A control method for automatic alignment of a vehicle end and a charging integrated interface

Abstract

This invention discloses a vehicle-side automatic alignment control method for an integrated battery swapping and charging interface, relating to the field of intelligent battery swapping technology for new energy vehicles. This method includes three innovations: First, by injecting a 1kHz sinusoidal micro-current excitation signal and using phase-locked loop amplification and demodulation to calculate the equivalent loop complex impedance, it matches the pre-stored calibration characteristic interval to achieve millisecond-level voltage platform identification, automatically retrieving the corresponding pre-charge configuration parameter set, eliminating the risk of cross-platform misconnection during communication delays; Second, it uses a suspension settlement dynamic model to drive an extended Kalman filter for predictive feedforward and deviation feedback dual-closed-loop dynamic alignment compensation, upgrading static calibration to dynamic real-time following; Third, by controlling the current slope to drop to zero-crossing point, it executes contactor disconnection and actively discharges residual bus voltage to a safe threshold within 200 microseconds, achieving equipotential connection and zero-current disconnection, eliminating arc erosion during mode switching. This invention effectively improves the safety and service life of the integrated battery swapping and charging interface.

Description

Technical Field

[0001] This invention relates to the field of intelligent battery swapping technology for new energy vehicles, specifically to a vehicle-side automatic alignment integrated interface control method for battery swapping and charging, applicable to a composite electrical interface control system that simultaneously supports three modes: battery swapping, charging, and vehicle-to-grid interactive discharge. Background Technology

[0002] With the rapid development of the new energy vehicle industry, battery swapping and charging stations, as composite energy replenishment infrastructure that combines physical battery swapping with high-voltage charging, are being widely deployed. The battery swapping and charging interface is the core electrical connection component of the station, and the reliability of its control method directly affects the safety of the battery swapping operation and the interface's lifespan.

[0003] The existing control method for integrated charging and swapping interfaces has the following three structural defects:

[0004] First, voltage platform identification relies entirely on communication handshakes. Current solutions, after the pilot pins make contact, require the vehicle-side battery management system and the station-side controller to complete a full communication handshake via CAN bus or Ethernet before obtaining vehicle voltage platform information and configuring pre-charge parameters. This communication handshake process has a delay of tens to hundreds of milliseconds. If physical mating occurs during this delay, a mismatch between the pre-charge resistor and the target voltage platform can trigger overcharging surges in the bus capacitor or even high-voltage breakdown, endangering equipment and personnel safety.

[0005] Secondly, the interface alignment reference uses a static, one-time calibration. Current solutions collect the chassis interface position once when the vehicle enters the station, using this static coordinate to drive the battery swapping robot to perform the alignment operation. However, during the vehicle's parking and battery swapping process, the air suspension experiences continuous nonlinear deformation ranging from millimeters to centimeters due to temperature changes, load release, and the robot's lifting reaction force, causing the interface alignment reference to drift over time. The static calibration value cannot compensate for this time-varying error in real time, resulting in the mechanical interface being forcibly plugged and unplugged under slight misalignment, accelerating the wear of the terminals and guide pins.

[0006] Third, the control logic is designed for unidirectional energy flow and does not support multi-mode zero-arc switching. Traditional battery swapping control logic does not have bidirectional current zero-crossing detection and active discharge control. When the interface switches from vehicle-to-grid interactive discharge mode to battery swapping mode, the residual voltage of the bus capacitor and reverse current trigger a high-voltage arc at the contactor contacts, causing rapid contact erosion and significantly shortening the interface lifespan, making it difficult to meet engineering life requirements. Summary of the Invention

[0007] The purpose of this invention is to provide a vehicle-side automatic alignment integrated charging and swapping interface control method to solve three core technical problems in the prior art: lack of safety protection during communication delay, inability of static alignment to adapt to continuous suspension settlement, and high-voltage arc erosion caused by multi-mode switching.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for controlling the automatic alignment of a vehicle-side charging and swapping interface, the method comprising the following steps:

[0010] After the vehicle enters the station, the station-end ultrasonic array performs an initial scan of the vehicle chassis to obtain the initial height of the vehicle chassis and the interface position; the vehicle-end inertial measurement unit is activated simultaneously and begins to collect three-dimensional acceleration and angular velocity baseline data; based on the extended Kalman filter algorithm, the ultrasonic array ranging data and the inertial measurement unit data are fused, and the suspension settlement dynamics model is used as the state transition equation for initialization, and the battery swapping robot enters the dynamic alignment compensation closed-loop control mode;

[0011] The battery swapping robot moves according to the initial alignment coordinates. The extended Kalman filter algorithm continues to run, and the predicted feedforward compensation amount and the end pose deviation feedback amount are superimposed to form servo commands, which correct the position of the robot's end effector in real time and guide the robot to dynamically follow the real position of the vehicle interface.

[0012] After the guide pin completes the mechanical coarse alignment, the pilot pin enters a semi-contact state with the corresponding pin at the station end. The vehicle-end interface controller injects a sinusoidal AC excitation signal with a frequency of 1kHz and an amplitude of 50mV peak-to-peak value through the pilot pin circuit, collects the response voltage signal, calculates the complex impedance modulus and phase angle of the equivalent circuit, matches the obtained feature vector with the pre-stored calibration feature intervals of each voltage platform, completes the voltage platform identification, and automatically retrieves the pre-charge configuration parameter set of the corresponding voltage platform based on the identification result. The above process is completed independently before the communication handshake is completed.

[0013] Adaptive precharging is performed based on the precharging configuration parameter set. The voltage difference between the two ends of the interface is monitored in real time. When the voltage difference drops to within 5V, the main power contactor is closed to complete the integrity confirmation of the high-voltage interlock circuit.

[0014] According to the dispatching instructions, one of the three modes of battery swapping, charging or vehicle-to-grid interactive discharge is executed; when mode switching is required, the zero-arc safety state machine is triggered, and the inverter output current is linearly reduced to the zero-crossing detection threshold according to the preset slope. After capturing the current zero-crossing point, the contactor disconnection command is output within a delay of no more than 200 microseconds. After disconnection, the active discharge circuit is closed to reduce the residual voltage of the bus capacitor to the safety threshold, and then the subsequent mode switching action is executed.

[0015] After the operation is completed, disconnect the interface according to the zero-arc timing sequence, and use the extended Kalman filter algorithm to guide the battery swapping robot to safely exit to the initial position.

[0016] In the dynamic alignment compensation step, the extended Kalman filter state vector is defined to include six components: vertical settlement displacement of the interface center, settlement rate, tilt angle around the vehicle's longitudinal axis, angular rate around the longitudinal axis, tilt angle around the vehicle's transverse axis, and angular rate around the transverse axis. The observation vector includes the interface center height observation value obtained by ultrasonic array interpolation, the vertical acceleration measured by the inertial measurement unit, and the angular rate around the two axes. The second-order damping system described by the suspension settlement rate attenuation coefficient is used as the state transition model. The algorithm alternately executes the prediction step and the update step, and uses the posterior state estimation to recursively predict the interface pose drift amount in the next 1 to 3 seconds, which is then superimposed on the robot servo command as a feedforward compensation amount, forming a dual closed-loop control structure of prediction feedforward and deviation feedback.

[0017] In the voltage platform identification step, the direct digital synthesis module built into the vehicle-end interface controller generates an excitation signal, which is injected into the pilot pin circuit through a DC blocking coupling capacitor. A high-precision analog-to-digital converter acquires the response voltage signal at a sampling frequency of 32kHz. Using the lock-in amplification principle, the response signal is multiplied by a cosine reference signal and a sine reference signal of the same frequency, and then integrated to obtain the in-phase component and the quadrature component. The amplitude and phase angle of the response signal are calculated based on the in-phase component and the quadrature component, and then the magnitude and phase angle of the complex impedance are calculated to form a feature vector, which is then matched with a pre-stored calibration feature interval. If the feature vector falls into the calibration feature interval of a certain voltage platform, the corresponding voltage platform identification result is output. If the confidence level does not meet the preset threshold, the identification is determined to be unsuccessful, the insertion is terminated, and a fault is reported. The entire impedance identification and configuration process takes no more than 10ms.

[0018] In the zero-arc safety state machine step, the state machine defines nine states: idle, pre-charging, pre-charging ready, charging, vehicle-to-grid interaction switching safety preparation, vehicle-to-grid interaction discharge, battery swapping ready, battery swapping, multi-mode switching safety stop, and fault. Taking the switch from vehicle-to-grid interaction discharge mode to battery swapping mode as an example, the controller samples the current bus voltage and bidirectional current. If the absolute value of the current exceeds the safety cut-off threshold, the immediate switch is prohibited. The controller controls the inverter to linearly reduce the discharge current set value at a slope of 5A / ms, while continuously sampling the real-time current at a period of 1ms. When the absolute value of the current drops to the zero-crossing detection threshold and the slope direction of the current change is consistent with the slope drop direction, the zero-crossing point is confirmed, and a contactor disconnection command is output within a delay of no more than 200 microseconds. After the contactor disconnects, the active discharge circuit is closed to discharge the bus capacitor through the discharge resistor in a controlled manner. When the bus voltage drops to the battery swapping safety voltage threshold, the discharge relay is closed, and then a battery swapping handshake signal is sent to the battery swapping robot.

[0019] The present invention achieves the following beneficial effects:

[0020] By identifying the microcurrent impedance of the pilot pin, the voltage platform is identified before the main power terminal is plugged in, completely eliminating the risk of cross-platform mixing during the communication handshake delay, and realizing the safe pre-charge configuration of adaptive multi-voltage platforms. The entire impedance identification process takes no more than 10ms, much earlier than the time when the communication handshake is completed.

[0021] By integrating the inertial measurement unit and the ultrasonic ranging array with extended Kalman filtering dynamic prediction compensation, the interface alignment is upgraded from static single calibration to dynamic real-time following, effectively eliminating alignment drift caused by air suspension settlement and reducing terminal wear.

[0022] By reconstructing the zero-arc safety state machine timing, equipotential connection and zero-current disconnection are achieved during the switching of the three modes of battery swapping, charging, and vehicle-to-grid interactive discharge, fundamentally suppressing contactor contact arc erosion and significantly extending the service life of the interface. Attached Figure Description

[0023] The invention will now be further described with reference to the accompanying drawings.

[0024] Figure 1 This is a block diagram of the overall system architecture upon which the control method described in this invention is based;

[0025] Figure 2 This is a flowchart of the overall control method described in this invention;

[0026] Figure 3 The first innovation of this invention is the flowchart of the pilot pin impedance identification sub-process.

[0027] Figure 4 This is the second innovative point of the invention, the dynamic alignment compensation dual closed-loop control structure diagram;

[0028] Figure 5 The third innovative point of this invention is the state transition diagram of the zero-arc safety state machine;

[0029] Figure 6 This is a diagram showing the collaborative relationship between the three innovative algorithms of this invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The overall system architecture of the present invention relies on a set of hardware and software co-operated charging and swapping interface control system. The system is divided into two main parts: vehicle terminal system and station terminal system. Status information is exchanged through CAN bus and Ethernet communication bus.

[0032] The core of the vehicle terminal system is the interface controller MCU, which integrates an impedance identification unit and a state machine control unit. It injects detection signals into the interface circuit through the pilot pin and collects the response. At the same time, it receives real-time attitude data from the inertial measurement unit, outputs voltage platform configuration instructions and pre-charge status to the vehicle battery management system, and interacts with the high-voltage interlock circuit to monitor the interlock status.

[0033] The core of the station terminal system is the edge computing node, which runs an extended Kalman filter alignment compensation algorithm, integrates ultrasonic array ranging data and vehicle-side inertial measurement unit data, and outputs dynamic compensation commands to the battery swapping robot servo controller; at the same time, it runs a zero-arc safety state machine, which controls the active discharge circuit and bidirectional pre-charge logic unit based on real-time data from the bus voltage and current detection unit, and manages the mode switching timing.

[0034] The overall control process of the present invention fully covers the entire process from vehicle entry into the station to the completion of battery swapping, charging or vehicle-to-grid interactive discharge operations, and is divided into five main stages.

[0035] Phase 1 is initialization and dynamic alignment preparation: After the vehicle enters the station, the station-end ultrasonic array initializes and scans to obtain the initial chassis height and interface position of the vehicle; the vehicle-end inertial measurement unit is activated and begins to collect three-dimensional acceleration and angular velocity baseline data; the extended Kalman filter algorithm is initialized to set the initial state vector and covariance matrix; the battery swapping robot moves according to the initial alignment coordinates and enters the dynamic alignment compensation closed-loop control mode.

[0036] Phase 2 is the pilot pin impedance identification: After the pilot pin completes the mechanical coarse alignment, the pilot pin enters the semi-contact state. The vehicle-end MCU injects an excitation signal to complete the voltage platform identification and automatically call the corresponding pre-charge sequence. The entire process does not require waiting for communication handshake and takes no more than 10ms.

[0037] Phase 3 is pre-charging and main contactor engagement: Adaptive pre-charging is performed based on the identified voltage platform parameters, and the voltage difference between the two ends of the interface is monitored in real time. After the voltage difference drops to within 5V, the main power contactor is closed to complete the physical engagement, and the extended Kalman filter dynamic compensation runs continuously throughout the process.

[0038] Phase four involves multi-mode operation and zero-arc switching: three modes are executed according to the scheduling instructions: battery swapping, charging, or vehicle-to-grid interactive discharge. When switching modes, the zero-arc safety state machine is triggered, and timing reconstruction is performed to ensure that the switching process is arc-free.

[0039] Phase 5 is safe exit: After the operation is completed, the interface is disconnected according to the zero-arc timing, and the robot safely exits to the initial position under the guidance of extended Kalman filter compensation.

[0040] Adaptive interlocking timing control for multi-voltage platforms based on pilot pin micro-current impedance characteristic identification: Due to differences in internal bus capacitance, insulation resistance, and harness distributed inductance, the integrated charging / swapping interfaces of different voltage platforms (400V, 800V, 900V) exhibit different high-frequency complex impedance characteristics in the equivalent RC circuit formed during the pilot pin half-contact stage. This method utilizes this inherent difference to achieve millisecond-level voltage platform identification by injecting a weak AC signal of a specific frequency and analyzing the response impedance before the communication handshake is completed.

[0041] The specific implementation is as follows:

[0042] Trigger detection: The vehicle-side interface controller MCU continuously monitors the DC on / off status of the pilot pin circuit. When the pilot pin enters the half-contact state with the corresponding pin at the station, the low-voltage conduction signal triggers the MCU to enter the impedance identification subroutine and starts the hardware protection timer. If identification is not completed within the preset time window of 50ms, the insertion abort command is triggered, the battery swapping robot returns to the safe position, and a fault code is reported.

[0043] AC excitation signal injection: The MCU's built-in direct digital synthesis module generates a sinusoidal AC excitation voltage signal with a frequency of 1kHz and an amplitude of 50mV peak-to-peak. This signal is injected into the interface's equivalent impedance circuit through the DC blocking coupling capacitor in the pilot pin circuit. 1kHz is chosen because the impedance differences between the 400V, 800V, and 900V interfaces are most significant at this frequency. The 50mV amplitude is chosen to ensure that the injected current does not exceed 1mA, far below the high-voltage interlock detection current threshold, thus avoiding false triggering of the high-voltage interlock protection.

[0044] Response signal sampling and synchronous demodulation: The MCU's high-precision analog-to-digital converter synchronously acquires the response voltage signal of the pilot pin circuit at a sampling frequency of 32kHz, collecting data for 64 complete cycles, with a total of 2048 sampling points, ensuring that the frequency resolution meets the identification accuracy requirements. Using the lock-in amplification principle, the response signal is multiplied by cosine and sine reference signals of the same frequency and then integrated to obtain the in-phase components. Orthogonal components :

[0045]

[0046]

[0047] in, For the first The response voltage value at each sampling point This represents the total number of sampling points (taken as 2048). The excitation signal frequency is 1kHz. The sampling frequency is 32kHz. This is the sampling point number.

[0048] Complex impedance calculation: based on in-phase components Orthogonal components Calculate the amplitude of the response signal. Phase angle relative to the excitation signal :

[0049]

[0050]

[0051] Then, the complex impedance magnitude of the equivalent circuit is calculated. With phase angle :

[0052]

[0053]

[0054] in, To determine the amplitude of the injected excitation current, a standard sampling resistor in series is measured. The voltage across the two ends is calculated, i.e. , This is the voltage across the sampling resistor.

[0055] Platform-based feature matching: The feature vectors calculated above are used for... Matching with three sets of calibration feature ranges pre-stored in the MCU flash memory:

[0056] 400V 180~220 -42~-35 800V 95~125 -55~-47 900V 70~95 -62~-55

[0057] If the feature vector falls within the feature range of a certain platform and the confidence level meets the preset threshold, the corresponding voltage platform identification result is output; otherwise, the identification is deemed to have failed, and a fault handling process is triggered. The above-mentioned calibration feature range values ​​must be confirmed by actual measurement calibration of no less than 200 sets of actual interface samples and be fixed to the MCU at the factory.

[0058] Adaptive precharge sequence call: Based on the identification results, the MCU retrieves the precharge configuration parameter set for the corresponding platform from the preset state machine, including the target resistance value of the precharge resistor, the maximum precharge time limit, the differential voltage threshold that allows the main contactor to close, and the upper limit of the precharge current limit. It then sends a configuration write command to the precharge circuit control module to complete the adaptive configuration for multiple voltage platforms. The entire impedance identification and configuration process takes no more than 10ms, far earlier than the completion of the communication handshake, fundamentally eliminating the risk of mixed connections during time delays.

[0059] For the prediction-correction dual-closed-loop dynamic alignment compensation method for nonlinear settlement of air suspension, this method constructs a suspension settlement rate prediction model based on extended Kalman filter, combines the pose observation values ​​obtained by sensor fusion with the physical-driven suspension settlement dynamic model, realizes the prediction feedforward of the interface alignment reference drift in the next 1 to 3 seconds, and converts the predicted compensation amount into servo motion commands for the battery swapping robot in real time, forming a dual-closed-loop control structure of prediction feedforward and deviation feedback.

[0060] System state vector and observation vector definition: Define the extended Kalman filter state vector It is a six-dimensional column vector containing the vertical settlement displacement of the vehicle interface center. (Unit: mm) Settlement rate (Unit: mm / s) Inclination angle about the vehicle's longitudinal axis (Unit: rad) Angular velocity about the longitudinal axis Inclination angle about the vehicle's transverse axis (Unit: rad) and angular velocity about the horizontal axis subscript Indicates the first One control cycle, control cycle .

[0061] Observation vector It is a four-dimensional column vector containing the observed interface center height values ​​obtained by multi-point ranging interpolation of the ultrasonic array at the station. Vertical acceleration measured by the vehicle-end inertial measurement unit angular rate about the longitudinal axis and angular rate about the horizontal axis .

[0062] Suspension settlement dynamics model establishment: The air suspension settlement behavior is modeled as a second-order damped system with slowly time-varying decay characteristics. In one control cycle... Within this framework, a discretized linear approximation of the state transition equations is used:

[0063]

[0064] in, The state transition matrix, taking the Z-axis settlement sub-block as an example, has the following structure:

[0065]

[0066] in, Suspension settlement rate attenuation coefficient (unit: This reflects the physical law that the settlement rate gradually decreases due to the slow decompression of the air suspension. Its initial value is given by the vehicle calibration data and is adaptively identified and updated through extended Kalman filtering in each battery swap operation. Let be the process noise vector, following a zero-mean Gaussian distribution, with a covariance matrix of... The structure of the sub-blocks with angles around the longitudinal and transverse axes is determined by the system calibration experiment; the same applies to the Z-axis settlement sub-block.

[0067] Extended Kalman Filter Prediction Step: At the beginning of each control cycle, based on the posterior state estimate from the previous time step... With covariance matrix Perform prior prediction:

[0068]

[0069]

[0070] in, For the first Prior state estimation at time 10:00 The corresponding prior error covariance matrix is ​​shown in the superscript. This indicates the matrix transpose.

[0071] Extended Kalman filter update step: when new sensor observation data... Upon arrival (ultrasonic ranging data is updated at 10Hz, and inertial measurement unit data is updated at 100Hz, using an asynchronous update strategy), calculate the Kalman gain. And correct the state estimate:

[0072]

[0073]

[0074]

[0075] in, The observation matrix maps the state vector to the observation vector space. To observe the noise covariance matrix, the standard deviation of each sensor was determined based on its accuracy specifications (1 mm for ultrasonic ranging noise and 0.01 mm for inertial measurement unit acceleration noise). The standard deviation of the angular rate noise of the inertial measurement unit is taken as 0.001. ); It is the identity matrix; The Kalman gain matrix adaptively assigns trust weights between predicted and observed values.

[0076] Calculation of feedforward prediction compensation: using the current posterior state estimate Forward recursion to predict the future The interface pose drift after one control cycle is used as the feedforward compensation vector. :

[0077]

[0078] in, The default value is 50 (corresponding to a 1-second prediction time domain), and the maximum supported value is 150 cycles, or 3 seconds. Each component of the feedforward compensation vector corresponds to the translation and rotation compensation of the robot's end effector in Cartesian space, which are directly superimposed on the target point coordinates of the robot trajectory planning to achieve predictive feedforward control.

[0079] Dual closed-loop servo command synthesis and output: feedforward compensation amount The feedback compensation amount output by the inner-loop PID feedback controller based on the measured deviation of the end-effector's pose. Linear superposition is performed to obtain the final servo command. :

[0080]

[0081] in, This refers to the reference alignment coordinate vector determined based on the initial scan upon entering the station. After being converted into joint angle commands through inverse kinematics, the commands are sent in real time to the robot servo driver via the EtherCAT bus, driving the robot's end effector to continuously and dynamically follow the actual position of the vehicle interface. This step is executed cyclically at a frequency of 50Hz until the interface completes physical engagement and the main contactor closes.

[0082] The zero-arc safety state machine timing reconstruction algorithm for the three modes of battery swapping, charging and vehicle-to-grid interactive discharge is designed. This method designs a finite state machine covering the three energy flow modes. By detecting the residual voltage and current direction slope of the bus, it drives the active discharge circuit and bidirectional pre-charge logic. During the mode switching process, the voltage difference between the two ends of the interface is dynamically clamped within 5V, and the flexible action sequence of zero current zero-crossing point cut-off and equipotential connection is strictly executed.

[0083] The state machine defines nine main states: Idle, Pre-charging, Pre-charging Ready, Charging, Vehicle-to-grid (V2G) Interaction Switching Safety Preparation, V2G Interaction Discharging, Battery Swapping Ready, Battery Swapping, Multi-mode Switching Safety Stop, and Fault State. Each state transition edge is labeled with a trigger condition to ensure that the state machine has a definite response in any scenario and that there are no deadlocks or undefined states.

[0084] Taking the most complex scenario of switching from vehicle-to-grid interactive discharge mode to battery swapping mode as an example, the complete execution process of the timing reconstruction algorithm is as follows:

[0085] Switching Request Reception and Security Assessment: After the station-side dispatching system issues a mode switching command, the controller samples the current bus voltage. With bidirectional current (Current direction is positive when the vehicle discharges into the power grid). If the absolute value of the current exceeds the safety cutoff threshold #imgpt82#, immediate switching is prohibited, and an orderly stop procedure is initiated; if the absolute value of the current does not exceed the safety cutoff threshold, a rapid zero-arc cutoff procedure is initiated directly.

[0086] Orderly Stopping Vehicle-to-Grid Interactive Discharge – Current Slope Control Reduced to Zero Crossing Point: The controller sends a power sag command to the vehicle-to-grid interactive inverter, linearly reducing the discharge current setpoint at a preset slope of 5 A / ms. Simultaneously, it continuously samples the real-time current at a period of 1ms and calculates the current change slope in real time. When the absolute value of the current drops to the zero-crossing detection threshold #imgpt83# (default 2A, this value must be less than the minimum arcing current of the selected contactor) and the sign of the current change slope is consistent with the sag direction, it is confirmed that the current has reached the zero crossing point, and a zero-crossing capture signal is immediately issued.

[0087] Zero-current contactor disconnection: After the zero-crossing capture signal triggers, the controller outputs a contactor disconnection command within a delay of no more than 200 microseconds, causing the contactor to complete contact separation at the moment the current crosses zero. At this time, the voltage difference across the contacts is only caused by the parasitic resistance of the line, which is far lower than the arc sustaining voltage, achieving zero-arc disconnection. This delay must be confirmed through actual measurement and calibration.

[0088] Active discharge of residual charge on the bus: After the contactor disconnects, the bus capacitor still retains the charge corresponding to the bus voltage before the power swap. The controller closes the active discharge circuit, which consists of a discharge resistor and a discharge relay connected in series, to discharge the bus capacitor in a controlled manner through the discharge resistor. The controller samples the bus voltage at a period of 1ms. When the bus voltage drops to the safe voltage threshold for power swapping, the discharge relay is closed, and the discharge is complete. The time constant of the discharge circuit is determined by the product of the discharge resistor and the capacitance of the bus capacitor. The selection of the discharge resistor must take into account both the discharge speed requirement (time constant not exceeding 2s) and the power dissipation level.

[0089] Switching to battery swapping mode and notifying the robot: After confirming the completion of busbar discharge, the state machine transitions to the battery swapping in progress state, sending a high-voltage safety and battery swapping permission handshake signal to the battery swapping robot controller. The robot then begins to physically remove the battery. High-voltage interlock circuit interruption detection continues to run; once a physical disconnection of the interface is detected, it is immediately reported to the state machine as a secondary confirmation condition for state transition.

[0090] Equipotential connection restoration after battery swapping: After the battery is replaced, if it is necessary to restore the charging or vehicle-to-grid interactive discharge mode, the state machine re-executes impedance identification to confirm the voltage platform of the new battery pack and executes the adaptive pre-charge process. After the voltage difference between the two ends of the interface drops to within 5V, the main contactor is closed in an equipotential manner to achieve arc-free connection and complete the mode restoration.

[0091] The three innovative algorithms work collaboratively, operating sequentially according to the operation phases: Before insertion, the extended Kalman filter dynamic alignment compensation algorithm runs continuously from vehicle entry to main contactor closure, guiding the robot to dynamically follow the interface position; during insertion, the impedance identification algorithm identifies the voltage platform within a millisecond-level time window of pilot pin half-contact and triggers adaptive pre-charging; during operation, the zero-arc safety state machine manages the three-mode switching safety timing throughout, with active discharge and zero-current disconnection working in tandem; in the exit phase, the extended Kalman filter compensation algorithm guides the robot to safely exit, and zero-arc timing control ensures arc-free disconnection. Together, these three algorithms form a complete closed-loop control system.

[0092] Alternative implementation methods:

[0093] In the pilot pin impedance identification algorithm, the lock-in amplification and demodulation method can be replaced with a spectrum analysis method based on fast Fourier transform: An N-point fast Fourier transform is performed on the acquired response voltage signal to extract the complex amplitude at the frequency corresponding to 1kHz. The real and imaginary parts correspond to the in-phase and quadrature components, respectively. Subsequent calculation steps are the same as the main scheme. This alternative method can analyze the impedance characteristics of multiple frequency points simultaneously and does not require a dedicated direct digital synthesis module, making it suitable for scenarios with high interface controller computing power. In terms of core innovation, the adaptive pre-charge configuration driven by impedance identification during the pilot pin half-contact stage is completely equivalent to the main scheme.

[0094] In the dynamic alignment compensation algorithm, the extended Kalman filter can be replaced by the particle filter algorithm to estimate the suspension settlement state. The particle filter does not require linearization approximation of the state transition equation, can handle highly nonlinear and non-Gaussian suspension settlement dynamics, and has higher estimation accuracy under extreme conditions, making it suitable for battery swapping scenarios with extreme climates or high load conditions. The trade-off is that the computational load is proportional to the number of particles, requiring stronger computing power for the station's edge computing nodes.

[0095] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for controlling an integrated charging and swapping interface with automatic alignment at the vehicle end, characterized in that, The method includes the following steps: After the vehicle enters the station, the station-end ultrasonic array scans the vehicle chassis to obtain the initial height of the vehicle chassis and the interface position. The vehicle-end inertial measurement unit is simultaneously activated and collects three-dimensional acceleration and angular velocity baseline data. The station-end edge computing node fuses the ultrasonic array ranging data and the inertial measurement unit data based on the extended Kalman filter algorithm, and initializes it with the suspension settlement dynamics model as the state transition equation. The battery swapping robot enters the dynamic alignment compensation closed-loop control mode. During the movement of the battery swapping robot toward the initial alignment coordinates, the extended Kalman filter algorithm continuously executes prediction and update steps, and combines the predicted feedforward compensation amount with the end pose deviation feedback compensation amount to form a servo command, which corrects the position of the robot's end effector in real time. After the guide pin completes the mechanical coarse alignment, the pilot pin enters the semi-contact state. The vehicle-end interface controller injects a sinusoidal AC excitation signal through the pilot pin circuit, collects the response voltage signal, calculates the complex impedance modulus and phase angle of the equivalent circuit through synchronous demodulation, matches the obtained feature vector with the pre-stored calibration feature intervals of each voltage platform, completes the voltage platform identification, and automatically retrieves the pre-charge configuration parameter set of the corresponding voltage platform based on the identification result. The identification process is completed independently before the communication handshake is completed. Adaptive precharging is performed based on the precharging configuration parameter set. The voltage difference between the two ends of the interface is monitored in real time. When the voltage difference drops to within the preset safety threshold, the main power contactor is closed and the integrity of the high-voltage interlock circuit is confirmed. According to the dispatching instructions, one of the three modes of battery swapping, charging or vehicle-to-grid interactive discharge is executed. When a mode switch is required, the zero-arc safety state machine is triggered, and the inverter output current is linearly reduced to the zero-crossing detection threshold according to the preset slope. After capturing the current zero-crossing point, the contactor disconnection command is output within the preset delay time. After disconnection, the residual voltage of the bus capacitor is reduced to the battery swapping safety voltage threshold through the active discharge circuit, and then the subsequent mode switching action is executed. After the operation is completed, disconnect the interface according to the zero-arc timing sequence, and use the extended Kalman filter algorithm to guide the battery swapping robot to exit to the initial position.

2. The method according to claim 1, characterized in that, In the step of injecting a sinusoidal AC excitation signal through the pilot pin circuit, the direct digital synthesis module built into the vehicle-end interface controller generates a sinusoidal AC excitation voltage signal with a frequency of 1kHz and an amplitude of 50mV peak-to-peak value, which is injected into the interface equivalent impedance circuit through the DC blocking coupling capacitor, and the injection current does not exceed 1mA. The synchronous demodulation adopts a phase-locked amplification method. The high-precision analog-to-digital converter collects the response voltage signal at a sampling frequency of 32kHz, and collects a total of 64 complete cycle data. The response signal is multiplied by a cosine reference signal and a sine reference signal of the same frequency and then integrated to obtain the in-phase component and the quadrature component. Based on this, the response amplitude and phase angle are calculated, and then the complex impedance magnitude and phase angle are calculated. In the step of matching the obtained feature vector with the pre-stored calibration feature ranges of each voltage platform, the calibration range of the complex impedance magnitude corresponding to the 400V platform is 180Ω to 220Ω, and the calibration range of the phase angle is -42° to -35°; the calibration range of the complex impedance magnitude corresponding to the 800V platform is 95Ω to 125Ω, and the calibration range of the phase angle is -55° to -47°; the calibration range of the complex impedance magnitude corresponding to the 900V platform is 70Ω to 95Ω, and the calibration range of the phase angle is -62° to -55°; when the feature vector falls into the calibration feature range of a certain platform and the confidence level meets the preset threshold, the corresponding voltage platform identification result is output; when the confidence level does not meet the preset threshold, the identification is determined to be unsuccessful, the interpolation is terminated and the fault is reported. The entire impedance identification process takes no more than 10ms; the vehicle-side interface controller is equipped with a hardware protection timer, which triggers a mating abort command if identification is not completed within 50ms.

3. The method according to claim 1, characterized in that, The state vector of the extended Kalman filter algorithm is a six-dimensional vector, which includes the settlement displacement and settlement rate of the vehicle interface center in the vertical direction, the tilt angle around the longitudinal axis of the vehicle, the angular rate around the longitudinal axis, the tilt angle around the transverse axis of the vehicle, and the angular rate around the transverse axis; the observation vector includes the interface center height observation value obtained by the ultrasonic array through multi-point ranging interpolation, the vertical acceleration measured by the inertial measurement unit, and the angular rate around the two axes. The suspension settlement dynamics model models the air suspension settlement behavior as a second-order damping system, and uses the suspension settlement rate attenuation coefficient to describe the physical law that the settlement rate gradually decreases over time. The initial value of this attenuation coefficient is given by the vehicle calibration data, and is adaptively identified and updated through extended Kalman filtering in each battery swap operation. The prediction step performs prior prediction based on the posterior state estimate and covariance matrix of the previous time step at the beginning of each control cycle; the update step calculates the Kalman gain and corrects the state estimate when new sensor observation data arrives. The ultrasonic ranging data is updated at 10Hz and the inertial measurement unit data is updated at 100Hz, and the two adopt an asynchronous update strategy. The predicted feedforward compensation amount is calculated by recursively predicting the interface pose drift amount 50 to 150 control cycles after the current posterior state estimate. The control cycle is 20ms. After being superimposed with the deviation feedback compensation amount output by the inner loop PID feedback controller, it forms the final servo command, which is cyclically sent to the robot servo driver at a frequency of 50Hz.

4. The method according to claim 1, characterized in that, The zero-arc safety state machine defines nine states: idle state, pre-charging state, pre-charging ready state, charging state, vehicle-to-grid interaction switching safety preparation state, vehicle-to-grid interaction discharging state, battery swapping ready state, battery swapping state, multi-mode switching safety stop state, and fault state. Each state transition edge is marked with a trigger condition, and the state machine has a definite response in any scenario. In the step of linearly reducing the inverter output current at a preset slope, the preset slope is 5A / ms; The zero-crossing detection threshold is 2A, which is less than the minimum arcing current of the selected contactor. After the captured current crosses zero, a contactor disconnect command is output within a preset delay time, wherein the preset delay time does not exceed 200 microseconds; The battery swapping safety voltage threshold is 60V; If it is necessary to restore the charging or vehicle-to-grid interactive discharge mode after the battery swap is completed, the voltage platform impedance identification and adaptive pre-charging process will be re-executed. After the voltage difference between the two ends of the interface drops to within 5V, the main contactor will be closed in an equipotential manner.

5. The method according to claim 1, characterized in that, In the step of capturing the zero-crossing point of the current, the controller continuously samples the real-time current with a period of 1ms and calculates the current change slope in real time. When the absolute value of the current drops to the zero-crossing detection threshold and the sign of the current change slope is consistent with the direction of the descent, it is confirmed that the current has reached the zero-crossing point.

6. The method according to claim 1, characterized in that, The active discharge route is composed of a discharge resistor and a discharge relay connected in series. The time constant determined by the product of the discharge resistor and the bus capacitor value does not exceed 2s. The controller samples the bus voltage with a period of 1ms. When the bus voltage drops to the safe voltage threshold for battery swapping, the discharge relay is turned off. During the bus discharge process, the high-voltage interlock circuit interruption detection continues to run. When a physical disconnection of the interface is detected, it is immediately reported to the state machine as a secondary confirmation condition for state transition.

7. The method according to claim 1, characterized in that, In the step of injecting a sinusoidal AC excitation signal through the pilot pin circuit, the synchronous demodulation adopts the fast Fourier transform spectrum analysis method instead of the phase-locked amplification method: an N-point fast Fourier transform is performed on the acquired response voltage signal to extract the complex amplitude at the corresponding frequency point of 1kHz. The real part and the imaginary part are respectively used as the in-phase component and the quadrature component. The subsequent complex impedance calculation and characteristic matching steps are the same as those of the phase-locked amplification method.

8. The method according to claim 1, characterized in that, In the dynamic alignment compensation step, the extended Kalman filter algorithm is replaced by a particle filter algorithm to estimate the suspension settlement state. Multiple particles are used to represent the posterior distribution of the state, and each particle carries a state vector and a corresponding weight. The prediction step is achieved by applying process noise perturbation to each particle and then propagating it according to the state transition equation. The update step updates the weights and normalizes them by calculating the observation likelihood function of each particle. When the number of effective particles is lower than a preset threshold, resampling is performed to prevent weight degradation.

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