Vehicle-mounted energy storage system dynamic anti-vibration and heat management integration method based on road condition prediction

Abstract

The invention discloses a dynamic anti-vibration and heat management integration method for a vehicle-mounted energy storage system based on road condition prediction. The method comprises the steps that a prospective event sequence containing at least one future disturbance event is generated based on prospective data obtained from at least one vehicle-mounted data source, and the future disturbance event comprises predicted occurrence time and intensity level; based on the prospective event sequence, generating an integrated pre-response control time sequence instruction for cooperatively regulating and controlling a dynamic anti-vibration system and a thermal management system of the energy storage system; and controlling the dynamic anti-vibration system and the thermal management system to execute a pre-response adjustment action at an execution moment indicated by the pre-response control time sequence instruction. Through a feedforward control mechanism, the response delay and the thermal lag of a traditional control strategy are eliminated, the mechanical stability and the thermal stability of the energy storage system can be synergistically and foreseeably optimized, and the operation safety, the reliability and the comprehensive energy efficiency of the system are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle energy system control technology, and in particular to an integrated method for dynamic vibration resistance and thermal management of vehicle energy storage systems based on road condition prediction. Background Technology

[0002] With the rapid development of new energy vehicle technology, the safety, reliability, and lifespan of on-board energy storage systems, as core components, have become key factors restricting overall vehicle performance. On-board energy storage systems face two persistent technical challenges during actual operation: mechanical vibration and impact from complex and variable road conditions, and the enormous thermal load generated under high-rate charging and discharging conditions. Traditional control strategies typically design and control vibration damping systems and thermal management systems as two independent subsystems. This separated management approach leads to significant technical problems. For example, vibration damping systems often employ passive or active control based on sensor feedback, with a response lag behind the occurrence of the impact; similarly, thermal management systems adjust based on temperature sensor feedback, and their adjustment effect is limited by the inherent thermal inertia of the battery pack, resulting in a response lag. These delays prevent the energy storage system from receiving optimal protection when facing sudden disturbances, thereby accelerating mechanical fatigue and cell aging, and even inducing safety risks. Therefore, how to overcome the delay bottleneck of traditional feedback control and achieve proactive and predictive management of vibration and thermal loads is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] The purpose of this application is to provide a method, system, device and storage medium for dynamic vibration resistance and thermal management integration of vehicle-mounted energy storage system based on road condition prediction, which aims to solve the problems of response delay, independent control strategies and inability to coordinate optimization in the existing technology of vibration resistance control and thermal management control.

[0004] To achieve the above objectives, in a first aspect, this application provides an integrated method for dynamic vibration resistance and thermal management of an on-board energy storage system based on road condition prediction. The method includes: generating a prospective event sequence containing at least one future disturbance event based on prospective data obtained from at least one on-board data source, wherein the future disturbance event includes a predicted occurrence time and intensity level; generating an integrated pre-response control timing command for coordinating the dynamic vibration resistance system and the thermal management system of the energy storage system based on the prospective event sequence; and controlling the dynamic vibration resistance system and the thermal management system to perform pre-response adjustment actions at the execution time indicated by the pre-response control timing command.

[0005] Optionally, the vehicle data source includes a high-precision map database, the forward-looking data is forward-looking path data, and the future disturbance event is a future road condition event.

[0006] Optionally, the future road condition events include vibration events and thermal load events; the step of generating a prospective event sequence containing at least one future road condition event includes: scanning fixed facility markers or road geometry parameters in the prospective path data to identify vibration events; and analyzing road slope parameters in the prospective path data to identify thermal load events.

[0007] Optionally, the step of generating integrated pre-response control timing instructions includes: for each vibration-type event in the prospective event sequence, determining the corresponding vibration-damping system control parameters, and generating vibration-damping system control instructions to be executed within a first preset time window before the predicted occurrence time of the vibration-type event; and for each thermal load-type event in the prospective event sequence, determining the corresponding thermal management system control parameters, and generating thermal management system control instructions to be executed within a second preset time window before the predicted occurrence time of the thermal load-type event.

[0008] Optionally, the step of determining the corresponding vibration-damping system control parameters includes: querying a preset vibration-damping strategy lookup table to obtain the corresponding vibration-damping system control parameters based on the intensity level of the vibration event and the vehicle speed in the current vehicle status data.

[0009] Optionally, the step of determining the corresponding thermal management system control parameters includes: querying a preset thermal management strategy lookup table to obtain the corresponding thermal management system control parameters based on the intensity level of the thermal load event.

[0010] Optionally, the vehicle-mounted data source includes a vehicle-to-everything communication unit, the forward-looking data is dynamic traffic information received from external devices, and the future disturbance event is a bumpy road section or high-load demand event parsed from the dynamic traffic information.

[0011] Optionally, the method further includes: real-time monitoring of the actual vibration response data and actual temperature data of the energy storage system; and closed-loop correction of the parameters in the vibration resistance strategy lookup table or the thermal management strategy lookup table based on the correlation between the actual vibration response data, the actual temperature data and the prospective event sequence.

[0012] Secondly, this application provides an integrated system for dynamic vibration resistance and thermal management of an on-board energy storage system based on road condition prediction, comprising: a data acquisition module for acquiring forward-looking data from at least one on-board data source; an event sequence generation module for generating a forward-looking event sequence containing at least one future disturbance event based on the forward-looking data, wherein the future disturbance event includes a predicted occurrence time and intensity level; a control strategy generation module for generating an integrated pre-response control timing command for coordinating the dynamic vibration resistance system and the thermal management system of the energy storage system based on the forward-looking event sequence; and a control execution module for controlling the dynamic vibration resistance system and the thermal management system to perform pre-response adjustment actions at the execution time indicated by the pre-response control timing command.

[0013] Thirdly, this application provides an in-vehicle device including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of the first aspects.

[0014] Fourthly, this application provides a computer-readable storage medium. When the computer program is executed by a processor, it implements the method described in any one of the first aspects.

[0015] This application utilizes data sources such as in-vehicle navigation systems, high-precision map data, or V2X communication to predict future disturbances that a vehicle will encounter, quantifying them into event sequences that include time, type, and intensity. Based on this prediction, the system can generate a feedforward, coordinated control command to adjust the vibration resistance parameters (such as suspension stiffness and damper damping) and the operating state of the thermal management system (such as coolant pump speed and fan power) of the energy storage system before the actual occurrence of impacts or high thermal loads. This "pre-response" mechanism fundamentally eliminates the physical delay and thermal hysteresis inherent in traditional feedback-based control systems, enabling the energy storage system to meet upcoming challenges in an optimal state. This method integrates the previously independent vibration resistance and thermal management subsystems into a unified prediction and decision-making framework, achieving global collaborative optimization and significantly reducing the peak mechanical and thermal stresses experienced by the energy storage system, thereby effectively extending its service life and improving the overall vehicle safety, reliability, and energy efficiency. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the functional modules of an integrated system for dynamic vibration resistance and thermal management of on-board energy storage system based on road condition prediction, provided in one embodiment of this application.

[0017] Figure 2 This is a flowchart illustrating an embodiment of the method for integrating dynamic vibration resistance and thermal management of on-board energy storage systems based on road condition prediction provided in this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0020] Example 1 This embodiment provides an integrated method for dynamic vibration resistance and thermal management of on-board energy storage systems based on road condition prediction. In a specific implementation, this method employs an integrated on-board computing unit to acquire and process high-precision map data and real-time vehicle status data, thereby enabling the predictive generation of coordinated control commands for the vibration resistance and thermal management subsystems of the energy storage system. This method solves the technical problems in existing technologies, such as insufficient protection of the energy storage system due to control delays and low global energy efficiency caused by the independent operation of factor systems. It achieves the beneficial effects of completing vibration resistance preparation before impact and pre-cooling before high loads, greatly improving the operational safety and durability of on-board energy storage systems.

[0021] Reference Figure 1 This illustration shows an embodiment of an integrated system 100 for dynamic vibration resistance and thermal management of on-board energy storage systems based on road condition prediction. The system 100 can be embedded in a vehicle's domain controller or a separate embedded computing unit. In one embodiment, the system 100 includes an onboard state and path data acquisition module 110, a forward-looking road condition event sequence generation module 120, an integrated pre-response control strategy generation module 130, and a control execution module 140. Optionally, the system 100 may further include a system state feedback and closed-loop correction module 150.

[0022] The vehicle status and path data acquisition module 110 can be a highly integrated hardware unit, which includes, but is not limited to, a Global Navigation Satellite System (GNSS) receiver, a Controller Area Network (CAN) bus transceiver, and an interface for communication with the vehicle-mounted Telematics Control Unit (TCU). The GNSS receiver, exemplarily, can be a chip supporting multiple frequency bands (such as L1 / L5) and multiple constellations (such as GPS, BeiDou, Galileo) to ensure sub-meter positioning accuracy and high-dynamic speed measurement accuracy even in signal-blocked environments such as urban canyons or complex overpasses. The GNSS receiver is configured to continuously output raw data frames containing longitude, latitude, altitude, and ground velocity at a frequency of not less than 10 Hz. The CAN bus transceiver is directly connected to the vehicle's high-speed CAN or chassis CAN bus. By implementing SAE J1939 or a similar vehicle communication protocol stack, it can listen to and decode key messages sent by the chassis controller, vehicle stability system, etc., in real time and non-intrusively, and extract high-frequency dynamic parameters such as longitudinal acceleration, lateral acceleration, steering wheel angle, and wheel speed. Its update frequency is usually above 100Hz. The communication interface with the TCU can be implemented through Ethernet or serial communication. Its function is to obtain high-definition map (HD map) data from cloud map service providers. This HD map data not only contains traditional road network topology, but more importantly, it includes rich static road attribute information, such as the precise slope, radius of curvature, and pavement type (asphalt, cement, gravel) of each road segment, as well as the location and type of fixed traffic facilities (such as speed bumps, manhole covers, and bridge expansion joints) that are precisely geotagged.

[0023] The forward-looking road condition event sequence generation module 120 receives continuous, heterogeneous data streams from the vehicle status and path data acquisition module 110 and transforms them into a discretized, timestamped list of future events that is more meaningful for control decisions. This module typically runs as pure software on the main processor. Its internal logic can be deconstructed into three sub-functions: a path projector, an event scanner, and an event quantizer. The path projector is responsible for optimally matching the discrete positioning point sequence provided by GNSS with the road centerline of a high-precision map. This process can employ algorithms such as Kalman filtering or particle filtering to eliminate positioning noise and ensure that the vehicle's position on the digital map is smooth and accurate. After the path is accurately determined, the event scanner scans the vehicle's forward path within a dynamically set look-ahead distance window (e.g., window length). It can be dynamically adjusted according to the current vehicle speed V. in A virtual scan is performed within a 10-15 second preview period. During the scan, a series of preset, physical-logic-based deterministic rules are applied to identify potential road condition events. For example, the rule set may include: "If a map marker of type 'standard speed bump' is detected on the scan path, an 'impact vibration event' is generated"; "If the average gradient of a continuous 500-meter section of the scan path is greater than 3%, a 'high-power discharge event' is generated"; "If the radius of curvature of the scan path is less than 50 meters and the current vehicle speed is higher than 40 kilometers per hour, a 'steering roll vibration event' is generated." Once an event is identified, the event quantizer immediately intervenes, converting the physical properties of the event into a standardized, dimensionless intensity level.

[0024] This embodiment provides an exemplary mapping relationship from physical parameters to intensity levels. This mapping relationship can be stored in the module's memory in the form of a table or a piecewise function.

[0025] For example, a quantization mapping table from physical parameters of a road condition event to its intensity level can be shown in Table 1:

[0026] Through the quantification process described above, each identified event is assigned a predicted occurrence time. By event distance Calculated from the current vehicle speed V, ) and an intensity level Ultimately, these quantified and timestamped events are organized into a forward-looking road condition event sequence according to the predicted order of their occurrence. The sequence of forward-looking road condition events is then passed to the next module. The integrated pre-response control strategy generation module 130 is used to generate the forward-looking road condition event sequence. This translates into a specific, time-sequential set of control instructions that can be understood by the physical actuators. Internally, this module can contain two parallel sub-modules: a dynamic vibration damping strategy generation sub-module and a predictive thermal management strategy generation sub-module. These two sub-modules share the same event sequence input but are each responsible for generating control strategies for different physical systems, thus achieving functional decoupling and collaborative decision-making. Its working mechanism is based on the engineering concept of "offline optimization, online lookup." During the product development phase, engineers have constructed two complete, multi-dimensional control strategy lookup tables (LUTs) through extensive simulation calculations or hardware-in-the-loop experiments. One is the vibration damping strategy lookup table LUT_VIB, and the other is the thermal management strategy lookup table LUT_THERM. When module 130 runs, it traverses each event in the event sequence and, based on the event type, intensity level, and current vehicle state (mainly vehicle speed), performs high-speed indexing in the corresponding lookup table to obtain the optimal control parameters.

[0027] The control execution module 140 receives a timing instruction list from the integrated pre-response control strategy generation module 130 and is responsible for converting these instructions into electrical signals that drive hardware actions at precise time points. This module can be implemented as a time-triggered scheduler. It maintains a high-precision clock internally and continuously compares the current time with the execution timestamps in the instruction list. Once a match is found, it sends specific control signals (such as target current, voltage, or PWM duty cycle) to the dynamic vibration damping system (such as an electronically controlled variable damping shock absorber or an electromagnetic active suspension actuator) and thermal management system (such as a variable frequency coolant pump or a pulse width modulation (PWM) controlled cooling fan) of the energy storage system via the drive circuit.

[0028] The system state feedback and closed-loop correction module 150, connected to the accelerometer and multi-point temperature sensors inside the energy storage system, acquires the actual physical response of the system after experiencing road condition events. By comparing the predicted events with the actual responses, this module can evaluate the effectiveness of the control strategy in a preset lookup table in the real world. If a persistent deviation is detected, for example, for a certain level of impact, the measured vibration peak is always higher than expected, the module can activate an adaptive learning algorithm to make small, gradual adjustments to the corresponding entries in the lookup table. This allows the control strategy to adapt to factors such as vehicle aging, load changes, or ambient temperature changes, maintaining optimal long-term performance.

[0029] The following will combine Figure 2This application provides a detailed description of an integrated method for dynamic vibration resistance and thermal management of on-board energy storage systems based on road condition prediction, as provided in an embodiment of this application. The method may include the following steps: S201: Real-time acquisition of vehicle current status data and forward path data within a predetermined distance ahead.

[0030] This step is performed by the onboard status and path data acquisition module 110. In one embodiment, the vehicle controller acquires and updates the vehicle's dynamic status at a relatively high frequency, such as 100Hz. This status data is organized into a real-time vehicle status vector, which may include, but is not limited to: current latitude and longitude coordinates (from GNSS), altitude (from GNSS or barometer), driving speed V (from wheel speed sensor or GNSS), longitudinal and lateral acceleration (from inertial measurement unit IMU), and steering wheel angle θ (from steering angle sensor). To ensure time alignment of data from different sensors, the system uses a unified time reference, which can be synchronized by a pulses per second (PPS) signal provided by the GNSS module, ensuring that the timestamp error of all data is within 1 millisecond.

[0031] Simultaneously, this module requests high-precision map data from the cloud-based map service backend, starting from the vehicle's current location and extending a predetermined distance (e.g., 2 kilometers) along the current driving direction. This request is initiated through an interface conforming to the RESTful API specification. For example, an API request could be GET / api / v2 / hdmap?lat=39.90&lon=116.39&heading=90&range=2000. The data returned by the cloud server is in JavaScript Object Notation (JSON) format, containing an array of waypoints. Each waypoint object has rich attribute information, such as {"lat": 39.90, "lon": 116.40, "elevation":50.5, "slope": 0.01, "curvature": 0.0005, "poi": {"type": "speed_bump", "height_cm": 5}}. The in-vehicle module parses the received JSON data and constructs the in-memory path data structure. To cope with abnormal situations such as network interruptions, the module also has a built-in path data caching mechanism. When the cloud connection is lost, the continuation of the most recently successfully obtained data can be used, and a more conservative pre-response mode can be entered.

[0032] For example, suppose at a certain moment The vehicle status and path data acquisition module 110 obtains the following current vehicle status data: driving speed V = 60 kilometers per hour (km / h), approximately equal to 16.7 meters per second (m / s). Simultaneously, the forward path data within 2 kilometers ahead, after analysis, reveals that: at a distance of... At a distance of [number] meters, there is a traffic facility clearly marked as "Type 2: Standard Speed ​​Bump for Urban Roads" by the map data; and, from the current location... Starting at a point [number] meters, the road enters a continuous uphill section of 1 kilometer in length with an average gradient of 4.5%. This raw data forms the basis for all subsequent predictions and decisions.

[0033] S202: Based on the current vehicle status data and the forward path data, generate a forward-looking road condition event sequence containing at least one future road condition event.

[0034] This step is performed by the prospective road condition event sequence generation module 120. The core task of this module is to transform the raw, continuous geographic and vehicle data obtained in step S201 into discrete, quantified events that are more instructive for the control system. This transformation process mainly includes event identification, quantification, and time series construction.

[0035] In a specific implementation, this step can be broken down into S202a (vibration event identification and quantization), S202b (thermal load event identification and quantization), and S202c (event sequence construction).

[0036] In S202a, the event scanner in module 120 traverses the look-ahead path data. It pays particular attention to two types of information: discrete traffic facility markings and continuous road geometry parameters. For traffic facility markings, the scanner identifies event points that may cause instantaneous impacts, such as speed bumps, manhole covers, and bridge joints. For road geometry parameters, it analyzes the road's radius of curvature and lateral slope to identify event segments that may cause significant vehicle roll during high-speed cornering. After identifying an event, the event quantizer immediately assigns it an intensity level according to preset mapping rules (as shown in Table 1).

[0037] In S202b, the scanner in module 120 focuses on analyzing elevation or gradient curves in the look-ahead path data. It identifies all continuous uphill and downhill sections whose length and gradient exceed preset thresholds. For uphill sections, it generates a "high-power discharge event" because the vehicle needs to output more power to overcome gravity, which leads to a large current discharge from the energy storage system and a sharp increase in heat generation. The intensity of the event is positively correlated with the gradient and section length. For downhill sections, it generates an "energy recovery event," which typically corresponds to a large current charge of the energy storage system. This also generates heat, but it also signifies the recovery of braking energy, serving as a low-cost energy window for performing thermal management actions.

[0038] In S202c, all identified and quantified events are given a predicted occurrence time. This time is determined based on the event's path distance from the vehicle and the vehicle's current (or predicted) speed. For example, a distance of... The event is predicted to occur at the following time. Finally, all event objects e_i(t_i, M_i, type_i) are sorted according to their timestamps. The events are sorted from smallest to largest to form the final forward-looking sequence of road conditions. .

[0039] Continuing the previous example, after receiving the data from S201, the forward-looking road condition event sequence generation module 120 performs the following operations. First, it identifies a speed bump 80 meters ahead. According to the mapping rules in Table 1, the height of a "standard speed bump on urban roads" is typically between 3 and 5 centimeters, therefore it is quantified as an "impact vibration event" with an intensity level of 2. Its predicted occurrence time is calculated as follows: Seconds later, module 120 detected a continuous uphill section starting 200 meters ahead, with an average gradient of 4.5%. According to Table 1, a 4.5% gradient corresponds to a "high-power discharge event" of intensity level 4. The predicted time of occurrence, i.e., the moment when the vehicle arrives at the start of the ramp, is calculated as follows: Seconds. Ultimately, the generated event sequence is: .

[0040] S203: Based on the aforementioned forward-looking road condition event sequence, generate integrated pre-response control timing instructions for the coordinated regulation of the dynamic vibration resistance system and thermal management system of the energy storage system.

[0041] This step is performed by the integrated pre-response control strategy generation module 130. Module 130 receives the event sequence. Then, it will iterate through each event and match it with an optimal control action with lead time.

[0042] Specifically, this step involves setting a pre-response time window for each event and querying control parameters from a pre-defined lookup table. For vibration-related events, a typical pre-response time window... This time window can be set from 300 to 800 milliseconds (ms). The basis for setting this time window is that it must be greater than or equal to the total delay required from the controller issuing a command to the physical actuator (such as a variable damping shock absorber) completing its action and reaching a steady state. For thermal load events, due to the greater inertia of the thermal system, its pre-response time window... It is usually necessary to set it for a longer period, such as 30 to 90 seconds, to ensure that the cooling system has enough time to build up an effective "cold reserve" before the peak heat generation arrives.

[0043] Then, module 130 will execute at the predicted execution time. According to the intensity level of the event For other relevant states (such as vehicle speed V), query the control parameters in the corresponding lookup table.

[0044] For example, a portion of the contents of a vibration damping strategy lookup table LUT_VIB can be shown in Table 2, with its index dimensions being event intensity and vehicle speed, and its output values ​​being normalized damping coefficients (0-1) and discrete suspension stiffness levels (1-5): Intensity level Vehicle speed (km / h) Damping coefficient Stiffness rating 2 40-60 0.75 3 2 60-80 0.85 4 3 40-60 0.85 4 3 60-80 0.95 5 For example, a portion of the contents of a thermal management policy lookup table LUT_THERM can be shown in Table 3, with its index dimension being event intensity and its output values ​​being the target speed of the coolant pump and the target power percentage of the radiator fan: Intensity level Pump speed (RPM) Fan power (%) 3 2500 50 4 3500 75 5 4500 90 This application further discloses an offline calibration method for the lookup table. This method is a systematic optimization process based on multiphysics simulation, the core of which lies in transforming the complex online control problem into an offline optimal parameter calibration problem.

[0045] First, a high-fidelity simulation environment needs to be constructed. This can typically be done on a co-simulation platform that integrates vehicle dynamics simulation software (such as MSC Adams Car or CarSim) and thermofluidic simulation software (such as ANSYS Fluent or GT-SUITE). In the dynamics model, a precise multibody dynamics model containing the energy storage system and its suspension system needs to be established. Key parameters of the model include the mass of the energy storage system (exemplarily, 450 kg), the position of its center of mass, and its moment of inertia (exemplarily, ...). The model also includes the nonlinear stiffness and damping characteristic curves of the suspension elements. In the thermal model, a three-dimensional heat transfer and fluid network model of the energy storage system needs to be established. Key parameters include the specific heat capacity of the battery cell (exemplarily, 1100 J / (kg·K)), thermal conductivity (exemplarily, radial 1.5 W / (m·K), axial 30 W / (m·K)), and the convective heat transfer coefficient of the coolant at different flow rates.

[0046] Secondly, a systematic virtual test case matrix needs to be designed. This matrix aims to cover most of the disturbance scenarios that vehicles may encounter in actual use. For example, for the calibration of the vibration resistance lookup table, the test case matrix design may include: combining vehicle speeds from 20 km / h to 100 km / h (in 10 km / h increments) with an AF-level road surface spectrum conforming to ISO 8608 standards, and triangular speed bumps with heights from 3 cm to 7 cm (in 1 cm increments). For the calibration of the thermal management lookup table, the test case matrix may include: combining ambient temperatures from -10°C to 40°C (in 10°C increments) with continuous uphill driving conditions (lasting 5 minutes) with gradients from 2% to 8% (in 1% increments).

[0047] Next, a clear and quantifiable optimization objective function needs to be defined for each test condition. For vibration control, the objective is to simultaneously suppress vibration and reduce energy consumption; its objective function can be defined as: .in, It is the peak value of the Z-axis acceleration at key measuring points of the energy storage system within the simulation period T (unit: m / s²). α is the instantaneous power consumption (in W) of the active suspension actuator, and α is a weighting factor (exemplarily 0.7, dimensionless) used to balance performance and energy consumption. For thermal management control, the objective function can be defined as: .in, It is the highest temperature of the battery cell (unit: °C). It is the total power consumption of the water pump and fan, and β is a weighting factor (for example, it can be 0.8, dimensionless).

[0048] Then, a parameter optimization algorithm is used to solve the above optimization problem. For each test condition, the system uses control parameters (e.g., damping coefficient, stiffness rating, pump speed) as variables and the above objective function as the optimization objective to perform iterative optimization. A genetic algorithm or a particle swarm optimization algorithm can be used. For example, if a genetic algorithm is used, its key parameters can be set as follows: population size 50, number of iterations 100, crossover probability 0.8, and mutation probability 0.1. The set of optimal control parameters obtained after the algorithm converges is the optimal solution for that specific test condition.

[0049] Finally, table construction and interpolation are performed. All test conditions and their corresponding optimal control parameter solutions are treated as discrete data points and filled into the grid of the lookup table. For conditions encountered during online operation that are not on the discrete grid points (e.g., vehicle speed of 65 km / h), the system can calculate the corresponding control parameters through multidimensional linear interpolation or spline interpolation, thereby constructing a smooth and continuous control surface and forming the final lookup tables LUT_VIB and LUT_THERM.

[0050] Continuing with the previous example, module 130 processes the event sequence. For vibration events... Assuming the pre-response time window Seconds, then the execution time After 4.3 seconds, the system will look up Table 2 based on the event intensity M=2 and the current vehicle speed V=60km / h. The query result will be the optimal control parameters for the vibration-damping system. It should be set to (damping coefficient = 0.75, stiffness level = 3). Therefore, the first instruction is generated: ( For thermal load events Assuming a pre-cooling time window Seconds, then the execution time The execution time is negative, meaning the action should have started 18 seconds ago; therefore, the system determines it should be executed immediately. Based on the event intensity M=4, the system looks up Table 3 to obtain the optimal thermal management system control parameters. It should be set to (pump speed = 3500 RPM, fan power = 75%). Therefore, the second instruction is generated: ( Ultimately, the generated integrated pre-response control timing instruction list is a set of these two instructions.

[0051] S204: At the execution time indicated by the pre-response control timing instruction, control the dynamic vibration damping system and the thermal management system to perform pre-response adjustment actions.

[0052] This step is performed by the control execution module 140. This module acts as a high-precision scheduler, continuously monitoring an internal system clock synchronized by a PPS signal. When the system time reaches the execution timestamp of a certain instruction (its scheduling accuracy is required to be better than 5 milliseconds), the module immediately sends the control parameters contained in the instruction to the corresponding underlying hardware controller via the vehicle's internal communication bus (such as CAN or FlexRay).

[0053] The process of converting upper-level logic control parameters into lower-level physical execution signals relies on a pre-defined mapping relationship. For example, for a vibration-damping system, a normalized target damping coefficient of 0.75 would be converted by a transformation function into a pulse-width modulation (PWM) signal to drive the solenoid valve. This function could be, for instance, a... Where D is the damping coefficient. Therefore, a damping coefficient of 0.75 corresponds to a PWM duty cycle value of 200 × 0.75 + 50 = 200 (within a range of 0-255). For the thermal management system, the target speed of 3500 RPM will be directly encoded into the CAN message sent to the pump controller, for example, in the 2nd and 3rd bytes of a message with ID 0x3F1, encoded at a resolution of 10 RPM / bit.

[0054] Furthermore, this module includes closed-loop monitoring and fault tolerance mechanisms for actuator actions. After issuing a control command, the module monitors the status feedback messages from the actuator controller. For example, it checks whether the actual rotational speed reported by the pump controller has reached more than 95% of the target rotational speed within 200 milliseconds. If it has not reached this target speed, or if a communication timeout occurs, the system determines that the execution has failed and triggers a fault tolerance strategy. For example, it sets the actuator to a preset, safe default operating mode and reports the fault status to the upper-layer application to ensure the basic safety of the system.

[0055] Continuing the previous example, the control execution module 140 immediately executes the thermal management command, increasing the coolant pump speed to 3500 RPM and adjusting the cooling fan power to 75%, initiating pre-cooling of the energy storage system. After 4.3 seconds, the module issues an anti-vibration command, adjusting the suspension stiffness and shock absorber damping of the energy storage system to the target values. Thus, when the vehicle actually drives over the speed bump after 4.8 seconds, the energy storage system is already in optimal shock resistance; and when the vehicle begins climbing the hill after 12.0 seconds, the battery pack has built up sufficient cooling reserves.

[0056] Optionally, this method may also include an adaptive closed-loop correction step for the control strategy. This step is executed by the system state feedback and closed-loop correction module 150. After the pre-response action is executed, the module continuously collects real data from sensors inside the energy storage system. For example, after the vehicle passes over a speed bump, it records the actual peak acceleration measured by the accelerometer; during the vehicle's ascent, it records the actual peak cell temperature measured by the temperature sensor. The module then compares these actual response values ​​with the predicted or expected response values ​​from the simulation model. If a systematic deviation is found (e.g., under all impacts of intensity 2, the actual vibration is 10% greater than expected), the adaptive algorithm is triggered to fine-tune the entry in the lookup table LUT_VIB corresponding to intensity 2 (e.g., increasing the damping coefficient or stiffness level by one level). This slow, data-driven closed-loop correction allows the control strategy to self-optimize, adapting to gradually changing factors such as vehicle component aging and load variations, ensuring the robustness and optimality of the system throughout its entire lifecycle.

[0057] Example 2 The main difference between this embodiment and Embodiment 1 is that the source of the forward-looking data is not limited to static high-precision map data, but also includes dynamic information obtained through vehicle-to-everything (V2X) communication units.

[0058] In this embodiment, the data acquisition module 110 also integrates a V2X on-board unit (OBU). This OBU can receive standardized communication messages from other vehicles (V2V), roadside infrastructure (V2I), or the cloud (V2N), such as Basic Safety Message (BSM), Signal Phase and Timing (SPAT), or Road Hazard Warning.

[0059] When a vehicle is in motion, the OBU may receive a message broadcast by a Roadside Unit (RSU) ahead, indicating a temporary, unmarked bumpy section of road 500 meters ahead due to road maintenance. This message contains the precise start and end geographic coordinates of the bumpy section and a suggested speed. After receiving and verifying the message, the data acquisition module 110 transmits its contents to the event sequence generation module 120.

[0060] The event sequence generation module 120 will similarly generate a "future disturbance event" from this dynamic information. It will quantify it as a high-intensity "impact vibration event" based on the danger level described in the message, and calculate the predicted occurrence time based on its distance and current vehicle speed.

[0061] The subsequent steps are exactly the same as in Example 1. The integrated pre-response control strategy generation module 130 will generate and issue vibration control commands in advance based on this event generated by V2X information. In this way, the method of the present invention can utilize dynamic, real-time traffic information to cope with sudden and temporary road disturbances that cannot be covered by map data, further broadening its applicable scenarios and robustness.

[0062] In summary, the method provided in this application deeply integrates and coordinates two independent systems—vibration resistance and thermal management—in terms of time and function by constructing a complete closed loop of "perception-prediction-decision-execution-feedback." It leverages "foresight" of the future to counteract the "delay" of the physical world, thereby achieving unprecedentedly refined and predictive protection for on-board energy storage systems without significantly increasing hardware costs.

[0063] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware.

[0065] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for integrating dynamic anti-vibration and thermal management of a vehicle energy storage system based on road condition prediction, characterized in that, The method comprises: generating a prospective event sequence comprising at least one future disturbance event based on prospective data obtained from at least one vehicle-mounted data source, wherein the future disturbance event comprises a predicted occurrence time and an intensity level; generating an integrated pre-response control timing instruction for coordinating the dynamic anti-vibration system and the thermal management system of the energy storage system based on the prospective event sequence; controlling the dynamic anti-vibration system and the thermal management system to perform a pre-response adjustment action at an execution time indicated by the pre-response control timing instruction.

2. The method of claim 1, wherein, The vehicle-mounted data source comprises a high-precision map database, the prospective data is prospective path data, and the future disturbance event is a future road condition event.

3. The method of claim 2, wherein, The future road condition event comprises a vibration event and a thermal load event. The step of generating a prospective event sequence comprising at least one future road condition event comprises: scanning fixed facility markers or road geometry parameters in the prospective path data to identify the vibration event; and analyzing road slope parameters in the prospective path data to identify the thermal load event.

4. The method of claim 3, wherein, The step of generating an integrated pre-response control timing instruction comprises: for each vibration event in the prospective event sequence, determining a corresponding anti-vibration system control parameter and generating an anti-vibration system control instruction to be executed within a first preset time window before the predicted occurrence time of the vibration event; and for each thermal load event in the prospective event sequence, determining a corresponding thermal management system control parameter and generating a thermal management system control instruction to be executed within a second preset time window before the predicted occurrence time of the thermal load event.

5. The method of claim 4, wherein, The step of determining the corresponding anti-vibration system control parameter comprises: According to the intensity level of the vibration event and the vehicle speed in the current state data, an anti-vibration system control parameter corresponding to the intensity level is queried from a preset anti-vibration strategy lookup table.

6. The method of claim 4, wherein, The step of determining the corresponding thermal management system control parameter comprises: According to the intensity level of the thermal load event, a thermal management system control parameter corresponding to the intensity level is queried from a preset thermal management strategy lookup table.

7. The method of claim 1, wherein, The vehicle-mounted data source comprises a vehicle-to-everything communication unit, the prospective data is dynamic traffic information received from an external device, and the future disturbance event is a front bumpy road section or a high load demand event analyzed based on the dynamic traffic information.

8. The method of claim 1, wherein, The method further comprises: monitoring actual vibration response data and actual temperature data of the energy storage system in real time; based on the correlation between the actual vibration response data, the actual temperature data, and the prospective event sequence, performing closed-loop correction on the parameters in the anti-vibration strategy lookup table or the thermal management strategy lookup table. 9.A dynamic anti-vibration and thermal management integrated system for a vehicle energy storage system based on road condition prediction, characterized in that, The method comprises: a data acquisition module configured to obtain prospective data from at least one vehicle-mounted data source; an event sequence generation module configured to generate a prospective event sequence comprising at least one future disturbance event based on the prospective data, wherein the future disturbance event comprises a predicted occurrence time and an intensity level; and an integrated pre-response control timing instruction generation module configured to generate an integrated pre-response control timing instruction for coordinating the dynamic anti-vibration system and the thermal management system of the energy storage system based on the prospective event sequence. a control strategy generation module configured to generate, based on the prospective event sequence, an integrated pre-response control timing instruction for cooperatively regulating a dynamic anti-vibration system and a thermal management system of the energy storage system; a control execution module configured to control the dynamic anti-vibration system and the thermal management system to perform a pre-response adjustment action at an execution time indicated by the pre-response control timing instruction.

10. An in-vehicle device comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the method of any one of claims 1-8 when executing the computer program.

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