Adaptive system and adaptive method for analysis of TAS sensor

Abstract

The invention discloses a self-adaptive system and a self-adaptive method for TAS sensor analysis. The self-adaptive system comprises a storage battery, an identification module, an algorithm switching module, an EEPROM module, a CAN module and a Power LDO. According to the self-adaptive method, aiming at two sensor signal characteristics of a Hairah sensor and a Faireo sensor, corresponding angles and torque analysis algorithms are automatically calibrated and matched. According to the invention, the problems that the cost is increased due to the switching of the Hairah sensor and the Faireo sensor, the corresponding controller is invalid and the after-sale is not timely when the capacity of one sensor is insufficient are solved. The production cost is reduced, the production efficiency is improved, the product utilization rate is improved, and the maintenance cost is reduced.

Description

Technical Field

[0001] The invention relates to an adaptive system and an adaptive method for TAS sensor analysis, belonging to the technical field of automobile electronic systems. Background Art

[0002] In the prior art, the angle and torque signals of the vehicle steering system are processed by using sensors to process the signals and transmit the calculated angle and torque signals to the actuator. The choice of sensor type is generally specified by the client to the supplier. The mainstream sensors on the market are Hella sensors and Valeo sensors. For the development of steering gears, one controller corresponds to the signal (angle and torque) solution algorithm of one sensor. Due to the production capacity of sensor supply, there is a need to switch between Hella sensors and Valeo sensors, which leads to a one-to-one correspondence between the controller and the sensor. This method will increase the cost of steering gear development. In the production process of steering gears, additional costs are also required to ensure the consistency of sensor control in production and assembly. In addition, when the production capacity of one of the sensors is insufficient, the corresponding controller will fail, resulting in waste. For the replacement of parts by the client in the later stage, there may also be problems of untimely after-sales due to insufficient sensor production capacity. Summary of the invention

[0003] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide an adaptive system and adaptive method for TAS sensor analysis. For Hella sensors and Valeo sensors, the system automatically calibrates and matches the corresponding angle and torque analysis algorithms by utilizing the signal characteristics of the two sensors.

[0004] The present invention adopts the following technical solutions to solve the above technical problems: an adaptive system for TAS sensor analysis, which automatically calibrates the signal characteristics of Hella sensors and Valeo sensors and matches the corresponding angles and torques to perform analysis algorithms;

[0005] Including battery, identification module, algorithm switching module, EEPROM module, CAN module and Power LDO;

[0006] The identification module is used to distinguish the type of TAS sensor used;

[0007] The algorithm switching module can adaptively switch the corresponding analysis algorithm according to the TAS sensor type, and generate specific feature values ​​after the analysis algorithm is adapted;

[0008] The EEPROM module stores the characteristic values ​​generated by the algorithm switching module;

[0009] The battery passes through the anti-reverse circuit and supplies power to the entire system through the Power LDO;

[0010] The TAS sensor transmits the sensor's period and duty cycle signals to the algorithm recognition module for frequency scanning to obtain characteristic values. After the algorithm switching module, the angle and torque values ​​are calculated to generate characteristic values. The CAN network interacts with the signal. The recognition module is connected to the CAN network through the circuit and transmits the signal to the CAN network. The characteristic values ​​obtained by the algorithm switching module are sent to the EEPROM module via the CAN network and the characteristic values ​​are stored in the EEPROM module.

[0011] Furthermore, the TAS sensor includes a Hella sensor and a Valeo sensor.

[0012] The angle signal of the Hella sensor includes a P signal and an S signal, and the torque signal includes a T1 signal and a T2 signal; the corresponding signal frequencies are: the P signal frequency is 850Hz to 1150Hz, the S signal frequency is 178Hz to 222Hz, and the T1 / T2 signal frequency is 1700Hz to 2300Hz;

[0013] The angle signal of the Valeo sensor includes AS signal and RS signal, and the torque signal includes T1' signal and T2' signal; the corresponding signal frequencies are: AS / RS signal frequency is 900Hz to 1100Hz, and T1' / T2' signal frequency is 1600Hz to 2400Hz;

[0014] The S signal in the Hella sensor signal and the RS signal in the Valeo sensor signal are used as the characteristic signals of the two sensors, and the frequencies of the S signal and the RS signal are used as characteristic points. The S signal and the RS signal of the sensor are swept by the identification module to obtain the signal frequency and generate the corresponding characteristic value.

[0015] An adaptive method for an adaptive system of TAS sensor analysis includes the following specific steps:

[0016] Step 1: After the steering gear is assembled, the battery is powered on, and the identification module scans the main and auxiliary signals of the sensor to obtain the corresponding frequency information; the type of the TAS sensor is determined based on the frequency information, and the signal is sent through the CAN network to generate a characteristic value;

[0017] Step 2, input the characteristic value into the algorithm switching module, and select the corresponding algorithm analysis channel according to the characteristic value. After the algorithm analysis channel is confirmed, a specific characteristic value will be generated again, and the flag bit will be stored in the EEPROM module.

[0018] Furthermore, in step 1, the CAN network uses 10 ms as a signal cycle, sets ten signal cycles for confirmation, and uses 100 ms as a judgment cycle. When the frequency range is between 178 Hz and 222 Hz, it is judged to be a Hella sensor, and 0X0-Hella is sent through the CAN network to generate a characteristic value of 0_Hella; when the frequency range is between 900 Hz and 1100 Hz, it is judged to be a Valeo sensor, and 0X1-Valeo is sent through the CAN network to generate a characteristic value of 1_Valeo; if the sensor fails, 0X3-Invalid is sent.

[0019] Furthermore, in step 2, the algorithm switching module includes two algorithm channels, which are defined as a first algorithm channel and a second algorithm channel respectively;

[0020] In the first algorithm channel, the P and S signals of the Hella sensor are initialized and calculated by the cursor algorithm to obtain the initial angle value, and the final angle value is obtained according to the angle tracking. The T1 and T2 signals of the Hella sensor are firstly verified by the protocol, and then the correctness of the signal is ensured by mutual verification of the T1 and T2 signals, and then the torque value is calculated;

[0021] In the second algorithm channel, the AS and RS signals of the Valeo sensor are initialized and calculated through the cursor algorithm to obtain the initial angle value, and the final angle value is obtained based on the angle tracking. The T1' and T2' signals of the Hella sensor are first verified by the protocol, and then the T1 and T2 signals are mutually verified to ensure the correctness of the signal before calculating the torque value.

[0022] Furthermore, in the step 2, the algorithm switching module includes analysis algorithms for two sensors, the Hella sensor and the Valeo sensor, and switches by judging the characteristic value; the algorithm switching module reads the characteristic value information sent by the identification module, and when the characteristic value is 0_Hella, the algorithm switching module selects to analyze the first algorithm channel, performs angle and torque analysis of the Hella sensor, and after the analysis is completed, sends 0X0-SensorValid through the CAN network to generate a characteristic value of 0X00; when the characteristic value is 1_Valeo, the algorithm switching module selects to analyze the second algorithm channel, performs angle and torque analysis of the Valeo sensor, and after the analysis is completed, sends 0X0-SensorValid through the CAN network to generate a characteristic value of 0X00; when the sensor is in a state where no analysis is performed, 0X1-SensorInvalid is sent to generate a characteristic value of 0XFF, and the characteristic value is stored in the EEPROM. After the product is offline, the characteristic value can be used to determine whether the sensor is successfully matched.

[0023] Compared with the prior art, the present invention adopts the above technical solution and has the following advantages: the present invention solves the problems of increased cost caused by switching between Hella sensors and Valeo sensors, failure of the corresponding controller when the production capacity of one of the sensors is insufficient, and delayed after-sales service. The present invention reduces production costs, improves production efficiency, improves product utilization, and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Schematic diagram of the system architecture of the present invention.

[0025] Figure 2 It is a schematic diagram of the process of the present invention.

[0026] Figure 3 This is a workflow diagram of the identification module of the present invention.

[0027] Figure 4 This is a workflow diagram of the algorithm switching module of the present invention.

[0028] Figure 5 This is a workflow diagram of the first algorithm channel in the algorithm switching module of the present invention.

[0029] Figure 6 This is a workflow diagram of the second algorithm channel in the algorithm switching module of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings:

[0031] This embodiment proposes an adaptive system for TAS sensor analysis, which automatically calibrates the signal characteristics of Hella sensors and Valeo sensors and matches the corresponding angles and torques to perform analysis algorithms. Its composition structure is as follows Figure 1 As shown, it includes a battery, an identification module, an algorithm switching module, an EEPROM module, a CAN module and a Power LDO.

[0032] The identification module is used to distinguish the type of TAS sensor used;

[0033] The algorithm switching module can adaptively switch the corresponding analysis algorithm according to the TAS sensor type. After the analysis algorithm is adapted, it generates a specific feature value.

[0034] The EEPROM module stores the characteristic values ​​generated by the algorithm switching module;

[0035] The battery passes through the anti-reverse circuit and supplies power to the entire system through the Power LDO;

[0036] The TAS sensor transmits the sensor's period and duty cycle signals to the algorithm recognition module for frequency scanning to obtain characteristic values. After the algorithm switching module, the angle and torque values ​​are calculated to generate characteristic values. The CAN network interacts with the signal. The recognition module is connected to the CAN network through the circuit and transmits the signal to the CAN network. The characteristic values ​​obtained by the algorithm switching module are sent to the EEPROM module via the CAN network and the characteristic values ​​are stored in the EEPROM module.

[0037] Among them, the TAS sensors include Hella sensors and Valeo sensors.

[0038] The angle signal of the Hella sensor includes a P signal and an S signal, and the torque signal includes a T1 signal and a T2 signal; the corresponding signal frequencies are: the P signal frequency is 850Hz to 1150Hz, the S signal frequency is 178Hz to 222Hz, and the T1 / T2 signal frequency is 1700Hz to 2300Hz;

[0039] The angle signal of the Valeo sensor includes AS signal and RS signal, and the torque signal includes T1' signal and T2' signal; the corresponding signal frequencies are: AS / RS signal frequency is 900Hz to 1100Hz, and T1' / T2' signal frequency is 1600Hz to 2400Hz;

[0040] Therefore, the S signal in the Hella sensor signal and the RS signal in the Valeo sensor signal are used as the characteristic signals of the two sensors, and the frequencies of the S signal and the RS signal are used as characteristic points. The S signal and the RS signal of the sensor are swept by the identification module to obtain the signal frequency and generate the corresponding characteristic value.

[0041] This embodiment also proposes an adaptive method for an adaptive system of TAS sensor analysis, such as Figure 2 As shown, the specific steps are as follows:

[0042] Step 1: After the steering gear is assembled, the battery is powered on, and the identification module scans the main and auxiliary signals of the sensor to obtain the corresponding frequency information; the type of TAS sensor is determined based on the frequency information, and the signal is sent through the CAN network to generate a characteristic value, such as Figure 3As shown. The CAN network uses 10ms as a signal cycle, sets ten signal cycles for confirmation, and uses 100ms as a judgment cycle. When the frequency range is between 178Hz and 222Hz, it is judged as a Hella sensor, and 0X0-Hella is sent through the CAN network to generate a characteristic value of 0_Hella; when the frequency range is between 900Hz and 1100Hz, it is judged as a Valeo sensor, and 0X1-Valeo is sent through the CAN network to generate a characteristic value of 1_Valeo; if the sensor fails, 0X3-Invalid is sent.

[0043] Step 2, the algorithm switching module includes two algorithm channels, which are defined as the first algorithm channel and the second algorithm channel respectively; the characteristic value is input into the algorithm switching module, and the algorithm switching module selects the corresponding algorithm analysis channel according to the characteristic value generated by the recognition module. After the algorithm analysis channel is confirmed, a specific characteristic value will be generated again, and the flag bit will be stored in the EEPROM module.

[0044] like Figure 4 As shown, in the first algorithm channel, the P and S signals of the Hella sensor are initialized, and then calculated by the cursor algorithm to calculate the initial angle value, and the final angle value is obtained according to the angle tracking. The T1 and T2 signals of the Hella sensor are firstly verified by the protocol, and then the correctness of the signal is ensured by mutual verification of the T1 and T2 signals, and then the torque value is calculated;

[0045] like Figure 5 As shown, in the second algorithm channel, the AS and RS signals of the Valeo sensor are initialized, calculated by the cursor algorithm, and the initial angle value is calculated. The final angle value is obtained according to the angle tracking. The T1' and T2' signals of the Hella sensor are first verified by the protocol, and then the correctness of the signal is ensured by mutual verification of the T1 and T2 signals, and then the torque value is calculated.

[0046] The algorithm switching module includes analysis algorithms for two sensors, Hella sensor and Valeo sensor, and switches by judging the characteristic value; the algorithm switching module reads the characteristic value information sent by the identification module. When the characteristic value is 0_Hella, the algorithm switching module selects to analyze the first algorithm channel, performs angle and torque analysis of the Hella sensor, and after the analysis is completed, sends 0X0-SensorValid through the CAN network to generate the characteristic value 0X00; when the characteristic value is 1_Valeo, the algorithm switching module selects to analyze the second algorithm channel, performs angle and torque analysis of the Valeo sensor, and after the analysis is completed, sends 0X0-SensorValid through the CAN network to generate the characteristic value 0X00; when the sensor is in a state of not being analyzed, it sends 0X1-SensorInvalid, generates the characteristic value 0XFF, and stores the characteristic value in EEPROM. After the product is offline, the characteristic value can be used to determine whether the sensor is successfully matched.

[0047] The system and method proposed in this embodiment can reduce production costs. In the traditional method, one sensor corresponds to one controller. When the sensor fails or the production capacity is insufficient, the corresponding controller will fail or be disabled, resulting in cost waste. This invention allows the controller to correspond to different sensors. When one sensor fails or the production capacity is insufficient, another sensor can still be used to match the controller.

[0048] At the same time, it can improve production efficiency. The traditional method requires additional processes or manual work to ensure the consistency of the sensor and the controller. When a mismatch occurs, rework is required, resulting in low efficiency. This invention realizes adaptive matching of the sensor and the controller, without adding additional processes or manual confirmation of the consistency of the sensor and the controller, thus improving production efficiency.

[0049] Improve after-sales efficiency. In the traditional method, when a problem of replacing a part occurs, after-sales personnel need to bring the corresponding sensor or controller for repair. However, when there is a shortage of sensors or controllers, after-sales service will be delayed. This invention improves the compatibility of the controller, which can avoid the above problems and reduce after-sales pressure.

[0050] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above specific embodiments. The above specific embodiments and the description in the specification are only for further illustrating the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of the present invention to be protected is defined by the claims and their equivalents.

Claims

1. An adaptive system for TAS sensor analysis, characterized in that: Automatically calibrate the signal characteristics of Hella sensors and Valeo sensors and match the corresponding angles and torques to perform analytical algorithms; Including battery, identification module, algorithm switching module, EEPROM module, CAN module and Power LDO; The identification module is used to distinguish the type of TAS sensor used; The algorithm switching module can adaptively switch the corresponding analysis algorithm according to the TAS sensor type, and generate specific feature values ​​after the analysis algorithm is adapted; The EEPROM module stores the characteristic values ​​generated by the algorithm switching module; The battery passes through the anti-reverse circuit and supplies power to the entire system through the Power LDO; The TAS sensor transmits the sensor's period and duty cycle signals to the algorithm recognition module for frequency scanning to obtain characteristic values. After the algorithm switching module, the angle and torque values ​​are calculated to generate characteristic values. The CAN network interacts with the signal. The recognition module is connected to the CAN network through the circuit and transmits the signal to the CAN network. The characteristic values ​​obtained by the algorithm switching module are sent to the EEPROM module via the CAN network and the characteristic values ​​are stored in the EEPROM module.

2. The adaptive system for TAS sensor analysis according to claim 1, characterized in that: The TAS sensors include Hella sensors and Valeo sensors. The angle signal of the Hella sensor includes a P signal and an S signal, and the torque signal includes a T1 signal and a T2 signal; the corresponding signal frequencies are: the P signal frequency is 850Hz to 1150Hz, the S signal frequency is 178Hz to 222Hz, and the T1 / T2 signal frequency is 1700Hz to 2300Hz; The angle signal of the Valeo sensor includes AS signal and RS signal, and the torque signal includes T1' signal and T2' signal; the corresponding signal frequencies are: AS / RS signal frequency is 900Hz to 1100Hz, and T1' / T2' signal frequency is 1600Hz to 2400Hz; The S signal in the Hella sensor signal and the RS signal in the Valeo sensor signal are used as the characteristic signals of the two sensors, and the frequencies of the S signal and the RS signal are used as characteristic points. The S signal and the RS signal of the sensor are swept by the identification module to obtain the signal frequency and generate the corresponding characteristic value.

3. An adaptive method for an adaptive system of TAS sensor analysis according to claim 1, characterized in that: The specific steps include: Step 1: After the steering gear is assembled, the battery is powered on, and the identification module scans the main and auxiliary signals of the sensor to obtain the corresponding frequency information; the type of the TAS sensor is determined based on the frequency information, and the signal is sent through the CAN network to generate a characteristic value; Step 2, input the characteristic value into the algorithm switching module, and select the corresponding algorithm analysis channel according to the characteristic value. After the algorithm analysis channel is confirmed, a specific characteristic value will be generated again, and the flag bit will be stored in the EEPROM module.

4. The adaptive method of the adaptive system of TAS sensor analysis according to claim 3, characterized in that: In step 1, the CAN network uses 10ms as a signal cycle, sets ten signal cycles for confirmation, and uses 100ms as a judgment cycle. When the frequency range is between 178Hz and 222Hz, it is judged as a Hella sensor, and 0X0-Hella is sent through the CAN network to generate a characteristic value of 0_Hella; when the frequency range is between 900Hz and 1100Hz, it is judged as a Valeo sensor, and 0X1-Valeo is sent through the CAN network to generate a characteristic value of 1_Valeo; if the sensor fails, 0X3-Invalid is sent.

5. The adaptive method of the adaptive system of TAS sensor analysis according to claim 3, characterized in that: In step 2, the algorithm switching module includes two algorithm channels, which are defined as a first algorithm channel and a second algorithm channel respectively; In the first algorithm channel, the P and S signals of the Hella sensor are initialized and calculated by the cursor algorithm to obtain the initial angle value, and the final angle value is obtained according to the angle tracking. The T1 and T2 signals of the Hella sensor are firstly verified by the protocol, and then the correctness of the signal is ensured by mutual verification of the T1 and T2 signals, and then the torque value is calculated; In the second algorithm channel, the AS and RS signals of the Valeo sensor are initialized and calculated through the cursor algorithm to obtain the initial angle value, and the final angle value is obtained based on the angle tracking. The T1' and T2' signals of the Hella sensor are first verified by the protocol, and then the T1 and T2 signals are mutually verified to ensure the correctness of the signal before calculating the torque value.

6. The adaptive method of the adaptive system of TAS sensor analysis according to claim 5, characterized in that: In the step 2, the algorithm switching module includes analysis algorithms for two sensors, the Hella sensor and the Valeo sensor, and switches by judging the characteristic value; the algorithm switching module reads the characteristic value information sent by the identification module, and when the characteristic value is 0_Hella, the algorithm switching module selects to analyze the first algorithm channel, performs angle and torque analysis of the Hella sensor, and after the analysis is completed, sends 0X0-SensorValid through the CAN network to generate a characteristic value of 0X00; when the characteristic value is 1_Valeo, the algorithm switching module selects to analyze the second algorithm channel, performs angle and torque analysis of the Valeo sensor, and after the analysis is completed, sends 0X0-SensorValid through the CAN network to generate a characteristic value of 0X00; when the sensor is in a state where no analysis is performed, 0X1-SensorInvalid is sent to generate a characteristic value of 0XFF, and the characteristic value is stored in the EEPROM. After the product is offline, the characteristic value can be used to determine whether the sensor is successfully matched.

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