Battery electrochemical impedance spectroscopy online detection method and device for hybrid vehicle, computer program product and hybrid vehicle

Abstract

The present application relates to the technical field of vehicles, in particular to a battery electrochemical impedance spectroscopy online detection method for a hybrid vehicle, which is executed when the vehicle is running, comprising the steps of: S100: predicting the future power demand of the hybrid vehicle; S200: judging whether the power demand of the vehicle meets preset conditions within a time window required for completing a single electrochemical impedance spectroscopy detection according to the prediction result; S300: when the preset conditions are met, controlling the generator of the hybrid vehicle to be connected or maintained in a connected state, providing the power output required for the operation of the hybrid vehicle by the generator, and making the vehicle battery be in a stable working state suitable for electrochemical impedance spectroscopy detection; S400: performing electrochemical impedance spectroscopy detection on the battery in the stable working state. The present application also relates to a battery electrochemical impedance spectroscopy online detection device for a hybrid vehicle, a computer program product and a hybrid vehicle. Thus, the executable frequency of EIS detection and the timeliness of data acquisition are significantly improved.

Description

Technical Field

[0001] This invention relates to the technical field of vehicles, specifically to an online detection method for battery electrochemical impedance spectroscopy in hybrid vehicles, an online detection device for battery electrochemical impedance spectroscopy in hybrid vehicles, a computer program product, and a corresponding hybrid vehicle. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the safety, reliability, and service life of power batteries, as the core energy storage components of hybrid and pure electric vehicles, are receiving increasing attention from the industry. The Battery Management System (BMS) plays a crucial role in real-time monitoring and estimation of battery status during vehicle operation. Accurate acquisition of key parameters such as State of Charge (SOC), State of Health (SOH), and thermal runaway warning directly affects the vehicle's power performance and operational safety. Currently, BMS primarily relies on traditional sensor signals such as voltage, current, and temperature, combined with equivalent circuit models or data-driven algorithms, to estimate battery status. However, these estimation methods based on macroscopic electrical parameters have limitations in identifying battery aging characteristics and sensing changes in internal microstates, especially within the battery voltage plateau range, where changes in terminal voltage alone cannot accurately reflect the evolution trend of the battery's internal state.

[0003] Electrochemical impedance spectroscopy (EIS) technology, as a detection method capable of deeply characterizing the internal electrochemical processes of batteries, has gained widespread research attention in the field of battery diagnostics in recent years. EIS detection involves applying a small-amplitude sinusoidal or square-wave excitation current within a certain frequency range to the battery, measuring the corresponding voltage response, and obtaining the real and imaginary impedance information of the battery based on the voltage-to-current ratio at different frequencies. Based on the impedance spectrum data obtained from EIS detection, key electrochemical parameters such as ohmic impedance, charge transfer impedance, and diffusion impedance within the battery can be effectively identified, thus providing highly sensitive auxiliary diagnostic evidence for SOC estimation and calibration, SOH assessment, and early warning of thermal runaway. It is widely believed in the industry that integrating EIS detection functionality into the BMS is one of the important development directions for the next generation of battery management systems.

[0004] However, the online application of EIS (Electronic Information System) testing technology under vehicle operating conditions faces significant technical obstacles. The fundamental principle of EIS testing requires the battery to be in a relatively static or stable electrochemical state during the testing process, meaning that no violent redox reactions should occur inside the battery. Specifically, the excitation current applied for EIS testing is typically small, generally in the range of hundreds of milliamperes to tens of amperes. However, during normal vehicle operation, due to driver acceleration, braking, steering, and other actions, the charging and discharging current of the power battery often experiences drastic fluctuations of tens or even hundreds of amperes in a short period. This large change in operating current severely interferes with the correspondence between the EIS excitation signal and the response signal, leading to distortion or even complete unusability of the acquired impedance spectrum data. Furthermore, during high-current charging and discharging, the internal electrochemical reactions of the battery are highly active, with significant polarization effects. Under these conditions, the results obtained from EIS testing are unlikely to accurately reflect the internal impedance characteristics of the battery.

[0005] Therefore, there is still a real need to improve the online detection of battery electrochemical impedance spectroscopy during vehicle operation. Summary of the Invention

[0006] In view of this, the object of the present invention is to provide an improved online detection method for battery electrochemical impedance spectroscopy for hybrid vehicles, an improved online detection device for battery electrochemical impedance spectroscopy for hybrid vehicles, an improved computer program product, and an improved hybrid vehicle, so as to at least solve some of the problems in the prior art and / or overcome other possible disadvantages not mentioned herein.

[0007] According to a first aspect of the present invention, an online method for detecting the electrochemical impedance spectroscopy (EIS) of a battery in a hybrid vehicle is provided, wherein the online EIS detection method is performed during the operation of the hybrid vehicle and includes the following steps: S100: predicting the future power demand of the hybrid vehicle; S200: based on the prediction result, determining whether the power demand of the hybrid vehicle meets preset conditions within the time window required to complete a single EIS detection; S300: when the preset conditions are met, controlling the generator of the hybrid vehicle to connect or maintain connection, and the generator provides the power output required for the operation of the hybrid vehicle, so that the battery of the hybrid vehicle is in a stable operating state suitable for EIS detection; S400: performing EIS detection on the battery in the stable operating state.

[0008] According to an optional embodiment of the present invention, in step S300, the stable operating state is the state in which the battery is charged at a constant micro-charging rate, preferably 0.05C to 0.1C.

[0009] According to an optional embodiment of the present invention, in steps S300 and S400, the energy regenerative braking function of the battery is disabled to avoid unexpected current disturbances in the battery due to braking energy recovery during the electrochemical impedance spectroscopy detection.

[0010] According to an optional embodiment of the present invention, the preset conditions include: within the time window, the predicted power demand of the hybrid vehicle is lower than a preset power threshold, and the change in the predicted power demand is lower than a preset change threshold.

[0011] According to an optional embodiment of the present invention, the duration of the time window is from 10 seconds to several minutes.

[0012] According to an optional embodiment of the present invention, the scenarios in which the preset conditions are met include: the hybrid vehicle is in a cruise control phase, especially in a highway cruise control phase, and the power required for cruise control can be covered by the generator.

[0013] According to an optional embodiment of the present invention, the scenarios that satisfy the preset conditions include: the hybrid vehicle is in a congested road section, and it is predicted that there will be no acceleration driving conditions that require the battery to participate in power supply within a preset time period in the future.

[0014] According to an optional embodiment of the present invention, the scenario that satisfies the preset conditions includes: there is a traffic light in front of the hybrid vehicle, and the predicted waiting time is not less than the duration of the time window.

[0015] According to an optional embodiment of the present invention, the online detection method for battery electrochemical impedance spectroscopy further includes step S500 after step S400: after the electrochemical impedance spectroscopy detection is completed, the power supply strategy adopted by the hybrid vehicle before step S300 is restored, and in particular, based on the actual driving conditions of the hybrid vehicle, it is determined whether the generator of the hybrid vehicle should continue to be connected or switched to disconnected.

[0016] According to an optional embodiment of the present invention, the online detection method for battery electrochemical impedance spectroscopy further includes a sub-step S410 during the execution of step S400: continuously monitoring whether the real-time power demand of the hybrid vehicle exceeds the current supply capacity of the generator or deviates from the preset conditions; when the real-time power demand exceeds the current supply capacity or deviates from the preset conditions, suspending the current electrochemical impedance spectroscopy detection and discarding the uncompleted detection data, and then executing step S500.

[0017] According to an optional embodiment of the present invention, step S100 includes the following sub-steps: S110: obtaining the driving intention of the driver of the hybrid vehicle and the road and traffic environment information where the hybrid vehicle is located; S120: based on the driving intention and the road and traffic environment information, identifying the driving scenario that the hybrid vehicle is currently facing and / or will face; S130: predicting the power demand of the hybrid vehicle within the time window according to the identified driving scenario.

[0018] According to an optional embodiment of the present invention, step S110 and / or step S120 are performed by an autonomous driving module mounted on the hybrid vehicle, wherein the autonomous driving module acquires traffic environment information around the hybrid vehicle in particular by means of a camera of the hybrid vehicle, so as to determine the road and traffic environment information and / or identify the driving scenario.

[0019] According to a second aspect of the present invention, an online electrochemical impedance spectroscopy (EIS) detection device for a hybrid vehicle is provided, wherein the online EIS detection device comprises: a prediction unit configured to predict the future power demand of the hybrid vehicle; a judgment unit configured to, based on the prediction result, determine whether the power demand of the hybrid vehicle meets preset conditions within a time window required to complete a single EIS detection; a control unit configured to, when the preset conditions are met, control the generator of the hybrid vehicle to connect or maintain a connected state, wherein the generator provides the power output required for the operation of the hybrid vehicle, so that the battery of the hybrid vehicle is in a stable operating state suitable for EIS detection; and a detection unit configured to perform EIS detection on the battery in the stable operating state, wherein the online EIS detection device is particularly configured to implement the steps of the online EIS detection method for the battery provided in the embodiments of the first aspect described above.

[0020] According to a third aspect of the present invention, a computer program product is provided, comprising computer program instructions, wherein, when executed by a processor, the computer program instructions implement the steps of the online detection method for battery electrochemical impedance spectroscopy provided in the embodiments of the first aspect described above.

[0021] According to a fourth aspect of the present invention, a hybrid vehicle, particularly a range-extended hybrid vehicle, is provided, wherein the hybrid vehicle includes a battery and a generator, as well as the online battery electrochemical impedance spectroscopy detection device provided in the embodiments of the second aspect above and / or the computer program product provided in the embodiments of the third aspect above.

[0022] According to an optional embodiment of the present invention, the hybrid vehicle has an autonomous driving module and a camera. The camera is configured to acquire traffic environment information around the hybrid vehicle so that the autonomous driving module can determine whether preset conditions for performing electrochemical impedance spectroscopy (EIS) detection are met. The autonomous driving module is configured to send a detection permission signal to the central computing controller or scheduling controller of the hybrid vehicle when it determines that the preset conditions are met. The central computing controller or scheduling controller is configured to, upon receiving the detection permission signal, issue an instruction to the battery management system of the hybrid vehicle to perform EIS detection and control the generator to connect to provide the power output required for the operation of the hybrid vehicle.

[0023] According to certain embodiments of the present invention, by sequentially executing four steps during the operation of a hybrid vehicle—power demand prediction, preset condition judgment, generator connection control, and electrochemical impedance spectroscopy (EIS) detection—online EIS detection of the power battery is achieved without stopping the vehicle. This fully utilizes the architectural advantages of the hybrid vehicle's generator, using the generator as a power substitute for the battery. It creates battery steady-state conditions that meet the requirements of EIS detection during vehicle operation, significantly improving the frequency of EIS detection and the timeliness of data acquisition. This provides the battery management system with more timely and abundant internal battery impedance parameters, which is beneficial for improving, for example, the accuracy of SOC estimation, the accuracy of SOH assessment, and the early warning capability of thermal runaway. Attached Figure Description

[0024] The invention will now be described in more detail with reference to the accompanying drawings, which will provide a better understanding of its principles, features, and advantages. The drawings include: Figure 1 A schematic flowchart of an online detection method for battery electrochemical impedance spectroscopy according to an embodiment of the present invention is shown; Figure 2 A schematic flowchart of an online detection method for battery electrochemical impedance spectroscopy according to an embodiment of the present invention is shown; Figure 3 A schematic structural framework diagram of an online battery electrochemical impedance spectroscopy detection device according to an embodiment of the present invention is shown. Figure 4 A structural framework diagram of a computer system according to an embodiment of the present invention is shown; and Figure 5 A schematic application scenario diagram of a hybrid vehicle according to an embodiment of the present invention is shown. Detailed Implementation

[0025] To make the technical problems to be solved, the technical solutions, and the beneficial technical effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and several exemplary embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the scope of protection of the present invention. Various embodiments may share the same view or multiple views for description, but not all features appearing in the same view should be interpreted as features that must be present in an embodiment.

[0026] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, wherein... Figures 1 to 2 The method embodiments can be combined in particular with Figure 3 The device structure framework diagram and Figure 5 Understand the illustrative application scenarios shown.

[0027] Figure 1 A schematic flowchart of an online battery electrochemical impedance spectroscopy detection method 1000 according to an embodiment of the present invention is shown. Here, the online battery electrochemical impedance spectroscopy detection method 1000 is performed during the operation of a hybrid vehicle 2000.

[0028] like Figure 1 As shown, the online detection method 1000 for battery electrochemical impedance spectroscopy includes steps S100, S200, S300 and S400.

[0029] In step S100, the future power demand of the hybrid vehicle 2000 is predicted. Specifically, the central computing controller of the hybrid vehicle 2000 can obtain information from various sources such as the autonomous driving module, navigation system, vehicle camera, and millimeter-wave radar, including current vehicle speed, road type ahead, traffic light status, distance and speed of the vehicle ahead, road congestion level, and the driver's current operating intention (such as whether cruise control is activated, whether entering a ramp, etc.). Based on a preset power prediction model, the power demand of the vehicle over a future period is predicted, generating a power demand curve that predicts the power change over time.

[0030] In step S200, based on the prediction results, it is determined whether the power demand of the hybrid vehicle 2000 meets the preset conditions within the time window required to complete a single electrochemical impedance spectroscopy (EIS) test. Specifically, the central computing controller of the hybrid vehicle 2000 can compare the power demand curve with the preset conditions to determine whether the predicted power demand is consistently lower than the preset power threshold (e.g., 15kW) and the power fluctuation amplitude is consistently lower than the preset variation threshold (e.g., ±3kW) within the time window required to complete a single EIS test (e.g., 10 seconds to several minutes). This confirms that the vehicle will not experience conditions such as rapid acceleration or deceleration that cause instantaneous high-current discharge or regenerative charging of the battery within the time window.

[0031] In step S300, when preset conditions are met, the generator 2200 of the hybrid vehicle 2000 is controlled to connect or remain connected, and the generator 2200 provides the power output required for the operation of the hybrid vehicle 2000, so that the battery 2100 of the hybrid vehicle 2000 is in a stable working state suitable for electrochemical impedance spectroscopy detection. Specifically, when the judgment result of step S200 is that the preset conditions are met, the central computing controller can send a command to the vehicle power domain controller to control the generator 2200 of the hybrid vehicle 2000 to connect or remain connected, so that the generator 2200 covers the entire driving power demand of the hybrid vehicle 2000 and the power consumption of the on-board electrical accessories within the time window with its current speed and load output. At the same time, the battery management system is controlled to disconnect the battery 2100 from the main power supply circuit or switch it to a mode of charging with a very small constant current, so that the battery 2100 no longer undergoes violent oxidation-reduction reactions and enters a stable working state suitable for electrochemical impedance spectroscopy detection.

[0032] In step S400, electrochemical impedance spectroscopy (EIS) detection is performed on the battery 2100, which is in a stable operating state. Specifically, after confirming that the battery 2100 is in the aforementioned stable operating state, the battery management system or a dedicated EIS detection circuit can apply a sinusoidal or square wave excitation current within a preset frequency range to the battery 2100, simultaneously acquiring the voltage response signal of the battery 2100. By performing frequency domain division on the voltage response signal and the excitation current signal at each frequency point, the real and imaginary impedances of the battery 2100 within that frequency range are obtained, forming complete electrochemical impedance spectroscopy data. This data is then transmitted to the battery management system or a cloud server for subsequent SOC calibration, SOH assessment, and thermal runaway early warning analysis.

[0033] Through the above steps, the online detection of the battery electrochemical impedance spectroscopy is completed during the normal driving of the hybrid vehicle, without waiting for it to stop or be taken offline for maintenance.

[0034] During the execution of step S300, after the central computing controller determines that the preset conditions for EIS detection are met and controls the generator 2200 to connect, the central computing controller can further send an instruction to the battery management system, requesting that the operating state of the battery 2100 be set to a constant, small charging rate. Specifically, the battery management system controls a small portion of the output power of the generator 2200 to supply a constant small current to the battery 2100 via the on-board DC / DC converter or directly via the high-voltage bus, so that the battery 2100 is continuously charged at a small charging rate of 0.05C to 0.1C. At this small charging rate, the lithium-ion insertion and extraction reactions inside the battery 2100 are extremely slow and uniform, the battery polarization effect is controlled at a very low level, and the impedance parameters of the equivalent circuit tend to stabilize.

[0035] While steps S300 and S400 are being executed—that is, when the generator 2200 is connected to provide vehicle drive power and EIS detection is in progress or about to occur—the central computing controller can synchronously send a command to the vehicle braking control unit to disable the regenerative braking function. In normal driving mode, when the driver releases the accelerator pedal or depresses the brake pedal, the drive motor of the hybrid vehicle 2000 typically switches to generator mode, using the vehicle's kinetic energy to regenerate charge for the battery 2100, thereby recovering some braking energy. However, during EIS detection, if the regenerative braking function remains active, any deceleration by the driver will cause the drive motor to reverse-feed a charging current to the battery 2100 with unpredictable amplitude that varies drastically with braking force and vehicle speed. This charging current will superimpose on the battery 2100's original stable, small charging current, disrupting the battery's internal electrochemical balance. This will cause the excitation signal applied by the EIS detection to be overwhelmed or severely interfered with by the fluctuations of the regenerative braking current, resulting in distorted or even completely unusable impedance spectrum data. Therefore, while the generator is engaged in step S300, the regenerative braking function of battery 2100 is disabled. This ensures that all braking needs of the vehicle during the entire EIS detection period are independently handled by the mechanical friction braking system (such as disc brakes). The drive motor no longer participates in regenerative braking. The port current of battery 2100 only includes the constant, small charging current supplied by the generator and the excitation current applied by the EIS detection device. Both are signals with known and controllable amplitudes, thus ensuring that the EIS detection is not affected by unexpected current disturbances caused by regenerative braking throughout the entire time window, guaranteeing the accuracy and integrity of the impedance spectrum data. When step S400 is completed or the EIS detection is terminated for other reasons, the central computing controller sends a command to the braking control unit to restore the regenerative braking function, and the vehicle's regenerative braking strategy returns to normal.

[0036] In a preferred embodiment, the preset condition determined in step S200 includes two parallel sub-conditions. The first sub-condition is: within the time window required to complete a single EIS test, the predicted power demand of the hybrid vehicle 2000 is consistently lower than a preset power threshold. This preset power threshold is set based on the maximum continuous power that the generator 2200 can stably output under the current operating conditions. Only when the predicted power demand does not exceed the preset power threshold throughout the entire time window is the generator considered capable of independently covering the entire power output of the vehicle without the battery's involvement in power supply. The second sub-condition is: within the aforementioned time window, the magnitude of change in the predicted power demand is consistently lower than a preset change threshold. This preset change threshold is set based on the power response speed of the generator 2200 and the torque adjustment rate of the range extender. This condition ensures that the generator can track power changes in real time without experiencing a power gap due to sudden jumps in vehicle power demand, thereby forcing the battery to instantly intervene and supplement the missing power. Only when both of the above sub-conditions are simultaneously satisfied is the determination result of step S200 satisfied, and only then can step S300 and subsequent EIS test procedures be executed. If any sub-condition is not met, the current time period is deemed unsuitable for EIS detection. The normal power supply strategy is maintained, and the cyclical monitoring of steps S100 and S200 continues until the next time window that meets the conditions appears. This dual-threshold preset condition design takes into account both the absolute power level and the rate of power change, effectively avoiding false triggering of EIS detection under operating conditions where the absolute power is low but fluctuates drastically (such as urban road sections with frequent start-stop cycles).

[0037] Figure 2 A schematic flowchart of an online detection method 1000 for battery electrochemical impedance spectroscopy according to an embodiment of the present invention is shown. Figure 2 As shown, the online detection method 1000 for battery electrochemical impedance spectroscopy further includes step S500 after step S400.

[0038] In step S500, after the electrochemical impedance spectroscopy (EIS) detection is completed, the power supply strategy adopted by the hybrid vehicle 2000 before step S300 is restored. Specifically, based on the actual driving conditions of the hybrid vehicle 2000, it is determined whether the generator 2200 of the hybrid vehicle 2000 should remain connected or switch to disconnection. Specifically, after step S400 is completed and the EIS detection device confirms that impedance spectrum data for all frequency bands has been successfully acquired, the battery management system can send an "EIS detection complete" signal to the central computing controller. Upon receiving this signal, the central computing controller can first query the power supply mode status information of the system before step S300, and simultaneously read the vehicle's current actual driving condition parameters, including current vehicle speed, accelerator pedal opening, current road type, current battery SOC level, and current generator operating status. Based on the above information, the central computing controller executes the power supply strategy restoration logic. For example, if the vehicle was in pure electric drive mode before step S300 and the current battery SOC was still higher than the range extender's starting threshold (e.g., SOC > 30%), the central computing controller would disconnect the generator 2200 (i.e., stop the range extender's engine), the vehicle would return to pure electric drive mode, and the battery 2100 would resume normal charging and discharging operation. As another example, if the vehicle was already in range extender mode before step S300 (i.e., the generator was already running to charge the battery and provide power), then in step S500, the generator 2200 would remain connected, but its output power and the battery's charging and discharging strategy would be readjusted by the vehicle energy management system according to the control logic of normal range extender mode, and the battery would resume participating in drive power supply and regenerative braking. Yet another example, if the vehicle's real-time power demand had changed significantly after the EIS detection (e.g., the driver pressed the accelerator pedal to prepare for a lane change and overtaking immediately after the detection), then regardless of the power supply mode before step S300, the central computing controller would prioritize determining the optimal power supply strategy based on the current actual driving conditions—possibly simultaneously keeping the generator connected and restoring the battery to participate in power supply to meet high-power acceleration demands. Simultaneously, in step S500, the central computing controller can also send a command to the braking control unit to restore the regenerative braking function (if the function was disabled in step S300), restoring all vehicle functions to normal operation. Step S500 ensures that the impact of the EIS testing process on the normal operation of the vehicle is limited to a finite period during the testing period, and the vehicle returns to normal driving after the testing is completed.

[0039] like Figure 2As shown, during the EIS detection process in step S400, sub-step S410 is preferably continuously executed for real-time safety monitoring. Specifically, after the EIS detection begins, the central computing controller can continuously collect real-time power demand information of the hybrid vehicle 2000, such as the driver's accelerator pedal opening, brake pedal opening, current drive motor output power or torque request, and the latest estimate of the instantaneous power demand by the autonomous driving module. During the EIS detection, the real-time power demand is compared with two judgment conditions: First, it is determined whether the real-time power demand exceeds the current supply capacity of the generator 2200 (i.e., the maximum power that the generator can output instantaneously under the current operating state). If the driver suddenly presses the accelerator pedal deeply to request a drive power of more than 60kW while the generator's current maximum output is only 55kW, the battery is immediately allowed to participate in power supply to meet the driver's acceleration demand. Second, it is determined whether the real-time power demand deviates from the preset conditions determined in step S200 (e.g., the power fluctuation suddenly exceeds the preset change threshold). Even if the absolute value of the power demand has not exceeded the generator's supply capacity, the violent power fluctuation itself will interfere with the measurement accuracy of the EIS detection through the coupling effect of the vehicle's electrical system. When any of the above-mentioned conditions is triggered, a stop-detection command is immediately sent to the battery management system and the EIS detection device. The EIS detection device stops applying excitation signals and discards the incomplete detection data. This is because incomplete EIS data may contain systematic errors caused by power surges, and using it for subsequent SOC or SOH calibrations could introduce erroneous results; therefore, it is discarded rather than retained. Step S500 is then executed to restore the normal power supply strategy. The battery quickly reconnects to the drive power supply circuit to meet the vehicle's current power requirements, ensuring that driving safety and driving experience are not affected. Since EIS detection is an auxiliary calibration method rather than a critical safety function, discarding incomplete detection data will not adversely affect the normal operation of the vehicle or the basic functions of the BMS. The system only needs to wait for the next time window that meets the conditions to re-execute a complete EIS detection. This real-time monitoring and safety abort mechanism ensures that in any sudden driving situation, the vehicle's power supply and driving safety always take precedence over the EIS detection task.

[0040] like Figure 2 As shown, step S100 includes sub-steps S110, S120 and S130.

[0041] In sub-step S110, the driving intention of the driver of the hybrid vehicle 2000 and the road and traffic environment information of the hybrid vehicle 2000 are obtained. The methods for obtaining the driver's driving intention information may include, but are not limited to: reading the driver's current acceleration or deceleration intention through the accelerator pedal opening sensor and the brake pedal opening sensor; reading the driver's steering intention through the steering angle sensor; reading the driver's lane change or turning intention through the turn signal switch signal; reading the driver's cruise intention through the activation status and set speed of the cruise control or adaptive cruise control system; and inferring the driver's driving route intention through the destination and recommended route set by the driver in the navigation system. The methods for obtaining road and traffic environment information may include, but are not limited to: obtaining the position, speed, and movement trend of traffic participants (such as vehicles in front, vehicles behind, pedestrians, and non-motorized vehicles) in front of, to the side, and behind the vehicle through vehicle-mounted cameras, millimeter-wave radar, ultrasonic radar, and / or lidar; identifying traffic signs, traffic lights, and their status through cameras; obtaining information on the road type (highway, urban expressway, urban arterial road, ramp, etc.), road speed limit, road slope, and curvature through high-precision maps and GPS positioning; and obtaining broadcast information on the movement status of surrounding vehicles and the phase and timing information of traffic lights through vehicle-to-everything (V2X) communication.

[0042] In sub-step S120, based on driving intention and road and traffic environment information, the driving scenarios that the hybrid vehicle 2000 is currently facing and / or about to face are identified. Specifically, based on the driving intention and road and traffic environment information obtained in sub-step S110, scene recognition algorithms (such as rule-based logical judgment or deep learning-based scene classification models) can be used to identify the driving scenarios that the vehicle is currently facing and / or about to face, and classify them into one of several predefined scene categories, such as highway constant speed cruising, urban congestion crawling, waiting at traffic lights, merging acceleration at ramps, and frequent starts and stops during normal urban driving.

[0043] In sub-step S130, the power demand of the hybrid vehicle 2000 within a time window is predicted based on the identified driving scenario. Specifically, based on the driving scenario identified in sub-step S120, the system calls the power demand prediction model corresponding to the scenario category or searches a pre-calibrated power demand feature parameter library to predict the power demand curve of the vehicle within the time window required for EIS detection, including the average power level and the maximum power fluctuation amplitude. The prediction result is then output to step S200 for preset condition judgment.

[0044] In a preferred embodiment, the acquisition of road and traffic environment information in sub-step S110 and the identification of driving scenarios in sub-step S120 are performed by the autonomous driving module 2300 mounted on the hybrid vehicle 2000. This autonomous driving module 2300 can be the core computing unit of the vehicle's Advanced Driver Assistance System (ADAS) or a higher-level autonomous driving system (e.g., L2+ or L3 level autonomous driving system), and it already possesses the ability to perceive, understand, and predict the vehicle's surrounding environment in real time. The autonomous driving module 2300 specifically utilizes the camera 2400 of the hybrid vehicle 2000 to acquire traffic environment information around the vehicle to determine road and traffic environment information and / or identify driving scenarios. The camera 2400 typically includes a forward-facing monocular or binocular camera mounted above the inner side of the windshield, side-view cameras mounted near the side mirrors, and a rear-view camera mounted at the rear of the vehicle. These cameras together constitute a surround-view perception system capable of capturing visual image information within a 360-degree range around the vehicle. The autonomous driving module 2300 can process the image data collected by the camera 2400 in real time. Through an object detection neural network, it identifies visual elements such as vehicles, pedestrians, traffic lights and their color status, traffic signs (e.g., speed limit signs, construction signs), lane lines, and road edges. Combined with high-precision map data and GPS positioning information, it constructs a complete traffic environment model around the vehicle. Based on this, the autonomous driving module 2300 uses its built-in behavior prediction and scene understanding algorithms to determine the driving scenarios the vehicle may encounter in the present and near future. In practical applications, the autonomous driving module 2300 can send its scene recognition results to the central computing controller via the in-vehicle Ethernet or CAN bus. The central computing controller then performs subsequent power demand prediction and EIS detection condition judgment. Since the autonomous driving module 2300 itself continuously runs environmental perception and scene understanding tasks for the vehicle's autonomous driving function, this method reuses the existing perception and computing capabilities of the autonomous driving module, eliminating the need for additional dedicated environmental perception hardware, thus achieving efficient utilization in terms of hardware cost and system complexity. Especially when the vehicle is in autonomous driving mode (such as highway automatic cruise mode), the autonomous driving module 2300 has more complete control over the vehicle's motion state, and can actively avoid sudden power changes caused by driver misoperation, further improving the stability of the EIS detection time window and the detection success rate.

[0045] Figure 3 A schematic structural framework diagram of an online battery electrochemical impedance spectroscopy detection device 3000 for a hybrid vehicle 2000 according to an embodiment of the present invention is shown.

[0046] like Figure 3As illustrated, the online battery electrochemical impedance spectroscopy detection device 3000 is integrated, for example, into the central computing controller and battery management system of the hybrid vehicle 2000. It comprises four functional units in a modular fashion: a prediction unit 3100, a judgment unit 3200, a control unit 3300, and a detection unit 3400. The prediction unit 3100, for example, runs as a software module on the processor of the central computing controller. It is configured to receive environmental perception data from the autonomous driving module 2300, route and road condition data from the navigation system, and driver operation data from the vehicle chassis domain controller. Based on a preset power prediction algorithm, it calculates and predicts the power demand of the hybrid vehicle 2000 within a future time window, generating prediction result data including parameters such as the predicted average power, peak power, and fluctuation amplitude. The judgment unit 3200 also runs as a software module on the central computing controller. It is configured to receive the prediction result data output by the prediction unit 3100, compare it with preset condition parameters (including preset power thresholds and preset change thresholds) stored in the central computing controller's memory, and output a judgment result indicating whether the conditions for starting EIS detection are met. The control unit 3300 is implemented as a combination of a software module and a low-level driver. It runs on the central computing controller and communicates with the vehicle's power domain controller and battery management system via the CAN bus. When the judgment unit 3200 outputs a "conditions met" judgment result, it sequentially executes the following control actions: sends a generator access command to the power domain controller to control the range extender to start and make the generator 2200 operate at the target power; sends a battery operating mode switching command to the battery management system to put the battery 2100 into a stable operating state with a small charging rate; and sends a command to the braking control unit to disable regenerative braking. The detection unit 3400 can be integrated onto the circuit board of the battery management system as a dedicated integrated circuit chip, or installed inside or near the battery pack as an independent EIS detection circuit module. It is configured to, upon receiving a detection start command from the control unit 3300, apply an excitation current of predetermined frequency and amplitude to the battery 2100 via an onboard signal generator, simultaneously acquire the voltage response of the battery 2100 via a high-precision analog-to-digital converter, calculate the impedance values ​​at each frequency point in a local digital signal processor, form complete electrochemical impedance spectroscopy data, and upload it to the battery management system for subsequent diagnostic analysis. These four functional units work collaboratively via an in-vehicle communication bus to achieve automated online detection of the battery electrochemical impedance spectrum during vehicle operation. Each functional unit of the online battery electrochemical impedance spectroscopy detection device 3000 is specifically configured to implement the corresponding steps of the provided online battery electrochemical impedance spectroscopy detection method 1000.

[0047] Figure 4A structural framework diagram of a computer system according to an embodiment of the present invention is shown. This computer system is configured to perform various steps of the online detection method 1000 for battery electrochemical impedance spectroscopy provided according to the above embodiments of the present invention. Figure 4 As shown, the computer system includes a memory 1, a processor 2, a communication interface 3, and a bus 4. Here, the memory 1, processor 2, and communication interface 3 are interconnected via the bus 4. According to one aspect of the invention, a computer program product is also provided, comprising computer program instructions, wherein when executed by a processor, particularly by the processor 2 of the aforementioned computer system, the computer program instructions implement the various steps of the online detection method 1000 for battery electrochemical impedance spectroscopy provided according to the above embodiments of the invention.

[0048] Figure 5 A schematic application scenario diagram of a hybrid vehicle 2000 according to an embodiment of the present invention is shown.

[0049] Here, the power system of the hybrid vehicle 2000 includes, for example, a ternary lithium battery pack as battery 2100 and a permanent magnet synchronous generator as generator 2200, which can supply power to the drive motor and / or the battery. The hybrid vehicle 2000 also includes the battery electrochemical impedance spectroscopy (EIS) online detection device 3000 mentioned in the above embodiments, wherein the prediction unit 3100, the judgment unit 3200, and the control unit 3300 are integrated in software form in the vehicle's central computing controller, and the detection unit 3400 is integrated in the form of a dedicated EIS detection chip on the main control circuit board of the battery management system. Alternatively or additionally, the hybrid vehicle 2000 may also include the computer program product mentioned in the above embodiments, which is stored in the flash memory of the central computing controller and executed by the processor to implement all the steps of the EIS online detection method. During the operation of the hybrid vehicle 2000, when the central computing controller determines that the EIS detection conditions are met, the generator 2200 connects to the high-voltage bus to bear the full power output of the vehicle, the battery 2100 exits the main power supply circuit and enters a steady state, and the detection unit 3400 performs EIS detection on the battery and uploads the impedance spectrum data to the battery management system to assist in battery diagnostic tasks such as SOC calibration, SOH assessment, and thermal runaway early warning. This hybrid vehicle 2000 integrates the EIS online detection function into the vehicle's existing electrical architecture, achieving high-precision online monitoring of battery health status without adding additional large hardware equipment or affecting normal vehicle operation.

[0050] Preferably, the hybrid vehicle 2000 is a range-extended hybrid vehicle, in which the engine (i.e. the internal combustion engine part of the range extender) does not directly drive the wheels mechanically, but is dedicated to driving the generator 2200 to generate electricity. The electrical energy generated by the generator is transmitted to the drive motor through the high-voltage bus to provide driving force for the vehicle and / or to the battery 2100 to charge the battery.

[0051] More preferably, the aforementioned range-extended hybrid vehicle 2000 includes an autonomous driving module 2300 and a camera 2400. The camera 2400 is configured to continuously acquire traffic environment information surrounding the hybrid vehicle 2000, including but not limited to the type, position, and relative speed of vehicles ahead; the direction and number of road lanes; the position of traffic lights and their current color and countdown information; the content of traffic signs; the markings of road construction areas; and visual information such as the distribution of pedestrians and non-motorized vehicles. This visual information is transmitted in real-time to the autonomous driving module 2300 via an in-vehicle Ethernet network. The autonomous driving module 2300 processes the received image data using perception algorithms such as object detection, semantic segmentation, and motion prediction. Combined with high-precision map data and vehicle dynamic information, it comprehensively judges the vehicle's current and short-term driving scenario and power demand trend, thereby determining whether the preset conditions for electrochemical impedance spectroscopy detection are met.

[0052] In one specific implementation, one typical scenario where the preset conditions are met is when the hybrid vehicle 2000 is in a constant-speed cruise phase, especially on a highway. For example, the driver is driving the hybrid vehicle at a set speed of 110 km / h on a highway, the vehicle's adaptive cruise control system is activated, and there is no obvious need to decelerate or change lanes ahead. Because the vehicle is in a constant-speed cruise state on the highway, the power demand fluctuation is minimal within a short time window (e.g., within 60 seconds), mainly affected only by minor changes in road longitudinal slope and wind speed disturbances, and the fluctuation is far below the preset change threshold. Therefore, when executing the online detection method 1000 for battery electrochemical impedance spectroscopy, in step S100, the system predicts, based on the road perception information of the autonomous driving module and the state information of the cruise control system, that the vehicle will continue to maintain a constant speed cruise for the next 60 seconds, with the power demand stabilizing at approximately 35 kW. Subsequently, in step S200, it is confirmed that the prediction result meets the preset conditions, thereby triggering the execution of steps S300 and S400. In this scenario, the generator operates at an output power of approximately 40kW (of which approximately 35kW is used to cover the power consumption of vehicle drive and accessories, and approximately 5kW is used to maintain the battery charging at a small rate of approximately 0.1C). After the battery exits the drive power supply circuit, it enters a steady state, under which accurate impedance spectrum measurements can be performed.

[0053] In one specific implementation, another typical scenario that meets the preset conditions is when the hybrid vehicle 2000 is in a congested road section. For example, during morning or evening rush hours, the hybrid vehicle is traveling on an urban main road or urban expressway, where there is widespread congestion, and the vehicle is moving slowly at a very low speed (e.g., 0 to 15 km / h) or intermittently stopping and waiting, following the traffic flow ahead. The vehicle's autonomous driving module 2300 comprehensively judges the real-time traffic information from the front camera, millimeter-wave radar, and navigation system, and identifies that the vehicle is currently in a congested road section, and that the traffic density is extremely high and vehicles are moving at very low speeds within a long distance ahead (e.g., within 500 meters to 1 kilometer ahead). Therefore, when executing the online detection method 1000 for battery electrochemical impedance spectroscopy, the system predicts in step S100 that the vehicle will not experience acceleration conditions requiring the battery 2100 to provide power within a preset time period in the future (e.g., within the next 2 to 3 minutes). Under these congested conditions, the vehicle's drive power demand is extremely low, requiring only about 2kW to 5kW of drive power when crawling at low speeds. Even with occasional short-distance following starts, the instantaneous power demand does not exceed 10kW to 15kW, which is well within the stable output capacity of the generator 2200. Therefore, after confirming in step S200 that the power demand meets the preset conditions within the time window, the system controls the generator to connect and provide the vehicle's full power output in step S300, allowing the battery to enter a stable, low-charge state. Since the vehicle is basically at low speed or stationary in congested sections, the vehicle vibration level is low and the electrical load is relatively constant, providing a relatively ideal electromagnetic environment for the EIS detection performed in step S400.

[0054] In one specific implementation, another typical scenario that meets the preset conditions is that there is a traffic light ahead of the hybrid vehicle 2000, and the predicted waiting time is not less than the duration of the time window. Also, Figure 5As illustrated, the hybrid vehicle 2000 is a range-extended hybrid vehicle equipped with an autonomous driving module 2300. It is driving on an urban road and gradually approaching an intersection with a traffic light 4000. At this time, the autonomous driving module 2300 uses the hybrid vehicle 2000's camera 2400 to collect real-time information about the road and traffic environment ahead of the vehicle. It identifies that the traffic light 4000 is currently red and, combined with the results of the V2X communication module or visual recognition of the countdown timer on the traffic light 4000, further obtains the remaining waiting time of the red light, for example, approximately 120 seconds. Simultaneously, the autonomous driving module 2300 also considers the driver's driving intentions (e.g., the current route plan indicates proceeding straight through the intersection rather than making an emergency right turn) and the surrounding traffic environment information (e.g., multiple stationary vehicles are already lined up ahead, no need to change lanes to overtake in adjacent lanes, the vehicle is already at a complete stop) to identify the current and upcoming driving scenario as a "long-duration red light wait" condition. Based on this prediction, within the time window (e.g., 10 seconds to several minutes) required to complete a single electrochemical impedance spectroscopy (EIS) test, the power demand of the hybrid vehicle 2000 will remain low and will not fluctuate significantly, thus determining that the preset conditions for performing EIS test are met. Subsequently, the autonomous driving module 2300 sends a test permission signal to the central computing controller or scheduling controller of the hybrid vehicle 2000. The central computing controller or scheduling controller then issues an instruction to the battery management system (BMS) to perform EIS test and simultaneously controls the generator 2200 to connect or maintain its connection state, so that the generator 2200 continuously provides auxiliary power output for the vehicle (e.g., air conditioning, on-board electronic equipment, and low-speed creep power when necessary) during the test phase, while applying, for example, 0.05C or 0.A constant, minute charging rate of 1C keeps battery 2100 in a relatively static and stable operating state, preventing violent oxidation-reduction reactions inside the battery caused by high-current charging and discharging. During this process, to further eliminate unexpected current disturbances, the energy regenerative braking function of battery 2100 is temporarily disabled. If the driver applies a slight operation to the brake pedal at this time, only the physical braking system responds, and kinetic energy is not allowed to enter the battery through the regenerative path. After the above steady-state condition is established, a sinusoidal or square wave excitation current is applied to battery 2100, and the corresponding excitation voltage response is measured. By performing complex division operations on voltage and current at different frequencies, the real and imaginary impedances of battery 2100 are obtained, which are used to support the subsequent... The system performs online calibrations for SOC and SOH estimations, as well as thermal runaway warnings. If, during the testing process, unexpected situations occur such as a vehicle ahead starting prematurely, a traffic light turning green unexpectedly, or a vehicle from an adjacent lane cutting in, causing the real-time power demand of the hybrid vehicle 2000 to momentarily exceed the current supply capacity of the generator 2200 or deviate from preset conditions, the electrochemical impedance spectroscopy (EIS) test is terminated, and the incomplete test data is discarded, awaiting a suitable opportunity to re-trigger. After successful completion of the test, the hybrid vehicle 2000 resumes the power supply strategy used before the EIS test and adaptively decides, based on actual driving conditions, whether the generator 2200 remains connected to continue range-extending power generation or switches to disconnected mode to return to pure electric drive mode. Since waiting at red lights is a frequent occurrence in daily urban driving, the technical solution of this invention can fully utilize these previously idle time windows for online battery diagnostics, significantly increasing the frequency of EIS testing without affecting the driver's normal driving experience. This provides the battery management system with richer and more timely internal battery status data, effectively improving the accuracy and reliability of vehicle battery health management.

[0055] Although specific embodiments of the invention have been described in detail herein, they are given for illustrative purposes only and should not be construed as limiting the scope of the invention. Various substitutions, alterations, and modifications can be conceived without departing from the spirit and scope of the invention.

Claims

1. An online detection method (1000) for battery electrochemical impedance spectroscopy in hybrid vehicles (2000), wherein, The online detection method (1000) for battery electrochemical impedance spectroscopy is performed during the operation of the hybrid vehicle (2000) and includes the following steps: S100: Predict the future power requirements of the hybrid vehicle (2000); S200: Based on the prediction results, determine whether the power demand of the hybrid vehicle (2000) meets the preset conditions within the time window required to complete a single electrochemical impedance spectroscopy detection. S300: When the preset conditions are met, control the generator (2200) of the hybrid vehicle (2000) to connect or maintain the connection state, and the generator (2200) provides the power output required for the operation of the hybrid vehicle (2000) so that the battery (2100) of the hybrid vehicle (2000) is in a stable working state suitable for electrochemical impedance spectroscopy detection. S400: Perform electrochemical impedance spectroscopy detection on the battery (2100) in the stable operating state.

2. The online detection method for battery electrochemical impedance spectroscopy according to claim 1 (1000), wherein, In step S300, the stable operating state is a state in which the battery (2100) is charged at a constant micro-charging rate, preferably 0.05C to 0.1C; and / or In steps S300 and S400, the energy regenerative braking function of the battery (2100) is disabled to avoid unexpected current disturbances in the battery (2100) due to braking energy recovery during the electrochemical impedance spectroscopy detection. and / or The preset conditions include: within the time window, the predicted power demand of the hybrid vehicle (2000) is lower than a preset power threshold, and the change in the predicted power demand is lower than a preset change threshold; and / or The duration of the time window is from 10 seconds to several minutes.

3. The online detection method for battery electrochemical impedance spectroscopy according to claim 1 or 2 (1000), wherein, Scenarios that satisfy the preset conditions include at least one of the following: The hybrid vehicle (2000) is in the cruise control phase, especially in the highway cruise control phase, and the power required for cruise control can be covered by the generator (2200). The hybrid vehicle (2000) is in a congested area, and it is predicted that there will be no acceleration conditions requiring the battery (2100) to provide power within a preset time period; and There is a traffic light ahead of the hybrid vehicle (2000), and the predicted waiting time is not less than the duration of the time window.

4. The online detection method for battery electrochemical impedance spectroscopy according to any one of claims 1 to 3 (1000), wherein, The online detection method for battery electrochemical impedance spectroscopy (1000) further includes step S500 after step S400: after the electrochemical impedance spectroscopy detection is completed, the power supply strategy adopted by the hybrid vehicle (2000) before step S300 is restored, and in particular, based on the actual driving conditions of the hybrid vehicle (2000), it is determined whether the generator (2200) of the hybrid vehicle (2000) should continue to be connected or switched to disconnected. Preferably, the online detection method for battery electrochemical impedance spectroscopy (1000) further includes a sub-step S410 during the execution of step S400: continuously monitoring whether the real-time power demand of the hybrid vehicle (2000) exceeds the current supply capacity of the generator (2200) or deviates from the preset condition; when the real-time power demand exceeds the current supply capacity or deviates from the preset condition, the current electrochemical impedance spectroscopy detection is stopped and the unfinished detection data is discarded, and then step S500 is executed.

5. The online detection method for battery electrochemical impedance spectroscopy according to any one of claims 1 to 4 (1000), wherein, Step S100 includes the following sub-steps: S110: Obtain the driving intention of the driver of the hybrid vehicle (2000) and the road and traffic environment information where the hybrid vehicle (2000) is located; S120: Based on the driving intention and the road and traffic environment information, identify the current and / or upcoming driving scenarios of the hybrid vehicle (2000); S130: Based on the identified driving scenario, predict the power demand of the hybrid vehicle (2000) within the time window.

6. The online detection method for battery electrochemical impedance spectroscopy according to claim 5 (1000), wherein, Step S110 and / or step S120 are performed by the autonomous driving module (2300) mounted on the hybrid vehicle (2000), which in particular uses the camera (2400) of the hybrid vehicle (2000) to acquire traffic environment information around the hybrid vehicle (2000) in order to determine the road and traffic environment information and / or identify the driving scenario.

7. An online detection device (3000) for battery electrochemical impedance spectroscopy in a hybrid vehicle (2000), wherein, The online electrochemical impedance spectroscopy detection device (3000) for batteries includes: A prediction unit (3100) is configured to predict the future power demand of the hybrid vehicle (2000); The judgment unit (3200) is configured to determine, based on the prediction result, whether the power demand of the hybrid vehicle (2000) meets the preset conditions within the time window required to complete a single electrochemical impedance spectroscopy detection. A control unit (3300) is configured to, when the preset conditions are met, control the generator (2200) of the hybrid vehicle (2000) to connect or maintain its connection state, so that the generator (2200) provides the power output required for the operation of the hybrid vehicle (2000), so that the battery (2100) of the hybrid vehicle (2000) is in a stable operating state suitable for electrochemical impedance spectroscopy detection; and Detection unit (3400), configured to perform electrochemical impedance spectroscopy detection on the battery (2100) in the stable operating state, The battery electrochemical impedance spectroscopy online detection device (3000) is particularly configured to implement the steps of the battery electrochemical impedance spectroscopy online detection method (1000) according to any one of claims 1 to 6.

8. A computer program product comprising computer program instructions, wherein, When the computer program instructions are executed by the processor, they implement the steps of the online detection method (1000) for battery electrochemical impedance spectroscopy according to any one of claims 1 to 6.

9. A hybrid vehicle (2000), particularly a range-extended hybrid vehicle, wherein, The hybrid vehicle (2000) includes a battery (2100) and a generator (2200), as well as an online battery electrochemical impedance spectroscopy detection device (3000) according to claim 7 and / or a computer program product according to claim 8.

10. The hybrid vehicle (2000) according to claim 9, wherein, The hybrid vehicle (2000) has an autonomous driving module (2300) and a camera (2400). The camera (2400) is configured to acquire traffic environment information around the hybrid vehicle (2000) so that the autonomous driving module (2300) can determine whether the preset conditions for performing electrochemical impedance spectroscopy detection are met. The autonomous driving module (2300) is configured to send an allow detection signal to the central computing controller or scheduling controller of the hybrid vehicle (2000) when it determines that the preset conditions are met. The central computing controller or scheduling controller is configured to, after receiving the allow detection signal, issue an instruction to the battery management system of the hybrid vehicle (2000) to perform electrochemical impedance spectroscopy detection, and control the generator (2200) to connect to provide the power output required for the operation of the hybrid vehicle (2000).

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