Battery full life cycle charging duration estimation method, device, equipment and vehicle

Abstract

The invention discloses a battery full life cycle charging duration estimation method, device and equipment and a vehicle. The battery full life cycle charging duration estimation method comprises the following steps: performing electrothermal simulation on a battery to obtain heating parameters of at least two battery packs; wherein the battery pack comprises at least one battery cell; determining a consistency characteristic value of the battery pack according to the heating parameter; according to the consistency characteristic value of each battery pack, calculating the charging duration of each battery pack from the beginning of charging to a charging target; and performing statistical calculation on each calculation result, and determining the full-life-cycle charging duration of the battery. The accuracy of estimating the charging time of the battery is improved by combining the electric heating simulation model of the battery system and estimating the heating parameters considering the problem of influencing the consistency of the battery.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and in particular to a method, apparatus, device, and vehicle for estimating the charging time of a battery throughout its entire life cycle. Background Technology

[0002] Battery charging time is a crucial aspect perceived by electric vehicle users. Typically, the duration of each charging current stage is calculated based on the current battery temperature, actual charging current, and state of charge (SOC), and the remaining charging time is obtained by summing the times for each stage. However, in practical applications, existing technologies suffer from inaccurate estimations of battery charging time. Summary of the Invention

[0003] This invention provides a method, apparatus, device, and vehicle for estimating the charging time of a battery throughout its entire life cycle, thereby improving the accuracy of battery charging time estimation.

[0004] According to one aspect of the present invention, a method for estimating the charging time of a battery throughout its entire life cycle is provided, the method comprising:

[0005] Electrothermal simulation of the battery was performed to obtain the heating parameters of at least two battery packs; wherein the battery packs include at least one cell;

[0006] The consistency characteristic value of the battery pack is determined based on the heating parameters;

[0007] Based on the consistency characteristic value of each battery pack, the charging time of each battery pack from the start of charging to the charging target is calculated;

[0008] Statistical calculations are performed on the results to determine the total charging time for the battery's entire lifespan.

[0009] Optionally, the step of calculating the charging time of each battery pack from the start of charging to the charging target based on the consistency characteristic value of each battery pack includes:

[0010] Based on the consistency characteristic values ​​described above, the charging current of each battery pack during the charging process is determined respectively;

[0011] The charging rate is obtained based on the charging current;

[0012] The cumulative value of the charging rate for each battery pack is calculated from the SOC value at the start of charging to the target SOC value, to obtain the charging time for each battery pack.

[0013] Optionally, the battery charging state includes BOL state and MOL state;

[0014] If the battery charging state is BOL, then the consistency characteristic value determined by the heating parameter is used to calculate the charging time in subsequent steps.

[0015] If the battery charging state is MOL, then the consistency characteristic value determined by the heating parameter is the first characteristic value;

[0016] Before calculating the charging time from the start of charging to the charging target for each battery pack based on the consistency characteristic value of each battery pack, the method further includes:

[0017] The first characteristic value is corrected according to the aging parameters of each battery pack to obtain the second characteristic value; the second characteristic value is used to calculate the charging time in subsequent steps.

[0018] Optionally, the heating parameters include at least one of the following: battery DC resistance, AC resistance, ohmic resistance, polarization resistance, and impedance; the aging parameters include at least the battery health status.

[0019] Optionally, the step of correcting each of the first characteristic values ​​based on the aging parameters of each of the battery packs includes:

[0020] A correction coefficient is constructed based on the battery health status; wherein the correction coefficient is based on a battery health status of 100%; if the battery health status is 100%, the correction coefficient is 1; if the battery health status is less than 100%, the correction coefficient is increased.

[0021] The second eigenvalue is obtained by multiplying the correction coefficient and the first eigenvalue.

[0022] Optionally, the step of performing statistical calculations on the calculation results to determine the charging time for the entire battery life cycle includes:

[0023] The average, mode, or median of each calculation result is used as the charging time for the entire battery life cycle.

[0024] Optionally, before performing electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs, the method further includes:

[0025] Based on the structural parameters, the battery cells contained in the battery system are grouped to obtain at least two battery packs; wherein, the structural parameters include at least the number of battery cells, the number of battery modules, and the number of battery regions.

[0026] Optionally, the step of grouping the battery cells included in the battery system according to structural parameters to obtain at least two battery packs includes at least one of the following:

[0027] One of the battery cells is divided into a battery pack, at least two adjacent battery cells are divided into a battery pack, one battery module is divided into a battery pack, at least two adjacent battery modules are divided into a battery pack, one battery area is divided into a battery pack, and at least two battery areas are divided into a battery pack.

[0028] Optionally, after determining the consistency characteristic value of the battery pack based on the heating parameter, the method further includes:

[0029] The maximum and minimum feature values ​​are selected from the consistency feature values, and the maximum and minimum feature values ​​are used to calculate the charging time in subsequent steps.

[0030] Alternatively, before determining the consistency characteristic value of the battery pack based on the heating parameters, the method further includes:

[0031] The maximum and minimum heat generation parameters are selected from the heat generation parameters, and the maximum and minimum heat generation parameters are used to calculate the charging time in subsequent steps.

[0032] Optionally, before or after performing electrothermal simulation on the battery, the method further includes:

[0033] Obtain the current battery temperature. If the current battery temperature is lower than the temperature threshold, preheat the battery and calculate the preheating time.

[0034] Furthermore, after performing statistical calculations on each calculation result, the method further includes:

[0035] The preheating time and the statistical results obtained from the statistical calculation are summed to obtain the total charging time of the battery's entire life cycle.

[0036] According to another aspect of the present invention, a battery life cycle charging time estimation device is provided, the battery life cycle charging time estimation device comprising:

[0037] An electrothermal simulation module is used to perform electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs; wherein the battery pack includes at least one cell;

[0038] The feature value acquisition module is used to determine the consistency feature value of the battery pack based on the heating parameters.

[0039] The battery pack charging time calculation module is used to calculate the charging time of each battery pack from the start of charging to the charging target based on the consistency characteristic value of each battery pack.

[0040] The full life cycle charging time calculation module is used to perform statistical calculations on the calculation results to determine the full life cycle charging time of the battery.

[0041] According to another aspect of the present invention, a battery life cycle charging time estimation device is provided, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the battery life cycle charging time estimation method described in the first aspect.

[0042] According to another aspect of the invention, a vehicle is provided that includes a battery life cycle charging time estimation device as described in the second aspect.

[0043] The technical solution of this invention provides a method, apparatus, device, and vehicle for estimating the charging time of a battery throughout its entire life cycle. The method includes: performing electrothermal simulation on the battery to obtain heating parameters for at least two battery packs; determining consistency characteristic values ​​for the battery packs based on the heating parameters; calculating the charging time for each battery pack from the start of charging to the charging target based on the consistency characteristic values; and performing statistical calculations on the calculation results to determine the total charging time of the battery throughout its life cycle. Unlike existing technologies, this invention uses consistency characteristic values ​​to characterize the consistency differences between battery packs, thus reflecting the impact of these consistency differences on the battery charging time estimation. Furthermore, combined with the battery system electrothermal simulation model, the estimated charging time for battery packs with different consistency characteristic values ​​is also different, thereby enabling the estimation of the charging time for each battery pack considering battery consistency issues; and determining the total charging time of the battery throughout its life cycle through statistical calculations. In summary, this invention improves the accuracy of battery charging time estimation by considering the impact of battery consistency issues in the charging time estimation.

[0044] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of a method for estimating the charging time of a battery throughout its entire life cycle, provided in an embodiment of the present invention.

[0047] Figure 2 This is a flowchart of another method for estimating the charging time of a battery throughout its entire life cycle, provided in an embodiment of the present invention.

[0048] Figure 3 This is a flowchart of another method for estimating the charging time of a battery throughout its entire life cycle, provided in an embodiment of the present invention.

[0049] Figure 4 This is a flowchart of another method for estimating the charging time of a battery throughout its entire life cycle, provided in an embodiment of the present invention.

[0050] Figure 5 This is a flowchart of another method for estimating the charging time of a battery throughout its entire life cycle, provided in an embodiment of the present invention.

[0051] Figure 6 This is a flowchart of another method for estimating the charging time of a battery throughout its entire life cycle, provided in an embodiment of the present invention.

[0052] Figure 7 This is a structural diagram of a battery life cycle charging time estimation device provided in an embodiment of the present invention;

[0053] Figure 8 This is a schematic diagram of a battery life cycle charging time estimation device provided in an embodiment of the present invention. Detailed Implementation

[0054] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0055] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0056] Figure 1 This is a flowchart of a battery lifecycle charging time estimation method provided in this embodiment of the invention. This embodiment can be applied to the process of improving the accuracy of battery charging time in a battery system. This method can be executed by a battery lifecycle charging time estimation device, which can be implemented in hardware and / or software. The battery lifecycle charging time estimation device can be configured in the controller or processor of the battery system. Figure 1 As shown, the method includes:

[0057] S110. Perform electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs.

[0058] The battery pack includes at least one battery cell. Due to differences in the consistency of the battery cells during charging and discharging, the charging and discharging consistency of the battery pack composed of the cells also varies. This difference in consistency is related to the heat generation parameters.

[0059] One approach is to use a battery system electrothermal simulation model to simulate the battery's thermal properties and obtain the battery pack's heating parameters. The battery system electrothermal simulation model includes the relationships and interactions between the electric field, thermal field, and flow field within the battery system. The methods for establishing the battery system electrothermal simulation model include one-dimensional and three-dimensional simulations; the specific method can be set according to the actual situation and is not limited here.

[0060] S120. Determine the consistency characteristic value of the battery pack based on the heating parameters.

[0061] Specifically, after obtaining the heating parameters of the battery pack, the consistency characteristic value of each battery pack can be obtained based on the overall trend of the heating parameters of the cells in each battery pack. Since the consistency characteristic value is selected based on the heating parameters characterizing battery consistency issues, the calculation accuracy of each characteristic value can be improved, which is beneficial to improving the estimation accuracy of the charging time of the battery's entire life cycle.

[0062] S130. Calculate the charging time for each battery pack from the start of charging to the charging target based on the consistency characteristic value of each battery pack.

[0063] Different battery packs have different consistency characteristic values, which correspond to different charging times. In this embodiment of the invention, the charging time is calculated based on the consistency characteristic values ​​of each battery pack, which helps to improve the accuracy of the charging time calculation.

[0064] S140. Perform statistical calculations on the calculation results to determine the charging time for the entire battery life cycle.

[0065] For example, statistical calculations could involve taking the average, mode, or median of each calculation result. The average, mode, or median of the charging time for each battery pack is then used as the total charging time for the battery's entire lifespan.

[0066] The technical solution of this invention provides a method, apparatus, device, and vehicle for estimating the charging time of a battery throughout its entire life cycle. The method includes: performing electrothermal simulation on the battery to obtain heating parameters for at least two battery packs; determining consistency characteristic values ​​for the battery packs based on the heating parameters; calculating the charging time for each battery pack from the start of charging to the charging target based on the consistency characteristic values; and performing statistical calculations on the calculation results to determine the total charging time of the battery throughout its life cycle. Unlike existing technologies, this invention uses consistency characteristic values ​​to characterize the consistency differences between battery packs, thus reflecting the impact of these consistency differences on the battery charging time estimation. Furthermore, combined with the battery system electrothermal simulation model, the estimated charging time for battery packs with different consistency characteristic values ​​is also different, thereby enabling the estimation of the charging time for each battery pack considering battery consistency issues; and determining the total charging time of the battery throughout its life cycle through statistical calculations. In summary, this invention improves the accuracy of battery charging time estimation by considering the impact of battery consistency issues in the charging time estimation.

[0067] Figure 2 This is a flowchart of another method for estimating the charging time of a battery throughout its entire life cycle, provided in an embodiment of the present invention. Based on the above embodiment, step S130, calculating the charging time of each battery pack from the start of charging to the charging target based on the consistency characteristic values ​​of each battery pack, is further defined. Optionally, refer to... Figure 2 The method includes the following steps:

[0068] S210. Perform electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs.

[0069] S220. Determine the consistency characteristic value of the battery pack based on the heating parameters.

[0070] S230. Based on each consistency characteristic value, determine the charging current of each battery pack during the charging process.

[0071] Specifically, when the battery system comprises n battery packs, the charging current of each battery pack, determined by the consistency parameter, can be expressed as I(X1), I(X2), ..., I(X... n ), where X1, X2, ..., X n These are the consistency characteristic values ​​for each battery pack.

[0072] S240. The charging rate is obtained based on the charging current.

[0073] Specifically, the charging rate can be obtained by taking the reciprocal of the charging current. The charging rate of each battery pack can be expressed as:

[0074] S250. Calculate the cumulative value of the charging rate for each battery pack from the SOC value at the start of charging to the target SOC value, and obtain the charging time for each battery pack.

[0075] For example, the charging time of each battery pack can be calculated using the following formula:

[0076]

[0077] Among them, X n Let SOC0 be the nth consistent characteristic value, and SOC1 be the range of SOC. SOC0 is the SOC when the battery starts charging, SOC1 is the target SOC of the battery, and I is the current charging current of the battery. For example, the range of SOC for SOC0 and SOC1 can be 20-100, which can be set according to the actual situation, and no specific limitation is made here.

[0078] Specifically, assuming there are n battery packs, substituting the n consistent characteristic values ​​into the above formula yields the charging times corresponding to each of the n consistent characteristic values, i.e., t1, t2, ..., t3. n .

[0079] The charging time t1 for the first battery pack from the start of charging to the target SOC is:

[0080]

[0081] The charging time t2 for the second battery pack from the start of charging to the target SOC is:

[0082]

[0083] The charging time t for the nth battery pack from the start of charging to the target SOC n for:

[0084]

[0085] S260. Perform statistical calculations on the calculation results to determine the charging time for the entire battery life cycle.

[0086] For example, the average charging time of each battery pack is used as the total battery lifespan charging time T, i.e.:

[0087]

[0088] In addition, the accuracy of the battery's total lifespan charging time can be estimated based on the variance of the charging time for each battery pack.

[0089] In the technical solution of this embodiment, the charging time is obtained by acquiring the charging current, the charging rate, and integrating the charging rate, which helps to improve the estimation accuracy.

[0090] Optionally, the heating parameters include at least one of the following: battery DC resistance, AC resistance, ohmic resistance, polarization resistance, and impedance.

[0091] It should be noted that the specific implementation method for determining the consistency characteristic values ​​of the battery system electrothermal simulation model based on the battery heating parameters can be one or more combinations of the battery DC resistance, AC resistance, ohmic resistance, polarization resistance and impedance. The specific settings can be made according to the actual situation, and no specific limitation is made here.

[0092] Based on the above embodiments, for example, suppose battery system A contains 6 battery packs, the current battery temperature is 25°C, and the battery SOC charging range is 20%-80%. Then, it can be determined that the battery system has 6 consistent characteristic values. The accuracy of the charging time and the full life cycle charging time corresponding to the consistent characteristic values ​​of the battery system is shown in Table 1. Wherein, σ is the accuracy of the full life cycle charging time of the battery.

[0093] Table 1. Charging time and accuracy of charging duration estimation for battery system A

[0094] <![CDATA[X1]]> <![CDATA[X2]]> <![CDATA[X3]]> <![CDATA[X4]]> <![CDATA[X5]]> <![CDATA[X6]]> T σ <![CDATA[t 1～6 ]]> 547 552 574 575 581 601 572 18

[0095] According to Table 1, the estimated charging time for battery system A at 25℃ and within the SOC range of 20%–80% is 9 minutes and 32 seconds (±18 seconds). The estimated charging time error is less than ±5%.

[0096] Based on the above embodiments, optionally, the battery charging state includes BOL state and MOL state, and the method for estimating the charging time of the battery's entire life cycle also varies for different charging states.

[0097] Figure 3 This is a flowchart of another method for estimating the charging time of a battery throughout its entire life cycle, provided in an embodiment of the present invention. (Reference) Figure 3 The method includes the following steps:

[0098] S310. Perform electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs.

[0099] S320: Determine the current charging status of the battery.

[0100] The current charging state of the battery includes the Beginning of Life (BOL) state and the Middle of Life (MOL) state.

[0101] S330. If the current charging state of the battery is BOL, then the consistency characteristic value determined by the heating parameter is used; based on the consistency characteristic value of each battery pack, the charging time of each battery pack from the start of charging to the charging target is calculated.

[0102] Specifically, when the battery is currently in the BOL (Best in Late) state of charge, the heat generated by the battery in the BOL state is considered. Based on the heat generation parameters of the battery in the BOL state, the consistency characteristic values ​​of each battery pack in the BOL state are determined. By taking the battery consistency problem into the calculation of each characteristic value of the battery electrothermal simulation model, the calculation accuracy of each characteristic value can be improved, which is conducive to improving the estimation accuracy of the charging time of the battery in the BOL state.

[0103] S340. Perform statistical calculations on the calculation results to determine the charging time for the entire battery life cycle.

[0104] Specifically, in the BOL state, after determining the consistency characteristic value of each battery pack in the BOL state based on the battery heating parameters, the full life cycle charging time of the battery in the BOL state is directly estimated and recorded as the first charging time T1.

[0105] In the technical solution of this embodiment, the method can estimate the full life cycle charging time of the battery in the BOL state, and the heating parameters characterizing the battery consistency problem in the BOL state can accurately determine the consistency characteristic values ​​of each battery pack in the BOL state. By taking the battery consistency problem into the calculation of each characteristic value of the battery electrothermal simulation model, the calculation accuracy of each characteristic value can be improved. Furthermore, by combining each consistency characteristic value with the battery system electrothermal simulation model to estimate the full life cycle charging time of the battery in the BOL state, the estimation accuracy can be improved.

[0106] Figure 4 This is a flowchart of another method for estimating the charging time of a battery's entire life cycle, provided in an embodiment of the present invention. Optionally, based on the above embodiments, refer to... Figure 4 The method includes the following steps:

[0107] S410. Perform electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs.

[0108] S420: Determine the current charging status of the battery.

[0109] S430. If the battery is currently in the MOL state of charging, then the consistency characteristic value determined by the heating parameters is the first characteristic value.

[0110] S440. Correct each first characteristic value according to the aging parameters of each battery pack to obtain the second characteristic value.

[0111] Specifically, when the battery is currently in the BOL state of charge, considering the heat generated by the battery in the MOL state, the first characteristic value of each battery pack in the BOL state can be corrected based on the aging parameters.

[0112] The battery aging parameters can be the State of Health (SOH) coefficient or other parameters that represent battery aging, such as electrical throughput, thermal throughput, operating conditions, and resting time. These can be set according to actual conditions and are not specifically limited here. Alternatively, aging parameters, such as current signals, voltage signals, temperature signals, and fault warnings, can be calculated based on charging and discharging information obtained from the cloud. These can also be set according to actual conditions and are not specifically limited here.

[0113] S450: Calculate the charging time for each battery pack from the start of charging to the charging target based on the second characteristic value of each battery pack.

[0114] Specifically, the second eigenvalue can be used to estimate the full life cycle charging time of the battery in the MOL state, and is denoted as the second charging time T2.

[0115] In the technical solution of this embodiment, the method can estimate the full life cycle charging time of the battery in the MOL state. By combining the aging parameters that characterize the actual heat generation state of the battery in the MOL state, the first characteristic values ​​of each battery pack in the BOL state, and the battery system electrothermal simulation model, the estimation accuracy of the full life cycle charging time of the battery in the BOL state is improved by taking battery aging factors and battery consistency issues into account in the estimation of battery charging time.

[0116] Figure 5 This is a flowchart of another method for estimating the charging time of a battery's entire life cycle, provided in an embodiment of the present invention. Based on the above embodiments, this embodiment further specifies the method for obtaining the second feature value. Optionally, refer to... Figure 5 The method includes the following steps:

[0117] S510. Perform electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs.

[0118] S520: Determine the current charging status of the battery.

[0119] S530. If the current charging state of the battery is MOL, then the consistency characteristic value determined by the heating parameters is the first characteristic value.

[0120] S540. Construct a correction coefficient based on the battery health status; wherein, the correction coefficient is based on a battery health status of 100%. If the battery health status is 100%, the correction coefficient is 1; if the battery health status is less than 100%, the correction coefficient is increased.

[0121] S550. Calculate the product of the correction coefficient and the first eigenvalue to obtain the second eigenvalue.

[0122] For example, taking the battery aging parameter SOH as an example, the first characteristic value of each battery pack is: X1, X2, ... X n Then, the first eigenvalues ​​(i.e., the second eigenvalues) after correction by SOH are: X1*(2 / SOH-1), X2*(2 / SOH-1), ... X n *(2 / SOH-1).

[0123] When the battery is currently in the MOL state of charging, it is in the middle stage, indicating that the battery has a certain degree of aging. By correcting the first characteristic value of each battery pack in the BOL state through the battery aging parameters, the second characteristic value of each battery system electrothermal simulation model in the MOL state is obtained. By taking the battery aging factor into the calculation of each characteristic value of the model, the calculation accuracy of each characteristic value can be improved, which is conducive to improving the estimation accuracy of the charging time of the battery in the MOL state.

[0124] S560. Calculate the charging time for each battery pack from the start of charging to the charging target based on the second characteristic value of each battery pack.

[0125] In the technical solution of this embodiment, the method can estimate the full life cycle charging time of the battery in the MOL state. By taking battery aging factors into account in the calculation of each characteristic value of the model, the calculation accuracy of each characteristic value of the battery in the MOL state can be improved. Furthermore, by combining the aging parameters that characterize the actual heat generation state of the battery in the MOL state, each first characteristic value of the battery system electrothermal simulation model in the BOL state, and the battery system electrothermal simulation model, the estimation accuracy of the full life cycle charging time of the battery in the BOL state can be improved by taking battery aging factors and battery consistency issues into account in the estimation of battery charging time.

[0126] Figure 6 This is a flowchart of another method for estimating the charging time of a battery throughout its entire life cycle, provided in this embodiment of the invention. Based on the above embodiments, this embodiment of the invention can also estimate the charging time for situations where the battery requires preheating. Optionally, refer to... Figure 6 The method includes the following steps:

[0127] S610. Obtain the current battery temperature. If the current battery temperature is lower than the temperature threshold, preheat the battery and calculate the preheating time.

[0128] For example, the current battery temperature can be obtained by inserting a charging gun.

[0129] For example, the specific implementation of determining the battery preheating time based on the current battery temperature can be as follows: Determine whether the battery is within the allowable charging range based on its current temperature. If the current battery temperature is within the allowable charging range, the battery enters the charging program, i.e., proceeds to step S620. If the current battery temperature is outside the allowable charging range, the battery enters the preheating program, i.e., the preheating time is calculated based on the current battery temperature and the target temperature, and is recorded as the third charging time. Therefore, the total charging time for the battery's entire lifespan is the preheating time plus the charging time of the charging program. For example, assuming that after entering the charging program, if the battery's current charging state is BOL (Best in Least Oxide) state, then the total charging time for the battery's entire lifespan is the charging time in the BOL state (i.e., the first charging time) plus the preheating time (i.e., the third charging time). If the battery's current charging state is MOL (Most Oxide) state, then the total charging time for the battery's entire lifespan is the charging time in the MOL state (i.e., the second charging time) plus the preheating time (i.e., the third charging time).

[0130] S620 performs electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs.

[0131] S630. Determine the consistency characteristic value of the battery pack based on the heating parameters.

[0132] S640. Calculate the charging time for each battery pack from the start of charging to the charging target based on the consistency characteristic value of each battery pack.

[0133] S650. Perform statistical calculations on each calculation result.

[0134] S660: Sum the preheating time and the statistical results obtained from statistical calculations to obtain the charging time for the entire battery life cycle.

[0135] For example, taking the battery's current charging state as BOL after the charging process, the calculation process for the battery preheating time (i.e., the third charging time) is as follows: The current temperature, current SOC, and battery SOH are detected by the charging gun. Assuming the current SOC is 20% and the current temperature t is -30℃, then the current SOC and current temperature do not meet the charging requirements (SOC [0, 100%) and t [-20, 50%), thus entering the preheating process. Entering the residual heat process, the calculation formula for the battery's third charging time T3 is:

[0136]

[0137] Where M is the battery pack mass, t0 is the minimum allowable charging temperature threshold, C is the total battery pack thermal fusion temperature, and P... PTC P 回收 and P 消耗 This refers to the heating power of each part of the battery system.

[0138] Given n first characteristic values, each first characteristic value is substituted into the battery system electrothermal simulation model for calculation. The charging time corresponding to each first characteristic value can be obtained. The first charging time T1 of the battery in the BOL state can be obtained based on the average value of the charging time corresponding to each first characteristic value. The accuracy of the charging time of the battery's entire life cycle can be obtained based on the variance of the charging time corresponding to each first characteristic value.

[0139] Assuming that the battery system's state of equilibrium (SOH) is 100% according to cloud data, the second charging time T2 is equal to the first charging time T1. Therefore, the total charging time for the battery's entire lifespan is T3 + T1 (or T3 + T2).

[0140] In the technical solution of this embodiment, the method can be used to estimate the charging time of the battery's entire life cycle. Since the consistency characteristic values ​​of each battery pack are selected based on the heating parameters characterizing the battery consistency problem, by taking the battery consistency problem into the calculation of each characteristic value of the battery electrothermal simulation model, the calculation accuracy of each consistency characteristic value can be improved. Thus, by combining each consistency characteristic value with the battery system electrothermal simulation model to estimate the charging time of the battery's entire life cycle, the estimation accuracy can be improved.

[0141] Based on the above embodiments, optionally, before performing electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs, the method further includes: grouping the cells contained in the battery system according to structural parameters to obtain at least two battery packs; wherein the structural parameters include at least the number of battery cells, the number of battery modules, and the number of battery regions.

[0142] In other words, the method of dividing the battery cells is not limited in the embodiments of the present invention, but can be limited as needed in practical applications. Specifically, based on structural parameters, the battery cells included in the battery system are grouped to obtain at least two battery packs, including at least one of the following: one battery cell is divided into one battery pack, at least two adjacent battery cells are divided into one battery pack, one battery module is divided into one battery pack, at least two adjacent battery modules are divided into one battery pack, one battery area is divided into one battery pack, and at least two battery areas are divided into one battery pack.

[0143] It should be noted that in the above embodiments, each battery pack is exemplarily shown to have a consistency characteristic value, and all consistency characteristic values ​​are used to estimate the charging time of the battery's entire life cycle. This setting helps to improve the accuracy of the estimation. In other embodiments, the estimation method can be simplified to improve the efficiency of the estimation. Specifically, after determining the consistency characteristic value of the battery pack based on the heat generation parameters, the method further includes: selecting the maximum and minimum characteristic values ​​from the consistency characteristic values, and using the maximum and minimum characteristic values ​​to calculate the charging time in subsequent steps; or, selecting the maximum and minimum heat generation parameters from the heat generation parameters, and using the maximum and minimum heat generation parameters to calculate the charging time in subsequent steps.

[0144] Figure 7 This is a structural diagram of a battery life cycle charging time estimation device provided in an embodiment of the present invention. The present invention also provides a battery life cycle charging time estimation device, see reference. Figure 7 The device 100 includes:

[0145] The electrothermal simulation module 101 is used to perform electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs; wherein the battery pack includes at least one cell.

[0146] The feature value acquisition module 102 is used to determine the consistency feature value of the battery pack based on the heating parameters.

[0147] The battery pack charging time calculation module 103 is used to calculate the charging time of each battery pack from the start of charging to the charging target based on the consistency characteristic value of each battery pack.

[0148] The full life cycle charging time calculation module 104 is used to perform statistical calculations on the calculation results to determine the full life cycle charging time of the battery.

[0149] Optionally, the battery pack charging time calculation module 103 includes:

[0150] The charging current determination unit is used to determine the charging current of each battery pack during the charging process based on each consistency characteristic value.

[0151] The charging rate determination unit is used to obtain the charging rate based on the charging current.

[0152] The charging time determination unit is used to calculate the cumulative value of the charging rate between the SOC value of each battery pack at the start of charging and the target SOC value, and obtain the charging time of each battery pack.

[0153] Optionally, the feature value acquisition module 102 includes:

[0154] The battery charging status determination unit is used to determine whether the battery charging status is BOL or MOL.

[0155] The first feature value acquisition unit is used to determine the consistency feature value using the heating parameters when the battery is in the BOL charging state.

[0156] The second feature value acquisition unit is used to determine the consistency feature value determined by the heating parameters as the first feature value when the battery charging state is MOL; to correct each first feature value according to the aging parameters of each battery pack to obtain the second feature value; and to use the second feature value as the consistency feature value.

[0157] Optionally, the second feature value acquisition unit is further configured to construct a correction coefficient based on the battery health status; wherein, the correction coefficient is based on a battery health status of 100%; if the battery health status is 100%, the correction coefficient is 1; if the battery health status is less than 100%, the correction coefficient is increased; the product of the correction coefficient and the first feature value is calculated to obtain the second feature value.

[0158] Optionally, the heat generation parameters include at least one of the following: battery DC resistance, AC resistance, ohmic resistance, polarization resistance, and impedance; the aging parameters include at least the battery health status.

[0159] Optionally, the full life cycle charging time calculation module 104 is also used to: calculate the average, mode or median of each calculation result as the full life cycle charging time of the battery.

[0160] Optionally, the battery life cycle charging time estimation device further includes: a battery pack grouping module, used to group the cells contained in the battery system according to structural parameters to obtain at least two battery packs; wherein the structural parameters include at least the number of battery cells, the number of battery modules, and the number of battery regions.

[0161] Optionally, the specific grouping method includes at least one of the following: one cell is divided into one battery pack, at least two adjacent cells are divided into one battery pack, one battery module is divided into one battery pack, at least two adjacent battery modules are divided into one battery pack, one battery area is divided into one battery pack, and at least two battery areas are divided into one battery pack.

[0162] Optionally, the feature value acquisition module 102 includes:

[0163] The eigenvalue filtering unit is used to filter out the maximum and minimum eigenvalues ​​from the consistent eigenvalues, and uses the maximum and minimum eigenvalues ​​to calculate the charging time in subsequent steps.

[0164] Optionally, the battery life cycle charging time estimation device also includes a heating device screening module, which is used to screen out the maximum heating parameters and the minimum heating parameters from the heating parameters, and use the maximum heating parameters and the minimum heating parameters to calculate the charging time in subsequent steps.

[0165] Optionally, the battery life cycle charging time estimation device also includes:

[0166] The preheating time acquisition module is used to obtain the current temperature of the battery. If the current temperature of the battery is lower than the temperature threshold, the battery is preheated and the preheating time is calculated.

[0167] The full life cycle charging time calculation module 104 is also used to perform statistical calculations on the calculation results, summing the preheating time and the statistical results obtained from the statistical calculations to obtain the full life cycle charging time of the battery.

[0168] Figure 8 This is a schematic diagram of a battery lifecycle charging time estimation device provided in an embodiment of the present invention. The present invention also provides a battery lifecycle charging time estimation device. Figure 8 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0169] like Figure 8 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0170] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0171] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for estimating the charging time over the entire battery life cycle.

[0172] In some embodiments, the battery lifespan charging time estimation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the battery lifespan charging time estimation method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the battery lifespan charging time estimation method by any other suitable means (e.g., by means of firmware).

[0173] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0174] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0175] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0176] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0177] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0178] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0179] This invention also provides a vehicle that includes the battery life cycle charging time estimation device provided in any embodiment of this invention.

[0180] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and no limitation is imposed herein.

[0181] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for estimating the charging time of a battery throughout its entire life cycle, characterized in that, include: Electrothermal simulation of the battery was performed to obtain the heating parameters of at least two battery packs; wherein the battery packs include at least one cell; The consistency characteristic value of the battery pack is determined based on the heating parameters; Based on the consistency characteristic value of each battery pack, the charging time of each battery pack from the start of charging to the charging target is calculated; Statistical calculations are performed on the results to determine the total charging time for the battery's entire lifespan.

2. The method for estimating the charging time of a battery throughout its entire life cycle according to claim 1, characterized in that, The step of calculating the charging time from the start of charging to the charging target for each battery pack based on the consistency characteristic value of each battery pack includes: Based on the consistency characteristic values ​​described above, the charging current of each battery pack during the charging process is determined respectively; The charging rate is obtained based on the charging current. The cumulative value of the charging rate for each battery pack is calculated from the SOC value at the start of charging to the target SOC value, to obtain the charging time for each battery pack.

3. The method for estimating the charging time of a battery throughout its entire life cycle according to claim 1, characterized in that, Battery charging status includes BOL status and MOL status; If the battery charging state is BOL, then the consistency characteristic value determined by the heating parameter is used to calculate the charging time in subsequent steps. If the battery charging state is MOL, then the consistency characteristic value determined by the heating parameter is the first characteristic value; Before calculating the charging time from the start of charging to the charging target for each battery pack based on the consistency characteristic value of each battery pack, the method further includes: The first characteristic value is corrected according to the aging parameters of each battery pack to obtain the second characteristic value; The second feature value is used to calculate the charging time in subsequent steps.

4. The method for estimating the charging time of a battery throughout its entire life cycle according to claim 3, characterized in that, The heating parameters include at least one of the following: battery DC resistance, AC resistance, ohmic resistance, polarization resistance, and impedance; the aging parameters include at least the battery health status.

5. The method for estimating the charging time of a battery throughout its entire life cycle according to claim 3, characterized in that, The step of correcting each of the first characteristic values ​​based on the aging parameters of each of the battery packs includes: A correction coefficient is constructed based on the battery health status; wherein, the correction coefficient is based on a battery health status of 100%; if the battery health status is 100%, the correction coefficient is 1; if the battery health status is less than 100%, the correction coefficient is increased. The second eigenvalue is obtained by multiplying the correction coefficient and the first eigenvalue.

6. The method for estimating the charging time of a battery throughout its entire life cycle according to claim 1, characterized in that, The statistical calculation of each calculation result to determine the charging time for the entire battery life cycle includes: The average, mode, or median of each calculation result is used as the charging time for the entire battery life cycle.

7. The method for estimating the charging time of a battery throughout its entire life cycle according to claim 1, characterized in that, Before performing electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs, the following steps are also included: Based on the structural parameters, the battery cells contained in the battery system are grouped to obtain at least two battery packs; wherein, the structural parameters include at least the number of battery cells, the number of battery modules, and the number of battery regions.

8. The method for estimating the charging time of a battery throughout its entire life cycle according to claim 7, characterized in that, The battery cells in the battery system are grouped according to structural parameters to obtain at least two battery packs, including at least one of the following: One of the battery cells is divided into a battery pack, at least two adjacent battery cells are divided into a battery pack, one battery module is divided into a battery pack, at least two adjacent battery modules are divided into a battery pack, one battery area is divided into a battery pack, and at least two battery areas are divided into a battery pack.

9. The method for estimating the charging time of a battery throughout its entire life cycle according to claim 1, characterized in that, After determining the consistency characteristic value of the battery pack based on the heating parameters, the method further includes: The maximum and minimum feature values ​​are selected from the consistency feature values, and the maximum and minimum feature values ​​are used to calculate the charging time in subsequent steps. Alternatively, before determining the consistency characteristic value of the battery pack based on the heating parameters, the method further includes: The maximum and minimum heat generation parameters are selected from the heat generation parameters, and the maximum and minimum heat generation parameters are used to calculate the charging time in subsequent steps.

10. The method for estimating the charging time of a battery throughout its entire life cycle according to claim 1, characterized in that, Before or after the electrothermal simulation of the battery, the following is also included: Obtain the current battery temperature. If the current battery temperature is lower than the temperature threshold, preheat the battery and calculate the preheating time. Furthermore, after performing statistical calculations on each calculation result, the method further includes: The preheating time and the statistical results obtained from the statistical calculation are summed to obtain the total charging time of the battery's entire life cycle.

11. A device for estimating the charging time of a battery throughout its entire life cycle, characterized in that, include: An electrothermal simulation module is used to perform electrothermal simulation on the battery to obtain the heating parameters of at least two battery packs; wherein the battery pack includes at least one cell; The feature value acquisition module is used to determine the consistency feature value of the battery pack based on the heating parameters. The battery pack charging time calculation module is used to calculate the charging time of each battery pack from the start of charging to the charging target based on the consistency characteristic value of each battery pack. The full life cycle charging time calculation module is used to perform statistical calculations on the calculation results to determine the full life cycle charging time of the battery.

12. A device for estimating the charging time of a battery throughout its entire life cycle, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the battery full life cycle charging time estimation method according to any one of claims 1-10.

13. A vehicle, characterized in that, Includes the battery life cycle charging time estimation device as described in claim 12.

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