An electric vehicle fast charging performance test evaluation method and system

Abstract

The application relates to the technical field of electric vehicle testing, in particular to a method and system for testing and evaluating the fast charging performance of an electric vehicle. The method comprises the following steps: starting a direct current fast charging process and synchronously collecting test data; extracting multi-dimensional features based on the test data, including a charging capacity retention factor representing charging capacity, a charging power smoothness index representing charging power smoothness, and a thermal management efficiency representing thermal management coordination; performing weighted summation on the charging capacity retention factor, the charging power smoothness index and the thermal management efficiency to obtain a comprehensive charging performance index; and dividing evaluation grades according to the comprehensive charging performance index. The application introduces environmental temperature correction, battery SOH dynamic compensation and charging power smoothness analysis, and establishes a charging performance quantitative evaluation system covering the whole link of "piles-vehicles-batteries".

Description

Technical Field

[0001] This application relates to the field of electric vehicle testing technology, and more specifically, to a method and system for testing and evaluating the fast charging performance of electric vehicles. Background Technology

[0002] With the rapid development of the electric vehicle industry, users' demands for charging convenience are increasing, and fast charging technology has become a key way to solve "range anxiety" and "charging wait anxiety". In recent years, the charging rate has gradually developed from 2C to 4C, 6C and even higher rates, and major automakers have launched ultra-fast charging technologies based on 800V high-voltage platforms and high-rate batteries.

[0003] However, while the high power and high rate characteristics of fast charging technology shorten charging time, they also pose serious challenges to battery systems. Studies have shown that high-current charging accelerates battery aging mechanisms, increases the heat dissipation requirements of battery packs and charging cables, and battery capacity, power, and internal resistance are all significantly negatively correlated with the charging rate. Furthermore, battery temperature rises significantly during fast charging; for every 10°C increase in battery temperature, the rate of degradation doubles during fast charging. High-rate charging also easily induces lithium deposition and dendrite growth on the negative electrode surface, causing battery capacity loss and safety risks. Therefore, evaluating fast charging performance is a complex issue involving multiple dimensions, including charging efficiency, battery life, temperature rise control, and safety.

[0004] Currently, existing technologies typically evaluate the fast-charging performance of electric vehicles using only charging time or charging rate as the main indicators. This single-dimensional evaluation method is insufficient to comprehensively reflect the overall performance in real-world usage scenarios.

[0005] Therefore, there is an urgent need to propose a more comprehensive and scientific method for evaluating the performance of fast charging for electric vehicles, so as to more accurately reflect the actual performance of fast charging and provide a reliable basis for the research and development optimization of fast charging technology and user selection. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for testing and evaluating the fast charging performance of electric vehicles. By introducing environmental temperature correction, dynamic compensation of battery SOH, and analysis of charging power smoothness, a quantitative evaluation system for charging performance covering the entire "charging pile-vehicle-battery" link is established.

[0007] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides a method for testing and evaluating the fast charging performance of electric vehicles, including: The electric vehicle under test was initialized in an environmental chamber and immersed in the vehicle at a set ambient temperature. Initiate the DC fast charging process and simultaneously collect test data, including: current ambient temperature, power requested by the vehicle battery management system, actual power received by the battery pack, battery state of charge, and power consumption of the thermal management system. Based on the experimental data, multidimensional features are extracted, including: the charging capacity retention factor characterizing charging capability, the charging power smoothness index characterizing charging power smoothness, and the thermal management efficiency characterizing thermal management coordination; wherein, the charging capacity retention factor characterizes the charging capability level that the battery can maintain under the current health state and measured ambient temperature, relative to the vehicle's factory condition. The comprehensive charging performance index is obtained by weighting and summing the charging capacity retention factor, the charging power smoothness index, and the thermal management efficiency. Evaluation levels are determined based on the comprehensive charging performance index.

[0008] Secondly, this application provides a fast-charging performance testing and evaluation system for electric vehicles, comprising: The initialization module is used to initialize the state of the electric vehicle under test in the environmental chamber and complete the immersion at a set ambient temperature. The data acquisition module is used to initiate the DC fast charging process and simultaneously collect test data, including: current ambient temperature, power requested by the vehicle battery management system, actual power received by the battery pack, battery state of charge, and power consumption of the thermal management system. The feature extraction module is used to extract multi-dimensional features based on the test data, including: the charging capacity retention factor characterizing charging capability, the charging power smoothness index characterizing charging power smoothness, and the thermal management efficiency characterizing thermal management coordination; wherein, the charging capacity retention factor characterizes the charging capability level that the battery can maintain under the current health state and measured ambient temperature, relative to the vehicle's factory condition. The calculation module is used to perform a weighted summation of the charging capacity retention factor, the charging power smoothness index, and the thermal management efficiency to obtain the comprehensive charging performance index. The classification module is used to classify the evaluation level based on the comprehensive charging performance index.

[0009] This application has the following substantial features and significant progress compared to the prior art: This application significantly improves the scientific rigor, accuracy, and engineering practicality of electric vehicle fast-charging performance testing by constructing a multi-dimensional quantitative evaluation system covering the entire chain from charging piles to vehicle control and power batteries. On one hand, it introduces an ambient temperature correction coefficient and a dynamic SOH (State of Charge) decay compensation mechanism to effectively eliminate the interference of extreme temperature zones and battery aging on charging capacity assessment, overcoming the misjudgment defects caused by traditional methods that rely solely on "charging time" as a single indicator. On the other hand, it relies on key data such as BMS requested power, actual battery received power, SOC (State of Charge), and thermal management system power consumption collected at high frequencies to accurately improve the performance of electric vehicles. By taking three key features—"charging capacity retention factor," "charging power smoothness index," and "thermal management coordination"—it can not only quantitatively identify transient anomalies such as power limiting, communication jitter, and thermal management overload, but also automatically trace the causes of low performance (such as insufficient low-temperature preheating or inconsistency between individual cells) and output targeted optimization suggestions. Finally, through the weighted fusion to generate a comprehensive charging performance index (CPI) and its four-level A / B / C / D evaluation, it has achieved a leap in fast charging performance from "able to charge" to "fast and stable, intelligent and accurate," providing reliable data support for vehicle thermal management optimization, charging strategy development, and regional adaptability design. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0011] Figure 1 This is a flowchart of a fast-charging performance testing and evaluation method for electric vehicles provided in an embodiment of this application. Detailed Implementation

[0012] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0013] Figure 1 This is a flowchart of a fast-charging performance testing and evaluation method for electric vehicles provided in this embodiment. This embodiment provides a testing and evaluation process covering the entire "charging station-vehicle-battery" chain, including the following operations: S110. Initialize the state of the electric vehicle under test in the environmental chamber and complete the immersion at the set ambient temperature.

[0014] First, the electric vehicle is initialized in a controlled environment chamber, including reading the battery thermal state and adjusting the state of charge (SOC) through the OBD / CAN interface. Then, the ambient temperature is set to the target value (such as -20℃, 25℃ or 40℃) and the vehicle is immersed in a constant temperature environment until the temperature difference between the battery and the ambient temperature meets the threshold requirements.

[0015] Optionally, the vehicle under test is placed in an environmental chamber, and the highest and lowest temperatures and state of charge of the battery are read through the on-board diagnostic interface using a diagnostic tool; the state of charge of the battery is adjusted to a preset initial point; and the ambient temperature in the environmental chamber is adjusted according to the test requirements until the immersion is completed.

[0016] Specifically, by turning on the air conditioner to discharge or by charging the battery with a charging station, the battery's SOC is adjusted to a preset initial point (e.g., 10%). Then, the ambient temperature is adjusted to the target test point (i.e., the target test temperature, which is also the current ambient temperature), and the difference between the battery temperature and the ambient temperature is continuously monitored. When the difference is less than or equal to 2°C and remains stable for more than 30 minutes without significant fluctuations, the immersion test is considered complete, and the test proceeds to the next stage.

[0017] S120. Start the DC fast charging process and simultaneously collect test data, including: current ambient temperature, power requested by the vehicle battery management system, actual power received by the battery pack, battery state of charge, and power consumption of the thermal management system.

[0018] The DC fast charging station is activated, and fast charging has begun. Simultaneously, the current ambient temperature is being collected. Synchronously acquire the power requested by the vehicle's battery management system (BMS) at a frequency of not less than 10Hz. Actual received power of the battery pack Battery pack SOC and thermal management system (including compressor, water pump, heater) power consumption The sampling frequency should be ≥10Hz to capture instantaneous derating caused by protocol handshakes or relay jitter.

[0019] Among them, the vehicle battery management system (BMS) requests power as follows: This is the charging power demand command value sent by the BMS to the charging pile via the CAN bus, measured in kW. It reflects the target charging power of the vehicle under its current SOC, temperature, and health conditions, and is the output signal of the charging control strategy. The battery pack's actual received power refers to the instantaneous effective power flowing into the positive and negative terminals of the battery pack from the high-voltage DC side, measured in kW. It is calculated from the measured values ​​of battery terminal voltage and current, representing the actual energy absorption rate of the battery. The battery's state of charge (SOC) is the percentage of its current usable capacity relative to its nominal capacity, expressed as 0~100%. The thermal management system's power consumption is the total electrical energy consumed by the thermal management system (including high-voltage accessories such as the compressor, electric water pump, and PTC heater) during fast charging, measured in kWh. It is obtained by high-frequency sampling and integration of the power supply circuits of each component using a high-voltage current sensor, and is used to assess the energy consumption cost of temperature control.

[0020] S130. Extract multidimensional features based on the test data, including: charging capability retention factor characterizing charging capability, charging power smoothness index characterizing charging power smoothness, and thermal management efficiency characterizing thermal management coordination.

[0021] Charging Capability Retention Factor: Characterizes the level of charging capability that the battery can maintain relative to the vehicle's factory condition under the current State of Health (SOH) and measured ambient temperature. Charging Power Smoothness Index: Characterizes the stability of power output during charging, used to quantify the consistency and fluctuation between the power requested by the BMS and the actual power received by the battery. Thermal Management Efficiency: Characterizes the energy coordination efficiency of the thermal management system during fast charging.

[0022] Optionally, a charge capability retention factor characterizing the charging capability may be extracted based on experimental data, including: Step 1: Read the capacity health status from the BMS message via the on-board diagnostic interface. The capacity health status is the percentage of the current maximum available capacity divided by the factory design capacity.

[0023] Step 2: Obtain the lifespan decay correction coefficient based on the capacity health status.

[0024] Since there is a strong coupling relationship between the internal resistance growth curve and the capacity decay curve, an empirical mapping can be performed using mathematical polynomials to construct a second-order polynomial mapping function: ; ; in, This is the capacity health status (e.g., 85%, substitute with 0.85); These are empirical fitting coefficients, which need to be obtained by least-squares fitting of a large number of "capacity-power decay two-dimensional scatter plots" of retired or operating batteries. For example, if we assume that capacity decay and power decay are approximately linearly positively correlated, then... ,at this time . It is the lifetime degradation correction factor. A sub-item.

[0025] if ,but The cutoff value is set to 1 to prevent inversion caused by testing errors; if In cases where this is not considered a valid fast charging evaluation criterion, then... The cutoff value is 0.6.

[0026] Step 3: Calculate the ambient temperature correction factor based on the current ambient temperature.

[0027] In the environmental chamber, based on the actual received power of the battery pack The maximum charging power was obtained under different ambient temperatures (e.g., -20°C to 50°C, in 5°C increments) and different states of charge (e.g., 10% to 80%, in 5% increments). In other words, it takes the maximum actual received power of the battery pack under a certain ambient temperature and a certain state of charge.

[0028] Assuming the current ambient temperature falls between two calibration temperatures, calculate the interpolation weights: ; ; ; ; in, It is the interpolation weight. This is the current ambient temperature. and These are two calibration temperatures. It's the temperature difference.

[0029] Based on the interpolation weights, calculate the maximum charging power at the current ambient temperature for each state of charge j: ; in, It is the charged state point j. This is the maximum charging power at the current ambient temperature and at the state of charge j. For calibration temperature The maximum charging power at state of charge j; For calibration temperature , the maximum charging power at state of charge point j. The output is a one-dimensional matrix, which corresponds to the maximum charging power across the entire SOC range under the current ambient temperature, and is also the theoretical maximum charging power.

[0030] Based on the difference between the maximum charging power at the current ambient temperature and the actual power received by the battery pack at the state of charge point j, the ambient temperature correction factor is obtained: ; in, The current ambient temperature. Here, m is the ambient temperature correction factor, and m is the number of charge state points. It is the charged state point j. It is the actual received power of the battery pack at the current ambient temperature and state of charge point j.

[0031] Step 4: Based on the lifetime degradation correction coefficient and the ambient temperature correction coefficient, obtain the charging capability retention factor, which characterizes the charging capability. .

[0032] ; Optionally, a charging power smoothness index characterizing charging power smoothness can be extracted based on experimental data, including: ; Where N is the number of power data points actually received by the battery pack, and also the number of power data points requested by the vehicle battery management system. This is the actual received power of the i-th battery pack (collected during the charging process from 10% to 80% SOC). This is the power request from the i-th vehicle battery management system (collected during the charging process from 10% to 80% SOC). This is the charging power smoothness index.

[0033] The charging power smoothness index is used to evaluate whether there are frequent power limits or communication handshake abnormalities during the charging process.

[0034] Optionally, thermal management efficiency, which characterizes the coordination of thermal management, is extracted based on experimental data. The formula for calculating the thermal management efficiency score is as follows: ; in, It is the cumulative power consumption of the thermal management system during charging; This refers to the net charge energy of the battery pack, where k is the adjustment coefficient. and These are the start and end times of charging, respectively. It is the power consumption of the thermal management system at time t. It is the actual received power of the battery pack at time t. It is the thermal management efficiency score.

[0035] In the above formula, the adjustment coefficient k is a dimensionless scalar, and its value is determined based on the industry's critical energy consumption tolerance boundary. The value range is generally 2.0≤k≤6.0. The value of k characterizes the evaluation system's sensitivity to the punishment of parasitic energy consumption in thermal management. Different step constants are assigned to different ambient temperature conditions to achieve precise quantitative constraints on the behavior of 'excessive energy consumption for charging rate' under different ambient temperature zones.

[0036] S140. The charging capacity retention factor, charging power smoothness index and thermal management efficiency are weighted and summed to obtain the comprehensive charging performance index.

[0037] The charging capacity retention factor, charging power smoothness index, and thermal management efficiency are weighted and summed to obtain the comprehensive charging performance index (CPI): ; In the above formula, , , The weighting coefficients are suggested to be 0.5:0.3:0.2.

[0038] S150 is evaluated based on its comprehensive charging performance index.

[0039] The CPI is mapped to four levels—A (Excellent), B (Good), C (Average), and D (Poor)—in descending order of value.

[0040] If the overall charging performance index is less than the set value, the cause will be automatically determined based on multi-dimensional characteristics. For example: S bound Excessively low charging power (i.e., actual charging power far lower than the expected theoretical power) may be due to insufficient preheating at low temperatures, leading to lithium plating protection current limiting. Mechanism analysis: At extremely low ambient temperatures, the battery electrolyte has high viscosity and a slow solid-phase diffusion rate. To prevent lithium plating on the negative electrode, the BMS forcibly clamps the charging current to a low rate (e.g., 0.2C~0.5C), and the onboard heating system fails to quickly heat the cell to its optimal temperature range. factor Excessively low power (i.e., drastic power fluctuations and frequent curve jumps) may be caused by a "over-temperature protection-recovery" step oscillation due to lag in thermal management. Mechanism analysis: The cooling system responds slowly, and the cell overheats, triggering the BMS to limit the current and derating it (e.g., from 200A to 80A); after derating, the temperature drops, and the BMS increases the current again, causing the temperature to spike again, forming a repeated "step sawtooth" operating condition. The anomaly (imbalance in thermal management) may be due to excessive cooling / misaligned cooling capacity distribution control (high energy consumption and inefficient strategy). Mechanism analysis: In order to maintain a low surface temperature, the vehicle's compressor and cooling pump operate at full load and maximum speed throughout the entire process, with parasitic power consumption consuming more than 15% of the charged electricity, resulting in extremely low energy efficiency.

[0041] This application also provides a fast-charging performance testing and evaluation system for electric vehicles, including: The initialization module is used to initialize the state of the electric vehicle under test in the environmental chamber and complete the immersion at a set ambient temperature. The data acquisition module is used to initiate the DC fast charging process and simultaneously collect test data, including: current ambient temperature, power requested by the vehicle battery management system, actual power received by the battery pack, battery state of charge, and power consumption of the thermal management system. The feature extraction module is used to extract multi-dimensional features based on the test data, including: the charging capacity retention factor characterizing charging capability, the charging power smoothness index characterizing charging power smoothness, and the thermal management efficiency characterizing thermal management coordination; wherein, the charging capacity retention factor characterizes the charging capability level that the battery can maintain under the current health state and measured ambient temperature, relative to the vehicle's factory condition. The calculation module is used to perform a weighted summation of the charging capacity retention factor, the charging power smoothness index, and the thermal management efficiency to obtain the comprehensive charging performance index. The classification module is used to classify the evaluation level based on the comprehensive charging performance index.

[0042] This system can execute the methods provided in any of the above embodiments and has the corresponding technical effects, which will not be elaborated here.

[0043] In summary, the present application has the following technical effects based on the above embodiments: 1. Scientific evaluation: The effects of charging ambient temperature and battery health (SOH) are taken into account to minimize misjudgments.

[0044] 2. Transparent Diagnosis: By collecting data from the entire charging station, vehicle, and battery chain, it is possible to accurately pinpoint whether slow charging or power fluctuations are caused by problems with the charging station, vehicle thermal management, or battery cell consistency.

[0045] 3. Full scenario coverage: It covers extreme temperature zones and can effectively evaluate the fast charging reliability of vehicles in cold winter regions and hot summer regions, which has extremely high engineering application value.

[0046] This embodiment provides a medium storing computer instructions for instructing a computer to perform the methods described above. The computer instructions on this medium, used to instruct a computer to perform the methods described above, thus possess at least the same advantages as the methods described above.

[0047] The medium in this application may be any combination of one or more computer-readable media. The medium may be a computer-readable signal medium or a computer-readable storage medium. The medium may be, for example,, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of the medium (a non-exhaustive list) include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, the medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0048] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0049] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF (Radio Frequency), or any suitable combination thereof.

[0050] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0051] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0052] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for testing and evaluating the fast charging performance of electric vehicles, characterized in that, include: The electric vehicle under test was initialized in an environmental chamber and immersed in the vehicle at a set ambient temperature. Initiate the DC fast charging process and simultaneously collect test data, including: current ambient temperature, power requested by the vehicle battery management system, actual power received by the battery pack, battery state of charge, and power consumption of the thermal management system. Based on the experimental data, multidimensional features are extracted, including: the charging capacity retention factor characterizing charging capability, the charging power smoothness index characterizing charging power smoothness, and the thermal management efficiency characterizing thermal management coordination; wherein, the charging capacity retention factor characterizes the charging capability level that the battery can maintain under the current health state and measured ambient temperature, relative to the vehicle's factory condition. The comprehensive charging performance index is obtained by weighting and summing the charging capacity retention factor, the charging power smoothness index, and the thermal management efficiency. Evaluation levels are determined based on the comprehensive charging performance index.

2. The method according to claim 1, characterized in that, The electric vehicle under test was initialized in an environmental chamber and immersed in the vehicle at a set ambient temperature, including: The vehicle under test is placed in an environmental chamber, and the highest temperature, lowest temperature and state of charge of the battery are read through the on-board diagnostic interface using a diagnostic tool. Adjust the battery's state of charge to the preset initial point; Adjust the ambient temperature inside the environmental chamber according to the testing requirements until the immersion is complete.

3. The method according to claim 2, characterized in that, Based on the experimental data, a charge retention factor characterizing the charging capability is extracted, including: Read the capacity health status from the BMS message through the on-board diagnostic interface; The lifetime decay correction coefficient is obtained based on the aforementioned capacity health status; Calculate the ambient temperature correction factor based on the current ambient temperature; Based on the lifetime degradation correction factor and the ambient temperature correction factor, the charging capability retention factor, which characterizes the charging capability, is obtained.

4. The method according to claim 3, characterized in that, The lifetime degradation correction factor is obtained based on the aforementioned capacity health, including: ； ； in, It refers to capacity health; These are empirical fitting coefficients; It is the lifetime degradation correction factor. A sub-item.

5. The method according to claim 4, characterized in that, Calculate the ambient temperature correction factor based on the current ambient temperature, including: In the environmental chamber, the maximum charging power under different ambient temperatures and different states of charge is obtained based on the actual power received by the battery pack. ; Assuming the current ambient temperature falls between two calibration temperatures, calculate the interpolation weights: ； ； ； ； in, It is the interpolation weight. This is the current ambient temperature. and These are two calibration temperatures. It's the temperature difference; Based on the interpolation weights, calculate the maximum charging power at the current ambient temperature for each state of charge j: ； in, This is the maximum charging power at the current ambient temperature and at the state of charge j. For calibration temperature The maximum charging power at state of charge j; For calibration temperature The maximum charging power at state of charge j; Based on the difference between the maximum charging power at the current ambient temperature and the actual power received by the battery pack at the state of charge point j, the ambient temperature correction factor is obtained: ； in, The current ambient temperature. Here, m is the ambient temperature correction factor, and m is the number of charge state points. It is the charged state point j. It is the actual received power of the battery pack at the state of charge point j.

6. The method according to claim 5, characterized in that, Based on the test data, a charging power smoothness index characterizing charging power smoothness is extracted, including: ； Where N is the number of power data points actually received by the battery pack, and also the number of power data points requested by the vehicle battery management system. It is the actual received power of the i-th battery pack. This is the i-th power request from the vehicle battery management system. This is the charging power smoothness index.

7. The method according to claim 6, characterized in that, Based on the experimental data, thermal management efficiency, which characterizes the coordination of thermal management, is extracted, including: ； in, It is the cumulative power consumption of the thermal management system during charging; This refers to the net charge energy of the battery pack, where k is the adjustment coefficient. and These are the start and end times of charging, respectively. It is the power consumption of the thermal management system at time t. It is the actual received power of the battery pack at time t. It refers to thermal management efficiency.

8. The method according to claim 7, characterized in that, After classifying the evaluation levels based on the comprehensive charging performance index, the following also applies: If the overall charging performance index is less than the set value, the cause will be automatically determined based on the multidimensional characteristics.

9. A fast-charging performance testing and evaluation system for electric vehicles, characterized in that, include: The initialization module is used to initialize the state of the electric vehicle under test in the environmental chamber and complete the immersion at a set ambient temperature. The data acquisition module is used to initiate the DC fast charging process and simultaneously collect test data, including: current ambient temperature, power requested by the vehicle battery management system, actual power received by the battery pack, battery state of charge, and power consumption of the thermal management system. The feature extraction module is used to extract multi-dimensional features based on the test data, including: the charging capacity retention factor characterizing charging capability, the charging power smoothness index characterizing charging power smoothness, and the thermal management efficiency characterizing thermal management coordination; wherein, the charging capacity retention factor characterizes the charging capability level that the battery can maintain under the current health state and measured ambient temperature, relative to the vehicle's factory condition. The calculation module is used to perform a weighted summation of the charging capacity retention factor, the charging power smoothness index, and the thermal management efficiency to obtain the comprehensive charging performance index. The classification module is used to classify the evaluation level based on the comprehensive charging performance index.

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