Electric vehicle external short circuit risk assessment method

Abstract

The invention provides an electric vehicle external short circuit risk assessment method, which comprises the following steps: acquiring historical air temperature data, fitting a temperature change curve within a year according to the historical air temperature data, and dividing the fitted temperature change curve into a plurality of temperature detection curves; acquiring a plurality of SOC detection nodes, randomly selecting a plurality of temperature detection curves, and generating a detection sequence according to the SOC detection nodes; and performing external short-circuit test on the vehicle according to the detection sequence, and performing risk scoring on the vehicle according to a test result. The problems that in the prior art, a battery system level short circuit test simulates a no-load static working condition, only a battery system is included, other loads necessary for charging are not included, and the real situation after external short circuit cannot be reflected on the whole vehicle level are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle testing, and in particular to a method for assessing external short-circuit risk of an electric vehicle. Background Art

[0002] At present, with the industry development trend of 800V and fast charging becoming increasingly popular, high-voltage, high-current, and high-power charging scenarios are increasing. However, once the charging interface ages or the insulation fails or the charging interface is soaked in water, a short circuit between the positive and negative electrodes may occur during the charging process. If the battery management system fails to effectively sense the external short-circuit fault in a timely manner and implement effective isolation measures, it may cause a short circuit in the battery system, irreversible thermal runaway of the battery cell, and even induce thermal diffusion of the entire vehicle, leading to a fire accident. Fire accidents in new energy vehicles occur from time to time during the charging process. At present, there is no effective vehicle-level external short-circuit test and evaluation method to evaluate the effectiveness of the safety protection mechanism.

[0003] Most existing solutions are to conduct short-circuit tests at the battery system level, when the battery system is not charging or discharging. First, close the positive and negative high-voltage relays, add a short-circuit resistor between 1 milliohm and 20 milliohm between the positive and negative loops, detect the short-circuit current, and after a period of time, cut off the short-circuit resistor between the positive and negative loops, let it stand for a while, and recheck the battery system, including BMS self-test, insulation resistance, fuse, etc.

[0004] This technical solution uses static and component-level testing solutions. On the one hand, the battery system-level test simulates no-load static conditions, while the actual scenarios are all loaded dynamic conditions. Secondly, the battery system-level test only includes the battery system, and does not include the charging piles, charging guns, charging sockets, OBD / DC-DC converters and other components required for charging. It cannot reflect the actual situation after an external short circuit at the vehicle level. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for assessing the risk of external short circuit of electric vehicles, which solves the problem that the battery system-level short-circuit test in the prior art simulates a no-load static condition, only includes the battery system, and does not include other loads required for charging, and cannot reflect the actual situation after an external short circuit at the vehicle level.

[0006] According to an embodiment of the present invention, a method for assessing external short circuit risk of an electric vehicle includes:

[0007] Acquire historical temperature data, fit a temperature change curve according to the historical temperature data, and divide the fitted temperature change curve into multiple temperature detection curves;

[0008] Acquire multiple SOC detection nodes, randomly select multiple temperature detection curves and generate a detection sequence according to the SOC detection nodes;

[0009] The vehicle is tested for external short circuits according to the detection sequence, and a risk score is assigned to the vehicle based on the test results.

[0010] Preferably, the method of dividing the fitted temperature change curve into a plurality of temperature change intervals comprises:

[0011] The temperature corresponding to the month on the temperature change curve is set as the division node;

[0012] Taking each division node as the starting point and counting the temperature corresponding to the 12th month as the end point, the temperature change curve is intercepted into multiple temperature detection curves.

[0013] Preferably, the SOC detection nodes include 20%, 40%, 60%, 80% and 99%.

[0014] Preferably, the step of randomly selecting a plurality of temperature detection curves comprises:

[0015] Obtain the number of vehicles registered each month and the total number of vehicles registered each year in the previous five years;

[0016] Calculate the average of the ratio of the number of vehicles registered in the same month of all years to the total number of vehicles registered in the corresponding year;

[0017] The average value of the proportion corresponding to each month is used as the selection probability for random selection, and the number of selections is equal to the number of SOC detection nodes.

[0018] Preferably, the detection sequence includes a plurality of detection state sets, and the detection state sets include detection temperature, SOC node and test time;

[0019] For each detection state set, the detection temperature and SOC node are randomly selected from the temperature detection curve and SOC detection node respectively, and the test time is randomly selected according to the Poisson distribution.

[0020] Preferably, each type of SOC node corresponds to the same number of detection status sets.

[0021] Preferably, the test time is selected in the range of 5 minutes to 25 minutes, and is randomly selected according to a Poisson distribution with an expectation of 10.

[0022] Preferably, each time the vehicle is tested according to the detection state set, the vehicle is scored according to the detection result, and then the vehicle state is restored to the state before the detection before the next test.

[0023] Preferably, when the vehicle cannot be charged after a certain test, the test is stopped, and the vehicle scores corresponding to the detection state set that has not been tested are set to 0.

[0024] Preferably, before performing the external short-circuit test, the vehicle needs to be charged and tested. If the vehicle can be charged normally, the external short-circuit test is performed.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The temperature changes in the environment of the electric vehicle are simulated by randomly selecting temperature detection curves with different temperature changes. The external short-circuit test is directly performed on the vehicle loaded with the battery to simulate the performance of the battery under actual dynamic conditions with load. The actual situation after the external short circuit is reflected at the vehicle level, so as to evaluate the external short-circuit risk of the electric vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The risk assessment flow chart of an embodiment of the present invention.

[0028] Figure 2 4 is a temperature variation trend diagram of an embodiment of the present invention.

[0029] Figure 3 A probability distribution diagram for test time selection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0031] like Figure 1 As shown, an embodiment of the present invention proposes a method for assessing the risk of external short circuit of an electric vehicle, comprising:

[0032] Obtain historical temperature data, fit a temperature change curve within a year based on the historical temperature data, and divide the fitted temperature change curve into multiple temperature detection curves.

[0033] The local temperature changes for each month in the past five years can be obtained from the official website of the Meteorological Bureau. This embodiment takes the annual temperature change of -20℃-40℃ as an example, and extracts division nodes from -20℃ to 40℃ at intervals of 10℃: -20℃, -10℃, 0℃, 10℃, 20℃, 30℃, 40℃. The temperature range of 0℃~20℃ is defined as spring, the temperature range of 20℃~40℃~20℃ is defined as summer, the temperature range of 20℃~0℃ is defined as autumn, and the temperature range of 0℃~-10℃~-20℃~-10℃~0℃ is defined as winter. The curve function between the two division nodes is a sine function, which simulates the temperature change trend of the four seasons of spring, summer, autumn and winter, see Figure 2 , Figure 2The temperature change of one year is represented in , and then each division node is taken as the starting point, and the temperature corresponding to the 12th month is counted as the end point (for example, March of the first year is taken as the starting point, and February of the second year is taken as the end point), and the temperature change curve is intercepted into multiple temperature detection curves, so as to obtain 12 temperature detection curves, as shown in Table 1.

[0034] Table 1 Temperature starting point extraction probability

[0035] Temperature detection curve serial number Temperature starting point Extraction probability 1 (0,0) <![CDATA[x3]]> 2 (1,10) <![CDATA[x4]]> 3 (2,20) <![CDATA[x5]]> 4 (3,30) <![CDATA[x6]]> 5 (4,40) <![CDATA[x7]]> 6 (5,30) <![CDATA[x8]]> 7 (6,20) <![CDATA[x9]]> 8 (7,10) <![CDATA[x 10 ]]> 9 (8,0) <![CDATA[x 11 ]]> 10 (9,-10) <![CDATA[x 12 ]]> 11 (10,-20) <![CDATA[x1]]> 12 (11,-10) <![CDATA[x2]]>

[0036] Acquire multiple SOC detection nodes, randomly select multiple temperature detection curves and generate a detection sequence according to the SOC detection nodes;

[0037] In this embodiment, the SOC detection nodes are set to 5, including 20%, 40%, 60%, 80% and 99%.

[0038] The detection sequence includes multiple (for example, 60) detection state sets, each of which includes a detection temperature, a SOC node, and a test time.

[0039] For each detection state set, the detection temperature and SOC node are randomly selected from the temperature detection curve and SOC detection node respectively, and the test time is randomly selected according to the Poisson distribution.

[0040] Selection of detection temperature:

[0041] Obtain the number of vehicles registered each month and the total number of vehicles registered each year in the previous five years;

[0042] Calculate the average of the ratio of the number of vehicles registered in the same month of all years to the total number of vehicles registered in the corresponding year;

[0043] The average value of the proportion corresponding to each month is used as the selection probability for random selection, and the number of selections is equal to the number of SOC detection nodes.

[0044] Let x i =The average value of the proportion of the number of vehicles registered in the i-th month of the previous five years to the total number of vehicles registered in the corresponding year. For example, x3 is the average value of the proportion of the number of vehicles registered in the third month of the previous five years to the total number of vehicles registered in the corresponding year. Since the temperature in March mostly varies between 0 and 10°C, the average value is used as the extraction probability of temperature detection curve 1.

[0045] Then, according to the order of the detection state set, a temperature detection curve is randomly selected multiple times and written into the detection state set in sequence.

[0046] Selection of SOC nodes:

[0047] 12 nodes of each of the five types of SOCs are set and 60 detection status sets are randomly written respectively.

[0048] Test time selection:

[0049] The duration of external short circuit ranges from 5 minutes to 25 minutes and follows a Poisson distribution with an expectation of 10, such as Figure 3 As shown in the figure, the horizontal axis represents the selected time length, and the vertical axis represents the probability of selecting the corresponding time length. In the previous step, 60 SOC feature points were selected, and 60 times will also be selected here. 60 external short-circuit durations are continuously generated according to probability and written into 60 detection state sets in the order of selection.

[0050] The vehicle is tested for external short circuits according to the detection sequence, and a risk score is assigned to the vehicle based on the test results.

[0051] Before testing the vehicle, first perform a battery charging test on the vehicle. If it can be charged normally, start testing according to the detection sequence.

[0052] There are 60 detection state sets in the detection sequence, so 60 consecutive detections are required. After each detection, the vehicle state must be restored to the state before the next detection.

[0053] After each test is completed, the short-circuit test equipment is removed from the circuit, the vehicle status at this time is tested, and the test is scored according to Table 2.

[0054] After recovery, check the vehicle status and score it according to Table 2 n , where n is the nth test (n=1, 2, 3...60).

[0055] Table 2 External short circuit test scoring table

[0056]

[0057] If the vehicle can be charged normally after the external short circuit is triggered for the Nth time, the test will continue until 60 external short circuit tests are completed. If the vehicle cannot be charged normally after the external short circuit is triggered for the Nth time, the test will be stopped and the scores of the unfinished tests 60-N will be 0.

[0058] Then the scores of the N short-circuit tests of the vehicle are summarized. And rate the vehicles according to Table 3.

[0059] Afterwards, 60 different models were selected to complete the above test, and the y of each model was counted. 总 , build a vehicle model score database, set y 总 It follows a normal distribution.

[0060] When a new vehicle is tested, the final y 总 , and rate them according to Table 3.

[0061] Table 3 Star rating comparison table

[0062] Serial number Star Rating Fraction 1 Sporadic Fraction <u-δ 2 One star u-δ≤score<u 3 Two Star u≤fraction <u+δ 4 Samsung u+δ≤fraction <u+2δ 5 Four Star u+2δ≤fraction<u+3δ 6 Five Star Score ≥ u+3δ

[0063] In summary, the temperature changes in the environment of the electric vehicle are simulated by randomly selecting temperature detection curves with different temperature changes. The external short-circuit test is directly performed on the vehicle loaded with the battery to simulate the performance of the battery under actual dynamic conditions with load. The actual situation after the external short circuit is reflected at the vehicle level, so as to evaluate the external short-circuit risk of the electric vehicle.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for assessing the risk of external short circuit of an electric vehicle, characterized in that: include: Acquire historical temperature data, fit a temperature change curve according to the historical temperature data, and divide the fitted temperature change curve into multiple temperature detection curves; Acquire multiple SOC detection nodes, randomly select multiple temperature detection curves and generate a detection sequence according to the SOC detection nodes; The vehicle is tested for external short circuits according to the detection sequence, and a risk score is assigned to the vehicle based on the test results.

2. The method for assessing the risk of external short circuit of an electric vehicle as claimed in claim 1, characterized in that: The method of dividing the fitted temperature change curve into multiple temperature change intervals includes: The temperature corresponding to the month on the temperature change curve is set as the division node; Taking each division node as the starting point and counting the temperature corresponding to the 12th month as the end point, the temperature change curve is intercepted into multiple temperature detection curves.

3. The method for assessing the risk of external short circuit of an electric vehicle as claimed in claim 1, characterized in that: The SOC detection nodes include 20%, 40%, 60%, 80% and 99%.

4. The method for assessing the risk of external short circuit of an electric vehicle as claimed in claim 3, characterized in that: The steps of randomly selecting a plurality of temperature detection curves include: Obtain the number of vehicles registered each month and the total number of vehicles registered each year in the previous five years; Calculate the average of the ratio of the number of vehicles registered in the same month of all years to the total number of vehicles registered in the corresponding year; The average value of the proportion corresponding to each month is used as the selection probability for random selection, and the number of selections is equal to the number of SOC detection nodes.

5. The method for assessing the risk of external short circuit of an electric vehicle as claimed in claim 4, characterized in that: The detection sequence includes a plurality of detection state sets, and the detection state sets include detection temperature, SOC node and test time; For each detection state set, the detection temperature and SOC node are randomly selected from the temperature detection curve and SOC detection node respectively, and the test time is randomly selected according to the Poisson distribution.

6. A method for assessing external short circuit risk of an electric vehicle as claimed in claim 5, characterized in that: Each SOC node corresponds to the same number of detection state sets.

7. The method for assessing the risk of external short circuit of an electric vehicle as claimed in claim 1, characterized in that: The test time ranges from 5 minutes to 25 minutes and is randomly selected according to a Poisson distribution with an expectation of 10.

8. The method for assessing the risk of external short circuit of an electric vehicle as claimed in claim 1, characterized in that: Each time the vehicle is tested according to the detection state set, the vehicle is scored according to the detection results, and then the vehicle state is restored to the state before the detection before the next test.

9. The method for assessing the risk of external short circuit of an electric vehicle as claimed in claim 1, characterized in that: If the vehicle cannot be charged after a test, the test is stopped and the vehicle scores corresponding to the detection state set that has not been tested are set to 0.

10. The method for assessing external short circuit risk of an electric vehicle as claimed in claim 1, characterized in that: Before performing an external short-circuit test, the vehicle must also be charged. If the vehicle can be charged normally, an external short-circuit test is performed.

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