In recent years, the rapid expansion of the electric vehicle industry, particularly in regions like China EV markets, has underscored the critical importance of high-voltage system safety and reliability. As a core component of electric vehicles, the high-voltage system operates under harsh conditions, leading to frequent component failures that threaten vehicle safety. My research focuses on developing timely and accurate diagnostic methods for high-voltage system faults, along with early warning mechanisms for potential hazards, to ensure the secure and dependable operation of electric vehicles. This article delves into the analysis of fault characteristics in high-voltage systems, with an emphasis on fault diagnosis and early warning technologies for key components such as high-voltage batteries, drive motors, and electronic control systems. By proposing innovative solutions, this work aims to enhance the safety and reliability of electric vehicles, contributing to the sustainable growth of the China EV sector and beyond.
The high-voltage system in electric vehicles comprises several critical elements, including the power battery, drive motor, motor controller, and DC/DC converter. The power battery supplies electrical energy to the drive motor via high-voltage busbars, while the motor controller regulates the motor’s speed and torque based on accelerator pedal signals. The DC/DC converter steps down high-voltage electricity to 12V for low-voltage devices. However, the intricate interplay of power supply, control, and cooling processes in these components results in diverse fault causes and manifestations. Through the analysis of vehicle operational data and maintenance cases, typical fault modes have been identified, such as battery cell overcharging/over-discharging, battery overheating, motor overheating, motor controller failure, high-voltage insulation faults, contactor erosion, and wiring short circuits. Each fault mode varies in its impact on vehicle safety and component reliability, necessitating tailored diagnostic and warning strategies. Factors influencing these faults are multifaceted, encompassing external aspects like operational conditions and usage patterns, as well as internal factors such as manufacturing processes and material selection. For instance, high ambient temperatures can accelerate battery aging and motor insulation degradation, while frequent fast charging may induce battery inconsistency. Prolonged high-load operation of the motor can lead to permanent magnet demagnetization, and quality defects in electronic components or poor connector contact are significant contributors to failures. A comprehensive understanding of the relationships between these influencing factors and faults forms the foundation for effective fault diagnosis and early warning.
| Fault Mode | Description | Key Influencing Factors |
|---|---|---|
| Battery Overcharging/Over-discharging | Excessive charge or discharge beyond safe limits | Charging habits, battery management system errors |
| Battery High Temperature | Elevated temperatures leading to thermal runaway | Ambient temperature, cooling system failure |
| Motor Overheating | Excessive heat in drive motor components | High load, insufficient cooling, insulation degradation |
| Motor Controller Failure | Malfunction in control electronics | Component quality, voltage spikes, thermal stress |
| High-Voltage Insulation Fault | Breakdown in insulation leading to leaks | Material defects, humidity, mechanical wear |
| Contactor Erosion | Wear and tear in switching components | Frequent cycling, current surges |
| Wiring Short Circuit | Unintended low-resistance paths in circuits | Physical damage, aging, poor installation |
Fault diagnosis plays a pivotal role as the cornerstone of early warning systems. Given the complexity and nonlinearity of electric vehicle high-voltage systems, I have developed an intelligent diagnostic approach based on multi-information fusion. This method considers system operational mechanisms and fault occurrence patterns, selecting multiple physical quantities such as voltage, current, and temperature as diagnostic indicators. Using signal processing techniques, I extract failure signatures and key features under different fault modes, constructing a fault-symptom knowledge base. Various intelligent algorithms, including fuzzy logic, neural networks, and support vector machines, are employed to establish nonlinear mapping relationships between fault modes and diagnostic indicators. Fuzzy logic excels in representing expert knowledge and handling linguistic information, neural networks offer self-learning and adaptive capabilities to approximate any nonlinear function, and support vector machines perform well with small sample sizes. To integrate the outputs of multiple diagnostic algorithms, I apply D-S evidence theory, an uncertainty reasoning method that effectively resolves conflicts among evidences and reduces uncertainties in the diagnostic process, leading to highly reliable fault conclusions. This multi-level fusion of information and algorithms significantly improves the fault detection rate in high-voltage systems, minimizing missed detections and false alarms, and laying a solid foundation for intelligent early warning.
The diagnostic process can be mathematically represented using a fusion model. For instance, the output of each algorithm is combined using D-S theory, where the basic probability assignment (BPA) for each fault hypothesis is computed. Let $m_1, m_2, \dots, m_n$ be the BPAs from different algorithms for a set of fault hypotheses $\Theta$. The combined BPA using Dempster’s rule is given by:
$$m(A) = \frac{\sum_{B \cap C = A} m_1(B) m_2(C)}{1 – K}$$
where $K = \sum_{B \cap C = \emptyset} m_1(B) m_2(C)$ represents the conflict between evidences. This approach enhances the robustness of fault diagnosis in electric vehicles, particularly in the dynamic China EV environment.
Building on accurate fault diagnosis, I further investigate the degradation patterns and early warning models for key components in high-voltage systems. Taking the power battery as an example, its capacity and internal resistance irreversibly deteriorate over repeated charge-discharge cycles until failure. I select capacity and internal resistance as indicators of the battery’s state of health (SOH), characterizing their evolution over time and cycle count. A mathematical model for capacity fade and internal resistance growth is constructed, accounting for the complex electrochemical processes involved in battery degradation. This process is influenced not only by design parameters but also by operational conditions such as charge-discharge rates and ambient temperature. Thus, the early warning model incorporates multiple internal and external factors to quantitatively describe the accelerating effects of operational stress on capacity fade and resistance growth, resulting in a dynamic, condition-integrated warning model. In practical applications, based on massive operational data collected by the onboard battery management system (BMS), model parameters are identified and calibrated. Intelligent optimization methods like genetic algorithms and particle swarm optimization are used to search for optimal parameter combinations, enabling adaptive alignment of the warning model with actual data. This allows for extrapolative prediction of the battery’s health state under varying conditions, estimating the remaining useful life (RUL) and issuing timely alerts. The condition-integrated adaptive warning method effectively overcomes the limitations of traditional empirical models, meeting the multi-condition, long-life warning requirements for electric vehicle power batteries in diverse China EV applications.
The battery degradation model can be expressed using empirical equations. For capacity fade, a common form is:
$$C(n) = C_0 – a \cdot n^b$$
where $C(n)$ is the capacity at cycle $n$, $C_0$ is the initial capacity, and $a$ and $b$ are parameters determined by operational conditions. Similarly, for internal resistance growth:
$$R(n) = R_0 + c \cdot \exp(d \cdot n)$$
where $R(n)$ is the internal resistance at cycle $n$, $R_0$ is the initial resistance, and $c$ and $d$ are degradation coefficients. These models are calibrated using real-world data from electric vehicles to ensure accuracy.
The reliable operation of high-voltage systems is a prerequisite for the safe use of electric vehicles. Engineering practices show that studying the failure behavior of individual components alone is insufficient; the complex interactions among components make system reliability issues more challenging. Traditional reliability theories, often based on historical failure data and statistical models, suffer from reduced predictability and adaptability when data are scarce or operational conditions vary widely. In my research, I introduce Bayesian networks as a powerful modeling tool to capture the failure behavior and evolution of the entire system, considering multiple factors such as component quality attributes, environmental stresses, and interactions. Bayesian networks are preferred for complex system reliability modeling due to their strong representational capacity and high推理 efficiency. During modeling, to address early data scarcity, I use expert knowledge to subjectively construct the network structure and conditional probability tables. As failure data accumulate, the expectation-maximization (EM) algorithm is employed to dynamically learn and optimize network parameters, continuously enhancing the model’s ability to characterize actual failure behaviors. Using Bayesian inference engines, I compute the failure probability and reliability of components and systems under future operational conditions, obtaining quantitative reliability assessment indicators. This provides a critical basis for failure risk-based warning decisions. The knowledge-driven and data-fused Bayesian modeling approach offers a new perspective for addressing reliability assessment challenges in electric vehicle high-voltage systems, supporting the advancement of China EV technologies.
The Bayesian network for reliability assessment can be represented as a directed acyclic graph where nodes represent components or factors, and edges denote dependencies. The joint probability distribution is given by:
$$P(X_1, X_2, \dots, X_n) = \prod_{i=1}^n P(X_i | \text{Parents}(X_i))$$
where $X_i$ are the random variables representing component states. The EM algorithm iteratively updates parameters to maximize the likelihood of observed data, refining the model for electric vehicle applications.
Based on the proposed fault diagnosis and early warning methods for electric vehicle high-voltage systems, I have developed a hardware-in-the-loop test platform and a corresponding diagnostic warning software prototype system. The system development follows a modular design philosophy, encompassing key modules such as data acquisition and preprocessing, fault feature extraction, fault diagnosis decision-making, and fault warning. The data acquisition and preprocessing module handles the collection, filtering, and normalization of sensor signals from high-voltage components, laying the data foundation for subsequent diagnosis and warning. The fault feature extraction module targets different fault modes, extracting statistical and frequency-domain features from indicators like voltage, current, and temperature. The fault diagnosis decision-making module integrates machine learning algorithms to map and classify fault modes from symptoms. The fault warning module focuses on critical components like batteries and motors, building degradation prediction models for early alerts. Considering the high-reliability requirements of onboard environments, the system incorporates various fault-tolerant mechanisms, such as watchdogs, data validation, and redundant storage. Through software functional safety analysis, the system’s anti-interference capability is enhanced, ensuring stable operation under adverse conditions. For instance, in a case involving a pure electric bus equipped with this system, continuous monitoring of components like the battery pack, drive motor, and high-voltage distribution box detected abnormal voltage drop and temperature rise in one battery pack, triggering a warning. The diagnostic system analyzed data including voltage, temperature, state of charge (SOC), and SOH, initially diagnosing increased internal resistance and aging in a cell. By extrapolating historical data trends, it predicted that the battery pack’s health would fall below 30% after 1,000 km, posing a combustion risk. Maintenance personnel replaced the battery pack based on this warning, averting a serious safety incident. In another instance, the system diagnosed local demagnetization in a permanent magnet based on abnormal motor temperature and three-phase current fluctuations, warning that motor efficiency would drop by 20% after 72 hours of operation, necessitating rotor replacement. This early warning allowed ample time for maintenance planning, preventing secondary damage from faulty operation and demonstrating the practical value of the diagnostic warning system in real-world electric vehicle scenarios, including those in the China EV market.
After developing the fault diagnosis and early warning system, extensive testing is essential to evaluate its diagnostic accuracy and warning timeliness. I designed a test verification scheme for typical components of high-voltage systems, such as battery packs, drive motors, and DC/DC converters. Based on fault modes like overcharge/over-discharge, short circuits, and high temperatures for batteries, and phase loss, rotor demagnetization, and bearing faults for drive motors, I developed fault injection devices that simulate typical and aging fault evolution processes. A test platform was built on physical components, comprising devices like an upper computer, data acquisition system, CAN communication analyzer, oscilloscope, multimeter, and temperature meter to synchronously collect multi-source heterogeneous data under fault conditions. Typical operational profiles were designed, covering combinations of speed, load, and ambient temperature that influence faults. Accelerated life tests were conducted on the platform to obtain full-process data from healthy to faulty states. For battery pack testing, fault injection devices simulated four typical faults, such as overcharging and high-temperature aging, with varying degrees of severity, yielding calibrated fault samples. The upper computer system designed six operational conditions, including charge-discharge cycles and high-low temperature cycles, forming typical operational profiles. Under each condition, healthy states and varying degrees of faulty states were simulated. The data acquisition system recorded physical quantities like terminal voltage, current, surface temperature distribution, and CAN messages, obtaining degradation data from health to fault under typical conditions. This fault data was imported into the developed diagnostic warning system to assess indicators such as diagnostic accuracy and warning lead time under different conditions and degradation levels. Comparisons with mature commercial diagnostic equipment from abroad were made to analyze the strengths and weaknesses of the developed system. After multiple iterations and debugging, the battery fault diagnosis model achieved an accuracy rate exceeding 95%, with a warning lead time of up to 5 days, meeting practical requirements. Similar tests were conducted for drive motors and electronic control systems, accumulating vast experimental data for training, optimization, and evaluation of diagnostic warning models, ensuring robustness for electric vehicle applications.
| Component | Diagnostic Accuracy (%) | Warning Lead Time | Key Test Conditions |
|---|---|---|---|
| Battery Pack | >95 | Up to 5 days | Temperature cycles, charge-discharge profiles |
| Drive Motor | >92 | Up to 72 hours | Load variations, thermal stress |
| Electronic Control System | >90 | Up to 48 hours | Voltage spikes, component aging |
Following product-level functional and performance testing on the developed platform, the actual application effectiveness of the diagnostic warning system in real vehicles must be evaluated. I selected three different types of electric vehicles: a pure electric sedan, a plug-in hybrid SUV, and a range-extended electric bus, integrating the developed high-voltage system fault diagnosis and early warning system into these vehicles for long-term, high-mileage real-road verification. By developing interface programs for the system’s upper and lower computers, data exchange with the vehicle controller via CAN bus was achieved, and integration testing with embedded software such as AUTOSAR ensured compatibility with other control units. Environmental conditions ranging from summer high temperatures to winter low temperatures, and driving conditions from urban slow speeds to highways, were designed for real-road trials. State data of high-voltage components during vehicle operation were continuously collected, and diagnostic and warning results were recorded. Finally, indicators such as accuracy, error rate, and false alarm rate of the diagnostic warning system under different vehicle types and conditions were compared and analyzed. Combined with data on maintenance efficiency and costs, the system’s practical application value was assessed. For example, in the pure electric sedan equipped with the system, accelerated life testing on ring highways simulated two years of use, accumulating 50,000 km. The diagnostic warning system operated stably throughout, with an error rate below 0.5% and a false alarm rate under 1%. At 15,000 km, the system accurately diagnosed a position sensor degradation fault by analyzing sharp fluctuations in motor temperature and torque, predicting that motor loss of control would occur after 3,000 km. The vehicle was promptly returned for sensor replacement. At 30,000 km, the system used big data analysis of historical charge-discharge data to identify超前 voltage decay in one battery pack, predicting that its capacity would fade by 30% after 5,000 km. Maintenance personnel replaced the battery pack two months in advance, effectively extending the vehicle’s range. Comprehensive driving data analysis showed that with the diagnostic warning system, the vehicle’s mean distance between failures increased by 20%, and per-failure maintenance costs decreased by 50%, saving significant operational costs for manufacturers and owners in the electric vehicle industry, including China EV stakeholders.
The economic impact of such systems can be quantified using cost-benefit analysis. For instance, the total cost savings $S$ over a vehicle’s lifetime can be expressed as:
$$S = \sum_{i=1}^N (C_{\text{repair,i}} – C_{\text{warning,i}}) + B_{\text{downtime}}$$
where $C_{\text{repair,i}}$ is the cost of repair without warning, $C_{\text{warning,i}}$ is the cost with early warning, $N$ is the number of incidents, and $B_{\text{downtime}}$ accounts for reduced downtime benefits. This highlights the value of proactive maintenance in electric vehicles.
In conclusion, fault diagnosis and early warning for electric vehicle high-voltage systems represent a complex, interdisciplinary challenge involving vehicle engineering, fault diagnosis, and reliability engineering, with significant implications for vehicle safety and reliability. My research addresses the multi-fault mode and strongly coupled characteristics of high-voltage systems by proposing a novel fault diagnosis and early warning method for complex systems, revealing the evolution patterns and warning mechanisms of typical faults. This provides theoretical guidance and technical references for subsequent studies. Considering the differences and personalized needs across various vehicle models, future research will focus on enhancing the adaptability and online learning capabilities of diagnostic warning models to further improve system intelligence and practicality. Additionally, strengthening fundamental theoretical research, conducting large-scale testing and verification, and establishing standards and specifications for fault diagnosis and warning are essential to promote the widespread application of these成果 in the electric vehicle industry, particularly in the rapidly growing China EV market. The integration of advanced algorithms and real-world data will continue to drive innovations, ensuring that electric vehicles remain safe, reliable, and efficient for years to come.
The future work can be summarized using a roadmap equation for adaptive models:
$$\theta_{t+1} = \theta_t + \eta \nabla L(\theta_t, D_t)$$
where $\theta_t$ represents model parameters at time $t$, $\eta$ is the learning rate, $L$ is the loss function, and $D_t$ is the incoming data stream. This iterative update process enables continuous improvement in electric vehicle systems, aligning with the dynamic nature of China EV deployments.