WO2023202491A1 - 一种电源***健康状态计算方法及装置 - Google Patents
一种电源***健康状态计算方法及装置 Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000004364 calculation method Methods 0.000 claims abstract description 49
- 230000003068 static effect Effects 0.000 claims abstract description 44
- 238000007599 discharging Methods 0.000 claims abstract description 16
- 230000003862 health status Effects 0.000 claims description 58
- 230000036541 health Effects 0.000 claims description 21
- 230000015654 memory Effects 0.000 claims description 17
- 239000000284 extract Substances 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 9
- 239000000178 monomer Substances 0.000 claims description 8
- 230000032683 aging Effects 0.000 claims description 7
- 230000001568 sexual effect Effects 0.000 claims 1
- 238000011156 evaluation Methods 0.000 abstract 1
- 230000009286 beneficial effect Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 4
- 238000013479 data entry Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000013480 data collection Methods 0.000 description 2
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
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- 229910021389 graphene Inorganic materials 0.000 description 1
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- 230000009191 jumping Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
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- 238000012360 testing method Methods 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Definitions
- the invention belongs to the technical field of power supply health status assessment, and specifically relates to a method and device for calculating the health status of a power supply system.
- LFP lithium iron phosphate
- SOC State of Charge, which is the available state of the remaining charge of the battery system
- SOH calculation method based on the battery model cannot accurately calculate vehicle SOH due to the lack of dynamic consideration of real operating conditions and time aging.
- the object of the present invention is to provide a power system health state calculation method to solve the problem of inaccurate health state calculation in the prior art; at the same time, the present invention also provides a power supply for implementing the above power supply system health state calculation method.
- System health status calculation device
- a power system health status calculation method of the present invention includes the following steps:
- a piece of charging and discharging data includes charging/discharging time, displayed state of charge, charging/discharging current, highest cell voltage and lowest cell voltage, and extract the data that meets the requirements Required charging scene data, the charging scene data includes multiple charging groups, and the data of one charging group is the data of a full charging process;
- Discharge data based on the highest cell voltage Vstatic high /lowest cell voltage Vstatic low of the last piece of charge and discharge data in this segment, and the SOC-OCV relationship (OCV: Open circuit voltage, open circuit voltage), determine the relationship with the last piece of data.
- the charge is calculated according to the following formula
- SOC true state of charge
- SOC true state of charge
- the SOC static display represents the displayed state of charge of the last piece of data in the charging and discharging data.
- the SOC 1 static display represents the displayed state of charge of the first piece of data in the charging group;
- the present invention extracts charging groups from a large amount of charge and discharge data, filters out static voltage data from the charging groups and multiple pieces of data before the charging groups, and uses the static voltage data and SOC- Through the OCV relationship, the true state of charge corresponding to the static voltage data can be determined. After obtaining the true state of charge, it is corrected and compensated to obtain the true state of charge of the highest unit at the beginning of charging. SOC high starting and charging The true state of charge SOC of the lowest cell starts at a low level ; combined with the charging capacity C N of this charge, the health state SOH of the highest cell can be calculated; finally, taking into account the consistency between batteries, the power supply is calculated System health status SOH system .
- This method takes into account many factors as a whole: First, this method does not directly use the displayed charge at the beginning of charging.
- the state is used as the state of charge at the beginning of charging for subsequent calculations, but considering the possible charging and discharging behavior between the static voltage value moment and the start of charging (a full charge process), calculated using the compensated static voltage data
- the state of charge is used as the state of charge at the beginning of charging, ensuring the accuracy of subsequent health state calculations;
- the health state of the highest cell is calculated. This is because the highest cell is generally All are fully charged, which makes it relatively easy to obtain data and facilitate subsequent calculations; finally, taking into account the consistency between multiple cells, a more accurate health status of the power system can be calculated.
- step 2) if the charging current of this charge is less than the set charging current threshold A1 , based on the highest cell voltage Vz high /the lowest cell voltage Vz low of the last piece of data before the charging group, As well as the SOC-OCV relationship, the state of charge corresponding to the highest cell voltage V z high / the lowest cell voltage V z low of the last piece of data before the charging group is used as the true charge of the highest cell at the start of charging for this charge.
- State SOC high start /charging start The true state of charge of the lowest cell SOC low start .
- SOC represents the state of charge corresponding to the static voltage OCV
- A1, A2, B1, and B2 all represent aging parameters
- t represents the use time
- the beneficial effects of the above technical solution are: taking into account the aging effect of the battery, the SOC-OCV relationship is corrected, and the corrected SOC-OCV relationship is used to calculate the health state SOH system of the power system, ensuring the accuracy of the health state calculation.
- usage time t is calculated using the following formula:
- k1 and k2 both represent usage time parameters; SOH represents the health status of the power system, which is the health status of the power system obtained in step 4).
- the beneficial effect of the above technical solution is to use the health status of the power system to characterize the usage time of the power system, rather than the current time minus the factory time or sales time as the usage time. This is because time inaccuracies and battery replacements are taken into account.
- the usage time calculated by the method is meaningless, so the method of the present invention ensures the accuracy of the usage time calculation.
- the usage time parameters k1 and k2 are determined in the following way: multiple sets of different usage time and different health status data of the power system are obtained, and the health status data with the most occurrences corresponding to a certain usage time is used as the health status data corresponding to the usage time. status; using different usage times and corresponding health status data, the formula Perform fitting and solve to obtain the usage time parameters k1 and k2.
- the beneficial effect of the above technical solution is: using big data to determine the relationship between the health status and usage time of the power system, making the usage time parameters obtained by fitting more accurate and more accurately reflecting the health status and usage time of the power system. The relationship between.
- the requirements include at least one of the following requirements:
- the ratio of the number of current jumps in this charge to the total number of current jumps in all charging groups is less than the set jump ratio threshold; the jump refers to the difference between the charging current of a piece of data and the charging current of the previous piece of data.
- the absolute value of is greater than the set current deviation threshold;
- Requirement 2 The positions of the highest cell and the second-highest cell are not adjacent or the voltage of the lowest cell is greater than the set low voltage threshold;
- Requirement 3 The total charging time of a charging group is greater than the set time threshold
- Requirement 4 The difference between the displayed state of charge of the last piece of data in a charging group and the displayed state of charge of the first piece of data is greater than the set state of charge difference threshold;
- Requirement 5 The displayed state of charge of the last piece of data in a charging group is greater than the set state of charge end threshold.
- condition 2 can eliminate the jump situation at the charging end.
- the method for determining the charging capacity C N of this charge in step 2) is: 1According to two adjacent pieces of data, Calculate the charging capacity C 1The method used is: if the interval time ⁇ t corresponding to two adjacent pieces of data is less than the set adjacent time low threshold, then the charging capacity between the adjacent moments corresponding to the two adjacent pieces of data is the charging current.
- the charging capacity between adjacent moments corresponding to the two adjacent pieces of data is the charging current multiplied by 10 seconds, and the set adjacent time low threshold is smaller than the set adjacent time high threshold; otherwise, the adjacent two pieces of data will The charging capacity between corresponding adjacent moments is the average value of the charging current corresponding to the two pieces of data multiplied by the interval time ⁇ t.
- the beneficial effect of the above technical solution is that the charging capacity C x between adjacent moments is calculated taking into account a variety of different actual situations, ensuring the accuracy of the calculation.
- a power system health status calculation device of the present invention includes a memory and a processor.
- the processor is used to execute instructions stored in the memory to implement the power system health status calculation method as introduced above, and achieve the same results as this method. beneficial effects.
- Figure 1 is a flow chart of the power system health status calculation method of the present invention
- Figure 2 is a schematic diagram of the health status error distribution of the present invention
- FIG. 3 is a data example diagram of the present invention.
- Figure 4 is a SOC-OCV curve fitted by new and old batteries of the present invention.
- Figure 5 is a schematic diagram of the deviation distribution and current deviation distribution of the SOC-OCV curve of the present invention after it becomes variable over the years;
- FIG. 6 is a schematic structural diagram of the power system health status calculation device of the present invention.
- a power supply system health status calculation method and a power supply system health status calculation device of the present invention will be described in detail below with reference to the drawings and embodiments.
- An embodiment of a power system health status calculation method of the present invention uses Apache
- the Hadoop data platform stores all vehicle data
- Step 1 Obtain the charge and discharge data of the LFP power system from the Apache Hadoop data platform and extract the charging scene data from it.
- T means today, T-1 means yesterday, T-3 means the day before yesterday.
- Each piece of data includes vehicle ID, charge/discharge time, maximum voltage of the unit, The lowest cell voltage, charge/discharge current, display state of charge (SOC), temperature, temperature and voltage position, rated capacity, and battery management system status information, etc.
- the battery management system status information is used to refer to the charging and discharging status. For example, if the information is 4, it indicates that the power system is in a discharge state, and if the information is 6, it indicates that the power system is in a charging state.
- Each piece of data adds the time of the previous data, the state of charge of the previous data, and the current of the previous data.
- Charging start tag When the interval state of charge ⁇ SOC between two pieces of data is greater than J (0 ⁇ 5%) and the interval time is less than K (0.5h ⁇ 1.5h), or the interval state of charge ⁇ SOC between two pieces of data is less than When L (0 ⁇ 5%), it is defined as charging has not started, otherwise it is defined as charging has started.
- Bounce refers to a large difference between the charging current of a piece of data and the charging current of the previous piece of data.
- N represents the set jump proportion threshold, and its value can be set to 10% ⁇ 80%
- charging start time T-2 days The data.
- the T-2 day data is extracted from the data from T-1 day to T-3 day because a single charge often spans days. For example, a complete charge starts from 10 pm on a certain day and ends at 3 am the next day. Considering this situation, in order to obtain all the complete charging data and identify all charging scenarios without omission, improve Calculate coverage to extract the complete T-2 day data from all data from T-1 day to T-3 day.
- the data of one charging group is the data of a full charge. Calculate the charging start time, charging end time, total capacity, and each charging group of each vehicle. Total energy, total charging time, maximum SOC interval, maximum time interval, maximum current, average current, and maximum cell voltage.
- E means setting Bounce ratio threshold, its value can be 0 ⁇ 20%)
- R means setting the low voltage threshold, its value can be 2.5V ⁇ 3.65V
- Step 2 After obtaining the charging scene data, calculate the health status of the power system for a charging group.
- step 1) Extract the first R pieces of data (R can be 10 to 100 pieces) before the charging group, and determine whether the charging current of this charge is greater than or equal to the set charging current threshold A 1 . If it is greater than or equal to, proceed to step 3) ⁇ 4), otherwise, perform step 2).
- a 2 means setting the static current threshold
- divide the continuous data into one section and identify the longest section of data (this time needs to be greater than 2 minutes)
- take the voltage of the last piece of data in the longest data of this period as the static voltage and calculate the real SOC based on the SOC-OCV relationship. That is: based on the highest cell voltage V static high of the last piece of data in this section of charge and discharge data, and the SOC-OCV relationship, determine the highest static voltage true state of charge SOC corresponding to the highest cell voltage V static high of the last piece of data.
- Static high based on the lowest cell voltage V static low of the last piece of charge and discharge data in this segment, and the SOC-OCV relationship, determine the true state of charge of the lowest static voltage corresponding to the lowest cell voltage V static low of the last piece of data. SOC is quiet low .
- the SOC static display indicates the displayed state of charge of the last piece of data in the charging and discharging data in step 3).
- the SOC 1 display indicates the displayed state of charge of the first piece of data in the charging group.
- the function of compensation in this step is: there may be charging and discharging behavior between the static voltage value moment and the charging start time, resulting in changes in the state of charge, so it is necessary to Compensation is done based on the state of charge calculated from the static voltage to ensure that the compensated state of charge corresponds to the state before charging.
- the correct calculation method of a total duration is: the last data time minus the time corresponding to the previous data of the first data. For example, if the battery is powered off at 15:00:00, the current is 100, 16 :00:00 The power-on current is 0, and charging starts at 16:00:20, so the previous storage time is 1 hour. 3 Calculate the shelving time of each section and select the section with the longest shelving time. Here it is 16:40:14-16:41:54. Its final highest cell voltage of 3.17875 and the lowest cell voltage of 3.15 are selected according to the static voltage. The SOC-OCV curves are converted to real SOC, corresponding to 15% and 14% respectively (example). 4At this moment, the BMS shows a state of charge of 20.4%.
- the BMS shows a state of charge of 19.2%. Therefore, the true state of charge SOC corresponding to the highest monomer before charging is: 15% + (19.2%-20.4%) , the true state of charge SOC corresponding to the lowest monomer is: 14%+(19.2%-20.4%).
- the SOC at the start of charging is equal to the SOC before charging, so the supplementary capacity is 0.
- C value represents the rated capacity of the highest unit.
- the SOH system calculates the vehicle usage time t based on the health status of the power system:
- k1 and k2 both represent usage time parameters. It can be seen from this formula that the definition of usage time here is not the current time minus the factory time or sales time, because the inaccurate time and battery replacement make the usage time calculated by this method meaningless, and the mileage also exists due to battery replacement and battery replacement. A similar issue with mileage resets, but instead using health status to determine usage time. Specifically, the two usage time parameters are determined in the following way: 1 Obtain multiple sets of different usage time and different health status data of the power system, and use the health status data with the most occurrences corresponding to a certain usage time as the health status data corresponding to the usage time. health status.
- SOC represents the state of charge corresponding to the static voltage OCV
- A1, A2, B1, and B2 all represent aging parameters.
- step 8 After obtaining the new SOC-OCV relationship, re-execute steps 1 to 5 of step 2 to obtain the latest state of charge SOH system of the power system. This is done because there are certain differences between the SOC-OCV relationship curves of new batteries and old batteries, as shown in Figure 4. Therefore, in this method, the SOC-OCV relationship curve that changes with use time is used to deal with the impact of battery aging on calculation accuracy. .
- FIG. 2 The health status error distribution calculated by the method of the present invention is shown in Figure 2.
- the test range is greater than 40,000 times, ⁇ 4% covers 87% of the vehicle, and the weighted accuracy is 2.13% (the weighted accuracy is the absolute value of the error multiplied by the corresponding proportion sum later).
- Figure 5 shows a schematic diagram of the deviation distribution and current deviation distribution of the SOC-OCV curve over the years. This figure is based on the following assumption: in a short period of time (3 months) at normal temperature, the SOH deviation calculated for the same vehicle should not be too large to enable an overall assessment of the results of the method of the present invention. Define the maximum SOH of each vehicle within 3 months minus the minimum SOH to be the SOH deviation of this vehicle.
- the distribution map of SOH deviation is drawn for all vehicles, as shown in Figure 5. After each iteration of the algorithm, the deviation distribution of SOH before and after the iteration is compared to determine the improvement effect of the algorithm. Moreover, the closer the deviation distribution is to the y-axis, the better the overall quality of the entire method is.
- this method has the following characteristics: 1) Extract charging data based on battery status bits, identify a single charging process, calculate key variables of the charging process, and filter the scene. 2) Identify and extract static voltage data before charging, solving the problem of identifying battery static voltage, and converting it into the corresponding real state of charge based on the SOC-OCV curve, and compensating for the loss of state of charge and charging data, ensuring health Accuracy of state calculations. 3) Aging processing can be performed to correct the impact of the aging of the SOC-OCV relationship curve on the calculated value of health status. 4) Overall, high-precision health status calculation of the power system is realized, which can be used for the core pricing model of the power system insurance business and early warning of abnormal vehicle attenuation.
- An embodiment of a power system health status calculation device of the present invention includes a memory, a processor and an internal bus.
- the processor and the memory complete mutual communication and data interaction through the internal bus.
- Store in memory Store at least one software function module, and the processor executes various functional applications and data processing by running software programs and modules in the memory to implement a power system health status calculation method introduced in the method embodiment of the present invention.
- the processor may be a microprocessor MCU, a programmable logic device FPGA and other processing devices.
- Memory can be various memories that use electrical energy to store information, such as RAM, ROM, etc.; it can also be various memories that use magnetic energy to store information, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, magnetic bubble memories, U disks, etc. ; It can also be various types of memories that use optical methods to store information, such as CDs, DVDs, etc.; Of course, it can also be other types of memories, such as quantum memory, graphene memory, etc.
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Abstract
一种电源***健康状态计算方法及装置,属于电源健康状态评估技术领域,该方法从大量的充放电数据中提取出充电分组数据,针对其中的充电分组数据以及充电分组前的多条数据,从中筛选出静态电压数据,利用静态电压数据以及SOC-OCV关系,便可确定静态电压数据对应的真实荷电状态,在得到该真实荷电状态后,又对其进行修正与补偿,从而得到充电开始最高单体的真实荷电状态和充电开始最低单体的真实荷电状态;结合该次充电的充电容量,便可计算得到最高单体的健康状态;最后,考虑到电池之间的一致性,计算得到电源***的健康状态。由此有效提高了电源***健康状态计算的准确性。
Description
本发明属于电源健康状态评估技术领域,具体涉及一种电源***健康状态计算方法及装置。
随着新能源车辆的广泛普及,部分车辆进入到寿命末期,电池的安全问题、二次利用及残值评估等问题也越来越受到整个行业的重视。但电池健康度的精准评估一直是行业内一个难题,因对每台车辆进行完整的深充需要耗费大量成本,所以急需一种基于运营数据的数据技术准确评估电池的健康状态SOH(State of Healthy,为电池实际容量和额定容量的比值)。
LFP(磷酸铁锂)电源***在新能源车辆上应用广泛,但因其材料特性,存在电压平台期,在平台期无法精确估算SOC(State of Charge,为电池***剩余电荷的可用状态),导致SOH计算困难。基于电池模型的SOH计算方法,因为缺少对现实工况和时间老化的动态考虑,导致无法精确计算车辆SOH。
发明内容
本发明的目的在于提供一种电源***健康状态计算方法,用以解决现有技术计算健康状态不准确的问题;同时,本发明还提供了一种用于实现上述电源***健康状态计算方法的电源***健康状态计算装置。
为解决上述技术问题,本发明所提供的技术方案以及技术方案对应的有益效果如下:
本发明的一种电源***健康状态计算方法,包括如下步骤:
1)获取电源***在不同时刻对应的多条充放电数据,一条充放电数据包括充/放电时刻、显示荷电状态、充/放电电流、最高单体电压和最低单体电压,从中提取出满足要求的充电场景数据,所述充电场景数据包括多个充电分组,一个充电分组的数据为一次充满电过程的数据;
2)对于一个充电分组,进行如下计算:
依据各个时刻及其对应的充电电流,确定该次充电的充电容量CN;
若此次充电的充电电流大于等于设定充电电流阈值A1,提取该充电分组前的多条充放电数据,从中筛选出放电电流小于设定静态电流阈值A2且持续时间最长的一段充放电数据,依据该段充放电数据中最后一条数据的最高单体电压V静高/最低单体电压V静低、以及SOC-OCV关系(OCV:Open circuit voltage,开路电压),确定与最后一条数据的最高单体电压V静高/最低单体电压V静低对应的最高静态电压真实荷电状态SOC静高/最低静态电压真实荷电状态SOC静低;依据如下公式计算得到此次充电的充电开始最高单体的真实荷电状态SOC高始和充电开始最低单体的真实荷电状态SOC低始:
SOC高始=SOC静高+(SOC1显-SOC静显),SOC低始=SOC静真+(SOC1显-SOC静显)
式中,SOC静显表示所述一段充放电数据中最后一条数据的显示荷电状态SOC静显,SOC1
显表示该充电分组第一条数据的显示荷电状态;
3)按照如下公式计算最高单体的健康状态SOH单:SOH单=CN/[(100%-SOC高始)*C额],C额表示最高单体的额定容量;
4)根据最高单体的健康状态SOH单得到电源***的健康状态。
进一步地,步骤4)中,按照如下公式计算电源***的健康状态SOH***:SOH***=SOH单*k一致性,k一致性表示一致性系数,k=100%-SOC高始-SOC低始。
上述技术方案的有益效果为:本发明从大量的充放电数据中提取出充电分组,针对其中的充电分组以及充电分组前的多条数据,从中筛选出静态电压数据,利用静态电压数据以及SOC-OCV关系,便可确定静态电压数据对应的真实荷电状态,在得到该真实荷电状态后,又对其进行修正与补偿,从而得到充电开始最高单体的真实荷电状态SOC高始和充电开始最低单体的真实荷电状态SOC低始;结合该次充电的充电容量CN,便可计算得到最高单体的健康状态SOH单;最后,考虑到电池之间的一致性,计算得到电源***的健康状态SOH***。
该方法整体考虑到多方面因素:首先,本方法并不是直接使用充电开始时的显示荷电
状态作为充电开始荷电状态进行后续计算,而是考虑到静态电压取值时刻和充电(一次满充过程)开始时刻之间可能存在的充放电行为,利用补偿后的静态电压数据所计算得到的荷电状态作为充电开始荷电状态,保证了后续健康状态计算的准确性;其次,在计算电池单体的健康状态时,计算的是最高单体的健康状态,这么处理是因为最高单体一般都充满电,相对来说数据获取较容易且方便后续计算;最后,考虑到多个单体之间的一致性,计算得到电源***更为准确的健康状态。
进一步地,步骤2)中,若此次充电的充电电流小于设定充电电流阈值A1时,依据该充电分组前最后一条数据的最高单体电压Vz高/最低单体电压Vz低、以及SOC-OCV关系,将与该充电分组前最后一条数据的最高单体电压Vz高/最低单体电压Vz低对应的荷电状态作为此次充电的充电开始最高单体的真实荷电状态SOC高始/充电开始最低单体的真实荷电状态SOC低始。
进一步地,还包括步骤5)~6);
5)利用如下公式修正SOC-OCV关系:
SOC=A×eB×OCV
A=A1×eA2×t
B=B1×eB2×t
SOC=A×eB×OCV
A=A1×eA2×t
B=B1×eB2×t
式中,SOC表示与静态电压OCV对应的荷电状态,A1、A2、B1、B2均表示老化参数,t表示使用时间;
6)利用修正后的SOC-OCV关系,重新执行步骤2)~4),以得到修正后的电源***的健康状态SOH***。
上述技术方案的有益效果为:考虑到电池老化作用对SOC-OCV关系进行修正,利用修正的SOC-OCV关系来计算得到电源***的健康状态SOH***,保证了健康状态计算的准确性。
进一步地,使用时间t采用如下公式计算得到:
式中,k1和k2均表示使用时间参数;SOH表示电源***的健康状态,为步骤4)得到的电源***的健康状态。
上述技术方案的有益效果为:利用电源***的健康状态来表征电源***的使用时间,而不是当前时间减去出厂时间或销售时间作为使用时间,是考虑到时间不准确和电池更换都让这种方法计算的使用时间没有意义,因而本发明方法保证了使用时间计算的准确性。
进一步地,使用时间参数k1和k2采用如下方式确定:获取电源***多组不同使用时间和不同健康状态数据,将某一使用时间对应出现次数最多的健康状态数据作为与该使用时间相对应的健康状态;利用不同使用时间和与之对应的健康状态数据,对公式
进行拟合求解得到使用时间参数k1和k2。
上述技术方案的有益效果为:利用大数据来确定电源***的健康状态和使用时间之间的关系,使得拟合得到的使用时间参数更为准确,更能准确反映电源***的健康状态和使用时间之间的关系。
进一步地,步骤1)中,所述要求包括以下要求中的至少一个:
要求1,此次充电的电流跳动次数与所有充电分组的电流跳动总次数的比值小于设定跳动比例阈值;跳动是指一条数据的充电电流与该条数据的上一条数据的充电电流的差值的绝对值大于设定电流偏差阈值;
要求2,最高单体与次高单体的位置不相邻或者最低单体电压大于设定低压阈值;
要求3,一个充电分组的充电总时长大于设定时长阈值;
要求4,一个充电分组的最后一条数据的显示荷电状态与第一条数据的显示荷电状态的差值大于设定荷电状态差值阈值;
要求5,一个充电分组的最后一条数据的显示荷电状态大于设定荷电状态结束阈值。
上述技术方案的有益效果为:条件2可以实现对充电末端跳变情况的排除。
进一步地,步骤2)中确定该次充电的充电容量CN的手段为:①根据相邻两条数据,
计算得到相邻两条数据所对应的相邻时刻之间的充电容量Cx;②将所有的相邻时刻之间的充电容量相加,得到该次充电的充电容量CN;具体地,步骤①所采用的手段为:若相邻两条数据所对应的间隔时间△t小于设定相邻时间低阈值,则该相邻两条数据所对应的相邻时刻之间的充电容量为充电电流乘以间隔时间△t;若相邻两条数据所对应的间隔时间大于设定相邻时间高阈值且相邻两条数据所对应的间隔荷电状态△SOC小于设定荷电状态阈值,则该相邻两条数据所对应的相邻时刻之间的充电容量为充电电流乘以10秒,设定相邻时间低阈值小于设定相邻时间高阈值;否则,该相邻两条数据所对应的相邻时刻之间的充电容量为两条数据所对应的充电电流的平均值乘以间隔时间△t。
上述技术方案的有益效果为:考虑到多种不同实际情况来计算相邻时刻之间的充电容量Cx,保证了计算的准确性。
本发明的一种电源***健康状态计算装置,包括存储器和处理器,所述处理器用于执行存储在存储器中的指令以实现如上述介绍的电源***健康状态计算方法,并达到与该方法相同的有益效果。
图1是本发明的电源***健康状态计算方法的流程图;
图2是本发明的健康状态误差分布情况示意图;
图3是本发明的数据实例图;
图4是本发明的新老电池拟合出的SOC-OCV曲线图;
图5是本发明的SOC-OCV曲线随年可变后偏差分布和当前偏差分布示意图;
图6是本发明的电源***健康状态计算装置的结构原理图。
下面结合附图和实施例,对本发明的一种电源***健康状态计算方法和一种电源***健康状态计算装置进行详细说明。
方法实施例:
本发明的一种电源***健康状态计算方法实施例,针对LFP电源***,采用Apache
Hadoop数据平台储存所有车辆数据,并采用Apache Spark计算平台计算数据,使用SQL语言提取并计算数据。下面结合图1,对本发明方法的整个过程进行描述。
步骤一,从Apache Hadoop数据平台获取LFP电源***的充放电数据,并从中提取充电场景数据。
1、提取基础数据。
提取T-1日至T-3日(T表示今天,T-1表示昨天,T-3表示大前天)的多条数据,每一条数据均包括车辆ID、充/放电时刻、单体最高电压、单体最低电压、充/放电电流、显示荷电状态(State of Charge,SOC)、温度、温度电压位置、额定容量、以及电池管理***状态信息等。其中,电池管理***状态信息用于指代充放电状态,例如该信息若为4,表明电源***处于放电状态,该信息若为6,表明电源***处于充电状态。
从中筛选出满足如下条件的数据:①充电状态,②电流>A(-800A<A<800A),③SOC>B(0<B<10%)、温度>C(-50℃<C<50℃),③数据入库时刻与数据采集时刻之间的时间间隔介于D与E之间(0s~3600s)。需说明的是,这里数据入库时刻是指数据上传至Apache Hadoop数据平台的时刻,如果数据入库时刻与数据采集时刻之间的时间间隔过大,则表明数据不够可靠,为了保证本发明方法计算的准确性,不够可靠的数据不纳入考虑范围。
2、添加相邻数据信息。
每条数据添加上一条数据时刻、上一条数据显示荷电状态、上一条数据电流。
3、关键变量计算。
1)计算间隔时间△t:本条数据时刻减去上条数据时刻。
2)计算间隔荷电状态△SOC:本条数据显示荷电状态减去上条数据显示荷电状态。
3)电流是否跳动:如果本条数据的充电电流和上条数据的充电电流之差的绝对值大于F(F表示设定电流偏差阈值,其范围为可设置为0A-10A),则定义为跳动,否则定义为不跳动。
4)容量计算:当两条数据的间隔时间△t小于G(G表示设定相邻时间低阈值,其取值可设置为60s~120s),则两条数据所对应的两个时刻之间的充电容量等于充电电流乘以间隔
时间△t;当两条数据的间隔时间△t大于H(H表示设定相邻时间高阈值,其取值可设置为120s~240s)且相邻两条数据所对应的间隔荷电状态△SOC小于I(I表示设定荷电状态阈值,其取值可设置为1A~5A),则两条数据所对应的两个时刻之间的充电容量等于充电电流乘以10s;其余情况下,两条数据所对应的两个时刻之间的充电容量等于两条充电电流的平均值乘以时间间隔△t。
5)能量计算:当两条数据的间隔时间△t小于G(G含义及范围同上),则两条数据所对应的两个时刻之间的充电能量等于充电电流乘以间隔时间△t再乘以电压;当两条数据的间隔时间△t大于H(H含义及范围同上)且相邻两条数据所对应的间隔荷电状态△SOC小于I(I含义及范围同上),则两条数据所对应的两个时刻之间的充电能量等于充电电流乘以10s再乘以电压;其余情况下,两条数据所对应的两个时刻之间的充电能量等于两条充电电流的平均值乘以时间间隔△t再乘以电压。
6)充电开始标签:当两条数据的间隔荷电状态△SOC大于J(0~5%)且间隔时间小于K(0.5h~1.5h),或两条数据的间隔荷电状态△SOC小于L(0~5%)时定义为充电未开始,反之定义为充电开始。
7)根据充电开始标签,对充电数据进行分组,为组添加标签,从而可确定多个充电分组。
4、过滤变量计算。
1)计算车辆在每个充电分组的最小时间,定义为充电开始时间。
2)计算车辆在每个充电分组中的最大时间,定义为充电结束时间。
3)计算电流跳动总次数。跳动是指一条数据的充电电流与该条数据的上一条数据的充电电流的差值较大。
5、充电场景数据获取。
保留充电开始时间以后、充电结束之前,电流跳动次数/电流跳动总次数<N(N表示设定跳动比例阈值,其取值可设置为10%~80%),充电开始时间=T-2天的数据。
从T-1天到T-3天的数据中提取出T-2天数据是因为,一次充电常常出现跨天的现象。例如,某一次的完整充电是从某一天的晚上10点开始到第二天的凌晨3点结束,考虑到这种情况,为了能够获取所有的完整的充电数据,无遗漏识别所有充电场景,提升计算覆盖率,从而在T-1天至T-3天的所有数据中提取出完整的T-2天数据。
6、充电场景数据汇总。
1)提取T-2天的充电场景数据。
2)提取T-2天的充电场景数据中的多个充电分组,一个充电分组的数据为一次充满电的数据,计算每台车辆每个充电分组的充电开始时间、充电结束时间、总容量、总能量、总充电时长、最大SOC间隔、最大时间间隔、最大电流、平均电流、以及最大单体电压。
3)提取每台车辆每个充电分组的开始/结束信息:开始/结束充电电流,开始/结束荷电状态,开始/结束最高(低)温度,开始/结束最高(低)温度位置,开始/结束最高(低)电压,开始/结束最高(低)电压位置,额定容量(如果固定信息表包含额定容量取固定信息表,如果固定信息表不包含,从BMS处获取得到)。
4)汇总2)、3)信息,保留满足如下要求的充电分组:一个充电分组的最后一条数据的显示荷电状态与第一条数据的显示荷电状态的差值大于O(O表示设定荷电状态差值阈值,其取值可为20%~100%),总充电时间>P(P表示设定时长阈值,其取值可为10min~30min),最后一条数据的显示荷电状态>Q(Q表示设定荷电状态结束阈值,其取值可为95%~100%),此次充电的电流跳动次数与所有充电分组的电流跳动总次数的比值<E(E表示设定跳动比例阈值,其取值可为0~20%),最高单体与次高单体的位置大于1或者最低单体电压>R(R表示设定低压阈值,其取值可在2.5V~3.65V)。需说明的是,正常情况下,最高单体与次高单体之间的位置应该为相邻位置,如果出现了最高单体与次高单体的位置序号差值大于1,则说明出现了充电末端跳变情况,此时应该排除该种情况对应的数据。
步骤二,在获取得到充电场景数据后,针对一个充电分组,计算得到电源***的健康状态。
1、计算此次充电的充电开始最高单体的真实荷电状态SOC高始和充电开始最低单体的真实荷电状态SOC低始。
1)提取该充电分组之前的前R条(R可取10~100条)数据,并判断此次充电的充电电流是否大于等于设定充电电流阈值A1,若大于等于,则执行步骤3)~4),否则,执行步骤2)。
2)依据该充电分组之前的前R条数据中最后一条数据的最高单体电压Vz高和最低单体电压Vz低,依据设置的SOC-OCV关系,将与该充电分组之前的前R条数据中最后一条数据的最高单体电压Vz高对应的荷电状态作为此次充电的充电开始最高单体的真实荷电状态SOC高始,将与该充电分组之前的前R条数据中最后一条数据的最低单体电压Vz低对应的荷电状态作为此次充电的充电开始最低单体的真实荷电状态SOC低始。
3)从前R条数据中,去掉电流大于A2(A2表示设定静态电流阈值)的部分,将连续的数据分为一段,识别其中时间最长的一段数据(该时间需要求大于2min),取该段时间最长的数据中的最后一条数据的电压为静态电压,并根据SOC-OCV关系计算真实的SOC。即:依据该段充放电数据中最后一条数据的最高单体电压V静高、以及SOC-OCV关系,确定与最后一条数据的最高单体电压V静高对应的最高静态电压真实荷电状态SOC静高,依据该段充放电数据中最后一条数据的最低单体电压V静低、以及SOC-OCV关系,确定与最后一条数据的最低单体电压V静低对应的最低静态电压真实荷电状态SOC静低。
4)因步骤3)中最后一条数据的时刻距离该充电分组的充电开始时刻之间会发生充放电,因此需要对荷电状态进行补偿,以得到充电开始最低单体的真实荷电状态SOC低始和充电开始最高单体的真实荷电状态SOC高始。具体方式为:
SOC高始=SOC静高+(SOC1显-SOC静显),SOC低始=SOC静真+(SOC1显-SOC静显)
式中,SOC静显表示步骤3)中一段充放电数据中最后一条数据的显示荷电状态SOC静
显,SOC1显表示该充电分组第一条数据的显示荷电状态。该步骤中进行补偿的作用为:因静态电压取值时刻和充电开始时间中间可能存在充放电行为,导致荷电状态有变化,因此需要对
根据静态电压计算的荷电状态做补偿,以保证补偿后荷电状态对应充电前的状态。
下面举一个具体的实例对步骤3)~步骤4)中的具体计算过程进行说明。例如针对图3的数据,过程如下:①根据电池管理***状态可知16:49:14为充电开始的第一条数据,向前提取30条放电数据(从16:38:14-16:47:54)。②去掉其中电流绝对值大于5A数据(放电为正,充电为负),这里排除所有大电流,以避免极化影响。根据数据的连续性,剩下的数据被分为三部分:分别为16:40:14-16:41:54,16:45:14-16:45:34,16:46:54:16:47:54。这里的问题需要注意,一个总时长的正确计算方式为:最后一条数据时刻减去第一条数据的前一条数据对应的时刻,例如,若电池在15:00:00下电电流为100,16:00:00上电电流为0,16:00:20开始充电,那么前面的搁置时间是是1个小时。③计算每一段的搁置时间,选搁置时间最长的一段,这里是16:40:14-16:41:54,其最后的最高单体电压3.17875和最低单体电压3.15被选中静态电压,根据SOC-OCV曲线转化为真实SOC,分别对应15%和14%(示例)。④此时刻BMS显示荷电状态为20.4%,充电前BMS显示荷电状态为19.2%,所以充电前最高单体对应的真实荷电状态SOC高始为:15%+(19.2%-20.4%),最低单体对应的真实荷电状态SOC低始为:14%+(19.2%-20.4%)。
2、计算该次充电的充电容量CN。
1)依据相邻两条数据所对应的间隔时间△t和充电电流,计算得到相邻两条数据所对应的相邻时刻之间的充电容量Cx。具体计算方式可见步骤一的“3、关键变量计算”的“4)容量计算”部分内容。
2)将所有的相邻时刻之间的充电容量相加,得到该次充电的充电容量CN=∑CX。
3)计算充电开始荷电状态和充电前1条数据荷电状态差值,定义为补偿容量,并补偿到计算的充电容量中。该次补偿的目的是为了防止充电前期数据丢失造成容量计算误差。具体的:
实际过程中会出现充电开始阶段数据丢失的情况,所以需要进行补偿,即虽然数据缺失,但是根据两条数据存在的SOC差值可以把这部分充电容量补上。补充容量=额定容量*
(充电开始SOC-充电前一条SOC)。
当数据正常、无缺失时,充电开始SOC和充电前一条SOC相等,所以补充容量为0。
3、计算最高单体的健康状态SOH单:
SOH单=CN/[(100%-SOC高始)*C额]
SOH单=CN/[(100%-SOC高始)*C额]
式中,C额表示最高单体的额定容量。
4、计算一致性系数:
k=100%-SOC高始-SOC低始
k=100%-SOC高始-SOC低始
5、计算电源***的健康状态SOH***:
SOH***=SOH单*k一致性
SOH***=SOH单*k一致性
6、根据电源***的健康状态SOH***计算车辆使用时间t:
式中,k1和k2均表示使用时间参数。从该公式可以看出,此处使用时间的定义,不是当前时间减去出厂时间或销售时间,因为时间不准确和电池更换都让这种方法计算的使用时间没有意义,里程也存在电池更换和里程重置的类似问题,而是利用健康状态来确定使用时间。具体地,两个使用时间参数采用如下方式来确定:①获取电源***多组不同使用时间和不同健康状态数据,将某一使用时间对应出现次数最多的健康状态数据作为与该使用时间相对应的健康状态。例如根据每1年左右(±0.2)的健康状态分布,确定大部分车辆的实际健康状态应该是多少,如根据统计分布得到(0.988,0.93),即0.988年(1年左右)对应的分布最多的SOH为0.93,根据统计数据得到实际年和SOH的最大概率对应关系。②利用不同使用时间和与之对应的健康状态数据,对公式进行拟合求解得到使用时间参数k1和k2,本实施例中,k1=0.6484,k2=0.07414。
7、进而根据使用时间t,计算新的SOC-OCV关系:
SOC=A×eB×OCV
A=A1×eA2×t
B=B1×eB2×t
SOC=A×eB×OCV
A=A1×eA2×t
B=B1×eB2×t
式中,SOC表示与静态电压OCV对应的荷电状态,A1、A2、B1、B2均表示老化参数。
8、在得到新的SOC-OCV关系后,重新执行步骤二的步骤1~5,以得到最新的电源***的荷电状态SOH***。这么处理是因为新电池和老电池的SOC-OCV关系曲线存在一定差异,如图4所示,所以该方法中使用随使用时间变化的SOC-OCV关系曲线来应对电池老化对计算准确性的影响。
采用本发明方法计算得到的健康状态误差分布情况如图2所示,测试范围大于4万次,±4%覆盖车辆87%,加权精度为2.13%(加权精度为误差绝对值乘以对应占比后求和)。如图5所示为SOC-OCV曲线随年可变后偏差分布和当前偏差分布示意图。该图基于如下假设:在常温短期内(3个月),同一车辆计算的SOH偏差不应该过大,以实现对本发明方法结果的整体情况进行评估。定义每台车辆3个月内的最大SOH减去最小SOH为此台车的SOH偏差。对所有车辆绘制SOH偏差的分布图,如图5所示。每次迭代算法后,对迭代前后的SOH的偏差分布进行对比,以确定算法的改进效果。而且,偏差分布越靠近y轴,整个方法的整体质量越好。
综上,该方法具有如下特点:1)根据电池状态位提取充电数据,识别单次充电过程,计算充电过程关键变量,并对场景进行过滤处理。2)识别并提取充电前静态电压数据,解决了电池静态电压的识别难题,并依据SOC-OCV曲线转化为对应真实荷电状态,并对荷电状态损失和充电数据损失进行补偿,保证了健康状态计算的准确性。3)能够进行老化处理,校正因SOC-OCV关系曲线老化对健康状态计算值的影响。4)总体实现了对电源***的高精度健康状态计算,可用于电源***保险业务的核心定价模型以及异常车辆衰减的提前预警。
装置实施例:
本发明的一种电源***健康状态计算装置实施例,如图6所示,包括存储器、处理器和内部总线,处理器、存储器之间通过内部总线完成相互间的通信和数据交互。存储器中存
储至少一个软件功能模块,处理器通过运行存储器中的软件程序以及模块,执行各种功能应用以及数据处理,实现本发明的方法实施例中介绍的一种电源***健康状态计算方法。
其中,处理器可以为微处理器MCU、可编程逻辑器件FPGA等处理装置。存储器可为利用电能方式存储信息的各式存储器,例如RAM、ROM等;也可为利用磁能方式存储信息的各式存储器,例如硬盘、软盘、磁带、磁芯存储器、磁泡存储器、U盘等;还可为利用光学方式存储信息的各式存储器,例如CD、DVD等;当然,还可为其他方式的存储器,例如量子存储器、石墨烯存储器等。
Claims (10)
- 一种电源***健康状态计算方法,其特征在于,包括如下步骤:1)获取电源***在不同时刻对应的多条充放电数据,一条充放电数据包括充/放电时刻、显示荷电状态、充/放电电流、最高单体电压和最低单体电压,从中提取出满足要求的充电场景数据,所述充电场景数据包括多个充电分组,一个充电分组的数据为一次充满电过程的数据;2)对于一个充电分组,进行如下计算:依据各个时刻及其对应的充电电流,确定该次充电的充电容量CN;若此次充电的充电电流大于等于设定充电电流阈值A1,提取该充电分组前的多条充放电数据,从中筛选出放电电流小于设定静态电流阈值A2且持续时间最长的一段充放电数据,依据该段充放电数据中最后一条数据的最高单体电压V静高/最低单体电压V静低、以及SOC-OCV关系,确定与最后一条数据的最高单体电压V静高/最低单体电压V静低对应的最高静态电压真实荷电状态SOC静高/最低静态电压真实荷电状态SOC静低;依据如下公式计算得到此次充电的充电开始最高单体的真实荷电状态SOC高始和充电开始最低单体的真实荷电状态SOC低始:
SOC高始=SOC静高+(SOC1显-SOC静显),SOC低始=SOC静真+(SOC1显-SOC静显)式中,SOC静显表示所述一段充放电数据中最后一条数据的显示荷电状态SOC静显,SOC1显表示该充电分组第一条数据的显示荷电状态;3)按照如下公式计算最高单体的健康状态SOH单:SOH单=CN/[(100%-SOC高始)*C额],C额表示最高单体的额定容量;4)根据最高单体的健康状态SOH单得到电源***的健康状态。 - 根据权利要求1所述的电源***健康状态计算方法,其特征在于,步骤4)中,按照如下公式计算电源***的健康状态SOH***:SOH***=SOH单*k一致性,k一致性表示一致性系数,k=100%-SOC高始-SOC低始。
- 根据权利要求1或2所述的电源***健康状态计算方法,其特征在于,步骤2)中,若此次充电的充电电流小于设定充电电流阈值A1时,依据该充电分组前最后一条数据的最高 单体电压Vz高/最低单体电压Vz低、以及SOC-OCV关系,将与该充电分组前最后一条数据的最高单体电压Vz高/最低单体电压Vz低对应的荷电状态作为此次充电的充电开始最高单体的真实荷电状态SOC高始/充电开始最低单体的真实荷电状态SOC低始。
- 根据权利要求3所述的电源***健康状态计算方法,其特征在于,还包括步骤5)~6);5)利用如下公式修正SOC-OCV关系:
SOC=A×eB×OCV
A=A1×eA2×t
B=B1×eB2×t式中,SOC表示与静态电压OCV对应的荷电状态,A1、A2、B1、B2均表示老化参数,t表示使用时间;6)利用修正后的SOC-OCV关系,重新执行步骤2)~4),以得到修正后的电源***的健康状态SOH***。 - 根据权利要求4所述的电源***健康状态计算方法,其特征在于,使用时间t采用如下公式计算得到:
式中,k1和k2均表示使用时间参数;SOH表示电源***的健康状态,为步骤4)得到的电源***的健康状态。 - 根据权利要求5所述的电源***健康状态计算方法,其特征在于,使用时间参数k1和k2采用如下方式确定:获取电源***多组不同使用时间和不同健康状态数据,将某一使用时间对应出现次数最多的健康状态数据作为与该使用时间相对应的健康状态;利用不同使用时间和与之对应的健康状态数据,对公式进行拟合求解得到使用时间参数k1和k2。
- 根据权利要求1所述的电源***健康状态计算方法,其特征在于,步骤1)中,所述 要求包括以下要求中的至少一个:要求1,此次充电的电流跳动次数与所有充电分组的电流跳动总次数的比值小于设定跳动比例阈值;跳动是指一条数据的充电电流与该条数据的上一条数据的充电电流的差值的绝对值大于设定电流偏差阈值;要求2,最高单体与次高单体的位置不相邻或者最低单体电压大于设定低压阈值;要求3,一个充电分组的充电总时长大于设定时长阈值;要求4,一个充电分组的最后一条数据的显示荷电状态与第一条数据的显示荷电状态的差值大于设定荷电状态差值阈值;要求5,一个充电分组的最后一条数据的显示荷电状态大于设定荷电状态结束阈值。
- 根据权利要求1所述的电源***健康状态计算方法,其特征在于,步骤2)中确定该次充电的充电容量CN的手段为:①根据相邻两条数据,计算得到相邻两条数据所对应的相邻时刻之间的充电容量Cx;②将所有的相邻时刻之间的充电容量相加,得到该次充电的充电容量CN。
- 根据权利要求8所述的电源***健康状态计算方法,其特征在于,步骤①所采用的手段为:若相邻两条数据所对应的间隔时间△t小于设定相邻时间低阈值,则该相邻两条数据所对应的相邻时刻之间的充电容量为充电电流乘以间隔时间△t;若相邻两条数据所对应的间隔时间大于设定相邻时间高阈值且相邻两条数据所对应的间隔荷电状态△SOC小于设定荷电状态阈值,则该相邻两条数据所对应的相邻时刻之间的充电容量为充电电流乘以10秒,设定相邻时间低阈值小于设定相邻时间高阈值;否则,该相邻两条数据所对应的相邻时刻之间的充电容量为两条数据所对应的充电电流的平均值乘以间隔时间△t。
- 一种电源***健康状态计算装置,其特征在于,包括存储器和处理器,所述处理器用于执行存储在存储器中的指令以实现如权利要求1~9任一项所述的电源***健康状态计算方法。
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