A battery system life simulation working condition formulation method

Abstract

The application discloses a battery system life simulation working condition making method. The method can comprise the following steps: determining a process of a battery system life simulation working condition; determining corresponding calculation parameters for each process of the battery system life simulation working condition respectively; and calculating the battery life attenuation of the battery system life simulation working condition. The application proposes a battery life working condition making method which is more suitable for actual use of users, combines the cycle and the standing state, and finely determines the discharging energy consumption, the charging preference and the different SOC static state strategy through big data respectively, and meanwhile, the life simulation working condition and the attenuation calculation method are given, so that the battery life design can be more accurately carried out.

Description

Technical Field

[0001] This invention relates to the automotive field, and more specifically, to a method for determining the simulation conditions for battery system lifespan. Background Technology

[0002] In the development of new energy vehicle projects, battery system life is one of the more important indicators. However, due to the long life test cycle, it is impossible to obtain accurate life test results in the early stages of development. Therefore, life simulation is introduced.

[0003] The most important aspect of life simulation is the design of operating conditions. How to closely resemble user scenarios and how to transform user vehicle scenarios into battery evaluation dimensions are the key points in extracting operating conditions for life simulation.

[0004] The extraction of operating conditions in life simulation relies on the collection of user driving data. User driving data is stored in two types: one is stored in real time on the vehicle. The advantage of real-time storage is high acquisition accuracy, which can reach the 0.1s level. The disadvantage is that the storage space on the vehicle is limited, and the storage SD card needs to be removed to read the data. It is difficult to collect the driving information of all users. The other type is uploaded to the cloud in real time. The advantage of cloud database is that it is more complete and can be downloaded and processed. The disadvantage is that the upload frequency is not as high as that of on-vehicle storage.

[0005] Currently, a method for determining the simulation conditions for battery system lifespan still needs to be developed.

[0006] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] This invention proposes a method for determining battery system life simulation conditions, which combines cycling and idle states, and uses big data to finely determine discharge energy consumption, charging preferences, and different SOC idle strategies. It also provides life simulation conditions and degradation calculation methods, which can more accurately carry out battery life design.

[0008] This disclosure provides a method for determining battery system lifespan simulation conditions, including: The process of determining the simulated operating conditions for battery system lifespan; The corresponding calculation parameters are determined for each process of the battery system life simulation conditions; Calculate battery life degradation under simulated battery system life conditions.

[0009] Preferably, the battery system life simulation conditions include the discharge process, the charging process, and the resting process.

[0010] Preferably, the calculation parameters for the discharge process include average daily mileage, driving energy consumption, and average energy consumption coefficient; Among them, the average daily mileage = required mileage / required years, and the average energy consumption coefficient is the sum of the energy consumption coefficient of each model and the market share of that model.

[0011] Preferably, the calculation parameters for the charging process include the depth of charge and the number of charging cycles; Wherein, depth of charge = end of charging SOC - start of charging SOC, and the number of charging cycles is counted according to different periods required by the simulation.

[0012] Preferably, the calculation parameters for the settling process include the settling SOC and its corresponding settling time; Among them, the SOC of the last frame of data before the static state is taken as the SOC of the current static state; Select two frames of data before and after the static state, and calculate the time difference as the duration of the static state.

[0013] Preferably, the battery life degradation calculation for the battery system life simulation conditions includes: Calculate the number of discharge cycles, the number of charge cycles, and the resting time for each SOC; The number of battery cycles is calculated based on the number of discharge cycles and the number of charge cycles, and is used as the basis for battery cycle life degradation. The battery storage time is calculated based on the resting time of each SOC, and is used as the battery storage life decay. The battery life degradation is obtained by adding the battery cycle life degradation to the battery storage life degradation.

[0014] Preferably, the number of discharge cycles is: Number of discharge cycles = Average daily mileage / Mileage per discharge cycle; Wherein, single discharge range = available battery system capacity / (design energy consumption) Average energy consumption coefficient).

[0015] Preferably, the number of charging cycles is: Number of charging cycles = Number of fast charging cycles + Number of slow charging cycles; Wherein, fast charging cycle count = number of fast charging cycles Fast charging depth, slow charging cycle count = number of slow charging cycles Slow charging depth.

[0016] Preferably, the number of discharge cycles is added to the number of charging cycles to obtain the number of battery cycles.

[0017] Preferably, the resting time of each SOC is added together to obtain the battery storage time.

[0018] Its beneficial effects are as follows:

[0019] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0020] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0021] Figure 1 A flowchart illustrating the steps of a method for determining battery system life simulation conditions according to an embodiment of the present invention is shown. Detailed Implementation

[0022] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0023] To facilitate understanding of the solutions and effects of the embodiments of the present invention, a specific application example is given below. Those skilled in the art should understand that this example is merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0024] Example 1

[0025] Figure 1 A flowchart illustrating the steps of a method for determining battery system life simulation conditions according to an embodiment of the present invention is shown.

[0026] like Figure 1 As shown, the method for determining the lifespan simulation conditions of this battery system includes: Step 101: Determine the battery system life simulation conditions. Step 102: Determine the corresponding calculation parameters for each process of the battery system life simulation conditions; Step 103: Calculate the battery life degradation under the battery system life simulation conditions.

[0027] In one example, the battery system life simulation conditions include the discharge process, the charging process, and the resting process.

[0028] In one example, the calculation parameters for the discharge process include average daily mileage, driving energy consumption, and average energy consumption coefficient; Among them, the average daily mileage = required mileage / required years, and the average energy consumption coefficient is the sum of the energy consumption coefficient of each model and the market share of that model.

[0029] In one example, the calculation parameters for the charging process include the depth of charge and the number of charging cycles; Wherein, depth of charge = end of charging SOC - start of charging SOC, and the number of charging cycles is counted according to different periods required by the simulation.

[0030] In one example, the calculation parameters for the settling process include the settling SOC and its corresponding settling duration; Among them, the SOC of the last frame of data before the static state is taken as the SOC of the current static state; Select two frames of data before and after the static state, and calculate the time difference as the duration of the static state.

[0031] In one example, calculating battery life degradation under simulated battery system life conditions includes: Calculate the number of discharge cycles, the number of charge cycles, and the resting time for each SOC; The number of battery cycles is calculated based on the number of discharge cycles and the number of charge cycles, and is used as the basis for battery cycle life degradation. The battery storage time is calculated based on the resting time of each SOC, and is used as the battery storage life decay. The battery life degradation is obtained by adding the battery cycle life degradation to the battery storage life degradation.

[0032] In one example, the number of discharge cycles is: Number of discharge cycles = Average daily mileage / Mileage per discharge cycle; Wherein, single discharge range = available battery system capacity / (design energy consumption) Average energy consumption coefficient).

[0033] In one example, the number of charging cycles is: Number of charging cycles = Number of fast charging cycles + Number of slow charging cycles; Wherein, fast charging cycle count = number of fast charging cycles Fast charging depth, slow charging cycle count = number of slow charging cycles Slow charging depth.

[0034] In one example, the number of discharge cycles is added to the number of charge cycles to obtain the battery cycle count.

[0035] In one example, the resting time of each SOC is summed to obtain the battery storage time.

[0036] Specifically, the extraction of operating conditions in lifespan simulation relies on the collection of user driving data. User driving data is stored in two types of locations: one is stored in real-time on the vehicle. The advantage of real-time storage is high acquisition accuracy, reaching the 0.1-second level. The disadvantage is limited on-vehicle storage space and the need to remove the SD card to read the data, making it difficult to collect driving information from all users. The other type is uploaded to the cloud in real-time. The advantage of cloud databases is their comprehensiveness, allowing for download and processing. The disadvantage is that the upload frequency is not as high as on-vehicle storage. However, since the lifespan simulation process aims to cover as many user scenarios as possible, a lower-precision data frequency is sufficient to reflect user operating conditions. Therefore, cloud data is selected for analysis and extraction.

[0037] The life simulation conditions consist of three parts: the discharge process, the charging process, which correspond to the battery cycle life, and the resting process, which corresponds to the battery storage life.

[0038] 1. Discharge process

[0039] Driving is the primary usage scenario for new energy vehicle users in their daily lives, corresponding to the battery discharge process, which involves driving range and driving energy consumption.

[0040] ① Mileage

[0041] Car manufacturers typically design warranty periods and mileage limits when selling vehicles, such as 8 years or 160,000 kilometers, whichever comes first. However, during the development process, stringent conditions are usually considered, such as both 8 years and 160,000 kilometers being reached simultaneously. Therefore, the average daily mileage = required mileage / required years = 160,000 kilometers / 8 years / 365 days = 54.8 km / day.

[0042] ②Driving energy consumption

[0043] Energy consumption = discharge energy / driving mileage. It reflects the intensity of the trip and is an important reference indicator for matching the battery and vehicle operating conditions. Energy consumption per 100 kilometers is usually used to reflect vehicle performance. The selection of this data is related to the energy consumption calculation method of different car manufacturers. We select vehicle energy consumption data, such as energy consumption = cumulative discharge energy / driving mileage, and select the energy consumption data of the last frame in each driving segment as the average energy consumption of this segment.

[0044] Since the selected data is all stored in the cloud, there may be data loss or frame skipping. If the decalculation energy consumption is 0 at the last moment of a certain driving process, we should look back to find the last non-zero value.

[0045] Energy consumption under operating conditions is greatly affected by the different designs of different vehicle models, such as wind resistance, wheel resistance, and weight. Therefore, we will calculate the energy consumption under operating conditions according to the same vehicle model and calculate the energy consumption of different proportions according to the distribution of energy consumption in different dimensions, such as 50%. This value covers the energy consumption under operating conditions for different proportions of users. Since vehicle design energy consumption is usually based on the announced operating conditions (CLTC or WLTC), the actual operating conditions cover a wide range of scenarios. The energy consumption coefficient = (operating condition energy consumption / design energy consumption) for the same vehicle model. This coefficient may be between 1.3 and 1.8. By weighted averaging the energy consumption coefficients of different vehicle models, we have: Average Energy Consumption Coefficient = Energy Consumption Coefficient of Vehicle Model A Market share of model A + Energy consumption coefficient of model B Market share of vehicle type B + ... + energy consumption coefficient of vehicle type n The market share of n models is given by (market share of model A + market share of model B + ... + market share of model n) = 1.

[0046] 2. Charging process

[0047] Plug-in charging, similar to refueling, is a way to replenish the power of new energy vehicles. It corresponds to the battery charging process, which involves driving range, state of charge (SOC) at the end of charging, depth of charge, number of charging cycles, and charging ratio.

[0048] ① Charging terminates SOC

[0049] The SOC of the last frame of the charging process is statistically analyzed and distributed according to different proportions to determine the charging termination condition in the life simulation. Usually, the SOC at the end of charging is 100%, which means that the user will charge directly to full.

[0050] ② Depth of charge

[0051] The depth of charging has a significant impact on battery life. For the same number of battery cycles, the deeper the charge, the worse the battery life. The depth of charging in a single cycle = SOC at the end of charging - SOC at the beginning of charging. By statistically analyzing the charging depth of all users, recommended charging depths are derived to cover different user proportions.

[0052] ③ Number of charging cycles and percentage

[0053] The number of charging cycles is counted according to different periods required by the simulation, such as daily, weekly, or monthly, to determine whether the charging status is fast charging or slow charging. The number of fast charging cycles and slow charging cycles within the period is counted in units. The charging ratio = number of fast charging cycles : number of slow charging cycles. This ratio needs to reflect the specific number of cycles. For example, a ratio of 1 may correspond to multiple situations, such as 1 fast: 1 slow, 2 fast: 2 slow.

[0054] 3. Settling process

[0055] When the vehicle is stationary, the corresponding battery storage state varies. Different storage states of charge (SOC) result in different amounts of battery degradation. Therefore, two factors are involved in this process: stationary SOC and stationary duration.

[0056] ① Let it stand for SOC

[0057] During a normal resting process, the State of Charge (SOC) hardly changes. Since the vehicle is powered off during this resting period, the cloud stops uploading data. Therefore, the SOC of the last frame of data before the resting period can represent the SOC of this resting state.

[0058] ②Settling time

[0059] When the vehicle is stationary, the high voltage is cut off, and no more data is transmitted to the cloud. The time difference between two frames containing data before and after the vehicle is stationary represents the duration of this stationary state.

[0060] It is recommended to group the calculated SOC and duration according to different ranges, such as 0-50% SOC, 50-80% SOC, and 80-100% SOC. Calculate the storage time in different ranges. Low SOC results in slow degradation, while high SOC results in fast degradation, which can better simulate battery usage.

[0061] Based on the analysis of the above information, the battery life degradation under this operating condition can be calculated: 1. Single discharge range = Available battery capacity / (Design energy consumption) Average energy consumption coefficient). Number of discharge cycles = Average daily mileage / Mileage per discharge cycle; 2. Fast charging cycle count = Number of fast charging cycles Fast charging depth, Slow charging cycle count = number of slow charging cycles Slow charging depth; 3. Battery cycle count = discharge cycle count + charge cycle count, which corresponds to the degradation of battery cycle life; 4. Battery storage time = SOC1 resting time + SOC2 resting time + ... + SOCn resting time, which corresponds to the degradation of battery storage life; 5. Lifetime decay = Cyclic decay + Storage decay.

[0062] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0063] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for determining the simulation conditions for battery system lifespan, characterized in that, include: The process of determining the simulated operating conditions for battery system lifespan; The corresponding calculation parameters are determined for each process of the battery system life simulation conditions; Calculate battery life degradation under simulated battery system life conditions.

2. The method for determining battery system lifespan simulation conditions according to claim 1, wherein, The battery system life simulation conditions include the discharge process, the charging process, and the resting process.

3. The method for determining battery system lifespan simulation conditions according to claim 2, wherein, The calculation parameters for the discharge process include average daily mileage, driving energy consumption, and average energy consumption coefficient. Among them, the average daily mileage = required mileage / required years, and the average energy consumption coefficient is the sum of the energy consumption coefficient of each model and the market share of that model.

4. The method for determining battery system lifespan simulation conditions according to claim 3, wherein, The calculation parameters for the charging process include the depth of charge and the number of charging cycles; Wherein, depth of charge = end of charging SOC - start of charging SOC, and the number of charging cycles is counted according to different periods required by the simulation.

5. The method for determining battery system lifespan simulation conditions according to claim 4, wherein, The calculation parameters for the settling process include the settling SOC and its corresponding settling time; Among them, the SOC of the last frame of data before the static state is taken as the SOC of the current static state; Select two frames of data before and after the static state, and calculate the time difference as the duration of the static state.

6. The method for determining battery system lifespan simulation conditions according to claim 5, wherein, The battery life degradation in the simulation conditions for calculating the battery system life includes: Calculate the number of discharge cycles, the number of charge cycles, and the resting time for each SOC; The number of battery cycles is calculated based on the number of discharge cycles and the number of charge cycles, and is used as the basis for battery cycle life degradation. The battery storage time is calculated based on the resting time of each SOC, and is used as the battery storage life decay. The battery life degradation is obtained by adding the battery cycle life degradation to the battery storage life degradation.

7. The method for determining battery system lifespan simulation conditions according to claim 6, wherein, The number of discharge cycles is: Number of discharge cycles = Average daily mileage / Mileage per discharge cycle; Wherein, single discharge range = available battery system capacity / (design energy consumption) Average energy consumption coefficient).

8. The method for determining battery system lifespan simulation conditions according to claim 6, wherein, The number of charging cycles is: Number of charging cycles = Number of fast charging cycles + Number of slow charging cycles; Wherein, fast charging cycle count = number of fast charging cycles Fast charging depth, slow charging cycle count = number of slow charging cycles Slow charging depth.

9. The method for determining battery system lifespan simulation conditions according to claim 6, wherein, Add the number of discharge cycles to the number of charge cycles to get the battery cycle count.

10. The method for determining battery system lifespan simulation conditions according to claim 6, wherein, The battery storage time is obtained by summing the resting time of each SOC.

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