As the adoption of electric cars continues to accelerate globally, ensuring their safe and efficient operation has become a critical focus in the automotive industry. In my research, I explore the dynamic performance detection methods for electric cars, drawing on technical frameworks such as GB/T 45688—2025, which standardizes real-time monitoring and safety assessments. This paper systematically outlines the core components of dynamic performance detection, including dynamic monitoring, fault monitoring, and hazard monitoring, and proposes innovative paths to enhance detection accuracy, establish dynamic warning mechanisms, refine standardized processes, and implement常态化 dynamic detection. By integrating platform data and analytical models, I aim to improve the comprehensiveness, precision, and efficiency of detecting performance issues in electric cars, thereby supporting their safe deployment and industrial growth. Throughout this discussion, I emphasize the importance of leveraging real-time data and advanced algorithms to address the unique challenges posed by electric car technologies, such as battery management and motor control.
To begin, it is essential to define the scope and key concepts related to electric cars and dynamic performance detection. An electric car, in a broad sense, encompasses various types of vehicles, including pure electric vehicles, hybrid electric vehicles, and fuel cell electric vehicles. However, for the purpose of this paper, I focus primarily on pure electric cars, which rely solely on electric power for propulsion. According to standards like GB/T 45688—2025, the applicable scope includes in-use pure electric cars and plug-in hybrid electric cars equipped with onboard terminals that comply with specific data reporting requirements. This delineation helps narrow down the research object to electric cars that are integral to modern transportation ecosystems.
Dynamic performance detection refers to the evaluation of a vehicle’s behavior under actual driving conditions, as opposed to static inspections that may not capture real-world operational nuances. For electric cars, this involves assessing key aspects such as power performance, maximum speed, climbing ability, braking efficiency, and steering responsiveness. GB/T 45688—2025 further formalizes this by defining dynamic performance detection as the use of data from electric car operation safety platforms or models to conduct real-time or near-real-time monitoring, analyzing potential safety faults or hazards. Specifically, “operation safety faults” refer to conditions that may cause specific electrical safety components or systems—like the power battery, drive motor, and electronic control system—to deviate from their designed operating parameters during electric car operation. Meanwhile, “operation safety hazards” denote risks that could lead to damage or threats to these electrical safety components. This definition aligns dynamic performance detection closely with the technical characteristics of electric cars, ensuring that evaluations are both relevant and actionable.
The core of dynamic performance detection for electric cars revolves around three main areas: dynamic monitoring, fault monitoring, and hazard monitoring. Each of these components leverages data from a two-tier platform architecture—comprising enterprise platforms and public platforms—to facilitate efficient data collection, analysis, transmission, and sharing. In the following sections, I delve into each area, supported by tables and mathematical models to illustrate the processes and criteria involved.
Dynamic Monitoring in Electric Cars
Dynamic monitoring forms the backbone of real-time performance assessment for electric cars. The GB/T 45688—2025 standard establishes a collaborative system between enterprise platforms and public platforms. Enterprise platforms are required to possess dynamic monitoring and warning capabilities, enabling them to receive data reported by onboard terminals in electric cars. This data must include information from before and after traffic incidents, such as 30 seconds preceding and following an event, with a sampling period of no more than 1 second. Public platforms, on the other hand, receive and process this data, along with dynamic monitoring results, fault handling information, and hazard mitigation details. They also provide feedback to enterprise platforms and disseminate dynamic monitoring outcomes for broader safety evaluations. This architecture ensures that electric car operations are continuously tracked, allowing for prompt interventions.
To quantify the monitoring process, consider the data flow equation: $$ D_{total} = \sum_{i=1}^{n} (D_{enterprise,i} + D_{public,i}) $$ where \( D_{total} \) represents the total data processed, \( D_{enterprise,i} \) denotes data from the i-th enterprise platform, and \( D_{public,i} \) refers to data handled by the public platform. This model highlights the scalability of dynamic monitoring for large fleets of electric cars.
| Monitoring Aspect | Description | Data Source |
|---|---|---|
| Power Performance | Evaluates acceleration and torque in electric cars | Onboard sensors |
| Braking Efficiency | Measures stopping distance and response time | Brake system data |
| Steering Responsiveness | Assesses handling and stability | Steering angle sensors |
In practice, dynamic monitoring for electric cars relies on algorithms that process streaming data to identify anomalies. For instance, the rate of data transmission can be modeled as: $$ R = \frac{N_{samples}}{T_{interval}} $$ where \( R \) is the data rate, \( N_{samples} \) is the number of samples, and \( T_{interval} \) is the time interval. This ensures that electric car data is analyzed in near-real-time, enhancing detection accuracy.
Fault Monitoring for Electric Cars
Fault monitoring focuses on identifying specific issues that could compromise the safety and performance of electric cars. According to GB/T 45688—2025, there are 12 key monitoring items for faults in electric cars, including power battery temperature, battery temperature differential, highest and lowest voltage of individual battery cells, voltage consistency, state of charge (SOC) anomalies, drive motor temperature, drive motor controller temperature, DC/DC converter temperature, DC/DC status, insulation resistance, and high-voltage interlock status. The monitoring procedure involves extracting general alarm flag data, analyzing these flags to generate results, and reporting any abnormalities in real-time through the enterprise platform.
A mathematical representation of fault detection can be expressed using a threshold model. For example, for battery temperature monitoring in an electric car: $$ T_{battery} > T_{threshold} \Rightarrow \text{Fault Detected} $$ where \( T_{battery} \) is the measured temperature and \( T_{threshold} \) is the safe operating limit. Similarly, voltage consistency can be assessed with: $$ \sigma_V = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (V_i – \bar{V})^2} $$ where \( \sigma_V \) is the standard deviation of cell voltages, \( V_i \) is the voltage of the i-th cell, and \( \bar{V} \) is the average voltage. If \( \sigma_V \) exceeds a predefined value, it indicates a fault in the electric car’s battery system.
| Fault Monitoring Item | Parameter | Threshold for Electric Cars |
|---|---|---|
| Power Battery Temperature | Temperature (°C) | Varies by battery type |
| SOC Anomaly | State of Charge (%) | Below 10% or above 90% |
| Insulation Resistance | Resistance (MΩ) | Less than 0.5 MΩ |
Furthermore, fault levels are categorized into three tiers for electric cars: Level 1 faults do not affect normal driving, Level 2 faults impair performance and may require speed or distance restrictions, and Level 3 faults necessitate immediate stopping or rescue. This分级 approach allows for prioritized responses, ensuring that critical issues in electric cars are addressed promptly.
Hazard Monitoring in Electric Cars
Hazard monitoring involves identifying potential risks that could lead to long-term damage or safety threats in electric cars. The 12 monitoring items for hazards include power battery maximum temperature during charging and discharging, individual cell voltage extremes, voltage differentials, drive motor temperature, drive motor controller temperature, DC/DC converter temperature, insulation resistance, voltage sampling anomalies, prolonged over-discharge, and extended stationary periods. The monitoring process uses hazard analysis models to evaluate data from the operation safety platform, with results reported in real-time by both enterprise and public platforms if abnormalities are detected.
For instance, the power battery maximum temperature model during charging for electric cars employs a high-temperature risk matrix. For ternary lithium batteries, the threshold is 60°C, while for lithium iron phosphate batteries, it is 65°C. The model assesses risk based on the duration and frequency of exceedances: $$ \text{Risk Score} = f(T, t, N) $$ where \( T \) is temperature, \( t \) is duration above threshold, and \( N \) is the number of exceedances. If the duration or frequency surpasses allowable limits, the model flags an anomaly. Similarly, the drive motor temperature model calculates the cumulative time proportion over 90 days that the temperature exceeds 80°C: $$ P = \frac{\sum_{j=1}^{m} t_{exceed,j}}{T_{total}} $$ where \( P \) is the proportion, \( t_{exceed,j} \) is the exceedance time for the j-th period, and \( T_{total} \) is the total time. If \( P > 0.2 \), it indicates a hazard in the electric car.
| Hazard Monitoring Item | Condition | Model Output for Electric Cars |
|---|---|---|
| Battery Max Temp (Charging) | Temperature > Threshold | Abnormal if exceedance limits breached |
| Voltage Sampling Anomaly | Inconsistent readings | Abnormal if deviation > 5% |
| Prolonged Stationary | Extended inactivity | Risk of battery degradation |
These models not only enhance the detection of hazards in electric cars but also facilitate predictive maintenance, reducing the likelihood of catastrophic failures.
Innovative Paths for Enhancing Dynamic Performance Detection
To address the limitations of traditional detection methods, I propose several innovative paths for improving dynamic performance detection in electric cars. These approaches leverage advanced technologies and standardized practices to achieve higher accuracy and efficiency.
Leveraging Platform Data to Improve Detection Accuracy
By utilizing the two-tier platform architecture, we can enhance the precision of detection for electric cars through real-time data acquisition and analytical computations. For example, integrating high-temperature risk matrix models allows for historical analysis of battery behavior during charging and discharging cycles. The general form of the risk assessment can be represented as: $$ \text{Risk} = \int_{0}^{T} I(T(t) > T_{threshold}) \, dt $$ where \( I \) is an indicator function, and the integral computes the total exceedance time. If this value exceeds a safe limit, it triggers an alert for the electric car. This data-driven approach minimizes risks during detection and improves overall efficiency for electric car fleets.
Establishing Dynamic Warning Mechanisms
Constructing a dynamic warning system that issues fault notifications and hazard alerts based on monitoring results is crucial for electric cars. This mechanism should incorporate graded responses aligned with fault levels and hazard frequencies. For instance, the warning priority can be modeled as: $$ W = \alpha \cdot L_{fault} + \beta \cdot F_{hazard} $$ where \( W \) is the warning level, \( L_{fault} \) is the fault level (1-3), \( F_{hazard} \) is the hazard frequency, and \( \alpha \) and \( \beta \) are weighting factors. This ensures that electric car operators receive timely notifications, enabling proactive measures to mitigate risks.
Refining Standardized Detection Processes
Developing a comprehensive system of technical specifications and operational guidelines is essential for standardizing detection and maintenance of electric cars. This involves creating detailed protocols that cover the entire lifecycle, from data collection to analysis and response. A standardized workflow can be summarized as: $$ \text{Process} = \{ \text{Data Input} \rightarrow \text{Analysis} \rightarrow \text{Output} \rightarrow \text{Action} \} $$ where each step is defined with specific criteria for electric cars. By promoting these standards through training and dissemination, we can elevate the consistency and effectiveness of detection efforts across the industry.
Implementing常态化 Dynamic Detection
Adopting a常态化 dynamic detection regime involves continuous monitoring through onboard terminals in electric cars, which record operational parameters, detect anomalies, and push alerts before faults escalate. This proactive system can be described by the equation: $$ A_{detection} = \frac{N_{alerts}}{T_{operation}} $$ where \( A_{detection} \) is the alert rate, \( N_{alerts} \) is the number of alerts, and \( T_{operation} \) is the operational time. A higher rate may indicate underlying issues in electric cars. Additionally, integrating self-diagnostic systems and data loggers provides multidimensional insights for fault root cause analysis, supporting both immediate repairs and long-term design optimizations for electric cars.
Conclusion
In summary, the rapid evolution of electric cars demands advanced dynamic performance detection methods that go beyond conventional approaches. By embracing platform data, dynamic warning systems, standardized processes, and常态化 monitoring, we can significantly enhance the safety and reliability of electric cars. These innovations not only facilitate early identification of faults and hazards but also contribute to the sustainable growth of the electric car industry. As I continue to refine these methods, the integration of real-time analytics and collaborative platforms will remain pivotal in addressing the unique challenges of electric car technologies.