New energy vehicle overload automatic detection method

Abstract

The invention relates to a new energy vehicle overload automatic detection method. The method comprises the following steps: step 100, setting a standard speed corresponding to a motor rated voltage UN under a rated load M in a motor driver; 200, acquiring historical kinematics feature data of the new energy vehicle, and extracting the most effective kinematics fragment; 300, in the running process of the new energy vehicle, the current motor speed speedt is obtained, and the standard speed standard speedt corresponding to the current motor running voltage Ut is calculated; step 400, a difference value diffracted between the current motor speed speedt and a standard speed standard speedt corresponding to the current motor operation voltage Ut is calculated, and an optimal threshold value threshold speed of the difference value between the current motor speed speedt and the standard speed standard speedt corresponding to the current motor operation voltage Ut is calculated, and the optimal threshold value threshold speed of the difference value between the current motor speed speedt and the standard speed standard speedt corresponding to the current motor operation voltage Ut is calculated; and step 500, comparing the difference value diffspeed with the optimal threshold value threshold speed, and if the difference value diffspeed is greater than the optimal threshold value threshold speed, judging that the new energy vehicle is overloaded, and giving an overload alarm, and if the difference value diffspeed is greater than the optimal threshold value threshold speed, judging that the new energy vehicle is overloaded, and if the difference value diffspeed is greater than the optimal threshold value threshold speed, judging that the new energy vehicle is overloaded. And judging that the new energy vehicle is not overloaded if the diffracted is smaller than the optimal threshold value thresholdspeed, and if the diffracted is smaller than the optimal threshold value thresholdspeed, judging that the new energy vehicle is not overloaded.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and in particular to an automatic overload detection method for new energy vehicles. Background Art

[0002] Currently, there are three main methods for identifying vehicle overload status: one is load identification based on vehicle static weighing, the second is load identification based on vehicle dynamic weighing, and the third is load identification based on on-board weighing system; however, there are the following problems: the vehicle static weighing system refers to measuring the vehicle load when the vehicle is completely stationary. This method either needs to be permanently installed on a concrete frame or platform, or requires the use of permanent trenches and road facilities. The vehicle load can only be measured at a fixed position or at a fixed time, and the identification system is huge in size, complex in structure, and difficult to install; the dynamic weighing system refers to measuring the weight of the vehicle without interrupting the traffic flow, and measuring the instantaneous load applied by the vehicle tires through force sensors. However, it can only be installed at fixed points, making it difficult to apply on a large scale, and the recognition accuracy is greatly affected by external factors (road smoothness, slope, vehicle tire pressure, etc.); the on-board weighing system is installed on the vehicle body, and the vehicle load is identified by the elastic deformation of the vehicle body components after being loaded. The transmission sensitivity of the on-board weighing system sensor directly affects the accuracy of vehicle load identification. At the same time, external factors such as vehicle body vibration, vehicle running speed and acceleration, and fatigue and mechanical creep during long-term use will affect the load identification accuracy. In addition, traditional on-board weighing equipment must install hardware equipment at the stress point of each axle of the vehicle. The installation process is relatively complicated, the installation cost is high, the maintenance is complicated, and after-sales service is difficult to maintain. The above vehicle overweight detection mostly relies on hardware equipment such as load sensors, which increases costs and maintenance difficulties. Summary of the invention

[0003] The purpose of the present invention is to provide a new energy vehicle overload automatic detection method in view of the deficiencies in the prior art.

[0004] The present invention is achieved by adopting the following technical solutions:

[0005] A new energy vehicle overload automatic detection method, the method comprising:

[0006] Step 100: The new energy vehicle includes a motor driver, in which the motor rated voltage U is set at a rated load M. N The corresponding standard speed standard_speed;

[0007] Step 200: Acquire historical kinematic feature data of the new energy vehicle, and extract the most effective kinematic segment according to the historical kinematic feature data;

[0008] Step 300: During the driving process of the new energy vehicle, the current motor speed speed_t is obtained, and the current motor operating voltage U is calculated. t The corresponding standard speed standard_speed_t;

[0009] Step 400: Calculate the current motor speed speed_t and the current motor operating voltage U t The corresponding standard speed standard_speed_t difference diff_speed, as well as the current motor speed speed_t and the current motor operating voltage U are calculated t The optimal threshold value threshold_speed of the corresponding standard speed standard_speed_t difference;

[0010] Step 500: Compare the difference diff_speed and the optimal threshold threshold_speed. If the difference diff_speed is greater than the optimal threshold threshold_speed, determine that the new energy vehicle is overloaded and issue an overload alarm; if diff_speed is less than the optimal threshold threshold_speed, determine that the new energy vehicle is not overloaded.

[0011] As a further explanation of the invention, the step 200 is divided into:

[0012] Step 201: Acquire historical operation data, and extract historical kinematic feature data from the historical operation data based on a correlation coefficient method;

[0013] Step 202: According to the historical kinematic feature data, abnormal data is eliminated based on the sliding quartering method to obtain a historical kinematic feature data matrix;

[0014] Step 203: Based on the historical kinematic feature data matrix, based on the principal component analysis algorithm and the clustering algorithm, the kinematic segments are divided, the effective kinematic segments are selected, and the deviation of the effective kinematic segments is calculated, so as to select the most effective kinematic segment.

[0015] As a further explanation of the invention, in step 202, eliminating abnormal data based on the sliding quartering method includes:

[0016] According to the voltage data set in the historical kinematic characteristic data, the voltage data set is sorted, and the lower quartile Q1 and the upper quartile Q3 are extracted; and the outlier limit is calculated, and the calculation formula is:

[0017] Outlier upper limit = Q3 + k (Q3 - Q1)

[0018] Outlier lower limit = Q1-k(Q3-Q1)

[0019] Where k is the coefficient;

[0020] Voltage data that exceeds the abnormal value limit is regarded as abnormal data and is removed.

[0021] As a further explanation of the invention, in step 203, the kinematic segments are divided according to the historical kinematic feature data matrix based on the principal component analysis algorithm and the clustering algorithm, including:

[0022] The historical kinematics characteristic data matrix includes velocity, acceleration, and displacement;

[0023] Based on the principal component analysis algorithm and clustering algorithm, the characteristic parameters related to velocity, acceleration and displacement are analyzed, and different kinematic segments are divided according to different operating states and modes.

[0024] As a further explanation of the invention, in step 203, selecting a valid kinematic segment and calculating the deviation of the valid kinematic segment to select the most effective kinematic segment includes:

[0025] The most effective kinematic segment is selected based on the deviation between each effective kinematic segment and the overall characteristic parameters of the current category, the deviations are sorted from small to large, and the segment with the smallest deviation is selected as the current most effective segment.

[0026] As a further explanation of the invention, in step 300, the current motor speed speed_t is obtained, and the current motor operating voltage U is calculated. t The corresponding standard speed standard_speed_t includes:

[0027] The motor start-up running time Time_set is preset, and the motor start-up running time Time_set is determined by the length of the most effective kinematic segment;

[0028] After the 1st time_set after the new energy vehicle motor starts running, the current motor running speed and running voltage are monitored in real time to obtain the current motor speed speed_t and calculate the current motor running voltage U t The corresponding standard speed is standard_speed_t.

[0029] As a further explanation of the invention, in step 300, the current motor operating voltage U t The corresponding standard speed standard_speed_t is calculated as follows:

[0030] standard_speed_t=standard_speed*U t / U N .

[0031] As a further explanation of the invention, in step 400, the current motor speed speed_t and the current motor operating voltage U t The optimal threshold value threshold_speed of the corresponding standard speed standard_speed_t difference includes:

[0032] Iteratively obtain the current motor speed speed_t and the current motor operating voltage U based on the simulated annealing algorithm t The corresponding standard speed standard_speed_t difference.

[0033] As a further explanation of the invention, the optimal threshold value threshold_speed calculation formula is:

[0034] The optimal threshold threshold_speed = 0.2*standard_speed_t-4*standard_speed_t.

[0035] A new energy vehicle includes an automatic overload detection method for the new energy vehicle.

[0036] Compared with the prior art, the present invention has the following beneficial technical effects:

[0037] The present invention can accurately and timely detect vehicle overloading and give a warning without adding additional hardware burden. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the automatic overload detection method for new energy vehicles of the present invention. DETAILED DESCRIPTION

[0039] Example 1

[0040] like Figure 1 As shown, an embodiment of the present invention provides a method for automatically detecting overload of a new energy vehicle, the method comprising:

[0041] Step 100: The new energy vehicle includes a motor driver, in which the motor rated voltage U is set at a rated load M. N Corresponding standard speed standard_speed; motor rated voltage U N The corresponding standard speed standard_speed satisfies the rated load M of new energy vehicles and the rated voltage U of the motor N The corresponding motor speed. Preferably, the motor rated voltage U N 24V; motor rated voltage U NThe corresponding standard speed standard_speed is set in the range of 1-100000 rpm.

[0042] Step 200: Acquire historical kinematic feature data of the new energy vehicle, and extract the most effective kinematic segment according to the historical kinematic feature data;

[0043] Step 300: During the driving process of the new energy vehicle, the current motor speed speed_t is obtained, and the current motor operating voltage U is calculated. t The corresponding standard speed standard_speed_t;

[0044] Step 400: Calculate the current motor speed speed_t and the current motor operating voltage U t The corresponding standard speed standard_speed_t difference diff_speed, as well as the current motor speed speed_t and the current motor operating voltage U are calculated t The optimal threshold value threshold_speed of the corresponding standard speed standard_speed_t difference;

[0045] Step 500: Compare the difference diff_speed and the optimal threshold threshold_speed. If the difference diff_speed is greater than the optimal threshold threshold_speed, the new energy vehicle is determined to be overloaded, and an overload alarm is issued. If necessary, the motor driver can stop driving the new energy vehicle; if diff_speed is less than the optimal threshold threshold_speed, the new energy vehicle is determined to be not overloaded. Preferably, the overload alarm can be linked to the new energy vehicle instrument. When the motor driver determines that the vehicle is overloaded, the new energy vehicle instrument will pop up an overload alarm window and sound an alarm. At the same time, the motor driver determines whether to power drive the new energy vehicle based on the driving scenario. For example, in a high-speed scenario, the vehicle can continue to drive for safety reasons. Except for this scenario, the motor driver stops driving the new energy vehicle.

[0046] Step 200 is divided into:

[0047] Step 201: acquiring historical operation data, and extracting historical kinematic feature data from the historical operation data based on a correlation coefficient method; that is, using a general correlation coefficient method, extracting data with a high correlation with kinematic features from the historical operation data as historical kinematic feature data;

[0048] Step 202: According to the historical kinematic feature data, abnormal data is eliminated based on the sliding quartering method to obtain a historical kinematic feature data matrix;

[0049] Step 203: Based on the historical kinematic feature data matrix, based on the principal component analysis algorithm and the clustering algorithm, the kinematic segments are divided, the effective kinematic segments are selected, and the deviation of the effective kinematic segments is calculated, so as to select the most effective kinematic segment.

[0050] In step 202, the abnormal data is eliminated based on the sliding quarter method, including:

[0051] According to the voltage data set in the historical kinematic characteristic data, the voltage data set is sorted, and the lower quartile Q1 and the upper quartile Q3 are extracted; and the outlier limit is calculated, and the calculation formula is:

[0052] Outlier upper limit = Q3 + k (Q3 - Q1)

[0053] Outlier lower limit = Q1-k(Q3-Q1)

[0054] Wherein, k is a coefficient; preferably, a general outlier monitoring method is used, and the coefficient k is usually set to 1.5 or 3;

[0055] Voltage data that exceeds the abnormal value limit is regarded as abnormal data and is removed.

[0056] In step 203, the kinematic segments are divided according to the historical kinematic feature data matrix based on the principal component analysis algorithm and the clustering algorithm, including:

[0057] The historical kinematics characteristic data matrix includes velocity, acceleration, and displacement;

[0058] Using the general principal component analysis algorithm and clustering algorithm, the characteristic parameters related to speed, acceleration and displacement are analyzed, and different kinematic segments are divided according to different operating states and modes. The kinematic segment is the speed interval between the new energy vehicle from the idle state to the next idle state. It includes the idle part and the driving part, and the driving part includes three modes: acceleration, constant speed and deceleration. By analyzing these segments, the driving characteristics of new energy vehicles under different road conditions can be obtained. For example, the kinematic segments can be divided into three categories, representing the driving conditions of unobstructed sections, general working conditions and congested sections. Secondly, in order to ensure that the selected kinematic segments are representative, it is necessary to extract features such as speed, acceleration and displacement from each segment, and then compare these features to find the most representative segments using the general principal component analysis algorithm and clustering algorithm. Such segments can reflect key information such as the overall driving speed and acceleration performance of new energy vehicles; by combining these representative short-trip segments, the driving condition curve of new energy vehicles can be constructed, which is helpful to simulate and evaluate the performance of commercial vehicles in actual driving.

[0059] In step 203, valid kinematic segments are selected, and deviations of the valid kinematic segments are calculated, so as to select the most effective kinematic segment, including:

[0060] The most effective kinematics segment is selected based on the deviation between each effective kinematics segment and the overall characteristic parameters of the current category, and the deviations are sorted from small to large, and the segment with the smallest deviation is selected as the current most effective segment. The most effective kinematics segment can accurately reflect the driving characteristics of commercial vehicles under different road conditions, making the analysis more representative.

[0061] In step 300, the current motor speed speed_t is obtained, and the current motor operating voltage U is calculated. t The corresponding standard speed standard_speed_t includes:

[0062] The motor start-up running time Time_set is preset, and the motor start-up running time Time_set is determined by the length of the most effective kinematic segment;

[0063] After the 1st time_set after the new energy vehicle motor starts running, the current motor running speed and running voltage are monitored in real time to obtain the current motor speed speed_t and calculate the current motor running voltage U t The corresponding standard speed standard_speed_t. The preset running time Time_set is determined based on the length of the most effective kinematic segment, which can reveal the typical driving conditions of new energy vehicles. The preset running time Time_set is preferably 120 seconds.

[0064] Furthermore, the calculation formula of the standard speed standard_speed_t corresponding to the current motor operating voltage Ut is:

[0065] standard_speed_t=standard_speed*U t / U N .

[0066] In step 400, the current motor speed speed_t and the current motor operating voltage U t The optimal threshold value threshold_speed of the corresponding standard speed standard_speed_t difference includes:

[0067] Iteratively obtain the current motor speed speed_t and the current motor operating voltage U based on the simulated annealing algorithm t The corresponding standard speed standard_speed_t difference.

[0068] Furthermore, the optimal threshold value threshold_speed calculation formula is:

[0069] The optimal threshold threshold_speed = 0.2*standard_speed_t-4*standard_speed_t.

[0070] Example 2

[0071] An embodiment of the present invention provides a new energy vehicle, including the new energy vehicle overload automatic detection method as described above.

Claims

1. A new energy vehicle overload automatic detection method, characterized in that: The method comprises: Step 100: The new energy vehicle includes a motor driver, in which the motor rated voltage U is set at a rated load M. N The corresponding standard speed standard_speed; Step 200: Acquire historical kinematic feature data of the new energy vehicle, and extract the most effective kinematic segment according to the historical kinematic feature data; Step 300: During the driving process of the new energy vehicle, the current motor speed speed_t is obtained, and the current motor operating voltage U is calculated. t The corresponding standard speed standard_speed_t; Step 400: Calculate the current motor speed speed_t and the current motor operating voltage U t The corresponding standard speed standard_speed_t difference diff_speed, as well as the current motor speed speed_t and the current motor operating voltage U are calculated t The optimal threshold value threshold_speed of the corresponding standard speed standard_speed_t difference; Step 500: Compare the difference diff_speed and the optimal threshold threshold_speed. If the difference diff_speed is greater than the optimal threshold threshold_speed, determine that the new energy vehicle is overloaded and issue an overload alarm; if diff_speed is less than the optimal threshold threshold_speed, determine that the new energy vehicle is not overloaded.

2. The automatic overload detection method for new energy vehicles according to claim 1, characterized in that: The step 200 is divided into: Step 201: Acquire historical operation data, and extract historical kinematic feature data from the historical operation data based on a correlation coefficient method; Step 202: According to the historical kinematic feature data, abnormal data is eliminated based on the sliding quartering method to obtain a historical kinematic feature data matrix; Step 203: Based on the historical kinematic feature data matrix, based on the principal component analysis algorithm and the clustering algorithm, the kinematic segments are divided, the effective kinematic segments are selected, and the deviation of the effective kinematic segments is calculated, so as to select the most effective kinematic segment.

3. The automatic overload detection method for new energy vehicles according to claim 2, characterized in that: In step 202, eliminating abnormal data based on the sliding quartering method includes: According to the voltage data set in the historical kinematic characteristic data, the voltage data set is sorted, and the lower quartile Q1 and the upper quartile Q3 are extracted; and the outlier limit is calculated, and the calculation formula is: Outlier upper limit = Q3 + k (Q3 - Q1) Outlier lower limit = Q1-k(Q3-Q1) Where k is the coefficient; Voltage data that exceeds the abnormal value limit is regarded as abnormal data and is removed.

4. The automatic overload detection method for new energy vehicles according to claim 3, characterized in that: In step 203, the kinematic segments are divided according to the historical kinematic feature data matrix based on the principal component analysis algorithm and the clustering algorithm, including: The historical kinematics characteristic data matrix includes velocity, acceleration, and displacement; Based on the principal component analysis algorithm and clustering algorithm, the characteristic parameters related to velocity, acceleration and displacement are analyzed, and different kinematic segments are divided according to different operating states and modes.

5. The automatic overload detection method for new energy vehicles according to claim 4, characterized in that: In step 203, valid kinematic segments are selected, and deviations of the valid kinematic segments are calculated, so as to select the most valid kinematic segment, including: The most effective kinematic segment is selected based on the deviation between each effective kinematic segment and the overall characteristic parameters of the current category, the deviations are sorted from small to large, and the segment with the smallest deviation is selected as the current most effective segment.

6. The automatic overload detection method for new energy vehicles according to claim 5, characterized in that: In step 300, the current motor speed speed_t is obtained, and the current motor operating voltage U is calculated. t The corresponding standard speed standard_speed_t includes: The motor start-up running time Time_set is preset, and the motor start-up running time Time_set is determined by the length of the most effective kinematic segment; After the 1st time_set after the new energy vehicle motor starts running, the current motor running speed and running voltage are monitored in real time to obtain the current motor speed speed_t and calculate the current motor running voltage U t The corresponding standard speed is standard_speed_t.

7. The automatic overload detection method for new energy vehicles according to claim 6, characterized in that: In step 300, the current motor operating voltage U t The corresponding standard speed standard_speed_t is calculated as follows: standard_speed_t=standard_speed*U t / U N 。 8. The automatic overload detection method for new energy vehicles according to claim 7, characterized in that: In step 400, the current motor speed speed_t and the current motor operating voltage U t The optimal threshold value threshold_speed of the corresponding standard speed standard_speed_t difference includes: Iteratively obtain the current motor speed speed_t and the current motor operating voltage U based on the simulated annealing algorithm t The corresponding standard speed standard_speed_t difference.

9. The automatic overload detection method for new energy vehicles according to claim 8, characterized in that: The optimal threshold value threshold_speed calculation formula is: The optimal threshold threshold_speed = 0.2*standard_speed_t-4*standard_speed_t.

10. A new energy vehicle, characterized in that: It includes the automatic overload detection method for new energy vehicles as described in any one of claims 1 to 9.

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