Protection system for intelligent controller of electric two-wheeled vehicle

Abstract

The invention provides an intelligent controller protection system for an electric two-wheeled vehicle, and the system is characterized in that a DC-DC converter is used for converting the input of a battery into low voltage, and supplying power to a low-voltage load system of the whole vehicle; the main controller is used for dynamically adjusting the PWM duty ratio according to the real-time load requirement of the whole vehicle low-voltage load system and maintaining the output voltage of the DC-DC converter to be stable. A high-speed comparator and an MOSFET switch are adopted to detect abnormal states of load current, input voltage and / or hardware temperature in real time, and hardware protection operation is executed according to a protection strategy matched with the abnormal states; based on a Kalman filtering algorithm which is optimized and expanded by introducing a genetic algorithm, dynamic compensation is carried out on a battery aging state and a temperature drift effect according to battery parameters of the high-voltage power battery, and an accurate target SOC value is obtained. According to the invention, the abnormity monitoring of the whole vehicle under different working conditions is supported, the active protection and intelligent management of the whole life cycle of the battery are realized, and the safety, cruising ability and service life of the two-wheeled electric vehicle are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric two-wheeler control, and particularly to an intelligent controller protection system for electric two-wheelers. Background Art

[0002] Traditional two-wheeled electric vehicle controllers generally have the following defects: lack of dynamic monitoring of battery status, unable to effectively avoid deep discharge or power feed caused by long-term parking, thus affecting battery life or even causing permanent damage; unable to accurately display power information, leading to user concerns during riding; there is no monitoring and warning for abnormal conditions when the whole vehicle is stationary, presenting potential safety hazards. In addition, existing independent DC-DC converters increase system complexity and cost, resulting in energy loss, and unable to achieve power path co-optimization with the controller. Summary of the Invention

[0003] For this reason, the present invention provides an intelligent controller protection system for electric two-wheelers, aiming to solve the technical problems of the lack of dynamic monitoring of battery status and the inability to accurately measure power information in the prior art.

[0004] To achieve the above object, the present invention adopts the following technical solutions: According to the first aspect of the present invention, the present invention provides an intelligent controller protection system for electric two-wheelers, the system includes: an integrated DC-DC converter, a main controller; the integrated DC-DC converter and the main controller are integrally installed; the main controller includes a hardware protection module and an SOC estimation module; The DC-DC converter is used to convert the input voltage of the high-voltage power battery into a low-voltage power supply to supply power to the low-voltage load system of the whole vehicle; The main controller is used to dynamically adjust the PWM duty cycle according to the real-time load demand of the low-voltage load system of the whole vehicle to maintain the stability of the output voltage of the DC-DC converter; The hardware protection module is used to adopt a high-speed comparator and a MOSFET switch to detect the abnormal states of load current, input voltage and / or hardware temperature in real time, and perform hardware protection operations according to the protection strategy matching the abnormal states; The SOC estimation module is used to dynamically compensate the battery aging state and temperature drift effect based on the extended Kalman filter algorithm optimized by introducing a genetic algorithm according to the battery parameters of the high-voltage power battery to obtain an accurate target SOC value for user reference and / or vehicle health monitoring.

[0005] Furthermore, the main controller further includes a dynamic / static protection module; the dynamic / static protection module is connected to the SOC estimation module and is used to obtain the target SOC value; The dynamic / static protection module is also used to monitor the operation data of the electric two-wheeler in different operation modes according to the target SOC value, and perform dynamic / static protection operations according to the active protection strategies matching different operation models; Among them, the operation modes include a charging mode, a riding mode, and a static mode.

[0006] Furthermore, the main controller and the vehicle central control device are bidirectionally communicatively connected based on a communication protocol, and are used to interact at least one of the vehicle charge and discharge status, fault codes, and SOC data in real time; Among them, the communication protocol includes at least one of a CAN protocol, an RS485 protocol, and a one-line communication protocol.

[0007] Furthermore, the integrated DC-DC converter adopts a synchronous rectification Buck topology and is equipped with a MOSFET switch with a low on-resistance; The main controller is also used to perform self-checking, register configuration, and initial PWM duty ratio setting when the high-voltage power battery is connected to the integrated DC-DC converter and starts to work, to ensure that the MOSFET switch is in the off state; The hardware protection module is built-in with a current detection circuit, which is used to detect the load current data in real time, and according to the high-speed comparator, when the load current exceeds a preset current threshold, trigger an overcurrent protection strategy; the overcurrent protection strategy includes disconnecting the MOSFET switch and recording a fault code; and / or, The hardware protection module is also used to detect in real time whether the input voltage is within the safe voltage range, and according to the high-speed comparator, when the input voltage is not within the safe voltage range, trigger an overvoltage / undervoltage protection strategy; the overvoltage / undervoltage protection strategy includes recording a fault code and locking the output; and / or, The hardware protection module is also used to detect the hardware temperature of the MOSFET switch through the low on-resistance, and according to the high-speed comparator, when the hardware temperature exceeds the safe temperature threshold, trigger a first over-temperature protection strategy; and / or, when the hardware temperature exceeds the safe temperature threshold and continues to rise, trigger a second over-temperature protection strategy; the first over-temperature protection strategy includes reducing the PWM duty ratio; the second over-temperature protection strategy includes triggering an alarm and cutting off the output.

[0008] Furthermore, the main controller is also used to: When the input voltage is within the safe voltage range, calculate the target value of the PWM duty ratio according to the real-time load demand of the vehicle low-voltage load system and the preset load priority; Perform real-time PWM duty cycle sampling using an ADC analog-to-digital converter, and dynamically adjust the voltage output of the DC-DC converter according to the error signal between the PWM duty cycle target value and the PWM duty cycle sampling value.

[0009] Furthermore, the SOC estimation module is also used for: Real-time collect the battery parameters of the high-voltage power battery through an ADC analog-to-digital converter; the battery parameters include battery voltage data, battery current data, battery temperature data, and battery internal resistance data; Establish an SOC prediction method based on the ampere-hour integration method, including: Calibrate the initial SOC value using the open-circuit voltage method, and calculate the real-time SOC data using the ampere-hour integration method. The calculation formula is as follows:

[0010] Wherein, Represents the real-time SOC value at time ; Represents the initial SOC value; Represents the nominal capacity; Represents the Coulomb efficiency; Represents the charge and discharge current at time ; And / or, Establish an SOC prediction method based on an improved extended Kalman filter algorithm, including: Taking the SOC value as the state variable, establish a discretized state model considering the nonlinear characteristics of the battery, and the formula is expressed as follows:

[0011] Wherein, Represents the SOC value at the current time ; Represents the SOC value at the previous time ; Represents the charge and discharge current at the current time ; Represents the sampling time interval; Represents the temperature Under the actual capacity of the battery; Taking the terminal voltage as the observed value, establish an SOC-voltage nonlinear relationship in combination with the second-order RC equivalent circuit model, and the formula is expressed as follows:

[0012] Wherein, Represents the terminal voltage at the current time ; Is the open-circuit voltage at the current time ; represents the internal resistance value of the battery affected by the SOC value and temperature at the current moment ; Dynamically update the process noise and the covariance matrix of the observation noise according to the internal resistance change rate and temperature drift amount; Use the dynamic fusion strategy of the electric two-wheeler under different working conditions to correct the SOC value, and obtain the corrected target SOC value.

[0013] Furthermore, the SOC estimation module is also used for: When the electric two-wheeler is in a low-current steady-state working condition, adopt the SOC prediction method based on the improved extended Kalman filter algorithm, and use the battery voltage data and the battery internal resistance data to correct the SOC value; When the electric two-wheeler is in a high-dynamic working condition, adopt the SOC prediction method based on the ampere-hour integration method, and calibrate the integration error in real time through the extended Kalman filter algorithm; Perform data synchronization at preset time intervals, dynamically allocate corresponding weights according to different working conditions, and calculate the target SOC value through the weighted average algorithm.

[0014] Furthermore, the SOC estimation module is also used for: Combine the actual measurement value of the OCV-SOC curve, and use the Kalman gain to dynamically correct the state prediction value. The formula is as follows:

[0015] where, represents the state prediction value of the state variable SOC and polarization voltage at the current moment ; represents the actual measurement value of the terminal voltage at the current moment ; represents the Kalman gain that determines the weight distribution between the state prediction value and the actual measurement value at the current moment ; represents the observation matrix that maps the state space of the OCV-SOC curve to the measurement space at the current moment. The formula is as follows:

[0016]

[0017] where, represents the terminal voltage at the current moment ; represents the polarization voltage; is obtained through the difference of the OCV-SOC curve.

[0018] Further, the SOC estimation module is also used for: Correcting the actual battery capacity and / or the internal resistance value of the battery, including: Establishing a capacity attenuation model based on the Arrhenius equation, and using the capacity attenuation model to correct the actual battery capacity. The formula is as follows:

[0019] Wherein, represents the actual battery capacity at temperature ; represents the nominal capacity; is the temperature coefficient; = 25°C; And / or, Fitting the internal resistance-temperature curve through experimental data, and using the internal resistance-temperature curve to correct the internal resistance value of the battery. The formula is as follows:

[0020] Wherein, represents the internal resistance value of the battery at temperature ; represents the internal resistance value of the battery at temperature ; α represents the internal resistance temperature coefficient.

[0021] Further, the dynamic / static protection module is also used for: When the electric two-wheeler is in the charging mode, based on the target SOC value, using a current sensor and a voltage sensor to collect first current data and first voltage data in real time during the charging process; when the first current data exceeds the first current threshold and the duration reaches the first time threshold, or, when the first voltage data reaches the first voltage threshold, disconnect the charging circuit through the built-in dual-redundancy protection circuit; When the electric two-wheeler is in the riding mode, based on the target SOC value, using a closed-loop Hall current sensor to monitor the motor load current data in real time; when the motor load current data exceeds the second current threshold or the increase rate exceeds the first speed threshold, cut off the power supply to the motor and turn off the power supply of the whole vehicle; When the electric two-wheeler is in the static mode, based on the target SOC value, using a zero-drift operational amplifier to detect micro current data in real time; when the micro current data exceeds the third current threshold and the duration reaches the second time threshold, trigger an alarm; when the micro current data exceeds the third current threshold and the duration reaches the third time threshold, cut off the main power supply; when the static time of the whole vehicle reaches the fourth time threshold and it is monitored that the SOC value drops by more than the second speed threshold, turn off the unnecessary loads.

[0022] The present invention adopts the above technical solutions and has at least the following beneficial effects: Through the present invention, an intelligent controller protection system for an electric two-wheeler is proposed, which at least includes: an integrated DC-DC converter and a main controller; the integrated DC-DC converter and the main controller are integrally installed; the main controller includes a hardware protection module and an SOC estimation module; the DC-DC converter is used to convert the input voltage of the high-voltage power battery into a low-voltage power supply to supply power to the low-voltage load system of the whole vehicle; the main controller is used to dynamically adjust the PWM duty cycle according to the real-time load demand of the low-voltage load system of the whole vehicle to maintain the stability of the output voltage of the DC-DC converter; the hardware protection module is used to adopt a high-speed comparator and a MOSFET switch to detect the abnormal states of the load current, input voltage and / or hardware temperature in real time, and perform hardware protection operations according to the protection strategy matching the abnormal states; the SOC estimation module is used to perform dynamic compensation on the battery aging state and temperature drift effect based on the extended Kalman filter algorithm optimized by introducing a genetic algorithm according to the battery parameters of the high-voltage power battery to obtain an accurate target SOC value for user reference and / or vehicle health monitoring. Through the present invention, abnormal monitoring under different working conditions of the whole vehicle is supported, active protection and intelligent management of the entire life cycle of the battery are realized, and the safety, endurance and service life of the two-wheeled electric vehicle are significantly improved.

[0023] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 Shows a schematic diagram of an intelligent controller protection system for an electric two-wheeler provided by an embodiment of the present invention; Figure 2 Shows a schematic diagram of the integrated design of an integrated DC-DC converter and a main controller provided by an embodiment of the present invention; Figure 3 Shows a schematic diagram of the working principle of a hardware protection module provided by an embodiment of the present invention; Figure 4 Shows a schematic diagram of the working principle of an SOC estimation module provided by an embodiment of the present invention; Figure 5 The schematic diagram of the working principle of the dynamic / static protection module provided by an embodiment of the present invention is shown. Detailed implementation manners

[0026] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0027] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, elements defined by the statement "comprising..." do not exclude the presence of additional identical elements in the process, method, article or device including the said elements.

[0028] An embodiment of the present invention provides an intelligent controller protection system for an electric two-wheeler, as Figure 1 shown, which at least includes an integrated DC-DC converter 110 and a main controller 120. The main controller 120 includes a hardware protection module 121, an SOC estimation module 122, and / or a dynamic / static protection module 123.

[0029] In an embodiment of the present invention, as Figure 2As shown, the integrated DC-DC converter 110 is integrated with the main controller 120 and is used to convert the input voltage of the high-voltage power battery (such as 72V) into a low-voltage power supply (such as 12V) to supply power to the low-voltage load system of the whole vehicle. The input terminal of the high-voltage power battery is connected to the integrated DC-DC converter, and the output terminal is divided into two paths: one is the positive pole of the low-voltage power supply, and the other is the negative pole of the low-voltage power supply. The output terminal is connected to the low-voltage load of the whole vehicle. In the embodiment of the present invention, through the integrated installation of the integrated DC-DC converter 110 and the main controller 120, and the highly integrated design of integrating the three functional modules of the hardware protection module 121, the SOC estimation module 122, and the dynamic / static protection module 123 into a single controller, the volume is reduced by 20% and the cost is reduced by more than 10%, solving the technical problems of high complexity and low reliability caused by discrete modules in the existing solutions.

[0030] In practical applications, the circuit design can adopt a synchronous rectification Buck topology structure and be equipped with MOSFET switches with low on-resistance (such as MOS devices), which can improve the conversion efficiency of the integrated DC-DC converter to more than 92%. The main controller 120 and the vehicle central control device are connected for two-way communication based on a communication protocol (such as CAN protocol, RS485 protocol, or one-wire communication protocol, etc.) and are used for real-time interaction of dynamic monitoring such as vehicle charge and discharge status, fault codes, and SOC data to achieve remote warning and ensure the safety of the whole vehicle in real time.

[0031] Specifically, in the normal operation state of the electric two-wheeler, the main controller 120 (MCU) can be used to dynamically adjust the PWM duty cycle according to the real-time load demand of the low-voltage load system of the whole vehicle (such as the power of the instrument panel and lights) to maintain the stability of the output voltage of the integrated DC-DC converter 110; the hardware protection module 121 can be used to adopt a high-speed comparator and MOSFET switches to detect the abnormal status of the load current, input voltage, and / or hardware temperature in real time and perform hardware protection operations according to the protection strategy matching the abnormal status; the SOC estimation module 122 can be used to dynamically compensate the battery aging state and temperature drift effect based on the extended Kalman filter algorithm optimized by introducing the genetic algorithm according to the battery parameters of the high-voltage power battery to obtain an accurate target SOC value for user reference and / or vehicle health monitoring.

[0032] As Figure 3 shown, when the main controller 120 (MCU) receives the access of the DC main power supply (high-voltage power battery) and the integrated DC-DC converter starts to work, it performs self-check, register configuration, and initial PWM duty cycle setting (such as the default 50%) to ensure that the MOSFET switch is in the off state to avoid power-on impact.

[0033] Furthermore, the hardware protection module 121 is built-in with a current detection circuit, which can be used to detect load current data in real time. When it is determined by a high-speed comparator that the load current exceeds a preset current threshold (e.g., 150% of the rated current value), an overcurrent protection strategy is triggered, that is, the MOSFET switch is disconnected and a fault code is recorded. In actual operation, the circuit can be cut off within 1 ms, and the response speed is more than 10 times faster than that of traditional software detection schemes.

[0034] The hardware protection module 121 can also be used to detect in real time whether the input voltage is within the safe voltage range (e.g., 36~90V). When it is determined by a high-speed comparator that the input voltage is not within the safe voltage range, an overvoltage / undervoltage protection strategy is triggered, that is, a fault code is recorded and the output is locked.

[0035] When the input voltage is within the safe voltage range, the main controller 120 (MCU) can calculate the PWM duty cycle target value according to the real-time load demand of the vehicle's low-voltage load system and the preset load priority; use the ADC analog-to-digital converter to sample the PWM duty cycle in real time, and dynamically adjust the voltage output of the integrated DC-DC converter according to the error signal between the PWM duty cycle target value and the PWM duty cycle sampled value.

[0036] Example 1: If the instrument power demand is detected to be 2A, the required duty cycle is calculated by the MCU, and the calculation formula is as follows:

[0037] where, represents the PWM duty cycle; represents the input voltage; represents the output voltage; represents the efficiency parameter.

[0038] Example 2: If the load suddenly increases (e.g., the lights are turned on), the PWM duty cycle can be corrected in real time through a feedback loop (such as PID control) to maintain the output voltage stability (within ±2%). According to the output voltage, after being divided by voltage-dividing resistors (e.g., 、 ), it drops to the 0.6V reference value. After being sampled by the ADC analog-to-digital converter, it is compared with the PWM duty cycle target value, and the error signal is used to adjust the duty cycle. The formula is expressed as follows:

[0039] Based on the load dynamic adjustment mechanism of the MCU, the optimization loop stability is achieved, voltage oscillation is prevented, and the stable operation of the vehicle is ensured.

[0040] In an optional embodiment, the hardware protection module 121 can also detect the hardware temperature of the MOSFET switch through a low on-resistance, and trigger a first over-temperature protection strategy, that is, reduce the PWM duty cycle, according to the high-speed comparator when the hardware temperature exceeds the safe temperature threshold (for example, 100 °C); when the hardware temperature exceeds the safe temperature threshold and continues to rise, trigger a second over-temperature protection strategy, that is, trigger an alarm and cut off the output.

[0041] Based on the abnormal states of the load current, input voltage, and / or hardware temperature detected in real time by the hardware protection module 121 and the corresponding hardware protection mechanism, software delay is avoided, adjusted in combination with dynamic thresholds, the risk of false triggering is reduced, the vehicle safety and reliability are significantly improved, and the service life is extended. It should be noted that after reconnecting the power supply, the MCU can also be used to restart the self-check process, and gradually restore the voltage output after confirming safety. Hardware-level protection mechanism.

[0042] Furthermore, the SOC estimation module 122 in the embodiment of the present invention adopts the Thevenin battery equivalent circuit model (ECM) and multi-dimensional data preprocessing, combines the open circuit voltage method (OCV) and the ampere-hour integration method, dynamically compensates for temperature and internal resistance changes, and also introduces a genetic algorithm to optimize the parameters of the extended Kalman filter (EKF) (such as the process noise covariance Q) to adapt to the parameter drift caused by battery aging.

[0043] Specifically, as Figure 4 shown, the SOC estimation module 122 first collects the battery parameters of the high-voltage power battery in real time through an ADC analog-to-digital converter. The battery parameters include battery voltage data, battery current data, battery temperature data, and battery internal resistance data. Among them, the battery voltage data acquisition can use a high-precision differential ADC analog-to-digital converter (such as a 24-bit Σ-Δ type) to measure the battery pack terminal voltage and the single-cell voltage in real time, and eliminate the instantaneous fluctuation through a moving average filter; the battery current data monitoring can collect the charge and discharge current based on a Hall sensor or a shunt resistor, with an accuracy of ±0.3%, and synchronously record the current direction (charging is positive, discharging is negative); the battery temperature data can be monitored by NTC sensors set on the battery surface and key connection points, with a resolution of 0.5 °C for the ambient and battery body temperatures; the battery internal resistance data can be obtained by the pulse discharge method (such as applying a 1C current pulse for 10 ms) to obtain the transient voltage change, and calculate the battery internal resistance value in combination with Ohm's law. The formula is as follows:

[0044] where represents the voltage difference before and after the pulse; represents the pulse current amplitude.

[0045] Furthermore, the SOC estimation module 122 predicts the SOC value using the Extended Kalman Filter algorithm (EKF). Specifically, an SOC prediction method based on the ampere-hour integration method is established, including: calibrating the initial SOC value using the open-circuit voltage method, and calculating the real-time SOC data using the ampere-hour integration method. The calculation formula is as follows:

[0046] Wherein, represents the real-time SOC value at time ; represents the initial SOC value; represents the nominal capacity; represents the Coulomb efficiency (charging = 0.95, discharging = 1.0); represents the charge and discharge current at time , positive for charging and negative for discharging.

[0047] An SOC prediction method based on the improved Extended Kalman Filter algorithm is established, including: taking the SOC value as the state variable, and establishing a discretized state model considering the battery's nonlinear characteristics. The formula is expressed as follows:

[0048] Wherein, represents the SOC value at the current time ; represents the SOC value at the previous time ; represents the charge and discharge current at the current time ; represents the sampling time interval; represents the temperature under which the actual capacity of the battery can be dynamically corrected through the built-in temperature compensation model.

[0049] Construct an observation equation, including: taking the terminal voltage as the observation value, and establishing a SOC-voltage nonlinear relationship in combination with the second-order RC equivalent circuit model. The formula is expressed as follows:

[0050] -Wherein, represents the terminal voltage at the current time ; is the open-circuit voltage at the current time ; represents the internal resistance of the battery affected by the SOC value and temperature at the current time ; During the process of predicting the SOC value based on the Extended Kalman Filter (EKF) algorithm mentioned above, the SOC estimation module 122 can also be used to dynamically update the process noise according to the internal resistance change rate and the temperature drift amount and the covariance matrix of the observation noise to improve the model robustness. Furthermore, the dynamic fusion strategy of the electric two-wheeler under different working conditions is used to correct the SOC value, and the corrected target SOC value is obtained. Among them, the dynamic fusion strategy means that when the electric two-wheeler is in a low-current steady-state working condition (for example, |I| < 0.1C), the SOC prediction method based on the improved Extended Kalman Filter algorithm is adopted, and the battery voltage data and the battery internal resistance data are used to correct the SOC value; when the electric two-wheeler is in a high-dynamic working condition (for example, acceleration, braking, etc.), the SOC prediction method based on the ampere-hour integration method is adopted, and the integration error is calibrated in real time through the Extended Kalman Filter algorithm; data synchronization is performed at preset time intervals, and corresponding weights are dynamically allocated according to different working conditions, and the target SOC value is calculated through the weighted average algorithm.

[0051] In the embodiment of the present invention, the error correction of the SOC value is achieved by updating the SOC prediction value in combination with the actual measurement value of the OCV-SOC curve, and the state prediction value is dynamically corrected using the Kalman gain. The formula is as follows:

[0052] wherein, represents the state prediction values of the state variables SOC and polarization voltage at the current time ; represents the actual measurement value of the terminal voltage at the current time ; represents the Kalman gain that determines the weight distribution between the state prediction value and the actual measurement value at the current time ; represents the observation matrix that maps the state space of the OCV-SOC curve to the measurement space at the current time .

[0053] It should be noted that the Kalman gain is used to weigh the influence of the prediction error and the measurement error. If the measurement noise is small (such as high sensor accuracy), is close to 1, and the correction amplitude is large; conversely, if the model prediction is more reliable, is close to 0, and the correction amplitude is small. For the observation matrix , in the battery model, the state variables usually include SOC and polarization voltage (such as the RC parallel voltage in the Thevenin model), and the observation matrix needs to map the state variables to the measurement voltage space. The formula is as follows:

[0054] Among them, represents the terminal voltage at the current moment ; represents the polarization voltage; It is obtained through the difference of the OCV-SOC curve. Therefore, the observation matrix has the form of:

[0055] That is to say, by taking the partial derivatives of SOC and the polarization voltage, the influence of the two on the measured voltage is reflected.

[0056] The SOC estimation module 122 can also be used to dynamically correct the actual battery capacity and / or the internal resistance value of the battery through a built-in temperature compensation model, that is, to correct the capacity and internal resistance parameters according to the temperature sensor data to improve the estimation accuracy in low-temperature / high-temperature scenarios.

[0057] Specifically, for capacity-temperature correction, the SOC estimation module 122 can establish a capacity decay model based on the Arrhenius equation and use the capacity decay model to correct the actual battery capacity. The formula is as follows:

[0058] Among them, represents the temperature ; the actual battery capacity at represents the nominal capacity; is the temperature coefficient (for example, the typical value of lead-acid batteries = 0.008 / °C); = 25°C.

[0059] For internal resistance-temperature correction, the SOC estimation module 122 can fit the internal resistance-temperature curve through experimental data and use the internal resistance-temperature curve to correct the internal resistance value of the battery. The formula is as follows:

[0060] Among them, represents the temperature ; the internal resistance value of the battery at represents the temperature ; α is the internal resistance temperature coefficient (the typical value α = 0.004 / °C).

[0061] In summary, based on the multi-parameter fusion extended Kalman filter algorithm, the battery aging and temperature drift effects are dynamically compensated to achieve an error rate of <2%. By combining the open circuit voltage method (OCV) with ampere-hour integration, the initial SOC deviation is corrected to avoid the risk of misjudging power failure. By optimizing the multi-model parallel SOC estimation, the optimal model is dynamically selected to adapt to different working conditions, improving the estimation accuracy and robustness. The accurate target SOC value obtained based on the above strategies is synchronously sent to the display instrument and the central control component for the user to intuitively apply.

[0062] Further, in the embodiment of the present invention, the dynamic / static protection module 123 is connected to the SOC estimation module 122, and can be used to obtain the target SOC value; it can also be used to monitor the operation data of the electric two-wheeler in different operation modes according to the target SOC value, and perform dynamic / static protection operations according to the active protection strategies matched with different operation models. Among them, the operation modes include a charging mode, a riding mode, and a static mode.

[0063] Specifically, as Figure 5 shown, when the electric two-wheeler is in the charging mode, the dynamic / static protection module 123, based on the target SOC value, uses a current sensor and a voltage sensor to collect the first current data and the first voltage data in real time during the charging process; when the first current data exceeds the first current threshold and the duration reaches the first time threshold, or when the first voltage data reaches the first voltage threshold, the charging circuit is disconnected through the built-in dual-redundancy protection circuit.

[0064] In actual operation, the current / voltage data during the charging process can be continuously collected by high-precision current sensors and voltage sensors. When the current exceeds 150% of the rated current value and the duration ≥ 10S, or the single-cell voltage reaches 14.8V (based on the 12V battery specification), the charging circuit is disconnected through the dual-redundancy protection circuit. The dual-redundancy protection circuit can be composed of a high-power MOSFET (such as CRST085N15N, Rds(on)=7.0mΩ) in parallel with a TVS diode to ensure that the MOSFET that can quickly and reliably cut off the charging circuit in case of an abnormality sends a protocol to the central control, the cloud, and the APP.

[0065] When the electric two-wheeler is in the riding mode, the dynamic / static protection module 123, based on the target SOC value, uses a closed-loop Hall current sensor to monitor the motor load current data in real time; when the motor load current data exceeds the second current threshold or the increase exceeds the first speed threshold, the power supply to the motor is cut off and the power supply to the whole vehicle is turned off.

[0066] In actual operation, the motor load current data can be collected by a high-precision closed-loop Hall current sensor (such as LEM LAH 100-P, with an accuracy of ±0.5%, a range of ±150A, and a response time <1μs). When the current increases by more than 50% within 2 seconds or exceeds 2 times the rated current of the controller, first turn off the motor drive MOSFET within 5ms to cut off the power supply of the motor, and then turn off the vehicle power MOSFET. When an abnormal current change is detected, if the short-circuit current persists, the main power MOSFET is disconnected after 10ms to completely isolate the fault.

[0067] When the electric two-wheeler is in the static mode, the dynamic / static protection module 123 detects the micro-current data in real time based on the target SOC value by using a zero-drift operational amplifier; when the micro-current data exceeds the third current threshold and the duration reaches the second time threshold, an alarm is triggered; when the micro-current data exceeds the third current threshold and the duration reaches the third time threshold, the main power supply is cut off; when the static duration of the whole vehicle reaches the fourth time threshold and the monitored SOC value drop exceeds the second speed threshold, non-essential loads are turned off.

[0068] In actual operation, a nanoampere-level current detection circuit can be constructed by using a zero-drift operational amplifier (such as ADI AD8629), with a detection range of 0 - 500mA, a resolution of 0.1mA, to monitor the micro-current situation of the battery in real time. If the micro-current exceeds 0.1A and persists for 30 seconds, an alarm is triggered. The alarm methods include waking up the MCU and sending an alarm to the vehicle control unit, the cloud, and the user APP through a protocol. At the same time, the vehicle's double-flash lights flash at a frequency of 0.5Hz. If the micro-current exceeds the limit and does not recover within 30 minutes, the main power MOSFET is cut off to completely isolate the fault. If the whole vehicle has been static for more than 168 hours or the monitored SOC drops by more than 0.5% per hour, it enters the sleep (low-power mode), the system turns off non-essential loads such as GPS and Bluetooth, and only retains the core monitoring circuit, with a standby power consumption <50μA. In this case, the MUC can periodically wake up to detect the battery SOC (such as once every 4 hours). If the SOC is lower than the threshold (such as 10%), the low-voltage power supply is automatically cut off to prevent deep discharge caused by long-term parking. When the vehicle is static, it can be woken up within 1 second by using the emergency key or vehicle vibration. When the MCU receives the disarming instruction sent by the remote control or detects vehicle vibration, the system quickly resumes power supply and enters the normal working state. Users can view the status information of the whole vehicle in real time through the APP to timely understand the health status and abnormal conditions of the battery. Thus, through the periodic low-power detection and threshold power-off strategy, the battery life can be extended by more than 20%, solving the pain point of power loss damage caused by users' long-term parking.

[0069] The embodiment of the present invention provides an intelligent controller protection system applied to electric two-wheel vehicles such as electric bicycles and electric motorcycles. Through the integrated design of an integrated DC-DC converter and a main controller, and by integrating functional modules such as a hardware protection module, an SOC estimation module, and dynamic / static protection modules in the main controller, the volume can be significantly reduced, the cost can be lowered, and the system efficiency and reliability can be improved; based on the accurate SOC algorithm of the extended Kalman filter, the risk of power failure can be effectively avoided, and the battery life can be extended; the hardware-level short-circuit protection response mechanism, the static power failure protection mechanism combined with the low-power wake-up mode solve the problem of battery damage caused by long-term parking; and it supports the realization of vehicle-wide collaborative monitoring. Through the above technical points, the vehicle-wide reliability and safety are significantly improved. The present invention fills the gap in the active protection and system integration of two-wheel vehicle controllers, and realizes quality improvement, energy consumption optimization, and environmental benefits through technological innovation, meeting the development trend of lightweight and intelligent in the electric vehicle industry.

[0070] Those skilled in the art can clearly understand that the specific working processes of the above-described systems, devices, modules, and units can refer to the corresponding processes in the foregoing method embodiments. For the sake of brevity, they will not be described in detail herein.

[0071] In addition, each functional unit in various embodiments of the present invention may be physically independent of each other, or two or more functional units may be integrated together, or all functional units may be integrated in a processing unit. The above-mentioned integrated functional units may be implemented in the form of hardware, or in the form of software or firmware.

[0072] Those of ordinary skill in the art can understand that if the above-mentioned integrated functional units are implemented in the form of software and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computing device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present invention when running the instructions. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0073] Alternatively, all or part of the steps of implementing the foregoing method embodiments may be completed by hardware related to program instructions (such as computing devices such as personal computers, servers, or network devices), and the program instructions may be stored in a computer-readable storage medium. When the program instructions are executed by a processor of the computing device, the computing device executes all or part of the steps of the methods described in the embodiments of the present invention.

[0074] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: within the spirit and principles of the present invention, it is still possible to modify the technical solutions described in the foregoing embodiments, or to equivalently replace some or all of the technical features therein; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the protection scope of the present invention.

Claims

1. An intelligent controller protection system for an electric two-wheeler, characterized in that, The system includes: an integrated DC-DC converter and a main controller; the integrated DC-DC converter is integrally installed with the main controller; the main controller includes a hardware protection module and an SOC estimation module; The integrated DC-DC converter is used to convert the input voltage of the high-voltage power battery into a low-voltage power supply to supply power to the vehicle's low-voltage load system; The main controller is used to dynamically adjust the PWM duty cycle according to the real-time load demand of the vehicle's low-voltage load system to maintain the stability of the output voltage of the integrated DC-DC converter; The hardware protection module is used to adopt a high-speed comparator and a MOSFET switch to detect the abnormal states of the load current, input voltage and / or hardware temperature in real time, and execute hardware protection operations according to the protection strategy matching the abnormal states; The SOC estimation module is used to dynamically compensate the battery aging state and temperature drift effect based on the extended Kalman filter algorithm optimized by introducing the genetic algorithm, according to the battery parameters of the high-voltage power battery, to obtain an accurate target SOC value for user reference and / or vehicle health monitoring.

2. The system according to claim 1, wherein The main controller further includes a dynamic / static protection module; the dynamic / static protection module is connected to the SOC estimation module and is used to obtain the target SOC value; The dynamic / static protection module is further used to monitor the operation data of the electric two-wheeler in different operation modes according to the target SOC value, and execute dynamic / static protection operations according to the active protection strategy matching different operation models; Wherein, the operation modes include a charging mode, a riding mode and a static mode.

3. The system according to claim 1, characterized in that The main controller is bidirectionally communicatively connected with the vehicle central control device based on a communication protocol, and is used to interact at least one of the vehicle charge and discharge state, fault code and SOC data in real time; Wherein, the communication protocol includes at least one of the CAN protocol, the RS485 protocol and the one-line communication protocol.

4. The system according to claim 1, wherein The integrated DC-DC converter adopts a synchronous rectification Buck topology structure and is equipped with a MOSFET switch with a low on-resistance; The main controller is further used to perform self-check, register configuration and initial PWM duty cycle setting when the high-voltage power battery is connected to the integrated DC-DC converter and starts to work, to ensure that the MOSFET switch is in the off state; The hardware protection module is internally provided with a current detection circuit, which is used to detect the load current data in real time, and trigger an overcurrent protection strategy according to the judgment of the high-speed comparator when the load current exceeds a preset current threshold; the overcurrent protection strategy includes disconnecting the MOSFET switch and recording the fault code; and / or, The hardware protection module is further used to detect in real time whether the input voltage is within the safe voltage range, and trigger an overvoltage / undervoltage protection strategy according to the judgment of the high-speed comparator when the input voltage is not within the safe voltage range; the overvoltage / undervoltage protection strategy includes recording the fault code and locking the output; and / or, The hardware protection module is also used to detect the hardware temperature of the MOSFET switch through the low on-resistance, and trigger the first over-temperature protection strategy according to the judgment of the high-speed comparator when the hardware temperature exceeds the safety temperature threshold; and / or, when the hardware temperature exceeds the safety temperature threshold and continues to rise, trigger the second over-temperature protection strategy; The first over-temperature protection strategy includes reducing the PWM duty cycle; The second over-temperature protection strategy includes triggering an alarm and cutting off the output.

5. The system according to claim 4, wherein The main controller is also used to: When the input voltage is within the safety voltage range, calculate the PWM duty cycle target value according to the real-time load demand of the vehicle low-voltage load system and the preset load priority; Use the ADC analog-to-digital converter to perform real-time PWM duty cycle sampling, and dynamically adjust the voltage output of the integrated DC-DC converter according to the error signal between the PWM duty cycle target value and the PWM duty cycle sampling value.

6. The system according to claim 1, wherein The SOC estimation module is also used to: Real-time collect the battery parameters of the high-voltage power battery through the ADC analog-to-digital converter; the battery parameters include battery voltage data, battery current data, battery temperature data, and battery internal resistance data; Establish an SOC prediction method based on the ampere-hour integration method, including: Calibrate the initial SOC value using the open-circuit voltage method, and calculate the real-time SOC data using the ampere-hour integration method. The calculation formula is as follows: Among them, represents the real-time value of SOC at ; represents the initial value of SOC; represents the nominal capacity; represents the Coulomb efficiency; represents the charge and discharge current at ; and / or, Establish an SOC prediction method based on the improved extended Kalman filter algorithm, including: Taking the SOC value as the state variable, establish a discretized state model considering the battery's non-linear characteristics, which is expressed by the formula as follows: Among them, represents the SOC value at the current moment ; represents the SOC value at the previous moment ; represents the charge and discharge current at the current moment ; represents the sampling time interval; represents the actual battery capacity at the temperature ; Taking the terminal voltage as the observation value, establish an SOC-voltage non-linear relationship in combination with the second-order RC equivalent circuit model, which is expressed by the formula as follows: Among them, represents the terminal voltage at the current moment; The terminal voltage at the current moment; is the open-circuit voltage at the current moment; The open-circuit voltage at the current moment; represents the internal resistance value of the battery affected by the SOC value and temperature at the current moment; The internal resistance value of the battery affected by the SOC value and temperature at the current moment; Dynamically update the process noise according to the internal resistance change rate and the temperature drift and the observation noise covariance matrix; Use the dynamic fusion strategy of the electric two-wheeler under different working conditions to correct the SOC value to obtain the corrected target SOC value.

7. The system according to claim 6, wherein The SOC estimation module is also used to: When the electric two-wheeler is in the low-current steady-state working condition, adopt the SOC prediction method based on the improved extended Kalman filter algorithm, and use the battery voltage data and the battery internal resistance data to correct the SOC value; When the electric two-wheeler is in the high-dynamic working condition, adopt the SOC prediction method based on the ampere-hour integration method, and calibrate the integration error in real time through the extended Kalman filter algorithm; Perform data synchronization at preset time intervals, dynamically allocate corresponding weights according to different working conditions, and calculate the target SOC value through the weighted average algorithm.

8. The system according to claim 6, wherein The SOC estimation module is also used to: Combined with the actual measurement value of the OCV-SOC curve, use the Kalman gain to dynamically correct the state prediction value, which is expressed by the formula as follows: Among them, represents the state prediction values of the state variables SOC and polarization voltage at the current moment; represents the actual measured value of the terminal voltage at the current moment; represents the Kalman gain that determines the weight distribution between the state prediction value and the actual measured value at the current moment; represents the observation matrix that maps the state space of the OCV-SOC curve to the measurement space at the current moment, and the formula is as follows: ​ Among them, represents the terminal voltage at the current moment ; represents the polarization voltage; It is obtained through the difference of the OCV-SOC curve.

9. The system according to claim 6, wherein The SOC estimation module is also used to: Correct the actual battery capacity and / or the battery internal resistance value, including: Establish a capacity attenuation model based on the Arrhenius equation, and use the capacity attenuation model to correct the actual battery capacity. The formula is as follows: Among them, represents the actual battery capacity at a certain temperature; represents the nominal capacity; is the temperature coefficient; = 25 °C; and / or, Fit the internal resistance-temperature curve through experimental data, and use the internal resistance-temperature curve to correct the battery internal resistance value. The formula is as follows: Among them, represents the internal resistance value of the battery at the temperature; represents the internal resistance value of the battery at the temperature; α represents the internal resistance temperature coefficient.

10. The system according to claim 2, characterized in that, The dynamic / static protection module is further configured to: When the electric two-wheeler is in the charging mode, based on the target SOC value, use the current sensor and the voltage sensor to collect the first current data and the first voltage data in real time during the charging process; when the first current data exceeds the first current threshold and the duration reaches the first time threshold, or when the first voltage data reaches the first voltage threshold, disconnect the charging circuit through the built-in dual-redundancy protection circuit; When the electric two-wheeler is in the riding mode, based on the target SOC value, use the closed-loop Hall current sensor to monitor the motor load current data in real time; when the motor load current data exceeds the second current threshold or the increase rate exceeds the first speed threshold, cut off the power supply to the motor and turn off the power supply of the entire vehicle; When the electric two-wheeler is in the static mode, based on the target SOC value, use the zero-drift operational amplifier to detect the micro-current data in real time; when the micro-current data exceeds the third current threshold and the duration reaches the second time threshold, trigger an alarm; when the micro-current data exceeds the third current threshold and the duration reaches the third time threshold, cut off the main power supply; when the static time of the entire vehicle reaches the fourth time threshold and the monitored SOC value drop exceeds the second speed threshold, turn off the non-essential loads.

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