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

Abstract

The invention provides a detector layout determination method and device in an electrochemical energy storage system, and relates to the technical field of electrochemical energy storage monitoring, and the method comprises the steps: obtaining the installation point location of a detector and the position of a battery unit; according to the relation between the installation point positions and the positions of the battery units, determining the coverage variables of the installation point positions on the battery units under the condition that the detectors are installed; according to a preset constraint condition, optimizing an installation decision variable by taking reduction of the sum of the total installation cost and the total detection effect as a target, and obtaining a target value of the installation decision variable; and according to the target value of the installation decision variable, arranging the detector on the corresponding installation point position. By reducing the sum of the total installation cost and the total detection effect as the target optimization installation decision variable, the target value of the installation decision variable is obtained, the position optimization of the detector is realized, 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, especially 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 essential in solving 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, and the location of the sensors is 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 total cost of setting up sensors. Summary of the invention

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

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

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

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

[0008] A fourth objective of the present application is to provide a computer-readable storage medium.

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

[0010] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for determining the layout of detectors in an electrochemical energy storage system, comprising:

[0011] Obtain the installation point of the detector and the location of the battery unit;

[0012] Determine, according to 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;

[0013] Determine the total installation cost and the total detection effect according to the cost information, fault information and detection efficiency information of the detector;

[0014] According to preset constraints, optimizing installation decision variables with the goal of reducing the sum of the total installation cost and the total detection effect, and obtaining target values ​​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 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 the installation point i cannot cover the 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 of 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 overall detection effect is formulated as:

[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 constraint conditions include:

[0024] Override constraints:

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

[0026] Constraints on detector installation and coverage: Where I is the installation point set, J is the battery unit set;

[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 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:

[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 with the smallest sum of the total installation cost and the total detection effect.

[0036] Optionally, the step of setting 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 purpose, the second embodiment of the present application proposes a detector layout determination device in an electrochemical energy storage system, comprising:

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

[0040] A coverage variable determination module, used to determine the coverage variables of each installation point for each battery cell when the detector is installed according to the relationship between the installation point and the position of the battery cell;

[0041] A calculation module, 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;

[0042] An optimization module, 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 variables;

[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, the third aspect of the present application provides an electronic device, including: 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 aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer execution instructions, and when the computer execution 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 purpose, the fifth aspect of the present application proposes a computer program product, which implements any method 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 the 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 schematic 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 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. DETAILED DESCRIPTION

[0054] Embodiments of the present application are described in detail below, and examples of the embodiments 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, especially 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 essential in solving the intermittent problem 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 a variety of faults that can lead to reduced efficiency, shortened lifetime, or even hazardous situations such as fire or explosion. Effective monitoring through strategically placed detectors is critical for timely detection and response to faults. However, the deployment of such detectors faces many challenges, including optimizing their locations to cover all potential fault points while minimizing associated costs and not exceeding operational constraints.

[0057] The evolution of electrochemical energy storage systems, especially in areas with high reliability requirements such as renewable energy integration and electric mobility, requires innovative approaches to system monitoring and fault detection. EES systems such as lithium-ion technology have played an important role in energy storage solutions. However, they also bring 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 the need for effective monitoring systems that can predict and mitigate failures before they escalate.

[0058] Traditional monitoring techniques in EES systems focus primarily on voltage, current, and temperature measurements. While these methods provide basic 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 is often heuristic or based on static configurations, without considering 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 problem, an embodiment of the present application provides a method for determining a detector layout in an electrochemical energy storage system. 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. Figure 1 As shown, the method comprises 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 according to 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 according to the cost information, fault information and detection efficiency information of the detector;

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

[0064] Step 105: installing 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 techniques 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 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 the ability to adapt to different failure scenarios. This study aims to fill this gap by developing an optimization model that integrates cost, detection reliability, and responsiveness to provide a comprehensive detector deployment approach based on various operating scenarios.

[0067] In this embodiment, the electrochemical energy storage system includes a plurality of battery cells (also referred to as battery cells), and these battery cells are distributed according to a certain pattern. Common arrangements include:

[0068] 1. I-shaped layout: Early electric vehicle battery packs often adopt an I-shaped layout, that is, the battery cells are arranged in the center of the chassis along the longitudinal direction of the vehicle, similar to the shape of the letter "I". This layout method utilizes the original space of the vehicle. For example, when a fuel vehicle is converted into an electric vehicle, the battery can be installed in the original engine compartment or trunk position.

[0069] 2. T-shaped layout T-shaped layout is usually adopted when the battery pack needs to adapt to the specific space constraints of the vehicle. For example, in extended-range electric vehicles, the battery pack will be designed in a T shape to accommodate the engine and fuel tank. This layout can utilize the central channel at the bottom of the vehicle and the space under the rear seats to improve space utilization.

[0070] 3. U-shaped or L-shaped layout: U-shaped or L-shaped layout is to further increase the battery capacity by expanding the size or shape of the battery pack and making the most of the space at the bottom of the vehicle. This layout can increase the volume and weight of the battery pack, thereby increasing the vehicle's range.

[0071] 4. Skateboard layout: Skateboard layout (also called flat-panel or integrated) is a common design in modern electric vehicles. The battery pack is designed as part of the vehicle chassis, and the battery cells are laid flat under the vehicle floor. This layout helps to lower the center of gravity of the vehicle, improve the handling and stability of the vehicle, while maximizing the size of the battery pack, improving energy density and endurance.

[0072] 5. Matrix or honeycomb layout: Matrix or honeycomb layout is that the battery cells are arranged in a regular array or honeycomb inside the battery pack, which is convenient for management and cooling, while improving the rigidity and safety of the structure. This layout is usually combined with an efficient thermal management system to ensure that the temperature of the battery cells is consistent and avoid the risk of thermal runaway.

[0073] 6. Modular layout: The modular layout combines battery cells into independently replaceable modules for easy production and maintenance, while allowing the capacity and shape of the battery pack to be flexibly adjusted to suit different vehicle models and needs.

[0074] The electric control system of electric vehicles controls the operation of the battery through the battery management system (BMS) to ensure that the battery operates under safe, efficient and reliable conditions. It is used to monitor the battery's voltage, current, temperature and other physical parameters to evaluate the battery's health status and remaining power (SOC). It adjusts the voltage and state of charge between the individual cells in the battery pack to prevent individual cells from being overcharged or over-discharged, and to extend the overall life of the battery pack. It controls the temperature of the battery to ensure that the battery operates within the optimal temperature range and avoids overheating or overcooling, which affects battery performance and life. It manages the battery's charge and discharge process, including controlling the charging current and voltage, to prevent the battery from being overcharged or deeply discharged, and to ensure the safe operation of the battery. By detecting faults in the battery system in real time, such as short circuits, open circuits, overtemperature, etc., it promptly issues alarms and takes necessary protective measures, such as cutting off the circuit.

[0075] Detectors are used to monitor parameters such as voltage, current, and temperature of each battery cell in the battery and to 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 each 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, 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 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 the installation point i cannot cover the 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 to install 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 operation scenarios of the electrochemical energy storage system include multiple scenarios. The overall environmental conditions of the system in each operation 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 of 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 overall detection effect is formulated as:

[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 obtained as The physical meaning of this part is that the economic costs of installing the detector and maintaining the operation are taken into account, and the goal is to minimize these costs.

[0087] Includes considerations of detection and response effectiveness, including It represents the sum of the response priorities of the battery cells in different scenarios. It represents the consideration of detection efficiency and failure probability. The physical meaning of this part is to balance cost and efficiency, optimize the layout strategy of detectors, and make the system efficient and safe in different scenarios. λ is a balancing factor that weighs the importance of cost and detection and response efficiency.

[0088] Optionally, the constraint conditions include:

[0089] Override 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: Among them, B is the total budget; this constraint limits the total cost to within the budget range. The physical meaning of this constraint is to ensure the economy and feasibility of the system.

[0091] Operational constraints, including the failure probability of the detector 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 meaning 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 reducing 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 monitoring of faults 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 the battery unit j, β is an adjustment parameter used to adjust the sensitivity of coverage and distance, and introduces nonlinear coverage effect attenuation; this constraint ensures timely response and processing of faults. The physical meaning of this constraint is to ensure the efficient response and safe processing of faults by the system.

[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 is the upper limit of the false alarm rate. This constraint is used to ensure the reliability of the detector and minimize the operational interference caused by false alarms.

[0099] Optionally, 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:

[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 with the smallest sum of the total installation cost and the total detection effect.

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

[0103] 1. Gradient descent method: Gradient descent method is an iterative optimization algorithm suitable for continuously differentiable objective functions. The algorithm updates parameters along the negative gradient direction so that the objective function gradually decreases. Gradient descent method is divided into batch gradient descent, stochastic gradient descent and mini-batch gradient descent.

[0104] 2. Newton's method: Newton's method is a second-order optimization algorithm that uses the second-order Taylor expansion of the objective function to find the minimum point. Newton's method has a faster convergence speed, but it needs to calculate the second-order derivative matrix (Hessian matrix) of the objective function, which may consume a lot of memory and computing resources for large problems.

[0105] 3. Quasi-Newton method: The quasi-Newton method is an improvement on the Newton method. It does not need to directly calculate the Hessian matrix, but 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. The conjugate gradient method can find the minimum value within a limited number of steps and does not need to store all gradient histories.

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

[0108] 6. Coordinate descent method: Coordinate descent method 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 each parameter. Coordinate descent method 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 their values ​​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: Genetic Algorithm is a heuristic optimization method that imitates the biological evolution process and gradually improves parameters through crossover, mutation and selection operations. Genetic Algorithm is suitable for multi-peak and non-convex objective functions.

[0111] 9. Particle Swarm Optimization: Particle swarm optimization is a group intelligence optimization algorithm that simulates the predation behavior of bird flocks 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 solid cooling process and allows jumping out of the local optimal solution. Simulated annealing is suitable for complex objective functions.

[0113] Optionally, the step of setting 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 embodiment, the electrochemical energy storage system includes 10 battery cells, each with different risk profiles and operating requirements. The system is operated under three different scenarios, reflecting different operating stresses and failure probabilities: Scenario 1: Normal operation under standard load conditions. Scenario 2: High load conditions, increasing the likelihood of battery failure. Scenario 3: Emergency situations, where a fast response of the system is critical.

[0116] The model is implemented to determine the optimal placement and configuration of detectors across the battery cells. The implementation considers predefined costs, probabilities, and scenario weights as input parameters to solve the optimization problem. The optimization process includes: Defining the problem constraints and objective function. Inputting system parameters and scenario data. Running the solver to find the best solution that minimizes cost and maximizes system reliability and responsiveness. Analyzing the solution to evaluate the placement of detectors and their assigned response priorities 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, and is applicable to the needs of different scales and energy storage applications.

[0118] In order to implement the above-mentioned embodiment, 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 comprises:

[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, configured to determine a total installation cost and a total detection effect according to the cost information, fault information and detection efficiency information of the detector;

[0122] The optimization module 240 is used to optimize the installation decision variables according to preset constraints to reduce the sum of the total installation cost and the total detection effect, and obtain the target value 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 should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0129] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0130] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means 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 representations 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, without contradiction.

[0131] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0132] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment 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 not be performed in 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 technicians in the technical field to which the embodiments of the present application belong.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, 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 in other suitable ways if necessary, and then stored in a computer memory.

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

[0135] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0136] In addition, each functional unit in each embodiment 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 one 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 can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify 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 installation point of the detector and the location of the battery unit; Determine, according to 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; Determine the total installation cost and the total detection effect according to the cost information, fault information and detection efficiency information of the detector; According to preset constraints, optimizing installation decision variables with the goal of reducing the sum of the total installation cost and the total detection effect, and obtaining target values ​​of the installation decision variables; The detector is set at the corresponding installation point according to the target value of the installation decision variable.

2. The method according to claim 1, characterized in that The installation point of the detector is i, the battery unit is j, and the coverage variable is y ij , where 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 the installation point i cannot cover the battery cell j.

3. The method according to claim 2, characterized in that The detector cost information includes the installation cost C of the detector at position i i , the maintenance cost of the detector at position i is O 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 .

4. The method according to claim 3, characterized in that The total installation cost is formulated as: Where M is the total number of positions i, x i For the installation decision variable at position i, when x i The value of 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; The overall detection effect is formulated as: 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.

5. The method according to claim 4, characterized in that The constraints include: Override constraints: Budget constraints: Among them, B is the total budget; Constraints on detector installation and coverage: Where I is the installation point set, J is the battery unit set; Detector failure probability constraints: Wherein, Pmin is the lower limit of the detector failure probability; Monitoring efficiency constraints: Wherein, Emin is the lower limit of the monitoring efficiency; Response success rate constraints: Among them, R min is the lower bound on the probability of successfully responding to a battery cell failure; Among them, d ij is the distance between the detector at position i and battery cell j, β is the adjustment parameter; 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.

6. The method according to claim 5, characterized in that The step of 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 with the smallest sum of the total installation cost and the total detection effect.

7. The method according to claim 6, characterized in that The step of setting 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.

8. A detector layout determination device in an electrochemical energy storage system, characterized in that: include: A position determination module, used to obtain the installation point of the detector and the position of the battery unit; A coverage variable determination module, used to determine the coverage variables of each installation point for each battery cell when the detector is installed according to the relationship between the installation point and the position of the battery cell; A calculation module, 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; An optimization module, 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 variables; 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.

9. 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 7.

10. 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 7 when executed by a processor.

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