A method and system for predicting the life of an energy storage unit based on double prediction

Abstract

A method and system for predicting the lifetime of an energy storage unit based on dual prediction includes: calculating the operating status index at the current moment; fitting the relationship between the operating status index and the health status into a predefined nonlinear function containing linear and bias terms, and adding the linear and bias terms to generate a linear function; inputting the operating status index into the linear and nonlinear functions respectively, and outputting the estimated conservative and ideal health status at the current moment; calculating the predicted first lifetime and second lifetime based on the current conservative and ideal health status respectively; determining whether the total number of times the ambient temperature exceeds a predefined maximum threshold within a set period exceeds a predefined time threshold; if so, outputting the predicted first lifetime; otherwise, outputting the result of weighted fusion of the predicted first lifetime and predicted second lifetime according to adaptive weights and rounded down. This invention considers the actual operating environment and provides more accurate predictions.

Description

Technical Field

[0001] This invention belongs to the field of lifetime prediction technology, and more specifically, relates to a method and system for predicting the lifetime of energy storage units based on dual prediction. Background Technology

[0002] With the rapid development of new energy power generation, electric vehicles, and smart grids, energy storage units (such as lithium-ion batteries and supercapacitors) have become crucial for system safety and economic operation, serving as core energy storage carriers. In practical applications, the performance of energy storage units slowly degrades over time due to factors such as operating environment and charge / discharge cycles. Failure to promptly and accurately assess their health status and predict their remaining lifespan can lead to system failures due to sudden performance drops, increase maintenance costs, and impact grid dispatch and economic efficiency. Traditional lifespan prediction methods often rely on accelerated aging data from laboratories or single health indicators (such as capacity and internal resistance), making it difficult to reflect the multi-parameter coupled aging process under complex actual operating conditions in real time, and lacking the ability to adaptively adjust to dynamic changes in the operating environment.

[0003] CN119179019A proposes a method and system for predicting the remaining life of lithium-ion batteries based on long-term and short-term models, including the following steps: establishing a lithium-ion battery database; obtaining a short-term model and a health value scale using data from the lithium-ion battery database; using data from the lithium-ion battery database, the health value scale, and a long-term iterative model; importing the data of the battery under test into the short-term model to obtain the predicted result of the battery's remaining life, and determining the health value scale of the battery under test; when the health value scale of the battery under test reaches the limit, importing the data of the battery under test into the long-term iterative model to obtain the predicted result of the remaining life of the battery under test. However, it only selects one predicted value for output, which may result in significant jumps in output at model switching points (i.e., when the health value scale fluctuates around the limit). Furthermore, both long-term and short-term predictions are simply outputs of the trained model. Both the short-term and long-term models are directly output by inputting the operating parameters into the short-term model, without considering whether it is reasonable to directly output the health value using the short-term model under different conditions (such as harsh operating environments). Moreover, the difference between short-term and long-term predictions is due to the different aging modes of energy storage units caused by environmental or other factors. Prediction needs to conform to the corresponding physical laws. Simply relying on the algorithm model output will result in large errors when the operating environment is different. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for predicting the lifetime of energy storage units based on dual prediction.

[0005] The present invention adopts the following technical solution.

[0006] A first aspect of the present invention proposes a method for predicting the lifetime of energy storage units based on dual prediction, comprising:

[0007] The system acquires the operating parameters of the energy storage unit at the current moment in real time. The operating parameters include operating temperature, output power and equivalent series resistance. The system calculates the operating status index at the current moment based on the operating parameters of the energy storage unit at the current moment and the operating status index at the previous moment.

[0008] Based on the acquired operational status indicators and health status at multiple historical moments, the relationship between the operational status indicators and the corresponding health status is fitted to a predefined nonlinear function containing linear and bias terms. The linear and bias terms are then added to generate a linear function. The operational status indicators at the current moment are input into the linear and nonlinear functions respectively, and the output values ​​are used as the estimated conservative and ideal health status at the current moment, respectively.

[0009] The health status changes under harsh environments are obtained through cyclic aging experiments. The difference between the estimated conservative health status at the current moment and the set minimum health status is used to calculate the predicted first lifetime. The number of charge-discharge cycles of the energy storage unit from the set initial health status to the set minimum health status is calculated as the maximum number of charge-discharge cycles. The number of charge-discharge cycles of the energy storage unit from the set initial health status to the estimated ideal health status at the current moment is calculated as the estimated current number of charge-discharge cycles. The predicted second lifetime is obtained by subtracting the estimated current number of charge-discharge cycles from the maximum number of charge-discharge cycles and rounding down.

[0010] Record the total number of times the ambient temperature exceeds the set maximum threshold within the current time and the set period before the current time. If the total number of times exceeds the set threshold, output the predicted first lifetime. Otherwise, calculate the adaptive weight based on the total number of times, and output the predicted first lifetime and the predicted second lifetime after weighted fusion according to the adaptive weight and rounded down.

[0011] Preferably, the step of calculating the operating status index at the current moment based on the operating parameters of the energy storage unit at the current moment and the operating status index at the previous moment specifically involves:

[0012] Set a first rated operating temperature and a second rated operating temperature; calculate the square of the difference between the current operating parameter and the first rated operating temperature, divided by twice the square of the set first difference threshold; use the negative of the division result as the exponent and the natural constant e as the base to perform exponential calculation to obtain the first temperature operating index; calculate the square of the difference between the current operating parameter and the second rated operating temperature, divided by twice the square of the set second difference threshold; use the negative of the division result as the exponent and the natural constant e as the base to perform exponential calculation to obtain the second temperature operating index; take the larger of the first temperature operating index and the second temperature operating index as the final temperature operating index;

[0013] The difference between the output power at the initial moment and the output power at the current moment is calculated and divided by the output power at the initial moment. The battery power operation index is obtained by subtracting the division result from 1.

[0014] Calculate the difference between the current equivalent series resistance and the rated value of the equivalent series resistance, divide it by the difference between the maximum value and the minimum value of the equivalent series resistance, multiply the result by the set influence factor, add 1, and calculate the reciprocal of the k-th power of the sum as the operating index of the equivalent series resistance.

[0015] The final operating temperature index, battery power index, and equivalent series resistance index are weighted and summed according to the set weights to obtain the uncorrected operating status index at the current moment. The uncorrected operating status index at the current moment is then merged with the operating status index at the previous moment to obtain the operating status index at the current moment.

[0016] Preferably, the step of fusing the uncorrected operating status index at the current moment with the operating status index at the previous moment to obtain the operating status index at the current moment specifically involves:

[0017] The initial operating status index is 1; if the current uncorrected operating status index is greater than or equal to the previous operating status index, then the current operating status index is equal to the previous operating status index minus the set value; otherwise, the current uncorrected operating status index is multiplied by the first smoothing coefficient and the difference between the previous operating status index and the set value is multiplied by the second smoothing coefficient; the sum of the first smoothing coefficient and the second smoothing coefficient is 1.

[0018] Preferably, the relationship between the operational status index at each historical moment and the health status at the corresponding historical moment is fitted to a predetermined linear function and a nonlinear function, respectively, specifically as follows:

[0019] The nonlinear function is as follows: First, calculate the operating status index at a certain time, multiply it by the first coefficient, add the square of the operating status index at the corresponding time multiplied by the second coefficient, add the inverse hyperbolic sine function of the operating status index at the corresponding time multiplied by the third coefficient, and add the bias equal to the ideal health state at the corresponding time.

[0020] Set a maximum threshold for ambient temperature, obtain the operating status indicators and corresponding health status of the energy storage unit at different historical moments when the ambient temperature is below the maximum threshold, and perform nonlinear function fitting to obtain the first coefficient, second coefficient, third coefficient and bias.

[0021] The linear function is: the operating status index at a given time multiplied by the first coefficient and the first bias equals the conservative health status at the corresponding time.

[0022] Preferably, the change in health status under harsh environments is obtained through cyclic aging experiments, and the predicted first lifespan is calculated by combining the difference between the estimated conservative health status at the current moment and the set minimum health status. Specifically:

[0023] Set the maximum threshold for ambient temperature; set the ambient temperature to the corresponding maximum threshold, conduct a cycle aging experiment with a set number of charge and discharge cycles, obtain the health status after each charge and discharge, and calculate the average change in health status after each charge and discharge as the change in health status under harsh conditions.

[0024] The first lifespan is obtained by dividing the difference between the estimated conservative health status at the current moment and the set minimum health status by the change in health status due to adverse environment, and then rounding down.

[0025] Preferably, the number of charge-discharge cycles required for the energy storage unit to reach a non-initial health state from a set initial health state is calculated as follows:

[0026] The ln function is used to calculate the ratio of the non-initial health state to the set initial health state. The result of the ln function calculation is divided by the set degradation coefficient. The result of the division is used as the base. The reciprocal of the set aging shape factor of the energy storage unit is used as the exponent. The exponent of the base is calculated. The negative of the exponent of the base is the number of charge and discharge cycles required for the energy storage unit to reach a non-initial health state from the set initial health state.

[0027] When the non-initial health state is the set minimum health state, the calculated number of charge / discharge cycles is the maximum number of charge / discharge cycles; when the non-initial health state is the estimated ideal health state at the current moment, the calculated number of charge / discharge cycles is the estimated current number of charge / discharge cycles.

[0028] Preferably, the step of calculating the adaptive weight based on the total number of time points specifically involves: calculating...

[0029] The total number of time points is divided by the set time threshold and then multiplied by the set curve steepness coefficient. The hyperbolic tangent function of the multiplication result is calculated. The hyperbolic tangent function of the set curve steepness coefficient is then calculated. The hyperbolic tangent function of the multiplication result is divided by the hyperbolic tangent function of the set curve steepness coefficient as the adaptive weight of the predicted first lifetime. The adaptive weight of the predicted first lifetime is obtained by subtracting 1 from the adaptive weight of the predicted second lifetime.

[0030] A second aspect of this invention proposes a dual-prediction-based energy storage unit lifetime prediction system using the method described in the first aspect of this invention, comprising: an operating status index calculation module, a health status prediction module, a dual-prediction module, and a lifetime output module, specifically:

[0031] Operating status index calculation module: Real-time acquisition of various operating parameters of the energy storage unit at the current moment, including operating temperature, output power and equivalent series resistance, and calculation of the operating status index at the current moment based on the various operating parameters of the energy storage unit at the current moment and the operating status index at the previous moment;

[0032] Health status prediction module: Based on the acquired operational status indicators and health status at multiple historical moments, the relationship between the operational status indicators and the corresponding health status is fitted to a set nonlinear function containing linear and bias terms. The linear and bias terms are added to generate a linear function. The operational status indicators at the current moment are input into the linear and nonlinear functions respectively, and the output values ​​are used as the predicted conservative and ideal health status at the current moment.

[0033] Dual prediction module: Obtain the health status change value in harsh environments through cyclic aging experiments, and calculate the predicted first lifetime by combining the difference between the estimated conservative health status at the current moment and the set minimum health status; calculate the maximum number of charge and discharge cycles of the energy storage unit from the set initial health status to the set minimum health status, and calculate the estimated current number of charge and discharge cycles of the energy storage unit from the set initial health status to the estimated ideal health status at the current moment; subtract the estimated current number of charge and discharge cycles from the maximum number of charge and discharge cycles and round down to obtain the predicted second lifetime;

[0034] Lifetime Output Module: Records the current time and the total number of times within the set period before the current time when the ambient temperature exceeds the set maximum threshold. If the total number of times exceeds the set threshold, the first predicted lifetime is output. Otherwise, the adaptive weight is calculated based on the total number of times, and the first predicted lifetime and the second predicted lifetime are output as a result of weighted fusion according to the adaptive weight.

[0035] A third aspect of the present invention provides an apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor performing steps using the method described in the first aspect of the present invention.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, uses the steps of the method described in the first aspect of the present invention.

[0037] The beneficial effects of this invention are as follows: Compared with the prior art, it dynamically calculates operating status indicators by real-time monitoring of multi-dimensional parameters such as operating temperature, output power, and equivalent series resistance, taking into account multi-dimensional parameters; it constructs a nonlinear function, obtains the coefficients of the nonlinear function by fitting historical data, and obtains a linear function based on the nonlinear function. The linear and nonlinear functions respectively predict the conservative and ideal health states at the current moment. When predicting the first and second lifetimes based on the two health states, the lifetime prediction is performed from two dimensions: the average decay in the short term under adverse conditions and the cyclic aging of the energy storage unit under normal conditions, which enhances the physical interpretability of the prediction; based on the judgment of the ambient temperature exceeding the standard, the first lifetime or the first and second lifetimes are selected for output, which takes into account the actual operating environment and provides more accurate predictions. Attached Figure Description

[0038] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0040] like Figure 1 As shown, Embodiment 1 of the present invention proposes a method for predicting the lifetime of energy storage units based on dual prediction, comprising:

[0041] The system acquires the operating parameters of the energy storage unit at the current moment in real time. The operating parameters include operating temperature, output power and equivalent series resistance. The system calculates the operating status index at the current moment based on the operating parameters of the energy storage unit at the current moment and the operating status index at the previous moment.

[0042] Based on the acquired operational status indicators and health status at multiple historical moments, the relationship between the operational status indicators and the corresponding health status is fitted to a predefined nonlinear function containing linear and bias terms. The linear and bias terms are then added to generate a linear function. The operational status indicators at the current moment are input into the linear and nonlinear functions respectively, and the output values ​​are used as the estimated conservative and ideal health status at the current moment, respectively.

[0043] The health status changes under harsh environments are obtained through cyclic aging experiments. The difference between the estimated conservative health status at the current moment and the set minimum health status is used to calculate the predicted first lifetime. The number of charge-discharge cycles of the energy storage unit from the set initial health status to the set minimum health status is calculated as the maximum number of charge-discharge cycles. The number of charge-discharge cycles of the energy storage unit from the set initial health status to the estimated ideal health status at the current moment is calculated as the estimated current number of charge-discharge cycles. The predicted second lifetime is obtained by subtracting the estimated current number of charge-discharge cycles from the maximum number of charge-discharge cycles and rounding down.

[0044] Record the total number of times the ambient temperature exceeds the set maximum threshold within the current time and the set period before the current time. If the total number of times exceeds the set threshold, output the predicted first lifetime. Otherwise, calculate the adaptive weight based on the total number of times, and output the predicted first lifetime and the predicted second lifetime after weighted fusion according to the adaptive weight and rounded down.

[0045] In this preferred embodiment, the calculation of the current operating status index based on the current operating parameters of the energy storage unit and the operating status index of the previous time step specifically involves:

[0046] Set a first rated operating temperature and a second rated operating temperature; calculate the square of the difference between the current operating parameter and the first rated operating temperature, divided by the square of twice the set first difference threshold; use the negative of the division result as the exponent and the natural constant e as the base to perform exponential calculation to obtain the first temperature operating index; calculate the square of the difference between the current operating parameter and the second rated operating temperature, divided by the square of twice the set second difference threshold; use the negative of the division result as the exponent and the natural constant e as the base to perform exponential calculation to obtain the second temperature operating index; take the larger of the first temperature operating index and the second temperature operating index as the final temperature operating index; the formula is:

[0047]

[0048] in, For the final temperature operating parameters; The operating temperature at time t is the current time. , These are the first rated operating temperature and the second rated operating temperature, respectively. , These are the first difference threshold and the second difference threshold, respectively.

[0049] It should be noted that this invention sets two independent locally optimal temperature points. As long as the current temperature does not deviate significantly from both optimal temperature points simultaneously, the energy storage unit remains in a relatively healthy state. Furthermore, since the temperature distribution may exhibit multiple patterns or peaks, additional rated operating temperatures and corresponding difference thresholds can be set.

[0050] The battery power performance index is obtained by dividing the difference between the initial output power and the current output power by the initial output power, and then subtracting the result from 1. The formula is as follows:

[0051]

[0052] in, This refers to battery power performance indicators; The output power at the current moment; The output power at the initial moment;

[0053] During operation, as the aging process progresses, the maximum output capacity will continue to decline due to factors such as loss of active lithium and increased internal resistance. Therefore, under the same demand, the measurable power will decrease over time. The power change rate between the current moment and the initial moment can characterize the operating status. The greater the power change rate, the worse the operating status.

[0054] Calculate the difference between the current equivalent series resistance and the rated value of the equivalent series resistance, divide it by the difference between the maximum value and the minimum value of the equivalent series resistance, multiply the result by the set influence factor, add 1, and calculate the reciprocal of the k-th power of the sum as the operating index of the equivalent series resistance.

[0055]

[0056] in, The operating parameters are based on the equivalent series resistance. This is the equivalent series resistance at the current moment; This is the rated value of the equivalent series resistance; This is the maximum value of the equivalent series resistance; This is the minimum value of the equivalent series resistance; The set impact factor; A constant that is set;

[0057] The final operating temperature index, battery power index, and equivalent series resistance index are weighted and summed according to the set weights to obtain the uncorrected operating status index at the current moment. The uncorrected operating status index at the current moment is then merged with the operating status index at the previous moment to obtain the operating status index at the current moment.

[0058] In this preferred embodiment, the step of fusing the uncorrected operating status index at the current moment with the operating status index at the previous moment to obtain the operating status index at the current moment specifically involves:

[0059] The initial operating status index is 1; if the current uncorrected operating status index is greater than or equal to the previous operating status index, then the current operating status index is equal to the previous operating status index minus the set value; otherwise, the current uncorrected operating status index is multiplied by the first smoothing coefficient and the difference between the previous operating status index and the set value is multiplied by the second smoothing coefficient; the sum of the first smoothing coefficient and the second smoothing coefficient is 1.

[0060]

[0061] in, , These are the operational status indicators for the current moment and the previous moment, respectively; The set value; , These are the first smoothing coefficient and the second smoothing coefficient, respectively. This represents the current, uncorrected operational status indicator.

[0062] It should be noted that aging involves irreversible chemical processes such as the continuous growth of the SEI film, permanent loss of active lithium ions, and degradation of the positive and negative electrode material structures. Therefore, the actual health status will always decline, and the operating status indicators positively correlated with the health status will also always decline. The set value is a relatively small value; in this embodiment, it is set to 0.02. Weighted summation according to the corresponding smoothing coefficients can smooth out the state changes. In this embodiment, the first smoothing coefficient is set to 0.8, and the second smoothing coefficient is set to 0.2.

[0063] In this preferred embodiment, the relationship between the operational status index at each historical moment and the health status at the corresponding historical moment is fitted to a set linear function and a nonlinear function, respectively, specifically:

[0064] The nonlinear function is as follows: First, calculate the operating status index at a given time, multiply by a first coefficient, add the square of the operating status index at that time multiplied by a second coefficient, then add the inverse hyperbolic sine function of the operating status index at that time multiplied by a third coefficient, and finally add the bias equal to the ideal health state at that time; the formula is:

[0065]

[0066] in, This represents the ideal state of health at the corresponding moment; , , These are the first coefficient, the second coefficient, and the third coefficient, respectively. for The inverse hyperbolic sine function; For bias;

[0067] Set a maximum threshold for ambient temperature, obtain the operating status indicators and corresponding health status of the energy storage unit at different historical moments when the ambient temperature is below the maximum threshold, and perform nonlinear function fitting to obtain the first coefficient, second coefficient, third coefficient and bias.

[0068] At this point, the linear function is: the operating status index at a given moment multiplied by the first coefficient plus the first bias equals the conservative health status at the corresponding moment.

[0069]

[0070] in, This represents the conservative health status at the corresponding moment.

[0071] In this preferred embodiment, the change in health status under harsh environments is obtained through cyclic aging experiments, and the predicted first lifespan is calculated by combining the difference between the estimated conservative health status at the current moment and the set minimum health status. Specifically:

[0072] Set the maximum threshold for ambient temperature; set the ambient temperature to the corresponding maximum threshold, conduct a cycle aging experiment with a set number of charge and discharge cycles, obtain the health status after each charge and discharge, and calculate the average change in health status after each charge and discharge as the change in health status under harsh conditions.

[0073] The first lifespan is obtained by dividing the difference between the estimated conservative health status at the current moment and the set minimum health status by the change in health status due to adverse environment, and then rounding down.

[0074] In this preferred embodiment, the number of charge-discharge cycles required for the energy storage unit to reach a non-initial health state from a set initial health state is calculated as follows:

[0075] The ln function is used to calculate the ratio of the non-initial health state to the set initial health state. The result of the ln function calculation is divided by the set degradation coefficient. The result of the division is used as the base. The reciprocal of the set aging shape factor of the energy storage unit is used as the exponent. The exponent of the base is calculated. The negative of the exponent of the base is the number of charge and discharge cycles required for the energy storage unit to reach a non-initial health state from the set initial health state.

[0076] When the non-initial health state is the set minimum health state, the calculated number of charge / discharge cycles is the maximum number of charge / discharge cycles; when the non-initial health state is the estimated ideal health state at the current moment, the calculated number of charge / discharge cycles is the estimated current number of charge / discharge cycles.

[0077] The formula is:

[0078]

[0079] in, ,when When it is 1, The maximum number of charge-discharge cycles; when When it is 2, The ideal health status at the current moment; The aging shape factor of the energy storage unit is set; The set degradation coefficient; The initial health status is set; For non-initial health states, when When it is 1, The minimum health status set; when When it is 2, The ideal state of health at the current moment is the estimated state of health. .

[0080] In a preferred embodiment, the step of calculating the adaptive weight based on the total number of time points specifically involves: calculating...

[0081] The total number of time points is divided by a set time threshold and then multiplied by a set curve steepness coefficient. The hyperbolic tangent function of the multiplied result is calculated. The hyperbolic tangent function of the set curve steepness coefficient is then calculated. The hyperbolic tangent function of the multiplied result is divided by the hyperbolic tangent function of the set curve steepness coefficient as the adaptive weight for the predicted first lifetime. 1 is subtracted from the adaptive weight of the predicted first lifetime as the adaptive weight for the predicted second lifetime. The formula is:

[0082]

[0083] in, It is the hyperbolic tangent function; The set curve steepness coefficient; Total number of moments; The set time threshold; Adaptive weights for the predicted first lifetime; The adaptive weights are used for predicting the second lifetime.

[0084] Embodiment 2 of the present invention proposes a dual-prediction-based energy storage unit lifetime prediction system using the method described in Embodiment 1 of the present invention, comprising: an operating status index calculation module, a health status prediction module, a dual-prediction module, and a lifetime output module, specifically:

[0085] Operating status index calculation module: Real-time acquisition of various operating parameters of the energy storage unit at the current moment, including operating temperature, output power and equivalent series resistance, and calculation of the operating status index at the current moment based on the various operating parameters of the energy storage unit at the current moment and the operating status index at the previous moment;

[0086] Health status prediction module: Based on the acquired operational status indicators and health status at multiple historical moments, the relationship between the operational status indicators and the corresponding health status is fitted to a set nonlinear function containing linear and bias terms. The linear and bias terms are added to generate a linear function. The operational status indicators at the current moment are input into the linear and nonlinear functions respectively, and the output values ​​are used as the predicted conservative and ideal health status at the current moment.

[0087] Dual prediction module: Obtain the health status change value in harsh environments through cyclic aging experiments, and calculate the predicted first lifetime by combining the difference between the estimated conservative health status at the current moment and the set minimum health status; calculate the maximum number of charge and discharge cycles of the energy storage unit from the set initial health status to the set minimum health status, and calculate the estimated current number of charge and discharge cycles of the energy storage unit from the set initial health status to the estimated ideal health status at the current moment; subtract the estimated current number of charge and discharge cycles from the maximum number of charge and discharge cycles and round down to obtain the predicted second lifetime;

[0088] Lifetime Output Module: Records the current time and the total number of times within the set period before the current time when the ambient temperature exceeds the set maximum threshold. If the total number of times exceeds the set threshold, the first predicted lifetime is output. Otherwise, the adaptive weight is calculated based on the total number of times, and the first predicted lifetime and the second predicted lifetime are output as a result of weighted fusion according to the adaptive weight.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for predicting the lifetime of an energy storage unit based on dual prediction, characterized in that, include: The system acquires the operating parameters of the energy storage unit at the current moment in real time. The operating parameters include operating temperature, output power and equivalent series resistance. The system calculates the operating status index at the current moment based on the operating parameters of the energy storage unit at the current moment and the operating status index at the previous moment. Based on the acquired operational status indicators and health status at multiple historical moments, the relationship between the operational status indicators and the corresponding health status is fitted to a predefined nonlinear function containing linear and bias terms. The linear and bias terms are then added to generate a linear function. The operational status indicators at the current moment are input into the linear and nonlinear functions respectively, and the output values ​​are used as the estimated conservative and ideal health status at the current moment, respectively. The changes in health status under harsh environments are obtained through cyclic aging experiments. The difference between the estimated conservative health status at the current moment and the set minimum health status is used to calculate the predicted first lifespan. The maximum number of charge / discharge cycles is calculated from the set initial health state to the set minimum health state of the energy storage unit, and the estimated number of charge / discharge cycles is calculated from the set initial health state to the estimated ideal health state at the current moment. The predicted second lifetime is obtained by subtracting the estimated current number of charge-discharge cycles from the maximum number of charge-discharge cycles and then rounding down. Record the total number of times the ambient temperature exceeds the set maximum threshold within the current time and the set period before the current time. If the total number of times exceeds the set threshold, output the predicted first lifetime. Otherwise, calculate the adaptive weight based on the total number of times, and output the predicted first lifetime and the predicted second lifetime after weighted fusion according to the adaptive weight and rounded down.

2. The method for predicting the lifetime of an energy storage unit based on dual prediction as described in claim 1, characterized in that: The calculation of the current operating status index based on the current operating parameters of the energy storage unit and the operating status index of the previous time step is specifically as follows: in, For the final temperature operating parameters; The operating temperature at time t is the current time. , These are the first rated operating temperature and the second rated operating temperature, respectively. , These are the first difference threshold and the second difference threshold, respectively. in, This refers to battery power performance indicators; The output power at the current moment; The output power at the initial moment; in, The operating parameters are based on the equivalent series resistance. This is the equivalent series resistance at the current moment; This is the rated value of the equivalent series resistance; This is the maximum value of the equivalent series resistance; This is the minimum value of the equivalent series resistance; The set impact factor; A constant that is set; The final operating temperature index, battery power index, and equivalent series resistance index are weighted and summed according to the set weights to obtain the uncorrected operating status index at the current moment. The uncorrected operating status index at the current moment is then merged with the operating status index at the previous moment to obtain the operating status index at the current moment.

3. The method for predicting the lifetime of an energy storage unit based on dual prediction as described in claim 2, characterized in that: The process of fusing the current uncorrected operating status index with the operating status index from the previous time step to obtain the current operating status index specifically involves: in, , These are the operational status indicators for the current moment and the previous moment, respectively; The set value; , These are the first smoothing coefficient and the second smoothing coefficient, respectively. The sum of the first smoothing coefficient and the second smoothing coefficient equals 1. This represents the current, uncorrected operational status indicator.

4. The method for predicting the lifetime of an energy storage unit based on dual prediction as described in claim 3, characterized in that: The relationship between the operational status indicators at each historical moment and the corresponding health status at that historical moment is fitted to a set linear function and a nonlinear function, respectively, as follows: The nonlinear function is: in, This represents the ideal state of health at the corresponding moment; , , These are the first coefficient, the second coefficient, and the third coefficient, respectively. for The inverse hyperbolic sine function; For bias; Set a maximum threshold for ambient temperature, obtain the operating status indicators and corresponding health status of the energy storage unit at different historical moments when the ambient temperature is below the maximum threshold, and perform nonlinear function fitting to obtain the first coefficient, second coefficient, third coefficient and bias. The linear function is: in, This represents the conservative health status at the corresponding moment.

5. The method for predicting the lifetime of an energy storage unit based on dual prediction as described in claim 4, characterized in that: The changes in health status under harsh environments are obtained through cyclic aging experiments. The predicted first lifespan is calculated by combining the difference between the estimated conservative health status at the current moment and the set minimum health status. Specifically: The ambient temperature was set to the corresponding maximum threshold, and a cycle aging experiment with a set number of charge and discharge cycles was conducted. The health status after each charge and discharge cycle was obtained, and the average change in health status after each charge and discharge cycle was calculated as the change in health status under harsh conditions. The first lifespan is obtained by dividing the difference between the estimated conservative health status at the current moment and the set minimum health status by the change in health status due to adverse environment, and then rounding down.

6. The method for predicting the lifetime of an energy storage unit based on dual prediction as described in claim 1, characterized in that: The number of charge-discharge cycles required for the energy storage unit to reach a non-initial health state from a set initial health state is calculated as follows: in, ,when When it is 1, The maximum number of charge-discharge cycles; when When it is 2, This represents the estimated number of charge / discharge cycles. The aging shape factor of the energy storage unit is set; The set degradation coefficient; The initial health status is set; For non-initial health states, when When it is 1, The minimum health status set; when When it is 2, The ideal state of health at the current moment is the estimated state of health. .

7. The method for predicting the lifetime of an energy storage unit based on dual prediction as described in claim 1, characterized in that: The calculation of adaptive weights based on the total number of time points is specifically as follows: in, It is the hyperbolic tangent function; The set curve steepness coefficient; Total number of moments; The set time threshold; Adaptive weights for the predicted first lifetime; The adaptive weights are used for predicting the second lifetime.

8. A dual-prediction-based energy storage unit lifetime prediction system using the method of any one of claims 1-7, comprising: The module comprising the operation status indicator calculation module, health status prediction module, dual prediction module, and lifespan output module is characterized by: Operating status index calculation module: Real-time acquisition of various operating parameters of the energy storage unit at the current moment, including operating temperature, output power and equivalent series resistance, and calculation of the operating status index at the current moment based on the various operating parameters of the energy storage unit at the current moment and the operating status index at the previous moment; Health status prediction module: Based on the acquired operational status indicators and health status at multiple historical moments, the relationship between the operational status indicators and the corresponding health status is fitted to a set nonlinear function containing linear and bias terms. The linear and bias terms are added to generate a linear function. The operational status indicators at the current moment are input into the linear and nonlinear functions respectively, and the output values ​​are used as the predicted conservative and ideal health status at the current moment. Dual prediction module: Obtain the health status change value in harsh environments through cyclic aging experiments, and calculate the predicted first lifetime by combining the difference between the estimated conservative health status at the current moment and the set minimum health status; calculate the maximum number of charge and discharge cycles of the energy storage unit from the set initial health status to the set minimum health status, and calculate the estimated current number of charge and discharge cycles of the energy storage unit from the set initial health status to the estimated ideal health status at the current moment; subtract the estimated current number of charge and discharge cycles from the maximum number of charge and discharge cycles and round down to obtain the predicted second lifetime; Lifetime Output Module: Records the current time and the total number of times within the set period before the current time when the ambient temperature exceeds the set maximum threshold. If the total number of times exceeds the set threshold, the first predicted lifetime is output. Otherwise, the adaptive weight is calculated based on the total number of times, and the first predicted lifetime and the second predicted lifetime are output as a result of weighted fusion according to the adaptive weight.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor performing steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program that, when executed by a processor, uses the steps of the method according to any one of claims 1-7.

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