An intelligent controller protection system for an electric two-wheeler

Abstract

The application provides an electric two-wheeled vehicle intelligent controller protection system, wherein: a DC-DC converter is used for converting battery input into low voltage to supply power for a whole vehicle low-voltage load system; a main controller is used for dynamically adjusting PWM duty cycle according to real-time load demand of the whole vehicle low-voltage load system to maintain the output voltage of the DC-DC converter stable; a high-speed comparator and a MOSFET switch are used to detect abnormal states of load current, input voltage and / or hardware temperature in real time, and to perform hardware protection operation according to protection strategies matched with the abnormal states; a Kalman filter algorithm optimized and extended based on introduction of a genetic algorithm is used to dynamically compensate battery aging state and temperature drift effect according to battery parameters of a high-voltage power battery to obtain a precise target SOC value. Through the application, abnormal monitoring under different working conditions of the whole vehicle is supported, active protection and intelligent management of the whole life cycle of the battery are realized, and the safety, endurance and service life of the two-wheeled electric vehicle are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric two-wheeled vehicle control, and in particular to an electric two-wheeled vehicle intelligent controller protection system. Background Art

[0002] Traditional two-wheeled electric vehicle controllers commonly suffer from the following flaws: They lack dynamic battery status monitoring, making it impossible to effectively prevent deep discharge or overcharging caused by prolonged parking, which can impact battery life and even cause permanent damage; they fail to accurately display battery charge information, leading to user concerns; and they lack monitoring and early warning for abnormal stationary vehicle conditions, posing a safety hazard. Furthermore, existing standalone DC-DC converters increase system complexity and cost, lead to energy loss, and fail to achieve coordinated power path optimization with the controller. Summary of the Invention

[0003] To this end, the present invention provides an intelligent controller protection system for an electric two-wheeled vehicle, aiming to solve the technical problems existing in the prior art of lacking dynamic monitoring of battery status and being unable to accurately measure power information.

[0004] To achieve the above objectives, the present invention adopts the following technical solutions:

[0005] According to a first aspect of the present invention, the present invention provides an intelligent controller protection system for an electric two-wheeled vehicle, the system comprising: an integrated DC-DC converter and a main controller; the integrated DC-DC converter and the main controller are integrally installed; the main controller comprises a hardware protection module and a SOC estimation module;

[0006] The DC-DC converter is used to convert the input voltage of the high-voltage power battery into a low-voltage power supply to power the low-voltage load system of the vehicle;

[0007] The main controller is used to dynamically adjust the PWM duty cycle according to the real-time load demand of the vehicle low-voltage load system to maintain the output voltage of the DC-DC converter stable;

[0008] The hardware protection module is used to use a high-speed comparator and a MOSFET switch to detect abnormal conditions of load current, input voltage and / or hardware temperature in real time, and perform hardware protection operations according to a protection strategy that matches the abnormal conditions;

[0009] The SOC estimation module is used to dynamically compensate for battery aging status and temperature drift effects based on the battery parameters of the high-voltage power battery based on a Kalman filter algorithm optimized and extended by a genetic algorithm, thereby obtaining an accurate target SOC value for user reference and / or vehicle health monitoring.

[0010] Further, the main controller further comprises a dynamic / static protection module; the dynamic / static protection module is connected with the SOC estimation module, and is configured to obtain the target SOC value;

[0011] The dynamic / static protection module is further configured to monitor running data of the electric two-wheeled vehicle in different running modes according to the target SOC value, and perform a dynamic / static protection operation according to an active protection strategy matched with different running modes;

[0012] The running modes include a charging mode, a riding mode and a static mode.

[0013] Further, the main controller is in bidirectional communication connection with a vehicle central control device based on a communication protocol, and is configured to interact at least one of a vehicle charging / discharging state, a fault code and an SOC data in real time;

[0014] The communication protocol includes at least one of a CAN protocol, an RS485 protocol and a one-wire protocol.

[0015] Further, the integrated DC-DC converter adopts a synchronous rectification Buck topology structure and carries a MOSFET switch with low conduction resistance;

[0016] The main controller is further configured to perform self-checking, register configuration and initial PWM duty ratio setting when the high-voltage power battery starts to work by being connected to the integrated DC-DC converter, so as to ensure that the MOSFET switch is in a closed state;

[0017] The hardware protection module is internally provided with a current detection circuit, which is configured to detect load current data in real time, and trigger an overcurrent protection strategy according to the high-speed comparator when the load current exceeds a preset current threshold; the overcurrent protection strategy includes disconnecting the MOSFET switch and recording a fault code; and / or,

[0018] The hardware protection module is further configured to detect whether the input voltage is within a safe voltage range in real time, and trigger an overvoltage / undervoltage protection strategy according to the high-speed comparator when the input voltage is not within the safe voltage range; the overvoltage / undervoltage protection strategy includes recording a fault code and locking an output; and / or,

[0019] The hardware protection module is further configured to detect a hardware temperature of the MOSFET switch through the low conduction resistance, and trigger a first over-temperature protection strategy according to the high-speed comparator when the hardware temperature exceeds a safe temperature threshold; and / or, trigger a second over-temperature protection strategy when the hardware temperature exceeds the safe temperature threshold and continuously rises; the first over-temperature protection strategy includes reducing a PWM duty ratio; and the second over-temperature protection strategy includes triggering an alarm and cutting off an output.

[0020] Furthermore, the main controller is also used for:

[0021] When the input voltage is within the safe voltage range, calculating a PWM duty cycle target value according to the real-time load demand of the vehicle low-voltage load system and a preset load priority;

[0022] The ADC analog-to-digital converter is used to perform real-time PWM duty cycle sampling, and the voltage output of the DC-DC converter is dynamically adjusted according to the error signal between the PWM duty cycle target value and the PWM duty cycle sampling value.

[0023] Furthermore, the SOC estimation module is further configured to:

[0024] The battery parameters of the high-voltage power battery are collected 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;

[0025] Establish an SOC prediction method based on the ampere-hour integration method, including:

[0026] The open circuit voltage method is used to calibrate the initial SOC value, and the ampere-hour integration method is used to calculate the real-time SOC data. The calculation formula is as follows:

[0027]

[0028] in, Indicates time Real-time SOC value; Indicates the initial value of SOC; Indicates nominal capacity; represents the Coulomb efficiency; Indicates time The charge and discharge current;

[0029] and / or,

[0030] Establish an SOC prediction method based on the improved extended Kalman filter algorithm, including:

[0031] Taking the SOC value as the state variable, a discretized state model considering the nonlinear characteristics of the battery is established. The formula is as follows:

[0032]

[0033] in, Indicates the current time SOC value; Indicates the last moment SOC value; Indicates the current time The charge and discharge current; Indicates the sampling time interval; Indicates temperature The actual capacity of the battery under

[0034] Taking the terminal voltage as the observation value, the SOC-voltage nonlinear relationship is established in combination with the second-order RC equivalent circuit model. The formula is as follows:

[0035]

[0036] in, Indicates the current time The terminal voltage; For the current moment The open circuit voltage; Indicates that the current moment The internal resistance of the battery is affected by the SOC value and temperature;

[0037] Dynamically update process noise based on internal resistance change rate and temperature drift and observation noise The covariance matrix of

[0038] The SOC value is corrected using the dynamic fusion strategy of the electric two-wheeled vehicle under different working conditions to obtain a corrected target SOC value.

[0039] Furthermore, the SOC estimation module is further configured to:

[0040] When the electric two-wheeled vehicle is in a low-current steady-state operating condition, the SOC prediction method based on the improved extended Kalman filter algorithm is adopted to correct the SOC value using the battery voltage data and the battery internal resistance data;

[0041] When the electric two-wheeled vehicle is in a high dynamic working condition, a SOC prediction method based on an ampere-hour integration method is adopted, and the integration error is calibrated in real time by an extended Kalman filter algorithm;

[0042] Data synchronization is performed once every preset time interval, and corresponding weights are dynamically assigned according to different working conditions, and the target SOC value is calculated through a weighted average algorithm.

[0043] Furthermore, the SOC estimation module is further configured to:

[0044] Combined with the actual measurement value of the OCV-SOC curve, the Kalman gain is used to dynamically correct the state prediction value. The formula is as follows:

[0045]

[0046] in, Indicates the current time State variables SOC and polarization voltage state prediction values; Indicates the current time The actual measured value of the terminal voltage; Indicates the current time The Kalman gain that determines the weight distribution between the state prediction value and the actual measurement value; Indicates the current time The state space of the OCV-SOC curve is mapped to the observation matrix of the measurement space. The formula is as follows:

[0047]

[0048]

[0049] in, Indicates the current time The terminal voltage; represents the polarization voltage; Obtained by OCV-SOC curve difference.

[0050] Furthermore, the SOC estimation module is further configured to:

[0051] Correcting the actual capacity of the battery and / or the internal resistance of the battery includes:

[0052] A capacity fading model is established based on the Arrhenius equation, and the actual capacity of the battery is corrected using the capacity fading model. The formula is as follows:

[0053]

[0054] in, Indicates temperature The actual capacity of the battery under Indicates nominal capacity; is the temperature coefficient; =25℃;

[0055] and / or,

[0056] The internal resistance-temperature curve is fitted by experimental data, and the internal resistance value of the battery is corrected using the internal resistance-temperature curve. The formula is as follows:

[0057]

[0058] in, Indicates temperature The internal resistance of the battery under Indicates temperature The internal resistance of the battery under α is the temperature coefficient of internal resistance.

[0059] Furthermore, the dynamic / static protection module is also used for:

[0060] When the electric two-wheeled vehicle is in a charging mode, first current data and first voltage data are collected in real time during the charging process using a current sensor and a voltage sensor based on the target SOC value; when the first current data exceeds a first current threshold and the duration reaches a first time threshold, or when the first voltage data reaches a first voltage threshold, the charging circuit is disconnected by a built-in dual-redundant protection circuit;

[0061] When the electric two-wheeled vehicle is in riding mode, based on the target SOC value, a closed-loop Hall current sensor is used to monitor the motor load current data in real time; when the motor load current data exceeds a second current threshold or the increase exceeds a first speed threshold, power supply to the motor is cut off and power supply to the entire vehicle is turned off;

[0062] When the electric two-wheeled vehicle is in static mode, based on the target SOC value, a zero-drift operational amplifier is used to detect microcurrent data in real time; when the microcurrent data exceeds the third current threshold and the duration reaches the second time threshold, an alarm is triggered; when the microcurrent data exceeds the third current threshold and the duration reaches the third time threshold, the main power supply is cut off; when the vehicle's static time reaches the fourth time threshold and the SOC value drop is detected to exceed the second speed threshold, non-essential loads are turned off.

[0063] The present invention adopts the above technical solution and has at least the following beneficial effects:

[0064] The present invention proposes an intelligent controller protection system for an electric two-wheeled vehicle, which at least includes: an integrated DC-DC converter and a main controller; the integrated DC-DC converter is installed integrally with the main controller; the main controller includes a hardware protection module and a 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 power the low-voltage load system of the entire 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 entire vehicle to maintain the output voltage of the DC-DC converter stable; the hardware protection module is used to use a high-speed comparator and a MOSFET switch to detect abnormal conditions of load current, input voltage and / or hardware temperature in real time, and perform hardware protection operations according to a protection strategy matching the abnormal conditions; the SOC estimation module is used to dynamically compensate for battery aging status and temperature drift effects according to the battery parameters of the high-voltage power battery based on a Kalman filter algorithm optimized and extended by introducing a genetic algorithm, so as to obtain an accurate target SOC value for user reference and / or vehicle health monitoring. Through the present invention, abnormal monitoring of the entire vehicle under different operating conditions is supported, active protection and intelligent management of the battery throughout its life cycle are achieved, and the safety, endurance and service life of two-wheeled electric vehicles are significantly improved.

[0065] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0067] Figure 1 A schematic diagram of an intelligent controller protection system for an electric two-wheeled vehicle according to an embodiment of the present invention is shown;

[0068] Figure 2 A simplified schematic diagram of an integrated DC-DC converter and a main controller according to an embodiment of the present invention is shown;

[0069] Figure 3 A schematic diagram showing the working principle of a hardware protection module provided by an embodiment of the present invention is shown;

[0070] Figure 4 A schematic diagram showing the working principle of an SOC estimation module provided by an embodiment of the present invention is shown;

[0071] Figure 5 A schematic diagram showing the working principle of a dynamic / static protection module provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0072] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying 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. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0073] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0074] The embodiment of the present invention provides an electric two-wheeled vehicle intelligent controller protection system, such as Figure 1 As shown, the system comprises at least an integrated DC-DC converter 110 and a main controller 120. The main controller 120 comprises a hardware protection module 121, an SOC estimation module 122, and / or a dynamic / static protection module 123.

[0075] In the embodiment of the present invention, Figure 2 As shown, the integrated DC-DC converter 110 and the main controller 120 are designed as an integrated whole, and are used to convert the input voltage of the high-voltage power battery (e.g., 72V) into a low-voltage power supply (e.g., 12V) to power the low-voltage load system of the entire vehicle. The input end of the high-voltage power battery is connected to the integrated DC-DC converter, and the output end is divided into two paths: one is the positive electrode of the low-voltage power supply, and the other is the negative electrode of the low-voltage power supply. The output end is connected to the low-voltage load of the entire vehicle. The embodiment of the present invention achieves a 20% reduction in volume and a cost reduction of more than 10% 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, thereby solving the technical problems of high complexity and low reliability caused by the discrete modules of the existing solution.

[0076] In practical applications, the circuit design can adopt a synchronous rectification Buck topology, paired with a low on-resistance MOSFET switch (e.g., MOS device), which can improve the conversion efficiency of the integrated DC-DC converter to over 92%. The main controller 120 and the vehicle's central control unit establish a bidirectional communication connection based on a communication protocol (e.g., CAN protocol, RS485 protocol, or a one-line protocol), enabling real-time interactive monitoring of the vehicle's charging and discharging status, fault codes, and SOC data, enabling remote early warning and ensuring real-time vehicle safety.

[0077] Specifically, under the normal operating state of the electric two-wheeled vehicle, the main controller 120 (MCU) can be used to dynamically adjust the PWM duty cycle according to the real-time load requirements of the low-voltage load system of the vehicle (such as the instrument panel, lighting power, etc.), and maintain the output voltage stability of the integrated DC-DC converter 110; the hardware protection module 121 can be used to use high-speed comparators and MOSFET switches to detect abnormal conditions of load current, input voltage and / or hardware temperature in real time, and perform hardware protection operations according to a protection strategy that matches the abnormal condition; the SOC estimation module 122 can be used to dynamically compensate for the battery aging status and temperature drift effects based on the battery parameters of the high-voltage power battery based on the Kalman filter algorithm optimized and extended by the introduction of genetic algorithms, and obtain an accurate target SOC value for user reference and / or vehicle health monitoring.

[0078] like Figure 3 As shown, when the main controller 120 (MCU) receives the DC main power supply (high-voltage power battery) and the integrated DC-DC converter starts working, it performs self-test, register configuration and initial PWM duty cycle setting (for example, the default is 50%) to ensure that the MOSFET switch is in the off state to avoid power-on shock.

[0079] Furthermore, the hardware protection module 121 includes a built-in current detection circuit that monitors load current data in real time. A high-speed comparator determines when the load current exceeds a preset threshold (e.g., 150% of the rated current), triggering the overcurrent protection strategy. This disconnects the MOSFET and records a fault code. In practice, this circuit can be disconnected within 1ms, a response speed over 10 times faster than traditional software-based detection solutions.

[0080] The hardware protection module 121 can also be used to detect in real time whether the input voltage is within a safe voltage range (for example, 36~90V), and based on the high-speed comparator, when the input voltage is not within the safe voltage range, trigger the overvoltage / undervoltage protection strategy, that is, record the fault code and lock the output.

[0081] When the input voltage is within the safe voltage range, the main controller 120 (MCU) can calculate the PWM duty cycle target value based on the real-time load demand and preset load priority of the vehicle's low-voltage load system; 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 based on the error signal between the PWM duty cycle target value and the PWM duty cycle sampling value.

[0082] Example 1: If the instrument power requirement is detected to be 2A, the MCU calculates the required duty cycle using the following formula:

[0083]

[0084] in, Indicates PWM duty cycle; Indicates input voltage; Indicates the output voltage; Represents the efficiency parameter.

[0085] Example 2: If the load suddenly increases (for example, when a light is turned on), the PWM duty cycle can be modified in real time through a feedback loop (such as PID control) to maintain a stable output voltage (within ±2%). 、 ) is reduced to a reference value of 0.6V. The ADC analog-to-digital converter samples it and compares it with the PWM duty cycle target value. The error signal is used to adjust the duty cycle. The formula is as follows:

[0086]

[0087] The MCU-based dynamic load adjustment mechanism optimizes loop stability, prevents voltage oscillations, and ensures stable vehicle operation.

[0088] 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 determine based on a high-speed comparator that when the hardware temperature exceeds a safety temperature threshold (for example, 100°C), it triggers a first over-temperature protection strategy, i.e., reduces the PWM duty cycle; when the hardware temperature exceeds the safety temperature threshold and continues to rise, it triggers a second over-temperature protection strategy, i.e., triggers an alarm and cuts off the output.

[0089] Hardware protection module 121 detects abnormal load current, input voltage, and / or hardware temperature in real time, and implements corresponding hardware protection mechanisms to avoid software delays. Dynamic threshold adjustments are combined to reduce the risk of false triggering, significantly improving vehicle safety and reliability and extending vehicle service life. It should be noted that after reconnecting to power, the MCU can also be used to restart the self-test process, gradually restoring voltage output after confirming safety. This is a hardware-level protection mechanism.

[0090] 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, combined with the open circuit voltage method (OCV) and the ampere-hour integration method, to dynamically compensate for temperature and internal resistance changes, and also introduces a genetic algorithm to optimize the extended Kalman filter (EKF) parameters (such as the process noise covariance Q) to adapt to parameter drift caused by battery aging.

[0091] Specifically, if Figure 4 As shown, the SOC estimation module 122 first collects the battery parameters of the high-voltage power battery in real time 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. Among them, the battery voltage data collection 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 single cell voltage in real time, and eliminate instantaneous fluctuations through sliding average filtering; the battery current data monitoring can collect the charge and discharge current based on the Hall sensor or shunt resistor with an accuracy of ±0.3%, and simultaneously record the current direction (charging is positive and discharging is negative); the battery temperature data can be monitored by the NTC sensor set on the battery surface and key connection points with a resolution of 0.5°C to monitor the ambient and battery body temperature; the battery internal resistance data can be obtained by the pulse discharge method (such as applying a 1C current pulse for 10ms) to obtain transient voltage changes, and the battery internal resistance value can be calculated in combination with Ohm's law. The formula is as follows:

[0092]

[0093] in, Indicates the voltage difference before and after the pulse; Indicates the pulse current amplitude.

[0094] Furthermore, the SOC estimation module 122 uses the extended Kalman filter (EKF) algorithm to predict the SOC value. Specifically, a SOC prediction method based on the ampere-hour integration method is established, including: using the open circuit voltage method to calibrate the initial SOC value, and using the ampere-hour integration method to calculate the real-time SOC data. The calculation formula is as follows:

[0095]

[0096] in, Indicates time Real-time SOC value; Indicates the initial value of SOC; Indicates nominal capacity; Indicates the coulombic efficiency (charge =0.95, discharge =1.0); Indicates time The charge and discharge current is positive for charging and negative for discharging.

[0097] An SOC prediction method based on an improved extended Kalman filter algorithm is established, including: using the SOC value as a state variable and establishing a discretized state model that takes into account the nonlinear characteristics of the battery. The formula is as follows:

[0098]

[0099] in, Indicates the current time SOC value; Indicates the last moment SOC value; Indicates the current time The charge and discharge current; Indicates the sampling time interval; Indicates temperature The actual capacity of the battery under load can be dynamically corrected through the built-in temperature compensation model.

[0100] The observation equation is constructed, including: taking the terminal voltage as the observation value and combining it with the second-order RC equivalent circuit model to establish the SOC-voltage nonlinear relationship. The formula is as follows:

[0101]

[0102] in, Indicates the current time The terminal voltage; For the current moment The open circuit voltage; Indicates that the current moment The internal resistance of the battery is affected by the SOC value and temperature;

[0103] In the process of predicting the SOC value based on the extended Kalman filter algorithm (EKF), the SOC estimation module 122 can also be used to dynamically update the process noise according to the internal resistance change rate and temperature drift. and observation noise The covariance matrix of the two-wheeled vehicle is used to improve the robustness of the model. Then, the dynamic fusion strategy of the electric two-wheeled vehicle under different working conditions is used to correct the SOC value to obtain the corrected target SOC value. Among them, the dynamic fusion strategy means that when the electric two-wheeled vehicle is in a low-current steady-state condition (for example, |I|<0.1C), the SOC prediction method based on the improved extended Kalman filter algorithm is used to correct the SOC value using battery voltage data and battery internal resistance data; when the electric two-wheeled vehicle is in a high-dynamic condition (such as acceleration, braking, etc.), the SOC prediction method based on the ampere-hour integration method is used, and the integral error is calibrated in real time through the extended Kalman filter algorithm; data synchronization is performed once every preset time interval, and corresponding weights are dynamically assigned according to different working conditions, and the target SOC value is calculated through the weighted average algorithm.

[0104] In the embodiment of the present invention, the error correction for the SOC value is to update the SOC prediction value by combining the actual measurement value of the OCV-SOC curve, and dynamically correct the state prediction value using the Kalman gain. The formula is as follows:

[0105]

[0106] in, Indicates the current time State variables SOC and polarization voltage state prediction values; Indicates the current time The actual measured value of the terminal voltage; Indicates the current time The Kalman gain that determines the weight distribution between the state prediction value and the actual measurement value; Indicates the current time The state space of the OCV-SOC curve is mapped to the observation matrix of the measurement space.

[0107] It should be noted that the Kalman gain Used to weigh the impact of prediction error and measurement error. If the measurement noise is small (such as high sensor accuracy), If it is close to 1, the correction is large; on the contrary, if the model prediction is more reliable, Close to 0, the correction amplitude is small. For the observation matrix In the battery model, the state variables usually include SOC and polarization voltage (such as RC parallel voltage in Thevenin model), and the observation matrix The state variables need to be mapped to the measured voltage space, which can be expressed as follows:

[0108]

[0109] in, Indicates the current time The terminal voltage; represents the polarization voltage; The difference between the OCV and SOC curves is obtained. Therefore, the observation matrix The form is:

[0110]

[0111] That is to say, by taking the partial derivative of SOC and polarization voltage, the influence of both on the measured voltage is reflected.

[0112] The SOC estimation module 122 can also be used to dynamically correct the actual battery capacity and / or battery internal resistance value 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 / high temperature scenarios.

[0113] 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 capacity of the battery. The formula is as follows:

[0114]

[0115] in, Indicates temperature The actual capacity of the battery under Indicates nominal capacity; is the temperature coefficient (e.g. typical value for lead-acid batteries =0.008 / ℃); =25℃.

[0116] 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 battery internal resistance value. The formula is as follows:

[0117]

[0118] in, Indicates temperature The internal resistance of the battery under Indicates temperature The internal resistance of the battery under α is the temperature coefficient of internal resistance (typical value α = 0.004 / °C).

[0119] In summary, based on the multi-parameter fusion extended Kalman filter algorithm, the effects of battery aging and temperature drift are dynamically compensated, and the error rate is less than 2%; by combining the open circuit voltage method (OCV) with the ampere-hour integral, the initial SOC deviation is corrected to avoid misjudgment of the power supply risk; by optimizing the multi-model parallel SOC estimation, the optimal model is dynamically selected to adapt to different working conditions and improve the estimation accuracy and robustness. The accurate target SOC value obtained based on the above strategy is sent to the display instrument and the central control component simultaneously, so that the user can intuitively apply it.

[0120] Further, the dynamic / static protection module 123 in the embodiment of the present application is connected with the SOC estimation module 122, and can be used to obtain a target SOC value; and can also be used to monitor running data of the electric two-wheeled vehicle in different running modes according to the target SOC value, and perform dynamic / static protection operations according to the active protection strategies matched with different running models. The running modes include a charging mode, a riding mode and a static mode.

[0121] Specifically, as shown in Figure 5 when the electric two-wheeled vehicle is in the charging mode, the dynamic / static protection module 123 acquires first current data and first voltage data in the charging process in real time based on the target SOC value by using a current sensor and a voltage sensor; when the first current data exceeds a first current threshold and the duration reaches a first time threshold, or the first voltage data reaches a first voltage threshold, the charging loop is disconnected through a built-in double-redundancy protection circuit.

[0122] In actual operation, the current / voltage data in the charging process can be continuously acquired by using high-precision current and voltage sensors. When the current exceeds 150% of the rated current value and the duration is greater than or equal to 10 seconds, or the single voltage reaches 14.8V (based on the 12V battery specification), the charging loop is disconnected through the double-redundancy protection circuit. The double-redundancy protection circuit can be composed of a large-power MOSFET (such as CRST085N15N, Rds(on)=7.0mΩ) and a TVS diode in parallel, so as to ensure that the MOSFET of the charging loop can be quickly and reliably cut off in abnormal conditions and send the central control, cloud and APP through the protocol.

[0123] When the electric two-wheeled vehicle is in the riding mode, the dynamic / static protection module 123 acquires motor load current data in real time based on the target SOC value by using a closed-loop Hall current sensor; when the motor load current data exceeds a second current threshold or the amplitude exceeds a first speed threshold, the motor power supply is cut off and the vehicle power supply is turned off.

[0124] In practice, motor load current data can be collected using a high-precision closed-loop Hall effect current sensor (such as the LEM LAH 100-P, with an accuracy of ±0.5%, a range of ±150A, and a response time of <1μs). If the current increases by more than 50% within 2 seconds or exceeds twice the rated current of the controller, the motor drive MOSFET is first turned off within 5ms to cut off power to the motor, followed by the vehicle power MOSFET. If an abnormal current change is detected and the short-circuit current persists, the main power MOSFET is disconnected after 10ms to completely isolate the fault.

[0125] When the electric two-wheeled vehicle is in static mode, the dynamic / static protection module 123 uses a zero-drift operational amplifier to detect microcurrent data in real time based on the target SOC value; when the microcurrent data exceeds the third current threshold and the duration reaches the second time threshold, an alarm is triggered; when the microcurrent data exceeds the third current threshold and the duration reaches the third time threshold, the main power supply is cut off; when the vehicle's static time reaches the fourth time threshold and the SOC value drop is detected to exceed the second speed threshold, non-essential loads are turned off.

[0126] In practice, a nanoampere current detection circuit can be constructed using a zero-drift operational amplifier (such as the ADI AD8629), with a detection range of 0-500mA and a resolution of 0.1mA, to monitor the battery's microcurrent in real time. If the microcurrent exceeds 0.1A for 30 seconds, an alarm is triggered. This alarm wakes up the MCU, which is then transmitted via a protocol to the central control, cloud, and user app. Simultaneously, the vehicle's hazard lights flash at a 0.5Hz frequency. If the microcurrent exceeds the limit and does not recover within 30 minutes, the main power MOSFET is disconnected, completely isolating the fault. If the vehicle is idle for more than 168 hours or if the SOC decreases by more than 0.5% per hour, it enters sleep (low-power mode), disabling non-essential loads such as GPS and Bluetooth, leaving only the core monitoring circuitry active, with standby power consumption below 50μA. In this case, the MUC can periodically wake up to check the battery SOC (e.g., every four hours). If the SOC falls below a threshold (e.g., 10%), the low-voltage power supply is automatically disconnected to prevent deep discharge caused by long-term parking. When the vehicle is stationary, it can be awakened within one second by an urgent key request or vehicle vibration. When the MCU receives the disarm command from the remote control or detects vehicle vibration, the system quickly restores power and enters normal operation. Users can use the app to view real-time vehicle status information and promptly monitor battery health and abnormal conditions. This, through periodic low-power detection and threshold power-off strategies, can extend battery life by over 20%, addressing the pain point of power supply damage caused by long-term parking.

[0127] The embodiment of the present invention provides an intelligent controller protection system for electric two-wheeled vehicles such as electric bicycles and electric motorcycles. Through the integrated design of the integrated DC-DC converter and the main controller, and the integration of functional modules such as the hardware protection module, the SOC estimation module, and the dynamic / static protection module in the main controller, the system can significantly reduce the volume, reduce the cost, and improve the system efficiency and reliability. The precise SOC algorithm based on the extended Kalman filter effectively avoids the risk of power feeding and prolongs the battery life. The hardware-level short-circuit protection response mechanism and the static power feeding protection mechanism combined with the low-power wake-up mode solve the problem of battery damage caused by long-term parking. It also supports the realization of coordinated monitoring of the entire vehicle. The above technical points significantly improve the reliability and safety of the entire vehicle. The present invention fills the gap in the active protection and system integration of two-wheeled vehicle controllers, achieves quality improvement, energy consumption optimization and environmental benefits through technological innovation, and is in line with the lightweight and intelligent development trend of the electric vehicle industry.

[0128] Those skilled in the art will clearly understand that the specific working processes of the systems, devices, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and for the sake of brevity, they will not be further described here.

[0129] In addition, the functional units 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 into a single processing unit. The above-mentioned integrated functional units may be implemented in the form of hardware, software, or firmware.

[0130] Those skilled in the art will understand that if the integrated functional unit is implemented in the form of software and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or all or part of it, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions that cause a computing device (such as a personal computer, server, or network device) to execute all or part of the steps of the method described in each embodiment of the present invention when executing these instructions. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0131] Alternatively, all or part of the steps of implementing the aforementioned method embodiments may be accomplished by hardware related to program instructions (such as a computing device such as a personal computer, a server, or a network device), 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 method described in each embodiment of the present invention.

[0132] 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 above embodiments, those skilled in the art should understand that within the spirit and principles of the present invention, they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not deviate from the scope of protection of the present invention.

Claims

1. An intelligent controller protection system for electric two-wheeled vehicles, characterized in that: The system 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 integrated DC-DC converter is used to convert the input voltage of the high-voltage power battery into a low-voltage power supply to power the low-voltage load system of the vehicle; The main controller is used to dynamically adjust the PWM duty cycle according to the real-time load demand of the vehicle low-voltage load system to maintain the output voltage of the integrated DC-DC converter stable; The hardware protection module is used to use a high-speed comparator and a MOSFET switch to detect abnormal conditions of load current, input voltage and / or hardware temperature in real time, and perform hardware protection operations according to a protection strategy that matches the abnormal conditions; The SOC estimation module is used to dynamically compensate for battery aging and temperature drift effects based on the battery parameters of the high-voltage power battery based on a Kalman filter algorithm optimized and extended by a genetic algorithm, thereby obtaining an accurate target SOC value for user reference and / or vehicle health monitoring; The SOC estimation module is further configured to, when the electric two-wheeled vehicle is in a low-current steady-state operating condition, adopt an SOC prediction method based on an improved extended Kalman filter algorithm to correct the SOC value using battery voltage data and battery internal resistance data; When the electric two-wheeled vehicle is in a high dynamic working condition, a SOC prediction method based on an ampere-hour integration method is adopted, and the integration error is calibrated in real time by an extended Kalman filter algorithm; Data synchronization is performed once every preset time interval, and corresponding weights are dynamically assigned according to different working conditions, and the target SOC value is calculated through a weighted average algorithm.

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 configured to monitor operating data of the electric two-wheeled vehicle under different operating modes according to the target SOC value, and perform dynamic / static protection operations according to active protection strategies matching different operating models; The operating modes include charging mode, riding mode and static mode.

3. The system according to claim 1, wherein: The main controller is connected to the vehicle central control device in a two-way communication manner based on a communication protocol, and is used for real-time interaction of at least one of the vehicle charging and discharging status, fault codes and SOC data; The communication protocol includes at least one of the CAN protocol, the RS485 protocol and the One-Line protocol.

4. The system according to claim 1, wherein: The integrated DC-DC converter adopts a synchronous rectification Buck topology and is equipped with a low on-resistance MOSFET switch; The main controller is also used to perform self-test, register configuration, and initial PWM duty cycle setting when the high-voltage power battery is connected to the integrated DC-DC converter and starts working, to ensure that the MOSFET switch is in the off state; The hardware protection module has a built-in current detection circuit for detecting load current data in real time and triggering an overcurrent protection strategy when the load current exceeds a preset current threshold according to the high-speed comparator; the overcurrent protection strategy includes disconnecting the MOSFET switch and recording a fault code; and / or, The hardware protection module is further configured to detect in real time whether the input voltage is within a safe voltage range, and trigger an overvoltage / undervoltage protection strategy when the input voltage is not within the safe voltage range, as determined by the high-speed comparator; the overvoltage / undervoltage protection strategy includes recording a fault code and locking the output; and / or, The hardware protection module is further configured to detect the hardware temperature of the MOSFET switch through the low on-resistance, and trigger a first over-temperature protection strategy when the hardware temperature exceeds a safety temperature threshold according to the high-speed comparator; and / or, when the hardware temperature exceeds a safety temperature threshold and continues to rise, triggering a 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, characterized in that The main controller is also used for: When the input voltage is within the safe voltage range, calculating a PWM duty cycle target value according to the real-time load demand of the vehicle low-voltage load system and a preset load priority; An ADC is used to perform real-time PWM duty cycle sampling, and the voltage output of the integrated DC-DC converter is dynamically adjusted according to an 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 further configured to: The battery parameters of the high-voltage power battery are collected 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; Establish an SOC prediction method based on the ampere-hour integration method, including: The open circuit voltage method is used to calibrate the initial SOC value, and the ampere-hour integration method is used to calculate the real-time SOC data. The calculation formula is as follows: in, Indicates time Real-time SOC value; Indicates the initial value of SOC; Indicates nominal capacity; represents the Coulomb efficiency; Indicates time The charge and discharge current; and / or, Establish an SOC prediction method based on the improved extended Kalman filter algorithm, including: Taking the SOC value as the state variable, a discretized state model considering the nonlinear characteristics of the battery is established. The formula is as follows: in, Indicates the current time SOC value; Indicates the last moment SOC value; Indicates the current time The charge and discharge current; Indicates the sampling time interval; Indicates temperature The actual capacity of the battery under Taking the terminal voltage as the observation value, the SOC-voltage nonlinear relationship is established in combination with the second-order RC equivalent circuit model. The formula is as follows: in, Indicates the current time The terminal voltage; For the current moment The open circuit voltage; Indicates that the current moment The internal resistance of the battery is affected by the SOC value and temperature; Dynamically update process noise based on internal resistance change rate and temperature drift and observation noise The covariance matrix of The SOC value is corrected using the dynamic fusion strategy of the electric two-wheeled vehicle under different working conditions to obtain a corrected target SOC value.

7. The system according to claim 6, characterized in that The SOC estimation module is further configured to: Combined with the actual measurement value of the OCV-SOC curve, the Kalman gain is used to dynamically correct the state prediction value. The formula is as follows: in, Indicates the current time State variables SOC and polarization voltage state prediction values; Indicates the current time The actual measured value of the terminal voltage; Indicates the current time The Kalman gain that determines the weight distribution between the state prediction value and the actual measurement value; Indicates the current time The state space of the OCV-SOC curve is mapped to the observation matrix of the measurement space. The formula is as follows: in, Indicates the current time The terminal voltage; represents the polarization voltage; Obtained by OCV-SOC curve difference.

8. The system according to claim 6, wherein: The SOC estimation module is further configured to: Correcting the actual capacity of the battery and / or the internal resistance of the battery includes: A capacity fading model is established based on the Arrhenius equation, and the actual capacity of the battery is corrected using the capacity fading model. The formula is as follows: in, Indicates temperature The actual capacity of the battery under Indicates nominal capacity; is the temperature coefficient; =25℃; and / or, The internal resistance-temperature curve is fitted by experimental data, and the internal resistance value of the battery is corrected using the internal resistance-temperature curve. The formula is as follows: in, Indicates temperature The internal resistance of the battery under Indicates temperature The internal resistance of the battery under α is the temperature coefficient of internal resistance.

9. The system according to claim 2, wherein: The dynamic / static protection module is also used for: When the electric two-wheeled vehicle is in a charging mode, first current data and first voltage data are collected in real time during the charging process using a current sensor and a voltage sensor based on the target SOC value; when the first current data exceeds a first current threshold and the duration reaches a first time threshold, or when the first voltage data reaches a first voltage threshold, the charging circuit is disconnected by a built-in dual-redundant protection circuit; When the electric two-wheeled vehicle is in riding mode, based on the target SOC value, a closed-loop Hall current sensor is used to monitor the motor load current data in real time; when the motor load current data exceeds a second current threshold or the increase exceeds a first speed threshold, power supply to the motor is cut off and power supply to the entire vehicle is turned off; When the electric two-wheeled vehicle is in static mode, based on the target SOC value, a zero-drift operational amplifier is used to detect microcurrent data in real time; when the microcurrent data exceeds the third current threshold and the duration reaches the second time threshold, an alarm is triggered; when the microcurrent data exceeds the third current threshold and the duration reaches the third time threshold, the main power supply is cut off; when the vehicle's static time reaches the fourth time threshold and the SOC value drop is detected to exceed the second speed threshold, non-essential loads are turned off.

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