A system for predicting the operation risks of an energy storage battery

Abstract

The present invention relates to the field of battery operation management, and discloses an energy storage battery operation risk prediction system. The system collects real-time operation data of multiple groups of energy storage batteries; an environmental data collection module and environmental data; then comprehensively calculates an operation risk coefficient based on the operation data and the environmental data, and after comparing it with a preset comparison range of the system, determines whether the energy storage battery is in a potential risk state; then gives an early warning to the energy storage battery in a potential risk state, conducts early warning monitoring and key marking on the energy storage battery in a critical state; then conducts a predictive hazard analysis on the key-marked energy storage battery to obtain a status value, and compares the status value with a preset status range; finally, executes corresponding emergency measures according to the comparison result. The present invention comprehensively predicts the operation risk of the energy storage battery through environmental data and operation data, issues an early warning in a timely and accurate manner, and conducts in-depth analysis on the energy storage battery in a critical state to establish a maintenance order.

Description

Technical Field

[0001] The present invention relates to the field of battery operation management, and particularly to a system for predicting the operation risks of energy storage batteries. Background Art

[0002] An energy storage battery is a device that can store electrical energy and release it when needed. It plays a crucial role in modern power systems. This battery stores electrical energy by converting it into chemical energy through chemical reactions and reconverts the chemical energy into electrical energy through reverse chemical reactions when needed.

[0003] There are various types of energy storage batteries. According to different principles and chemical reactions, they can be divided into lead-acid batteries, nickel-based batteries, lithium-based batteries, flow batteries, sodium-sulfur batteries, and other types. Each type of battery has its specific application scenarios, advantages, and disadvantages. For example, lead-acid batteries are a relatively traditional type with mature technology and low cost, but their energy density and cycle life are relatively low; while lithium-ion batteries have a high energy density and a long cycle life, so they are widely used in fields such as electric vehicles and mobile energy storage.

[0004] For the energy storage battery module for solar power generation, due to different environments or differences in the product quality itself, as the usage time extends, there are performance differences among the energy storage batteries in each group within the energy storage battery module, resulting in the problem of accelerated aging of the energy storage battery module. Summary of the Invention

[0005] The purpose of the present invention is to provide a system for predicting the operation risks of energy storage batteries to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A system for predicting the operation risks of energy storage batteries includes:

[0008] An operation data acquisition module for real-time acquisition of operation data of multiple groups of energy storage batteries;

[0009] An environmental data acquisition module for real-time acquisition of environmental data during the operation of the energy storage batteries;

[0010] A risk analysis module for comprehensively calculating an operation risk coefficient based on the operation data and environmental data, comparing the operation risk coefficient with a preset comparison interval of the system, and judging whether the energy storage battery is in a potential risk state according to the result;

[0011] An early warning and monitoring module for giving an early warning to the energy storage batteries in a potential risk state according to the judgment result of the risk analysis module, and performing early warning monitoring and key marking on the energy storage batteries in a critical state;

[0012] Deep hazard analysis module, used to perform predictive hazard analysis on the key-marked energy storage batteries, continuously using the current time point t i as a reference, and obtaining the time point t after spanning backward by a preset time period Δt i-Δt , fitting to obtain the curve of the operation risk coefficient within t i-Δt -t i with the time axis as the abscissa, processing this curve to obtain a state value, and comparing the state value with a preset state interval;

[0013] Emergency module, used to execute corresponding emergency measures according to the comparison result between the state value and the preset state interval.

[0014] As a further technical solution, the process of obtaining the operation risk coefficient is as follows:

[0015] Through the formula:

[0016] Calculate to obtain the operation risk coefficient F of the i-th group of energy storage batteries i ;

[0017] In the formula, K x is the operation scoring coefficient, D y is the environment scoring coefficient, ρ x , τ y are preset conversion coefficients; m is the total number of detection time points at continuous equally spaced time intervals.

[0018] As a further technical solution, the process of obtaining the operation scoring coefficient is as follows:

[0019] Through the formula: Obtain the operation scoring coefficient K x ;

[0020] In the formula, μ a is the proportionality coefficient corresponding to the a-th operation parameter, X a is the real-time detection value of the a-th operation parameter, is the detection mean value of the a-th operation parameter, n is the total number of operation parameters, D y0 is the environment scoring reference coefficient.

[0021] As a further technical solution, the process of obtaining the environment scoring coefficient is as follows:

[0022] Through the formula:

[0023]

[0024] Calculate to obtain the environment scoring coefficient D y ;

[0025] In the formula, p is the value of the electrolyte leakage gas, θT , θ Z is the weight coefficient, and T(t) is the curve of the environmental temperature varying with time within the time period of t A -t B , T’(t) is the curve of the predicted environmental temperature varying with time within the time period of t A -t B , Z(t) is the curve of the vibration value varying with time within the time period of t A -t B , Z’(t)t is the curve of the predicted vibration value varying with time within the time period of t A -t B .

[0026] As a further technical solution, the process of determining whether the energy storage battery is in a potential risk state is as follows:

[0027] Compare the calculated F i with the preset interval [F i- , F i+ ;

[0028] If F i exceeds [F i- , F i+ , it is determined that the current energy storage battery is in a potential risk state;

[0029] If F i falls within [F i- , F i+ , it is determined that the current energy storage battery is in a critical state;

[0030] If F i is lower than [F i- , F i+ , it is determined that the current energy storage battery is in a safe state.

[0031] As a further technical solution, the process of obtaining the state value of the key-marked energy storage battery is as follows:

[0032] Obtain the number N of the time points corresponding to dF i-Δt -t i (t) / dt = 0 on the curve with the time axis as the abscissa of the operation risk coefficient within the time period of t i and

[0033] Through the formula: Calculate to obtain the state value of the current key-marked energy storage battery;

[0034] is the maximum peak value of the operation risk coefficient curve within the time period of t i-Δt -t i . For t i-Δt -t i The minimum valley value of the operating risk coefficient curve within the time period, μ is the coefficient for removing dimensions, and ΔF is the reference value within the time period of t i-Δt -t i time period.

[0035] As a further technical solution, the working process of the emergency module is as follows:

[0036] Compare the obtained state value P with the preset state interval [P 0 , P 1 ;

[0037] If P is greater than P 1 , it is determined that the currently key-marked energy storage battery is in a deteriorated state;

[0038] If P belongs to [P 0 , P 1 , it is determined that the currently key-marked energy storage battery is in a normal state;

[0039] If P is less than P 0 , it is determined that the currently key-marked energy storage battery is in a repaired state.

[0040] As a further technical solution, the working process of the emergency module further includes:

[0041] Arrange the key-marked energy storage batteries in descending order according to the magnitude of the state value;

[0042] Remove the energy storage batteries in the repaired state from the current arrangement, and the remaining energy storage batteries are filled in sequentially;

[0043] Mark the energy storage batteries in the deteriorated state for emergency and send them to the background management personnel for priority maintenance.

[0044] The beneficial effects of the present invention:

[0045] (1) The present invention calculates the operation risk coefficient comprehensively according to the operation data and environmental data through the risk analysis module, compares the operation risk coefficient with the comparison interval preset by the system, and judges whether each group of energy storage batteries is in a potential risk state according to the result. If so, the early warning monitoring module gives an early warning to the energy storage batteries in the potential risk state, and conducts early warning monitoring and key marking on the energy storage batteries in the critical state. Obviously, the performance of the energy storage batteries in the potential operation risk state is worse than that of the normal energy storage batteries. In order to prevent the accelerated aging of the overall energy storage battery module, after warning the energy storage batteries in the potential operation risk state, that is, the energy storage batteries with poor performance, the staff will replace them, so as to keep the performance states of each group of energy storage batteries in the whole energy storage battery module relatively consistent, achieve a better service life, and the replaced energy storage batteries can be recycled for other uses to save costs. Then, the deep risk analysis module conducts a deep analysis on the energy storage batteries in the critical state, so as to preferentially maintain the energy storage batteries in the deteriorating trend, reduce their risks, and prevent the energy storage batteries from operating in a continuously deteriorating state, affecting the operation efficiency of the overall energy storage battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 is the logic block diagram of the present invention;

[0048] Figure 2 is the structural block diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0049] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] Please refer to Figure 1 - Figure 2 as shown, the present invention is an energy storage battery operation risk prediction system, including:

[0051] An operation data acquisition module for real-time acquisition of operation data of multiple groups of energy storage batteries during operation;

[0052] An environmental data acquisition module for real-time acquisition of environmental data during the operation of the energy storage battery;

[0053] A risk analysis module, which is used to comprehensively calculate an operation risk coefficient based on operation data and environmental data, compare the operation risk coefficient with a comparison interval preset in the system, and judge whether the energy storage battery is in a potential risk state according to the result;

[0054] An early warning and monitoring module, which is used to give an early warning to the energy storage battery in a potential risk state according to the judgment result of the risk analysis module, and conduct early warning monitoring and key marking on the energy storage battery in a critical state;

[0055] A deep - danger analysis module, which is used to conduct predictive danger analysis on the key - marked energy storage battery. By continuously using the current time point t i as a reference, and spanning backward by a preset time period Δt to obtain the time point t i-Δt , fitting to obtain a curve with the operation risk coefficient within t i-Δt -t i as the abscissa of the time axis, processing this curve to obtain a state value, and comparing the state value with a preset state interval;

[0056] An emergency module, which is used to execute corresponding emergency measures according to the comparison result between the state value and the preset state interval.

[0057] In the present invention, the risk analysis module comprehensively calculates an operation risk coefficient based on operation data and environmental data, compares the operation risk coefficient with a comparison interval preset in the system, and judges whether each group of energy storage batteries is in a potential risk state according to the result. If so, the early warning and monitoring module gives an early warning to the energy storage battery in a potential risk state, and conducts early warning monitoring and key marking on the energy storage battery in a critical state; Obviously, the performance of the energy storage battery in a potential operation risk state is worse than that of a normal energy storage battery. In order to prevent the overall aging of the energy storage battery module from accelerating, after warning the energy storage battery in a potential operation risk state, that is, the energy storage battery with poor performance, the staff replaces it, so as to keep the performance states of each group of energy storage batteries in the whole energy storage battery module relatively consistent, achieving a better service life. The replaced energy storage battery can be recycled for other uses to save costs; then, the deep - danger analysis module conducts in - depth analysis on the energy storage battery in a critical state, so as to preferentially maintain the energy storage battery in a deteriorating trend to reduce its risk and prevent the energy storage battery from operating in a continuously deteriorating state, affecting the operation efficiency of the whole energy storage battery.

[0058] It should be noted that the operation data of the energy storage battery includes working voltage, working current, power, state of charge, battery pack temperature, etc. The above parameters are all detected by existing sensors or detection instruments, and will not be elaborated here;

[0059] The process of obtaining the operation risk coefficient is as follows:

[0060] Through the formula:

[0061] Calculate the operation risk coefficient F of the i-th group of energy storage batteries i ;

[0062] In the formula, K x is the operation scoring coefficient, D y is the environment scoring coefficient, ρ x , τ y are preset conversion coefficients; m is the total number of detection time points of continuous equally spaced time durations.

[0063] The process of obtaining the operation scoring coefficient is as follows:

[0064] Through the formula: Obtain the operation scoring coefficient K x ;

[0065] In the formula, μ a is the proportionality coefficient corresponding to the a-th operation parameter, X a is the real-time detection value of the a-th operation parameter, is the detection average value of the a-th operation parameter, n is the total number of operation parameters, D y0 is the environment scoring reference coefficient.

[0066] The process of obtaining the environment scoring coefficient is as follows:

[0067] Through the formula:

[0068]

[0069] Calculate to obtain the environment scoring coefficient D y ;

[0070] In the formula, p is the electrolyte leakage gas value, θ T , θ Z are weight coefficients, T(t) is the curve of environmental temperature changing with time within the time period from t A -t B to t A -t B to t A -t B to t A -t B to t

[0071] In this embodiment, a method for obtaining the operation risk coefficient is provided. First, through the formulas and Calculate the operation scoring coefficient K respectively x and environmental rating coefficient D y , and then substitute both into The operation risk coefficient F of the i-th group of energy storage batteries is obtained by calculation i ; It can be clearly seen from the above technical solution that The larger the value, the higher the running score coefficient K x The larger the value, the greater the volatility of the operating parameters of the energy storage battery, that is, the more unstable the energy storage battery is. Obviously, the lower the safety of the energy storage battery and the worse the performance. The larger the |T(t)-T'(t)|, the greater the difference between the actual ambient temperature of the energy storage battery and the predicted ambient temperature. Therefore, the greater the change in the environment in which the energy storage battery is operating, the greater the impact on the performance of the energy storage battery. Z(t)-Z'(t) indicates that the environment in which the energy storage battery is located often vibrates, which has a greater impact on the operating performance of the energy storage battery. Therefore, the environmental rating coefficient D y The larger the value, the higher the risk factor F i The higher the risk, the more accurately the operation risk of the energy storage battery can be predicted through the above scheme.

[0072] The process of determining whether the energy storage battery is in a potential risk state is as follows:

[0073] The calculated F i With the preset interval [F i- , F i+ ] for comparison;

[0074] If F i Beyond [F i- , F i+ ], it is judged that the current energy storage battery is in a potential risk state;

[0075] If F i Fall into [F i- , F i+ ], it is judged that the current energy storage battery is in a critical state;

[0076] If F i Lower than [F i- , F i+ ], it is judged that the current energy storage battery is in a safe state.

[0077] The process of obtaining the status value of the energy storage battery marked as key is as follows:

[0078] Get in t i-Δt -t i The curve of the operation risk coefficient within the period with time as the horizontal axis is in dF i The number of time points N corresponding to (t) / dt=0 and

[0079] By formula: Calculate and obtain the status value of the energy storage battery currently marked;

[0080] t i-Δt -t i The maximum peak value of the operating risk factor curve within the period, t i-Δt -t i The minimum valley value of the operation risk coefficient curve within the time period, μ is the dimensionless coefficient, ΔF is t i-Δt -t i Reference value within the time period.

[0081] The working process of the emergency module is as follows:

[0082] The obtained state value P is compared with the preset state interval [P 0 , P 1 ]Compare;

[0083] If P is greater than P 1 , it is judged that the energy storage battery currently marked is in a deteriorated state;

[0084] If P belongs to [P 0 , P 1 ], it is judged that the energy storage battery currently marked is in normal state;

[0085] If P is less than P 0 , it is determined that the energy storage battery currently marked is in a repair state.

[0086] The working process of the emergency module also includes:

[0087] Arrange the highlighted energy storage batteries in descending order according to the size of their status values;

[0088] The energy storage batteries in the repair state are removed from the current arrangement, and the remaining energy storage batteries are replaced in sequence;

[0089] Energy storage batteries that are in a deteriorating state are marked as emergency batteries and sent to back-end management personnel for priority maintenance.

[0090] In this embodiment, the F obtained by calculation is i With the preset interval [F i- , F i+ ] to compare; if F i Beyond [F i- , F i + ], it is judged that the current energy storage battery is in a potential risk state; if F i Fall into [Fi- , F i+ , then it is determined that the current energy storage battery is in a critical state; if F i is lower than [F i- , F i+ , then it is determined that the current energy storage battery is in a safe state; then perform a deep analysis on the energy storage battery in the critical state to obtain the number N of the time points corresponding to dF i-Δt -t i during the time period, where the curve of the operation risk coefficient with the time axis as the abscissa is 0, and i And Then, through the formula calculate to obtain the state value of the currently key-marked energy storage battery. Obviously, within this time period , the greater the difference, the faster the change of the operation risk coefficient of the energy storage battery, and the more the number of peaks and valleys, the higher the frequency of the operation change. Therefore, it can be further determined that the energy storage battery is in a stage of unstable performance, and corresponding emergency treatment is carried out to repair the energy storage battery to achieve stable operation, reduce unnecessary replacement of the energy storage battery, and reduce costs.

[0091] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A risk prediction system for energy storage battery operation, characterized in that, Including: An operation data acquisition module, which is used to acquire the operation data of multiple groups of energy storage batteries in real time; An environmental data acquisition module, which is used to acquire the environmental data during the operation of the energy storage battery in real time; A risk analysis module, which is used to comprehensively calculate the operation risk coefficient based on the operation data and environmental data, compare the operation risk coefficient with the comparison interval preset by the system, and judge whether the energy storage battery is in a potential risk state according to the result; An early warning monitoring module, which is used to give early warnings to the energy storage batteries in a potential risk state according to the judgment result of the risk analysis module, and conduct early warning monitoring and key marking on the energy storage batteries in a critical state; A deep - risk - analysis module is used to perform predictive risk analysis on the key - marked energy - storage batteries. By continuously using the current time point t i as a reference, after stepping backward across a preset time period Δt, the time point t i-Δt is obtained. Then, a curve with the operating risk coefficient within t i-Δt -t i as the abscissa of the time axis is fitted. A state value is obtained by processing this curve, and the state value is compared with a preset state interval; An emergency module, which is used to execute corresponding emergency measures according to the comparison result between the state value and the preset state interval; The process of obtaining the operation risk coefficient is as follows: Through the formula: Calculate the operating risk coefficient F of the i-th energy storage battery pack i ; Where K x is the operation scoring coefficient, D y is the environmental scoring coefficient, ρ x , τ y are preset conversion coefficients; m is the total number of detection time points at consecutive equally spaced time intervals; The process of obtaining the operation scoring coefficient is as follows: Through the formula: the operation rating coefficient K is obtained x ; where μ a is the proportionality coefficient corresponding to the a-th operating parameter, X a is the real-time detected value of the a-th operating parameter, is the average detected value of the a-th operating parameter, n is the total number of operating parameters, and D y0 is the environmental score reference coefficient; The process of obtaining the environmental scoring coefficient is as follows: Through the formula: Calculate to obtain the environmental score coefficient D y ; Where p is the value of the electrolyte leakage gas, θ T , θ Z are weight coefficients, T(t) is the curve of the ambient temperature varying with time within the time period of t A -t B , T’(t) is the curve of the predicted ambient temperature varying with time within the time period of t A -t B , Z(t) is the curve of the vibration value varying with time within the time period of t A -t B , Z’(t) is the curve of the predicted vibration value varying with time within the time period of t A -t B ; The process of judging whether the energy storage battery is in a potential risk state is as follows: Compare the calculated F i with the preset interval [F i- , F i+ ; If F i exceeds [F i- , F i+ , it is determined that the current energy storage battery is in a potential risk state; If F i falls within [F i- , F i+ , it is determined that the current energy storage battery is in a critical state; If F i is lower than [F i- , F i+ , it is determined that the current energy storage battery is in a safe state; The process of obtaining the state value of the key-marked energy storage battery is as follows: Obtain the curve of the operation risk coefficient within the time period from t i-Δt -t i with the time axis as the abscissa at the number N of time points corresponding to dF i (t) / dt = 0 and Obtained through the formula: Calculate the state value of the energy storage battery with the current key mark; is t i-Δt -t i The maximum peak value of the operation risk coefficient curve within the time period, is t i-Δt -t i The minimum valley value of the operation risk coefficient curve within the time period, μ is the dimensionless coefficient, and ΔF is the reference value within the time period of t i-Δt -t i Time period reference value.

2. The energy storage battery operation risk prediction system according to claim 1, wherein The working process of the emergency module is as follows: Compare the obtained status value P with the preset status interval [P 0 , P 1 . If P is greater than P 1 , it is determined that the energy storage battery with the current key mark is in a deteriorated state; If P belongs to [P 0 , P 1 , then it is determined that the currently key-marked energy storage battery is in a normal state; If P is less than P 0 , it is determined that the energy storage battery with the current key mark is in a repaired state.

3. The energy storage battery operation risk prediction system according to claim 2, wherein The working process of the emergency module further includes: Arranging the key-marked energy storage batteries in descending order according to the size of the state value; Excluding the energy storage batteries in the repair state from the current arrangement, and the remaining energy storage batteries are filled in turn; Making an emergency mark on the energy storage batteries in the deteriorating state and sending them to the background management personnel for priority maintenance.

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