A method and apparatus for predicting battery life

Abstract

This invention discloses a method for estimating battery life, comprising the following steps: simulating the SOH (State of Health) aging curves of the battery under different operating conditions based on the lifespan degradation influencing factors of the battery to be estimated, wherein the operating conditions include: cell temperature and / or battery SOC; and stitching together the SOH aging curves of the battery under different operating conditions to obtain the estimated battery life. This invention, by simulating and stitching together the SOH aging curves of the battery to be estimated under different operating conditions, including: cell temperature and / or battery SOC, yields the estimated battery life. It no longer uses a single cell temperature or SOC to predict battery life, thus more objectively reproducing the real battery environment and providing a more accurate prediction of battery life.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and more specifically to a method and apparatus for estimating battery life. Background Technology

[0002] Objectively and accurately predicting the actual lifespan of batteries has always been a goal pursued in the field of lifespan simulation. Current empirical mathematical models are used for single-condition lifespan performance evaluation (pure cycle / calendar decay).

[0003] Existing single-condition life models use calendar empirical models Q. TSOC Taking (t)~t as an example, this study investigates the functional relationship between recovery capacity Q and time t, where temperature T and storage state of charge (SOC) are parameters affecting decay. For n different temperature scenarios (or SOCs), the function Q... TSOC (t)~t has n decay curves.

[0004] Applying the aforementioned calendar-based empirical model to real-world lifespan assessment scenarios, such as a fully charged battery cell undergoing a calendar-based storage period with its temperature cyclically changing between 25°C and 45°C, alternating by 1°C every minute, the simulation predicts the State of Health (SOH) after one year of storage, where SOH represents battery health. However, for real-world lifespan requirements, when battery temperature is no longer uniform but fluctuates, the limitations of traditional lifespan prediction methods become apparent. Traditional lifespan prediction methods use temperature or SOC equivalence, such as using 35°C as the equivalent value for the scenario temperature, and obtaining an aging curve from 35°C to 100% SOC according to the calendar model. But this approach is clearly deviating from the application scenario and cannot meet the needs of higher-precision simulations. Summary of the Invention

[0005] The purpose of this invention is to address the limitations of traditional single-condition lifespan models in the existing technology when the battery temperature is no longer uniform and exhibits variations, which makes traditional lifespan prediction methods inadequate for real-world lifespan requirements. This invention proposes a method and device for predicting battery lifespan.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0007] A method for predicting battery life includes the following steps:

[0008] Based on the life degradation influencing factors of the battery to be estimated, simulate the SOH aging curve of the battery to be estimated under different operating conditions, including: cell temperature and / or battery SOC;

[0009] The SOH aging curves of the battery under different operating conditions are spliced ​​together to obtain the estimated battery life.

[0010] By subjecting the battery to be estimated to different operating conditions, including cell temperature and / or state of charge (SOC), the battery life is predicted. This method no longer uses a single temperature or SOC to predict battery life, but rather more objectively reflects the actual environment of the battery, thus providing a more accurate prediction of battery life.

[0011] As a preferred embodiment of the present invention, the following steps are also included:

[0012] Let the battery cell be in α i ℃, β i Under the condition of %SOC, record the corresponding calendar storage time as t. i And based on the calendar life equation, the aging curve of the i-th SOH is simulated; where i = 1, 2, 3...n, and n is a positive integer;

[0013] The SOH i The starting point of the aging curve is spliced ​​to other SOH curves with different i values. i At the end of the aging curve, the subsequent SOH aging curve is obtained;

[0014] The SOH i The SOH value at the beginning of the aging curve has a different i value than the SOH values ​​described above. i The SOH values ​​at the end of the aging curves are equal; based on the SOH values ​​corresponding to the end of the continuous SOH aging curves, the sum of battery degradation is obtained, thus yielding the battery life prediction result.

[0015] As a preferred embodiment of the present invention, a calendar life equation for the battery is defined, and the aging characteristics of the cell are described in accordance with an empirical model, so that the SOC and T values ​​of the battery have corresponding SOH aging curves, expressing the decay relationship between the SOH and t of the battery.

[0016] Where T represents cell temperature, SOC represents state of charge, t represents calendar storage time, and SOH represents battery health.

[0017] As a preferred embodiment of the present invention, the cell aging characteristics are described using an empirical model, as shown in the following formula.

[0018] Qloss(T, SOC, t)=A(C)*exp(Ea / (8.314*T))*t Z

[0019] SOH = Qloss(T, SOC, t)

[0020] Where Qloss represents battery capacity decay, T represents cell temperature, SOC represents state of charge, t represents calendar storage time, A(C) represents the correlation function between SOC and T, Ea represents activation energy, SOH represents battery health, and C represents the ratio of battery charge and discharge current. This formula can be used to simulate the corresponding SOH aging curve of the battery.

[0021] In a preferred embodiment of the present invention, in step S1, -10≤α i ≤60, 0≤β i ≤100; Defines the applicable range of battery temperature and SOC.

[0022] As a preferred embodiment of the present invention, step S1 further includes: defining the boundary conditions of the battery; by defining the boundary conditions of the battery, it is possible to avoid large deviations in the battery life prediction. For example, the battery life will decrease significantly when the battery is at -30℃ or 80℃, which will cause the SOH aging curve to change too much, thus making the battery life prediction result inaccurate.

[0023] As a preferred embodiment of the present invention, the boundary conditions include: the same SOH value corresponds to the same battery aging state.

[0024] As a preferred embodiment of the present invention, the boundary conditions further include:

[0025] Battery calendar life, SOC: 0-100%, T: -10℃~60℃;

[0026] Battery cycle life, DOD: 0-100%, C: BEV 0-1C, PHEV / HEV 1C-10C, T: -10℃-60℃;

[0027] Where T represents battery temperature; SOC represents state of charge, reflecting the remaining capacity of the battery; DOD represents the depth of discharge of the battery; C represents the ratio of battery charge and discharge current; BEV represents the ratio of charge and discharge current of pure electric vehicle batteries; PHEV / HEV represents the ratio of charge and discharge current of hybrid electric vehicle batteries. The range of the ratio of conventional battery charge and discharge current varies for different vehicle types. For example, the ratio of charge and discharge current of BEV pure electric vehicle batteries is usually in the range of 0-1C, while the ratio of charge and discharge current of PHEV / HEV hybrid electric vehicle batteries is usually in the range of 1C-10C.

[0028] The present invention also provides a device for estimating battery life, comprising at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the methods described above.

[0029] As a preferred embodiment of the present invention, the device further includes a model input terminal and a model output terminal;

[0030] The processor is connected to the model input terminal and the model output terminal respectively;

[0031] The model input terminal is used to input battery information, which includes battery temperature, SOC, cycle current, and DOD information.

[0032] The processor includes a Matlab program encapsulation module, which encapsulates a calendar life equation that describes the aging characteristics of the battery cell in an empirical manner.

[0033] The model output terminal is used to output the SOH after processing by the processor, that is, the battery life prediction result corresponding to the SOH, wherein the SOH includes at least one of DCR growth rate and capacity retention rate;

[0034] The battery information input at the model input terminal is processed by the processor and then transmitted to the model output terminal for output.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] 1. This invention simulates and synthesizes the SOH aging curves of batteries under different operating conditions, including cell temperature and / or battery SOC, to obtain the estimated battery life. It no longer uses a single temperature or SOC to predict battery life, but more objectively restores the real environment of the battery, thus more accurately predicting battery life. Attached Figure Description

[0037] Figure 1 This is a schematic diagram illustrating the splicing principle of the SOH curves in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of a device for estimating battery life according to the present invention;

[0039] Figure 3 This is a schematic diagram illustrating the boundary conditions for introducing the battery in this invention. Detailed Implementation

[0040] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0041] Example 1

[0042] Please refer to the attached document. Figure 1 As shown, this embodiment provides a method for estimating battery life, including the following steps:

[0043] Based on the life degradation influencing factors of the battery to be estimated, simulate the SOH aging curve of the battery to be estimated under different operating conditions, including: cell temperature and / or battery SOC;

[0044] The SOH (Surface Endurance) continuous method involves stitching together the SOH aging curves of the battery under different operating conditions to obtain the estimated battery life. The SOH continuous method includes the following steps:

[0045] Under specified conditions (e.g., 25°C, 100% SOC), the corresponding calendar storage time of the battery cell is recorded as t1, and the SOH1 aging curve is simulated according to the calendar life equation.

[0046] Under another specified condition (e.g., 45°C, 100% SOC), the corresponding calendar storage time of the battery cell is recorded as t2, and the SOH2 aging curve is simulated according to the calendar life equation.

[0047] The starting end of the SOH1 aging curve is spliced ​​to the end of the SOH2 aging curve to obtain a continuous SOH aging curve.

[0048] The SOH value at the beginning of the SOH1 aging curve is equal to the SOH value at the end of the SOH2 aging curve.

[0049] Based on the SOH value corresponding to the end of the continuous SOH aging curve, the sum of battery degradation is obtained, thus yielding the battery life prediction result.

[0050] By subjecting the battery to be estimated to different operating conditions, including cell temperature and / or state of charge (SOC), the battery life is predicted. This method no longer uses a single temperature or SOC to predict battery life, but rather more objectively reflects the actual environment of the battery, thus providing a more accurate prediction of battery life.

[0051] Define the calendar life equation of the battery, describe the cell aging characteristics according to the empirical model, so that the SOC and T values ​​of the battery have corresponding SOH aging curves, and express the decay relationship between the SOH and t of the battery.

[0052] Where T represents cell temperature, SOC represents state of charge, t represents calendar storage time, and SOH represents battery health.

[0053] The cell aging characteristics are described using an empirical model, as shown in the following formula.

[0054] Qloss(T, SOC, t)=A(C)*exp(Ea / (8.314*T))*t Z

[0055] SOH = Qloss(T, SOC, t)

[0056] Where Qloss represents battery capacity decay, T represents cell temperature, SOC represents state of charge, t represents calendar storage time, A(C) represents the correlation function between SOC and T, Ea represents activation energy, SOH represents battery health, and C represents the ratio of battery charge and discharge current. This formula can be used to simulate the corresponding SOH aging curve of the battery.

[0057] In step S1, -10≤α i ≤60, 0≤β i ≤100; Defines the applicable range of battery temperature and SOC.

[0058] Step S1 further includes: defining the boundary conditions of the battery; by defining the boundary conditions of the battery, it is possible to avoid large deviations in the battery life prediction. For example, the battery life will decrease significantly when the battery is at -30℃ or 80℃, which will cause the SOH aging curve to change too much, thus making the battery life prediction result inaccurate.

[0059] The boundary conditions include: the same SOH value corresponds to the same battery aging state.

[0060] The boundary conditions also include:

[0061] Battery calendar life, SOC: 0-100%, T: -10℃~60℃;

[0062] Battery cycle life, DOD: 0-100%, C: BEV 0-1C, PHEV / HEV 1C-10C, T: -10℃-60℃;

[0063] Where T represents battery temperature; SOC represents state of charge, reflecting the remaining capacity of the battery; DOD represents the depth of discharge of the battery; C represents the ratio of battery charge and discharge current; BEV represents the ratio of charge and discharge current of pure electric vehicle batteries; PHEV / HEV represents the ratio of charge and discharge current of hybrid electric vehicle batteries. The range of the ratio of conventional battery charge and discharge current varies for different vehicle types. For example, the ratio of charge and discharge current of BEV pure electric vehicle batteries is usually in the range of 0-1C, while the ratio of charge and discharge current of PHEV / HEV hybrid electric vehicle batteries is usually in the range of 1C-10C.

[0064] Example 2

[0065] Please refer to the attached document. Figure 2 As shown, the present invention also provides a device for estimating battery life, including at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described in Embodiment 1.

[0066] The device also includes a model input terminal and a model output terminal;

[0067] The processor is connected to the model input terminal and the model output terminal respectively;

[0068] The model input terminal is used to input battery information, which includes battery temperature, SOC, cycle current, and DOD information.

[0069] The processor includes a Matlab program encapsulation module, which encapsulates a calendar life equation that describes the aging characteristics of the battery cell in an empirical manner.

[0070] The model output terminal is used to output the SOH after processing by the processor, that is, the battery life prediction result corresponding to the SOH, wherein the SOH includes at least one of DCR growth rate and capacity retention rate;

[0071] The battery information input at the model input terminal is processed by the processor and then transmitted to the model output terminal for output.

[0072] Please refer to the attached document. Figure 3As shown, battery information is input through the model input terminal. The battery information includes the preset boundary conditions of the battery and the calendar life equation. The battery information is processed by the Matlab program encapsulation module. The Matlab program encapsulation module adopts the battery life estimation method described in Example 1. After the battery information is processed by the Matlab program encapsulation module, the battery life estimation result is output by the model output terminal.

[0073] A set of battery boundary conditions is imported every 0.1 seconds. These boundary conditions include battery temperature, SOC, cycle current, and DOD information. The Matlab program encapsulation module automatically reads the battery temperature, SOC, cycle current, and DOD information per unit time and outputs the battery life prediction result through the model output terminal. This yields the cumulative SOH decay of the cell over the entire operating cycle, completing the scenario life simulation calculation. This achieves intelligent battery life prediction, eliminating the need for extensive manual calculations.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of estimating battery life, characterized by, The method comprises the following steps: According to the life attenuation influencing factor of the battery to be estimated, the SOH aging curve corresponding to the battery to be estimated under different working conditions is simulated, and the working conditions include the cell temperature and the SOC of the battery. The SOH aging curves corresponding to the battery to be estimated under different working conditions are spliced to obtain the life of the estimated battery, which comprises: Let the battery core be in the condition of α i ℃, β i % SOC, record the corresponding calendar storage time as t i i, and simulate the SOH i aging curve according to the calendar life equation; wherein i=1, 2, 3…n, and the n is a positive integer; SOH i the beginning of the aging curve, splicing to other SOH with different i values i the end of the aging curve, obtaining the SOH aging curve in succession wherein the SOH i the SOH value at the beginning of the aging curve is equal to the SOH value at the end of the aging curve for the other SOH i the SOH value at the beginning of the aging curve is equal to the SOH value at the end of the aging curve for the other SOH According to the SOH value corresponding to the end of the spliced SOH aging curve, the sum of the battery attenuation amounts is obtained, and the life prediction result of the battery is obtained.

2. The method for estimating the life of a battery according to claim 1, wherein a calendar life equation of the battery is defined, the cell aging characteristics are described in an empirical model manner, so that the SOC and T value of the battery have a corresponding SOH aging curve, and the attenuation relationship between the SOH and t of the battery is expressed. Wherein T represents the cell temperature, SOC represents the state of charge, t represents the calendar storage time, and SOH represents the battery health degree. The cell aging characteristics are described in an empirical model manner as shown in the following formula:

3. The method of claim 2, wherein, Wherein Qloss represents the battery capacity attenuation, T represents the cell temperature, SOC represents the state of charge, t represents the calendar storage time, A(C) represents the SOC and T related function, Ea represents the activation energy, SOH represents the battery health degree, and C represents the ratio of the battery charge and discharge current size. Further comprising:

4. The method of claim 1, wherein, where -10 < a i ≤ 60, 0 < β i ≤ 100.

5. The method of estimating battery life according to claim 1, wherein, Defining the boundary conditions of the battery. The boundary conditions include that the battery aging states corresponding to the same SOH value are the same.

6. The method of estimating battery life according to claim 5, wherein, The boundary conditions further include:

7. The method of estimating battery life according to claim 5, wherein, Battery calendar life, SOC: 0-100%, T: -10℃-60℃; Battery cycle life, DOD: 0-100%, C: BEV 0-1C, PHEV / HEV 1C-10C, T: -10℃-60℃; Wherein T represents the battery temperature; SOC represents the state of charge, reflecting the remaining capacity of the battery; DOD represents the discharge depth of the battery; C represents the ratio of the battery charge and discharge current size; BEV represents the ratio of the battery charge and discharge current size of the battery of a pure electric vehicle; PHEV / HEV represents the ratio of the battery charge and discharge current size of the battery of a hybrid vehicle. The device further comprises at least one processor and a memory connected in communication with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method of any one of claims 1 to 7.

8. A device for predicting battery life, characterized in that, The device further comprises a model input end and a model output end; 9. The apparatus for estimating the life of a battery of claim 8, wherein, The processor is connected with the model input end and the model output end respectively; The model input end is used for inputting battery information, and the battery information includes the temperature, SOC, cycle current and DOD information of the battery; The processor comprises a Matlab program packaging module, and the Matlab program packaging module packages the calendar life equation for describing the cell aging characteristics in an empirical model manner; The model output end is used for outputting SOH processed by the processor, and the SOH includes at least one of the DCR growth rate and the capacity retention rate. ​ The battery information input by the model input is transmitted to the model output for output after being processed by the processor.

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