios. This factor represents a consideration of detection efficiency and failure probability. The physical meaning of this component is to balance cost and efficiency, optimizing the detector deployment strategy to ensure the system remains efficient and safe in various scenarios. λ is a balancing factor that weighs the importance of cost against detection and response efficiency.

[0088] Optionally, the constraints include:

[0089] Coverage constraints: This constraint ensures that each battery cell is covered by at least one detector. The physical meaning of this constraint is to ensure that the system fully monitors all battery cells.

[0090] Budget constraints: Where B is the total budget; this constraint limits the total cost to within the budget. The physical meaning of this constraint is to ensure the economy and feasibility of the system.

[0091] Operational constraints, including the detector's failure probability and the required detection and response efficiency:

[0092] Constraints on detector installation and coverage: Where I is the set of installation points and J is the set of battery units. This constraint ensures the consistency of the detector layout and coverage relationship. The physical significance of this constraint is to avoid invalid detector layout.

[0093] Detector failure probability constraints: This constraint ensures the reliability and stability of the detector. The physical meaning of this constraint is to prevent the system from losing monitoring efficiency due to detector failure, where Pmin is the lower limit of the detector failure probability.

[0094] Monitoring efficiency constraints: This constraint ensures timely detection of faults. The physical meaning of this constraint is to ensure efficient fault monitoring by the system, where Emin is the lower limit of the monitoring efficiency.

[0095] Response success rate constraints:

[0096] Among them, R min is the lower bound on the probability of successfully responding to a battery cell failure;

[0097] Among them, d ij is the distance between the detector at position i and battery cell j, and β is an adjustment parameter used to adjust the sensitivity of coverage to distance, introducing nonlinear coverage effect attenuation. This constraint ensures timely response and handling of faults. The physical significance of this constraint is to ensure the system's efficient response and safe handling of faults.

[0098] False alarm rate constraint: F i ≤F max , where F i is the false alarm rate of the detector at position i, F max This constraint is used to ensure the reliability of the detector and minimize the operational disruption caused by false alarms.

[0099] Optionally, optimizing the installation decision variable with the goal of reducing the sum of the total installation cost and the total detection effect, and obtaining the target value of the installation decision variable, includes:

[0100] Adjusting the values ​​of the installation decision variables corresponding to each installation point according to the optimization algorithm to reduce the sum of the total installation cost and the total detection effect;

[0101] Obtain a target value of the installation decision variable corresponding to the solution that minimizes the sum of the total installation cost and the total detection effect.

[0102] In one possible embodiment, the minimum value of the objective function (total installation cost + total detection effect) is calculated using a minimum algorithm, for example:

[0103] 1. Gradient Descent: Gradient descent is an iterative optimization algorithm suitable for continuously differentiable objective functions. It updates parameters along the negative gradient, gradually decreasing the objective function. Gradient descent comes in many varieties, including batch gradient descent, stochastic gradient descent, and mini-batch gradient descent.

[0104] 2. Newton's method: The Newton method is a second-order optimization algorithm that uses the second-order Taylor expansion of the objective function to find the minimum. The Newton method has a fast convergence rate, but it requires calculating the second-order derivative matrix (Hessian matrix) of the objective function, which can consume a large amount of memory and computing resources for large problems.

[0105] 3. Quasi-Newton method: The quasi-Newton method is an improvement on the Newton method. Instead of directly calculating the Hessian matrix, it accelerates the optimization process by approximating the Hessian matrix. Typical quasi-Newton methods include BFGS (Broyden-Fletcher-Goldfarb-Shanno) and L-BFGS (Limited-memory BFGS).

[0106] 4. Conjugate Gradient Method: The conjugate gradient method is a first-order optimization algorithm suitable for quadratic objective functions. It can find the minimum value within a finite number of steps and does not require storing the entire gradient history.

[0107] 5. Chalk Method: The Chalk Method (Conjugate Direction Method) is a first-order optimization algorithm suitable for non-quadratic objective functions. The Chalk Method accelerates the optimization process by constructing a set of conjugate directions.

[0108] 6. Coordinate Descent: Coordinate descent is a first-order optimization algorithm that updates only one parameter at a time. It is suitable for situations where the objective function is independent of all parameters. Coordinate descent can be parallelized and is suitable for large-scale problems.

[0109] 7. Random Search: Random search is a simple optimization method that randomly generates parameters and evaluates them on the objective function to find the minimum. Random search is suitable for situations where there is no clear gradient information or the gradient is difficult to calculate.

[0110] 8. Genetic Algorithm: A genetic algorithm is a heuristic optimization method that mimics the biological evolution process and gradually improves parameters through crossover, mutation, and selection operations. Genetic algorithms are suitable for multimodal and non-convex objective functions.

[0111] 9. Particle Swarm Optimization: Particle swarm optimization is a swarm intelligence optimization algorithm that simulates the hunting behavior of flocks of birds to find the minimum value. Particle swarm optimization is applicable to global optimization problems.

[0112] 10. Simulated Annealing: Simulated annealing is a heuristic optimization method that simulates the cooling process of a solid and allows the solution to escape from the local optimum. Simulated annealing is suitable for complex objective functions.

[0113] Optionally, the step of placing the detector at a corresponding installation point according to the target value of the installation decision variable includes:

[0114] The detector is installed at an installation point where the value of the installation decision variable is 1.

[0115] In one possible implementation, an electrochemical energy storage system includes 10 battery cells, each with a different risk profile and operating requirements. The system operates under three different scenarios, reflecting varying operating stresses and failure probabilities: Scenario 1: Normal operation under standard load conditions. Scenario 2: High load conditions, which increase the likelihood of battery failure. Scenario 3: Emergency situations, where rapid system response is crucial.

[0116] The model was implemented to determine the optimal detector placement and configuration across the battery cells. The implementation considered predefined costs, probabilities, and scenario weights as input parameters to solve the optimization problem. The optimization process involved defining the problem constraints and objective function. System parameters and scenario data were input. The solver was run to find the optimal solution that minimized cost and maximized system reliability and responsiveness. The solution was analyzed to evaluate the detector placement and its assigned response priority under each scenario.

[0117] This study successfully developed a comprehensive model for optimizing the deployment of detectors in electrochemical energy storage systems, taking into account cost, efficiency and safety. The model not only helps to deploy detectors cost-effectively, but also ensures the system's responsiveness under various fault conditions. It takes into account potential detector failures and monitoring efficiency, ensuring that the system can maintain the necessary operating performance even when some detectors fail. In addition, the introduction of scenario weights enables the model to optimize the detector response strategy according to different operating conditions, enhancing the adaptability and robustness of the system. Optimized detector deployment can significantly improve the safety and reliability of electrochemical energy storage systems while adhering to budget constraints. The model serves as a decision support tool for designing and operating systems, suitable for the needs of different scales and energy storage applications.

[0118] In order to implement the above embodiments, the present application also proposes a detector layout determination device in an electrochemical energy storage system. Figure 2 This is a schematic diagram of the structure of a detector layout determination device in an electrochemical energy storage system provided in an embodiment of the present application. Figure 2 As shown, the device includes:

[0119] A position determination module 210 is used to obtain the installation point of the detector and the position of the battery unit;

[0120] a coverage variable determination module 220 for determining, based on the relationship between the installation point and the position of the battery cell, the coverage variable of each installation point for each battery cell when the detector is installed;

[0121] A calculation module 230 is used to determine the total installation cost and the total detection effect according to the cost information, fault information and detection efficiency information of the detector;

[0122] An optimization module 240 is configured to optimize the installation decision variables according to preset constraints with the goal of reducing the sum of the total installation cost and the total detection effect, and obtain target values ​​of the installation decision variables;

[0123] The detector setting module 250 is used to set the detector at the corresponding installation point according to the target value of the installation decision variable.

[0124] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0125] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0126] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0127] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0128] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0129] This application contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0130] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0131] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0132] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0133] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0134] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0135] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0136] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0137] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for determining detector layout in an electrochemical energy storage system, characterized in that: The following steps are involved: Obtain the detector's installation location and the battery unit's position; Determining, based on the relationship between the installation points and the positions of the battery cells, coverage variables of each battery cell at each installation point when the detector is installed; Determining the total installation cost and the total detection effect based on the cost information, fault information and detection efficiency information of the detector; According to preset constraints, optimizing the installation decision variables with the goal of reducing the sum of the total installation cost and the total detection effect, and obtaining the target value of the installation decision variables; Setting the detector at the corresponding installation point according to the target value of the installation decision variable; The preset constraints include: Coverage constraint: ensure that each battery cell is covered by at least one detector; Budget constraints: limit the total cost to within the budget; Detector installation and coverage relationship constraints: ensure the consistency of detector layout and coverage relationship; Detector failure probability constraint: prevents the system from losing monitoring efficiency due to detector failure; Monitoring efficiency constraints: ensuring timely detection of faults; Response success rate constraints: ensure timely response and handling of faults; and false alarm rate constraints: ensure detector reliability and minimize operational disruptions due to false alarms.

2. The method according to claim 1, characterized in that Optimizing the installation decision variables with the goal of reducing the sum of the total installation cost and the total detection effect, and obtaining the target value of the installation decision variables, includes: Adjusting the values ​​of the installation decision variables corresponding to each installation point according to the optimization algorithm to reduce the sum of the total installation cost and the total detection effect; Obtain a target value of the installation decision variable corresponding to the solution that minimizes the sum of the total installation cost and the total detection effect.

3. The method according to claim 2, characterized in that The step of placing the detector at the corresponding installation point according to the target value of the installation decision variable includes: The detector is installed at an installation point where the value of the installation decision variable is 1.

4. A detector layout determination device in an electrochemical energy storage system, characterized in that: include: A position determination module is used to obtain the installation point of the detector and the position of the battery unit; a coverage variable determination module, configured to determine, based on the relationship between the installation point and the position of the battery cell, the coverage variable of each installation point for each battery cell when the detector is installed; A calculation module, configured to determine a total installation cost and a total detection effect based on cost information, fault information, and detection efficiency information of the detector; An optimization module is configured to optimize the installation decision variables according to preset constraints with the goal of reducing the sum of the total installation cost and the total detection effect, and obtain a target value of the installation decision variable; A detector setting module, configured to set the detector at a corresponding installation point according to a target value of the installation decision variable; The preset constraints include: Coverage constraint: ensure that each battery cell is covered by at least one detector; Budget constraints: limit the total cost to within the budget; Detector installation and coverage relationship constraints: ensure the consistency of detector layout and coverage relationship; Detector failure probability constraint: prevents the system from losing monitoring efficiency due to detector failure; Monitoring efficiency constraints: ensuring timely detection of faults; Response success rate constraints: ensure timely response and handling of faults; and false alarm rate constraints: ensure detector reliability and minimize operational disruptions due to false alarms.

5. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 3 when executed by a processor.

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