Vehicle evaluation method and system based on extreme working condition pool and performance boundary search

Abstract

The application discloses a whole vehicle evaluation method for limited working condition pool and performance boundary search, comprising the following steps: step S1, constructing a vehicle operation parameter space; step S2, constructing a limited working condition pool database; step S3, simulating each working condition in the limited working condition pool database under a virtual simulation environment, and obtaining vehicle dynamic response data; step S4, extracting whole vehicle key performance indexes based on the vehicle dynamic response data, and performing normalization processing on the indexes; step S5, constructing a hierarchical weight system according to index grouping; step S6, calculating the comprehensive performance score of each working condition by an exponential weighting method, and generating a whole vehicle comprehensive performance index; and step S7, performing performance risk evaluation. The application can evaluate the whole vehicle limit performance in a virtual data stage, avoid data freezing in a later stage, cause irreparable loss and performance weakness, and obtain the vehicle performance boundary performance under the limit working condition.

Description

Technical Field

[0001] This invention relates to the field of automotive performance development and virtual evaluation technology. Background Technology

[0002] In the traditional vehicle development process, vehicle performance evaluation mainly relies on the following three methods. The first is typical operating condition simulation. For example, the published document CN121364352A, published on January 20, 2026, entitled "A Test Method and System for Motor Driver of On-board Motor of New Energy Vehicle", discloses a method including the following steps: S1. Dynamic environment and operating condition parameter acquisition: Under preset dynamic environmental conditions and simulated operating conditions, the performance parameters of the motor driver are acquired; the dynamic environmental conditions include a combination of temperature -40℃~85℃, humidity 10%~95%, and vibration frequency 10-2000Hz; the simulated operating conditions include NEDC cycle operating conditions, WLTC cycle operating conditions, and rapid acceleration / deceleration dynamic operating conditions; S2. Multimodal anomaly detection: Based on the parameters acquired in step S1, multimodal anomaly detection algorithms that integrate random forest, isolated forest, and autoencoder are used, combined with Bayesian optimization of dynamic thresholds, to identify the abnormal modes of the motor driver; S3. Adaptive parameter optimization and performance evaluation; S4. Test result generation. In addition, there is the use of proving ground testing, which involves testers driving test vehicles or production vehicles to test sites or road tests, and collecting user road verification data to obtain the required simulation results based on the integrated data.

[0003] However, existing technologies have obvious problems:

[0004] 1. Insufficient coverage of operating conditions; traditional simulation operating conditions typically only include: double lane change, moose test, steady-state turn, and hill climbing limit; these operating conditions cannot cover the extreme usage scenarios of the whole vehicle in real complex environments. For example: high altitude + full load + hill climbing; low adhesion + high speed lane change; overload + continuous curves; these extreme situations are often the real trigger points for vehicle performance failure.

[0005] 2. Lack of performance limit identification capability; existing evaluation methods only determine whether the standard is met. However, they cannot answer where the vehicle's true performance limit lies. For example, the maximum stable lateral acceleration, maximum controllable yaw response, and maximum stable braking capability under multiple extreme conditions.

[0006] 3. Low development efficiency; many problems can only be discovered in the prototype and testing phases, leading to extended development cycles and increased costs; therefore, a new technical method is needed to identify the performance limits of the entire vehicle during the virtual development phase. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to realize a vehicle performance evaluation method that can cover extreme working conditions and obtain the vehicle performance boundary.

[0008] To achieve the above objectives, the technical solution adopted by this invention is: a vehicle evaluation method based on a limited operating condition pool and performance boundary search, comprising the following steps:

[0009] Step S1: Construct the vehicle operating parameter space;

[0010] Step S2: Construct the extreme condition pool database;

[0011] Step S3: Simulate each working condition in the extreme working condition pool database in a virtual simulation environment and obtain vehicle dynamic response data.

[0012] Step S4: Extract key performance indicators of the whole vehicle based on vehicle dynamic response data, and normalize the indicators.

[0013] Step S5: Construct a hierarchical weighting system by grouping indicators;

[0014] Step S6: Calculate the comprehensive performance score for each working condition using the exponential weighting method, and summarize to generate the overall vehicle performance.

[0015] Step S7: Conduct a performance risk assessment.

[0016] In step S1, the basic variable set of the vehicle operating parameter space is P;

[0017] P={v, μ, δ, m, θ, T};

[0018] Where v is the vehicle speed, μ is the road surface adhesion coefficient, δ is the steering wheel angle, m is the vehicle load, θ is the road slope, and T is the ambient temperature.

[0019] In step S2, the extreme working condition pool database contains combined working conditions of load type and environment type. The load type includes, but is not limited to: no load, full load, and overload. The environment type includes, but is not limited to: vehicle speed, tire adhesion, slope, temperature, humidity, and altitude.

[0020] The extreme condition pool database is C;

[0021] C = {C1, C2 … Cn};

[0022] Where C1, C2 … Cn are working condition groups composed of different load types and different environmental types.

[0023] In step S3, vehicle performance simulation is performed based on the extreme condition pool database, and vehicle dynamic response data is output. The vehicle dynamic response data includes yaw rate, sideslip angle, lateral acceleration, body roll angle, tire lateral force, maximum gradeability, wading depth, and brake fade rate.

[0024] The vehicle dynamic response data is R;

[0025] R = {R1,R2,R3...Rn};

[0026] Where R1, R2, R3...Rn represent the vehicle dynamic response data for each working condition group.

[0027] In step S4, the method for extracting key performance indicators of the whole vehicle is as follows:

[0028] 1) Extract the original data of the whole vehicle;

[0029] 2) Process the raw data of the entire vehicle;

[0030] 3) Feature extraction;

[0031] 4) Obtain performance metrics based on the extracted features;

[0032] 5) Label and identify performance indicators;

[0033] The performance indicators include, but are not limited to, power performance indicators, handling and stability indicators, braking performance indicators, vehicle stability indicators, and safety performance indicators.

[0034] In step S4, the method for normalizing performance evaluation metrics is as follows:

[0035] Convert all indicators to the 0-1 range. Divide the indicators into cost-type indicators and revenue-type indicators. The larger the value of the cost-type indicator, the higher the cost. The larger the value of the revenue-type indicator, the higher the revenue.

[0036] The normalized formula for the performance metrics of tagging is as follows:

[0037] These are the performance indicators obtained from simulations under the current extreme operating conditions;

[0038] This is the minimum value of the indicator in the database or under reference operating conditions;

[0039] This is the maximum value of the indicator in the database or under reference operating conditions;

[0040] The normalized index value ranges from 0 to 1;

[0041] After normalizing different performance indicators, a vehicle performance indicator matrix is ​​formed, which is each performance indicator under each working condition group.

[0042] In step S5, the method for constructing a hierarchical weight system by grouping indicators is as follows:

[0043] 1) Grouping and hierarchizing each performance metric;

[0044] 2) Weighting system: A combination of the analytic hierarchy process (AHP) and the entropy method is used for weighting.

[0045] Subjective weights: Based on vehicle development experience and expert evaluation, a judgment matrix was constructed using the analytic hierarchy process (AHP) to obtain subjective weights. ;

[0046] Objective weights: Based on the dispersion of the simulation sample data, objective weights are calculated using the entropy method. ;

[0047] Combined weights: based on weight adjustment coefficients Calculate the final weights; ;

[0048] 3) Adjustment of the importance of operating conditions: Based on the impact of different extreme operating conditions on the vehicle performance, operating condition weights are introduced. Extreme operating conditions have a high weight, while normal operating conditions have a low weight.

[0049] In step S6, the comprehensive performance scoring function is constructed as follows: A comprehensive vehicle performance evaluation function is then established.

[0050] Where: F represents the overall vehicle performance score, and n represents the number of performance indicators. For indicator permissions, For normalized indicators, As the weight of the operating conditions, the larger the value of F, the better the performance of the whole vehicle under extreme operating conditions.

[0051] In step S7, within the extreme operating condition pool, the performance scoring critical point is identified, and the vehicle performance boundary function is obtained through interpolation, thus obtaining the vehicle performance boundary region.

[0052] The vehicle evaluation system based on extreme operating condition pool and performance boundary search includes an input device for inputting a vehicle operating parameter space and an extreme operating condition pool database, a storage device for storing the vehicle operating parameter space and the extreme operating condition pool database, a display device, and a processing device. The input device is connected to and inputs data to the processing device. The processing device interacts with the storage device. The processing device is connected to and displays data information to the display device. The processing device executes the vehicle evaluation method based on extreme operating condition pool and performance boundary search.

[0053] This invention can evaluate the ultimate performance of the whole vehicle in the virtual data stage (i.e. before M2 data is frozen and before the parts are molded), avoiding irreparable losses and performance weaknesses caused by data freezing in the later stage, and can obtain the vehicle's performance boundary performance under extreme conditions. Attached Figure Description

[0054] The following is a brief explanation of the content represented by each figure in this specification:

[0055] Figure 1 This is a schematic diagram of the vehicle evaluation method based on extreme condition pool and performance boundary search.

[0056] Figure 2 This is a schematic diagram illustrating the method for constructing the extreme condition pool based on key vehicle operating parameters in a vehicle evaluation method based on extreme condition pools and performance boundary search.

[0057] Figure 3 This is a schematic diagram of the vehicle's extreme performance space parameters and performance boundaries in a vehicle evaluation method based on extreme condition pools and performance boundary searches.

[0058] Figure 4 This is a schematic diagram illustrating the method for extracting multi-dimensional performance indicators in a vehicle evaluation method based on extreme condition pools and performance boundary search. Detailed Implementation

[0059] The following description, with reference to the accompanying drawings, details the specific implementation of the present invention, including the shape and structure of each component, the relative positions and connections between the parts, the function and working principle of each part, the manufacturing process, and the operation and use methods, to help those skilled in the art to have a more complete, accurate, and in-depth understanding of the inventive concept and technical solution of the present invention.

[0060] This invention is mainly for a system for the development and virtual evaluation of vehicle performance. It can evaluate and analyze the virtual performance of the whole vehicle based on the extreme condition pool and performance boundary search. The results of the evaluation and analysis can be used for the evaluation of the virtual development stage of vehicle power, handling stability and safety performance.

[0061] like Figure 1 As shown, the vehicle evaluation method based on the limited operating condition pool and performance boundary search includes the following steps:

[0062] Step S1: Constructing the vehicle operating parameter space. The core of vehicle operating parameters is parameter selection → data collection → preprocessing → feature extraction → dimensionality reduction → spatial modeling → verification and optimization, quantifying the multi-dimensional state of vehicle operation into an analyzable and modelable structured space. The following details the core concepts, construction steps, key technologies and examples, and application scenarios.

[0063] (1) The goals and dimensions need to be clearly defined during the construction process.

[0064] First determine the intended use (operating condition identification / energy consumption optimization / autonomous driving), then select the dimensions:

[0065] Basic motion: time t, position (x, y), velocity v, acceleration a, and mean acceleration / deceleration.

[0066] Energy parameters: drive power P, battery SOC, energy consumption rate, and regenerative braking power.

[0067] Environmental parameters: road slope (theta), curvature, wind speed, and road surface adhesion coefficient.

[0068] Driving characteristics: acceleration intensity, braking frequency, cruise ratio, idling time.

[0069] (2) Data collection and sample library construction

[0070] Data collected from real vehicles: OBD, GPS, IMU, Battery Management System (BMS), and slope sensor, with a sampling frequency of 1–10 Hz.

[0071] Scene coverage: urban / suburban / highway, congested / smooth traffic, different inclines, different driving styles.

[0072] Data volume: Single scene ≥ 500 km, total sample size ≥ 10 6 A time sequence record.

[0073] (3) Data preprocessing (cleaning → alignment → filtering → completion)

[0074] Outlier removal: 3σ principle, sliding window detection (e.g., (v>120) km / h or (|a|>5) m / s²).

[0075] Time alignment: Unify timestamps and interpolate to fill in missing values ​​(linear / spline interpolation).

[0076] Smoothing filters: moving average, Kalman filter, to remove velocity / acceleration jitter.

[0077] Segment division: divided according to motion state (idle, acceleration, cruise, deceleration, stop).

[0078] (4) Feature engineering (raw data → physical features)

[0079] Macro characteristics: total duration, average vehicle speed, proportion of highways, idling rate, number of start-stop cycles.

[0080] Mesoscopic features: acceleration distribution histogram, velocity-acceleration joint distribution, cruise stability index.

[0081] Microscopic characteristics: single acceleration slope, braking intensity, slope dwell time, and energy recovery efficiency.

[0082] Feature matrix: \(N \times D\) (N is the number of samples, D is the feature dimension, usually \(D=20–50\)).

[0083] (5) Dimensionality reduction and spatial reconstruction (high dimension → low dimension interpretable space)

[0084] Principal Component Analysis (PCA):

[0085] Calculate the covariance matrix → Eigenvalue decomposition → Select the top k principal components (cumulative variance ≥ 90%).

[0086] Key dimensions for load matrix identification include "high-frequency start-stop strength" and "medium-speed cruise stability".

[0087] Orthogonal transformation: preserves physical meaning and avoids the inability to interpret due to pure mathematical dimensionality reduction.

[0088] Output: A low-dimensional feature space (k=3–5), with each dimension having a clear physical meaning.

[0089] (5) Spatial modeling and state division

[0090] Clustering partitioning (K-means / FCM):

[0091] Optimal number of clusters: contour coefficient, Calinski-Harabasz index, typically 6–8 clusters cover 92%+ of the scene.

[0092] Cluster center: Represents typical operating conditions (such as urban congestion, highway cruising, and suburban commuting).

[0093] State transition matrix (Markov):

[0094] Multidimensional state transition probability: \(P(X_{t+1}|X_t)\), describes the law of state evolution.

[0095] Driving area (free space):

[0096] Voronoi diagram: Divides the feasible region into the obstacle region for motion planning.

[0097] (7) Validation, visualization and iterative optimization

[0098] Accuracy verification:

[0099] Reconstruction error: The error between the original data and the spatial projection is ≤5%.

[0100] Operating condition coverage: Test scenario coverage ≥ 90%.

[0101] Visualization:

[0102] 2D / 3D scatter plot: Principal component spatial clustering distribution.

[0103] Trajectory heatmap: velocity-acceleration joint distribution.

[0104] Iteration: Add a new scenario → Update the sample library → Retrain PCA / clustering → Optimize the spatial boundary.

[0105] The essence of constructing a vehicle operating parameter space is to quantify complex operating states into a structured, computable multi-dimensional space. The key lies in: accurate dimension selection, comprehensive data coverage, physically meaningful features, dimensionality reduction to retain core elements, and scenario-appropriate segmentation. By executing a 7-step closed-loop process, parameter spaces adapted to different applications can be quickly constructed, supporting operational condition analysis, energy consumption optimization, and autonomous driving decision-making.

[0106] Step S2: Construct the extreme condition pool database;

[0107] Extreme operating conditions generally refer to extreme driving scenarios where vehicles exceed their normal driving range and approach the safety boundaries of power, heat, electricity, structure, and braking. These scenarios include five categories: extreme driving, extreme environment, extreme road, extreme load, and extreme vehicle condition. The extreme operating condition pool database is a standardized storage of structured datasets containing extreme operating condition scenarios, operating parameters, boundary thresholds, test data, simulation data, and failure samples. These datasets are used for vehicle calibration, extreme verification, safety protection, and fault prediction.

[0108] Define the categories and boundary thresholds for extreme operating conditions, including:

[0109] Extreme driving conditions: rapid acceleration, full throttle launch, continuous emergency braking, high-speed emergency lane change, extreme hill climbing, long-distance high-speed driving, and full-load extreme speed driving.

[0110] Extreme road conditions: extremely steep longitudinal slopes, continuous long downhill slopes, continuous sharp bends, bumpy and bad roads, extreme water wading depths, pothole-riding off-road conditions, and ultra-high altitude slopes.

[0111] Extreme environmental conditions: extreme cold, extreme heat, high humidity during the plum rain season, high altitude and low pressure, strong winds and sandstorms, heavy rain and dense fog, and icy and snowy roads.

[0112] Extreme load conditions: full vehicle load / overload, extreme towing of trailer, continuous high power during long-term uphill climbing, and battery full discharge / full charge limits;

[0113] Extreme system boundary conditions: motor limit speed / torque, battery limit charge / discharge rate, electronic control limit output, continuous brake thermal fade, and gearbox limit shift shock.

[0114] The extreme working condition pool constructed this time includes a variety of load (including but not limited to: no load, full load, overload) and environmental (including but not limited to: vehicle speed, tire adhesion, slope, temperature, humidity) combination conditions.

[0115] Step S3: Simulate each working condition in the extreme working condition pool database in a virtual simulation environment and obtain vehicle dynamic response data.

[0116] The virtual simulation process relies on a multi-domain joint simulation model of the whole vehicle. It artificially sets extreme scenarios, extreme loads, extreme environments, and extreme driving inputs that exceed the boundaries of normal driving, drives the model to calculate, and outputs a batch of dynamic response time-series data of vehicle dynamics, power performance, thermal state, electrical state, chassis attitude, braking, steering, etc., replacing high-risk and high-cost real vehicle extreme tests. This invention is based on a proposed extreme condition pool database, selects data combinations from the extreme condition pool database, and performs simulations one by one to obtain the required vehicle dynamic response data.

[0117] The simulation process mainly includes:

[0118] First, a high-precision vehicle simulation model is built;

[0119] (1) Subsystem modeling:

[0120] Powertrain: Engine / Motor, Gearbox, Driveshaft, Differential; Input peak torque, limiting speed, continuous maximum power, and external characteristic limit MAP.

[0121] Battery system: Record maximum charge / discharge rate, high and low temperature internal resistance limits, SOC upper and lower limits, and thermal runaway temperature boundaries;

[0122] Chassis system: suspension hard points, spring damping limit travel, tire extreme lateral slip characteristics, braking system thermal fade characteristic curve;

[0123] Braking system: Establish a coupled model of braking pressure, deceleration, and braking temperature, and write the continuous braking temperature rise limit;

[0124] Thermal management system: extreme heat transfer coefficient and extreme temperature control strategy for motor, electronic control, and battery heat dissipation.

[0125] (2) Model calibration;

[0126] The accuracy of the model is corrected by benchmarking with real vehicle test data to ensure that the error under normal working conditions is less than 3% and to ensure that the response trend under extreme working conditions is real and reliable.

[0127] Secondly, define the input boundaries for the limit simulation;

[0128] Forced limit inputs are set from five extreme dimensions;

[0129] Extreme driving inputs include continuous input at 100% throttle opening, step emergency braking, continuous high-frequency braking, rapid full turn of the steering angle, and high-speed constant speed cruise.

[0130] Extreme road input includes setting ultra-slope long slopes, continuous undulating extreme road conditions, pothole pulse road surfaces, ultra-high curvature sharp bends, and extreme water crossing resistance.

[0131] Extreme environmental input, ambient temperature: -40℃ (extreme cold) / +55℃ (extreme heat);

[0132] Atmospheric pressure: High-altitude low pressure, strong headwind / tailwind extreme wind resistance;

[0133] Road surface adhesion coefficient: μ=0.1~0.2 for low adhesion in ice and snow, μ=1.0 for high adhesion limit;

[0134] Ultimate load input;

[0135] Full load, overload, ultimate traction and towing load, axle load limit distribution;

[0136] System policy limit unlock;

[0137] Temporarily release VCU / MCU / BMS power limits, temperature control torque limits, and SOC protection (for simulation purposes only, simulating critical boundary conditions);

[0138] Next, configure the simulation solver and sampling frequency;

[0139] Solver: Fixed-step / variable-step rigid solver, adapted for transient ultimate impact conditions;

[0140] Sampling frequency: 50~100Hz, to ensure that data is not lost during transient impacts and peak abrupt changes;

[0141] Simulation duration: Short transient limit 3~30s; Long period limit endurance 300~3600s;

[0142] Finally, the output list of dynamic response data under extreme conditions (which can be directly imported into the database) generally includes:

[0143] (1) Dynamic response of the whole vehicle: vehicle speed, longitudinal acceleration, lateral acceleration, pitch angle, roll angle, yaw rate, wheel slip ratio, vehicle body vibration acceleration, and suspension travel.

[0144] (2) Dynamic response of the power system, real-time torque / speed / power, peak power output, power response lag, full throttle acceleration response curve, and power margin for extreme climbing.

[0145] (3) Electric drive & battery dynamic response, bus voltage, instantaneous peak current, dynamic changes in charge and discharge rate, single cell voltage difference, SOC change rate, battery temperature rise rate, and low temperature start-up voltage drop.

[0146] (4) Braking system limit response, braking pressure, real-time deceleration, brake pad temperature change, braking force decay during thermal fade process, and continuous braking distance change.

[0147] (5) Thermal management limit response, motor temperature, electronic control temperature, battery pack maximum / minimum temperature, cooling fan limit speed, and heat dissipation margin under extreme operating conditions.

[0148] (6) Vehicle energy consumption and boundary protection response, instantaneous energy consumption under extreme conditions, peak energy consumption, system trigger power limiting time, torque reduction, and high temperature / low pressure / low voltage protection intervention sequence.

[0149] Step S4: Extract key performance indicators of the whole vehicle based on vehicle dynamic response data, and normalize the indicators.

[0150] The data to be extracted is obtained directly from vehicle sensors or through vehicle parameter information calculated using sensors. Then, the simulated / measured extreme condition time-series response data is used as the data source. The raw data is then processed using five types of algorithms: time domain statistics, peak extraction, feature fitting, boundary determination, and efficiency calculation. From seven dimensions—motion, power, braking, thermal, electric drive, handling, and durability—specific vehicle performance indicators for extreme conditions are quantified in batches and output. These indicators can be directly used for benchmarking, calibration, rating, and storage in the condition pool to complete feature extraction.

[0151] For example, through the following methods:

[0152] Data slicing: Divided according to extreme operating condition stages (start-up, steady state, peak, decay, and protection intervention stage);

[0153] Feature filtering: Remove noise and identify limiting peak values, steady-state mean, rate of change, and critical points;

[0154] Formula quantification: Substitute the physical calculation formulas to calculate performance indicators;

[0155] Grading determination: Based on the design threshold, classify the levels as compliant / critical / exceeding limits;

[0156] Structured archiving: Indicators are bound to operating condition tags and stored in the extreme operating condition database.

[0157] Step S5: Construct a hierarchical weighting system by grouping indicators;

[0158] For example, a hierarchical indicator system (with fixed evaluation dimensions) can be used.

[0159] We will continue to use the seven major performance metrics extracted earlier as the primary evaluation dimensions:

[0160] Then, the individual indicators are standardized and normalized in both forward and reverse directions;

[0161] For example, set the measured value, optimal ideal value, worst limit / failure threshold for a single indicator, and then determine the positive indicators (the larger the better: climbing ability, acceleration ability, heat dissipation margin, etc.), as well as the negative indicators (the smaller the better: braking distance, temperature rise rate, energy consumption, voltage drop, etc.), and the moderate range indicators (such as optimal operating temperature, reasonable slip ratio). Use trapezoidal / normal membership function normalization, and the highest score is obtained when it falls within the reasonable range.

[0162] Next, the hierarchical weights are determined (two commonly used weighting methods): Method A: Analytic Hierarchy Process (AHP) (most commonly used in engineering and preferred for vehicle calibration), Method B: Simple experience weights (quick implementation and applicable to extreme condition pools), i.e., default weights for extreme condition scenarios (safety first, reliability first).

[0163] Finally, the single-dimensional score is calculated using a hierarchical weighted calculation model, and the overall performance score of the vehicle under extreme conditions can also be converted to a percentage system (intuitive scoring).

[0164] Step S6: Calculate the comprehensive performance score for each working condition using the index weighting method, and summarize to generate the overall vehicle performance index;

[0165] Performance levels (rating standards) are derived from the comprehensive performance score of each operating condition. The dynamic weights for different operating conditions are adaptively adjusted. A single set of fixed weights is unsuitable for all extreme operating conditions. By shifting the weights based on operating condition types, the scoring method can be adjusted, such as...

[0166] For braking conditions on long downhill slopes: increase the weight of braking safety and decrease the weight of power and energy consumption;

[0167] Extreme cold / extreme heat environment conditions: Improve the weighting of thermal management and the reliability of the three electrical systems;

[0168] High-speed and extreme-endurance operating conditions: Improve power continuity and thermal management;

[0169] Low-adhesion instability conditions: Significantly increase the weight of handling stability;

[0170] Add scenario tags to each extreme working condition → match preset weight templates → automatically switch weight matrices → recalculate the overall score.

[0171] Step S7: Conduct a performance risk assessment.

[0172] Figure 2This is a schematic diagram of a method for constructing an extreme condition pool based on key operating parameters of the vehicle according to the present invention. Figure 3 This is a schematic diagram of the vehicle's limit performance space parameters and performance boundaries according to the present invention. In order to characterize the comprehensive influence of different working conditions on the vehicle's performance, the working condition intensity index is composed of multiple key parameters, including influencing factors such as vehicle speed, road gradient, vehicle load, road surface adhesion coefficient, ambient temperature and altitude. The higher the working condition intensity, the greater the load on the vehicle system under that working condition, and the closer it is to the vehicle's performance limit state.

[0173] The following detailed explanation is based on the definition of the vehicle's operating parameters as vehicle speed, road surface adhesion coefficient, steering wheel angle, vehicle load, road slope, and ambient temperature.

[0174] Step 1: Establish the vehicle operating parameter space. Define the set of vehicle operating parameters; this parameter space constitutes the basic set of variables for the vehicle's operating environment.

[0175] P = {v, μ, δ, m, θ, T} ;

[0176] Where v is the vehicle speed, μ is the road surface adhesion coefficient, δ is the steering wheel angle, m is the vehicle load, θ is the road slope, and T is the ambient temperature.

[0177] Step 2: Construct the extreme condition pool. Select typical extreme values ​​in the parameter space to generate the vehicle's extreme condition combinations.

[0178] For example:

[0179] Operating condition: Case 1, speed 80 kph, adhesion 0.3, full load, slope 10%;

[0180] Operating condition: Case 2, speed 90 kph, adhesion 0.3, full load, slope 5%;

[0181] Operating condition: Case 3, speed 100 kph, adhesion 0.5, overload, slope 3%;

[0182] Operating condition: Case 4, speed 110 kph, adhesion 0.6, no load, slope 0%;

[0183] Operating condition: Case 5, speed 40 kph, adhesion 0.65, no load, slope 30%;

[0184] Form a database of extreme operating conditions C = {C1, C2 … Cn}

[0185] Step 3: Virtual simulation calculation of the whole vehicle. Each condition in the extreme condition pool is broken down and the whole vehicle performance is simulated. The output vehicle dynamic response data includes yaw rate, sideslip angle, lateral acceleration, body roll angle, tire lateral force, maximum gradeability, wading depth, brake fade rate, and other key vehicle performance data.

[0186] The vehicle response index is formed as R = {R1,R2,R3...Rn}.

[0187] Step 4: Establish performance evaluation indicators. Based on the vehicle performance development evaluation standards, extract multi-dimensional performance indicators from the key performance data response of the vehicle, including but not limited to:

[0188] Performance metrics: These describe a vehicle's power output capability under extreme acceleration conditions. 0-100 km / h acceleration time, maximum climbing ability, high-speed re-acceleration capability, and drive wheel slip ratio.

[0189] Handling and stability metrics: used to evaluate the stability and response characteristics of a vehicle under extreme handling conditions. These include steady-state yaw rate gain, sideslip response time, peak yaw rate, understeer gradient, and extreme lateral acceleration.

[0190] Braking performance indicators: used to evaluate the safety performance of a vehicle under emergency braking conditions. These include 100-0 km / h braking distance, peak braking deceleration, and braking stability index.

[0191] Vehicle stability indicators: used to evaluate vehicle stability under complex and extreme operating conditions. These include yaw rate overshoot, body roll angle, rate of change of lateral acceleration at the center of gravity, and vehicle trajectory deviation.

[0192] Safety performance indicators: These are used to evaluate a vehicle's safety performance under collision conditions. These include passenger compartment intrusion, occupant injury levels, number of airbags, and airbag holding time.

[0193] Methods for extracting multidimensional performance indicators, such as Figure 4 The key steps are data processing. First, the raw data needs to be processed. This raw data consists of vehicle parameters directly obtained from sensors, including vehicle speed, steering wheel angle, ambient temperature, road gradient, altitude, yaw rate, sideslip angle, lateral acceleration, and body roll angle. This raw data requires noise reduction processing, such as filtering and de-scratching. Second, segmented processing is performed based on the vehicle's overall state, such as the starting phase, steady-state phase, extreme phase, and recovery phase. Third, peak detection is also performed.

[0194] Performance evaluation metrics are normalized. Since different performance metrics have different dimensions, they need to be normalized. Normalization transforms all metrics into the 0-1 range, facilitating weighted aggregation.

[0195] The metrics are categorized into cost metrics and benefit metrics. For cost metrics, a higher value indicates higher cost; conversely, for benefit metrics, a higher value indicates higher benefit. In step four, the performance metrics are labeled.

[0196] The normalization formula is as follows:

[0197]

[0198] The index values ​​obtained from simulation under the current extreme working conditions.

[0199] This is the minimum value of the indicator in the database or under reference operating conditions.

[0200] This is the maximum value of the indicator in the database or under reference operating conditions.

[0201] The normalized index value ranges from 0 to 1.

[0202] After normalizing different performance indicators, a matrix of vehicle performance indicators can be formed.

[0203] Operating condition number Case1, power performance index 0.75, handling stability index 0.81, braking index 0.88, body stability index 0.85;

[0204] Operating condition number Case2, power performance index 0.6, handling stability index 0.71, braking index 0.92, body stability index 0.75;

[0205] Operating condition number Case3, power performance index 0.5, handling stability index 0.61, braking index 0.95, body stability index 0.62;

[0206] Operating condition number: Case 4...

[0207] Step 5: Comprehensive Performance Scoring Model

[0208] Indicator grouping and hierarchical classification:

[0209] Level L1, the indicator group is power performance, and the indicator examples are 0-100 km / h acceleration time, climbing ability, and engine output rate;

[0210] Level L1, the indicator group is handling stability, and the indicator examples are yaw rate, sideslip angle, and steering response.

[0211] Level L1, the indicator group is braking performance, and the indicator examples are braking distance, braking deceleration value, and wheel lock-up coefficient;

[0212] Level L2, the indicator group is vehicle stability, and the indicator examples are vehicle roll angle, center of gravity lateral velocity, and trajectory deviation;

[0213] Level L2, the indicator group is comfort, and the indicator examples are vehicle body acceleration RMS and vibration rate response;

[0214] Level L2, the indicator group is durability / safety margin, and the indicator examples are brake fade rate and suspension stress.

[0215] Note: L1 is the core safety performance indicator, and L2 is the comfort and durability auxiliary indicator.

[0216] For example, the calculation method is as follows:

[0217]

[0218] Weighting system design: The weighting method is combined with the analytic hierarchy process (AHP) and the entropy method.

[0219] Subjective weights: Based on vehicle development experience and expert evaluation, a judgment matrix was constructed using the Analytic Hierarchy Process (AHP) to obtain subjective weights. .

[0220] Objective weights: Based on the dispersion of the simulation sample data, objective weights are calculated using the entropy method. .

[0221] Combined weights: based on weight adjustment coefficients Calculate the final weight

[0222]

[0223] (3) Design adjustment based on the importance of operating conditions;

[0224] Different extreme operating conditions have different impacts on the overall vehicle performance, so operating condition weights are introduced. Extreme working conditions (high temperature, high load, steep slope) have a high weight, while normal working conditions have a low weight.

[0225] Step 6: Comprehensive performance scoring function: Construct a comprehensive performance evaluation function for the whole vehicle.

[0226]

[0227] Where: F represents the overall vehicle performance score, and n represents the number of performance indicators. For indicator permissions, For normalized indicators, This represents the weighting of operating conditions. The larger the value of F, the better the vehicle's performance under extreme operating conditions.

[0228] Step 7: Limit Performance Boundary Criterion

[0229] Within the extreme operating condition pool, identify the performance scoring critical point, i.e. The boundary function of the vehicle performance is obtained through interpolation.

[0230] Additionally: A performance limit is determined to be reached when one of the following conditions is met: A key performance indicator exceeds a preset safety threshold, and the comprehensive evaluation function... Below the target value The simulation stability index shows a divergent trend; based on this, the boundary region of the vehicle performance can be obtained.

[0231] The vehicle evaluation system based on extreme condition pooling and performance boundary search mainly includes input devices, processing devices, storage devices, and display devices. The details of each device are described below:

[0232] Input devices connect to and input data to processing devices. These can be manual input devices such as keyboards, mice, or lights, or they can be vehicle-mounted sensing and acquisition layers, real vehicle data acquisition devices (real vehicle road + track extreme testing), and powertrain chassis integrated test benches.

[0233] (1) On-board vehicle data acquisition hardware:

[0234] Vehicle CAN bus recorder: Supports CAN / CANFD / LIN, sampling frequency 10~100Hz, and collects data from all protocols including VCU / MCU / BMS / ESP / ABS / TCU;

[0235] High-precision vehicle-mounted IMU inertial navigation system;

[0236] Measurement range: longitudinal ±5g, lateral ±3g, yaw rate ±300° / s, output vehicle speed, acceleration, attitude angle, slip ratio, and vehicle attitude;

[0237] Distributed temperature acquisition module: K-type thermocouple + multi-channel temperature monitoring instrument to collect the temperature of motor, electronic control, battery, brake disc, coolant, and intake and exhaust.

[0238] High-precision wheel speed / brake pressure sensor: monitors dynamic changes in brake hydraulic pressure, wheel speed difference, and braking force;

[0239] GPS / BeiDou dual-mode positioning module: collects slope, altitude, vehicle speed, and driving trajectory for identification of extreme road conditions;

[0240] (2) Powertrain chassis integrated test bench;

[0241] Chassis dynamometer (four-drum / single-drum);

[0242] Power rating: Matches the peak power of the vehicle, and can simulate road resistance, gradient resistance, wind resistance, and rolling resistance.

[0243] Supports: constant slope loading, dynamic variable slope loading, long downhill inertial resistance simulation, and ultimate drag load loading.

[0244] Electric dynamometer (motor / engine test bench) enables full throttle limit torque output, continuous high power endurance, high and low temperature start-up, and extreme charge and discharge loading;

[0245] Braking performance test bench, simulating multiple consecutive extreme braking, brake heat fade, and low-adhesion road surface braking force attenuation test.

[0246] The suspension road simulation vibration table reproduces bumpy, potholed, and extreme off-road road inputs, and collects the chassis's extreme dynamic response.

[0247] Battery charge and discharge test cabinet, simulating extreme rate charge and discharge, low temperature high current discharge, and high voltage power supply extreme conditions;

[0248] (3) Data aggregation and edge computing hardware:

[0249] Real-time data aggregation server, industrial-grade edge computing gateway: aggregates vehicle-mounted data, benchtop sensor data, and environmental chamber parameters, and unifies data protocol conversion: analog to digital, CAN message parsing, timing alignment, and unified sampling clock;

[0250] The processing equipment is a combination of a real-time data aggregation server and a real-time computing hardware configuration;

[0251] Real-time computing hardware configuration;

[0252] High-performance industrial control host / Embedded industrial computing board

[0253] Features included:

[0254] Real-time time-domain feature calculation: peak value, mean value, rate of change, duration;

[0255] Automatic extraction of key performance indicators for the whole vehicle online (matching the indicator system mentioned above);

[0256] Automatic boundary search algorithm hardware deployment: progressive loading → capturing performance critical thresholds, protecting trigger points, and failure boundaries;

[0257] Normalization processing and real-time calculation of dimensional scores;

[0258] The processing device executes the vehicle evaluation method based on the extreme condition pool and performance boundary search.

[0259] The storage device is a local high-speed solid-state drive: it stores real-time extreme response data, connects to the extreme operating condition pool database, and completes the unified storage of operating condition data, indicator data, and scoring data.

[0260] Display devices are displays in a broad sense, and can include:

[0261] Visualized large screen: Extreme operating condition status, vehicle dynamic response curve, performance boundary cloud map, and scoring trend display;

[0262] Print output terminal: Hardware output for vehicle extreme performance evaluation report;

[0263] Remote operation and maintenance terminal: Access the operating condition pool remotely, issue test tasks, and view evaluation results.

[0264] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution of the present invention to other occasions without modification, are all within the protection scope of the present invention.

Claims

1. A vehicle evaluation method based on extreme condition pool and performance boundary search, characterized in that, Includes the following steps: Step S1: Construct the vehicle operating parameter space; Step S2: Construct the extreme condition pool database; Step S3: Simulate each working condition in the extreme working condition pool database in a virtual simulation environment and obtain vehicle dynamic response data. Step S4: Extract key performance indicators of the whole vehicle based on vehicle dynamic response data, and normalize the indicators. Step S5: Construct a hierarchical weighting system by grouping indicators; Step S6: Calculate the comprehensive performance score for each working condition using the exponential weighting method, and summarize to generate the overall vehicle performance. Step S7: Conduct a performance risk assessment.

2. The vehicle evaluation method based on extreme condition pool and performance boundary search according to claim 1, characterized in that: In step S1, the basic variable set of the vehicle operating parameter space is P; P={v, μ, δ, m, θ, T}; Where v is the vehicle speed, μ is the road surface adhesion coefficient, δ is the steering wheel angle, m is the vehicle load, θ is the road slope, and T is the ambient temperature.

3. The vehicle evaluation method based on extreme condition pool and performance boundary search according to claim 2, characterized in that: In step S2, the extreme working condition pool database contains a combination of load type and environment type working conditions. The load type includes at least one of the following: no load, full load, and overload. The environment type includes at least one of the following: vehicle speed, tire adhesion, slope, temperature, humidity, and altitude. The extreme condition pool database is C; C = {C1, C2 … Cn}; Where C1, C2 … Cn are working condition groups composed of different load types and different environmental types.

4. The vehicle evaluation method based on extreme condition pool and performance boundary search according to claim 3, characterized in that: In step S3, vehicle performance simulation is performed based on the extreme condition pool database, and vehicle dynamic response data is output. The vehicle dynamic response data includes yaw rate, sideslip angle, lateral acceleration, body roll angle, tire lateral force, maximum gradeability, wading depth, and brake fade rate. The vehicle dynamic response data is R; R = {R1,R2,R3...Rn}; Where R1, R2, R3...Rn represent the vehicle dynamic response data for each working condition group.

5. The vehicle evaluation method based on extreme condition pool and performance boundary search according to claim 1, 2, 3 or 4, characterized in that: In step S4, the method for extracting key performance indicators of the whole vehicle is as follows: 1) Extract the original data of the whole vehicle; 2) Process the raw data of the entire vehicle; 3) Feature extraction; 4) Obtain performance metrics based on the extracted features; 5) Label and identify performance indicators; The performance indicators include, but are not limited to, power performance indicators, handling and stability indicators, braking performance indicators, vehicle stability indicators, and safety performance indicators.

6. The vehicle evaluation method based on extreme condition pool and performance boundary search according to claim 5, characterized in that: In step S4, the method for normalizing performance evaluation metrics is as follows: Convert all indicators to the 0-1 range. Divide the indicators into cost-type indicators and revenue-type indicators. The larger the value of the cost-type indicator, the higher the cost. The larger the value of the revenue-type indicator, the higher the revenue. The normalized formula for the performance metrics of tagging is as follows: These are the performance indicators obtained from simulations under the current extreme operating conditions; This is the minimum value of the indicator in the database or under reference operating conditions; This is the maximum value of the indicator in the database or under reference operating conditions; The normalized index value ranges from 0 to 1; After normalizing different performance indicators, a vehicle performance indicator matrix is ​​formed, which is each performance indicator under each working condition group.

7. The vehicle evaluation method based on extreme condition pool and performance boundary search according to claim 1 or 6, characterized in that: In step S5, the method for constructing a hierarchical weight system by grouping indicators is as follows: 1) Grouping and hierarchizing each performance metric; 2) Weighting system: A combination of the analytic hierarchy process (AHP) and the entropy method is used for weighting. Subjective weights: Based on vehicle development experience and expert evaluation, a judgment matrix was constructed using the analytic hierarchy process (AHP) to obtain subjective weights. ; Objective weights: Based on the dispersion of the simulation sample data, objective weights are calculated using the entropy method. ; combination Weight: Adjusted according to weighting coefficient Calculate the final weights; ; 3) Adjustment of the importance of operating conditions: Based on the impact of different extreme operating conditions on the vehicle performance, operating condition weights are introduced. Extreme operating conditions have a high weight, while normal operating conditions have a low weight.

8. The vehicle evaluation method based on extreme condition pool and performance boundary search according to claim 7, characterized in that: In step S6, the comprehensive performance scoring function is constructed as follows: A comprehensive vehicle performance evaluation function is then established. Where: F represents the overall vehicle performance score, and n represents the number of performance indicators. For indicator permissions, For normalized indicators, As the weight of the operating conditions, the larger the value of F, the better the performance of the whole vehicle under extreme operating conditions.

9. The vehicle evaluation method based on extreme condition pool and performance boundary search according to claim 1 or 8, characterized in that: In step S7, within the extreme operating condition pool, the performance scoring critical point is identified, and the vehicle performance boundary function is obtained through interpolation, thus obtaining the vehicle performance boundary region.

10. A vehicle evaluation system based on extreme condition pool and performance boundary search, the system comprising an input device for inputting a vehicle operating parameter space and an extreme condition pool database, a storage device for storing the vehicle operating parameter space and the extreme condition pool database, a display device, and a processing device; wherein the input device is connected to and inputs data to the processing device, the processing device interacts with the storage device, and the processing device is connected to and displays data information to the display device, characterized in that: The processing device executes the vehicle evaluation method based on extreme condition pool and performance boundary search as described in any one of claims 1-9.

Images