In recent years, the rapid expansion of the global electric vehicle industry, driven by energy transition and environmental policies, has placed significant emphasis on the performance and safety of power batteries. As a critical component, the thermal management system for EV power batteries ensures optimal operating temperatures, preventing issues such as performance degradation, safety hazards, and thermal runaway. In this paper, we explore the fault diagnosis and maintenance technologies for China EV battery thermal management systems, addressing the limitations of traditional methods, which often suffer from delayed detection, low accuracy, and high false-alarm rates. We propose an integrated approach combining multi-sensor fusion, machine learning algorithms, and preventive maintenance strategies to enhance system reliability, safety, and longevity. The widespread adoption of EVs, particularly in regions like China, underscores the importance of advancing these technologies for sustainable development.
The thermal management system for EV power batteries is designed based on thermodynamic principles to regulate temperature through multi-level control networks. Liquid cooling systems utilize forced convection of coolant through battery modules, with micro-channel cooling plates featuring channel widths of 2–5 mm to maintain Reynolds numbers between 800 and 2000, optimizing heat transfer coefficients. The heat transfer rate in such systems can be described by Newton’s law of cooling: $$q = hA(T_b – T_c)$$ where \(q\) is the heat flux, \(h\) is the heat transfer coefficient, \(A\) is the surface area, \(T_b\) is the battery temperature, and \(T_c\) is the coolant temperature. Air cooling systems, on the other hand, rely on axial fans and heat sinks designed with fin spacings of 3–8 mm and thicknesses of 0.8–1.2 mm to enhance convective heat transfer, governed by Bernoulli’s equation and heat transfer correlations. Phase change material (PCM) technologies employ paraffin-based materials with latent heats of 180–220 J/g operating in the 25–35°C range, absorbing excess heat through phase transitions to buffer temperature fluctuations. These mechanisms are essential for maintaining the stability of China EV battery systems under varying operational conditions.
To evaluate the performance of EV power battery thermal management systems, a multi-dimensional quantitative index system is established based on fundamental heat transfer laws. Temperature uniformity is characterized by standard deviation and temperature difference coefficients, with the system aiming to maintain a temperature standard deviation within 2°C: $$\sigma_T = \sqrt{\frac{1}{N}\sum_{i=1}^{N}(T_i – \bar{T})^2} \leq 2 \, ^\circ\text{C}$$ where \(N\) is the number of measurement points, \(T_i\) is the temperature at point \(i\), and \(\bar{T}\) is the average temperature. Heat dissipation efficiency is assessed using thermal resistance analysis, with optimal performance achieved when the heat dissipation power density reaches 500–800 W/(m²·K). The total thermal resistance \(R_{\text{total}}\) is calculated as: $$R_{\text{total}} = \frac{\Delta T}{q}$$ where \(\Delta T\) is the temperature difference and \(q\) is the heat flow rate. Energy consumption optimization is based on the second law of thermodynamics, incorporating the coefficient of performance (COP) as a key parameter. The COP is defined as: $$\text{COP} = \frac{Q_{\text{cooling}}}{W_{\text{input}}}$$ where \(Q_{\text{cooling}}\) is the cooling capacity and \(W_{\text{input}}\) is the input work. Under typical conditions, the COP should exceed 3.5, while minimizing pump and fan power consumption to enhance overall efficiency for China EV battery applications.
Experimental characterization of EV power battery thermal management systems involves multi-condition tests based on heat and mass transfer theories. Tests are conducted in environmental chambers with temperature control accuracy of ±0.1°C, simulating temperatures from -20°C to 60°C, and using resistor heating modules to replicate battery heat generation. Data indicate that liquid cooling systems can control the maximum temperature difference within battery modules to 3.2°C under 2C discharge rates, with coolant flow rates maintained at 4–6 L/min for a散热功率 of 1,200 W. Furthermore, a 10°C increase in ambient temperature reduces散热效率 by 12–15%, necessitating higher coolant flow rates to compensate. The Fourier heat conduction equation and convective heat transfer correlations are applied to derive optimal control strategies: $$\frac{\partial T}{\partial t} = \alpha \nabla^2 T$$ where \(\alpha\) is the thermal diffusivity. These experiments provide a theoretical basis for parameter optimization in China EV battery systems.
Faults in EV power battery thermal management systems often exhibit coupling characteristics due to thermodynamic and electrochemical failures. Typical faults include coolant leakage, temperature sensor drift, fan abnormalities, and electronic valve malfunctions. For instance, coolant leakage leads to pressure drops and reduced heat transfer coefficients, causing local heat accumulation. Temperature sensor faults, with deviations exceeding ±2°C, result in control strategy errors and imbalanced cooling power distribution. Fan issues, such as speed fluctuations over 10% of rated values, impair convective heat transfer, while valve failures disrupt fluid distribution in cooling circuits. The severity of these faults is categorized into levels, guiding response measures from monitoring to emergency shutdowns. The following table summarizes the coupling characteristics of these faults:
| Coupling Type | Manifestation | Impact Scope |
|---|---|---|
| Thermodynamic-Electrochemical Coupling | Temperature anomalies leading to battery performance degradation | Vehicle powertrain system |
| Fluid-Heat Transfer Coupling | Coolant abnormalities affecting散热效率 | Battery pack temperature distribution |
| Mechanical-Electronic Control Coupling | Fan/valve faults impacting control accuracy | Thermal management system coordination |
Another table outlines the fault severity levels and corresponding responses:
| Severity Level | Description | Response Measures |
|---|---|---|
| Minor | Parameters deviate 5–10% from normal range | Monitoring and warning |
| Moderate | Parameters deviate 10–20% from normal range | Reduced power operation |
| Severe | Parameters deviate >20% from normal range | System protective shutdown |
| Critical | Multiple fault couplings | Emergency safety handling |
To address these faults, we develop a multi-parameter fusion fault diagnosis algorithm based on Bayesian inference and Kalman filtering theory. This algorithm constructs a state-space model for dynamic estimation of physical quantities like temperature, pressure, and flow fields. The extended Kalman filter (EKF) handles nonlinear system transitions, while particle filtering resolves high-dimensional integration issues in parameter estimation. The state update in EKF is given by: $$\hat{x}_{k|k-1} = f(\hat{x}_{k-1|k-1}, u_k)$$ $$P_{k|k-1} = F_k P_{k-1|k-1} F_k^T + Q_k$$ where \(\hat{x}\) is the state estimate, \(P\) is the error covariance, \(F\) is the state transition matrix, and \(Q\) is the process noise covariance. Support vector machine (SVM) classifiers map fault feature vectors to high-dimensional spaces using kernel functions, constructing optimal hyperplanes for precise fault identification. Additionally, deep neural networks with residual connections and attention mechanisms capture temporal features in fault evolution. At the decision layer, D-S evidence theory weights multi-source information confidence, outputting diagnostic results that include fault type, severity, and confidence intervals. This approach significantly improves the accuracy and efficiency of fault diagnosis for EV power battery systems.
The intelligent fault diagnosis system adopts a layered, modular architecture. The data acquisition and preprocessing module uses CAN bus and Ethernet protocols for real-time sensor data collection, with signal conditioning circuits employing low-noise amplifiers and high-precision A/D converters to maintain accuracy within 0.1%. The fault diagnosis engine, based on edge computing, deploys lightweight deep learning models compressed by 60% to meet real-time response requirements. Cloud data management platforms integrate fault mode libraries and maintenance experience databases, enabling big data analysis for fault trend prediction and preventive maintenance decision support. Human-machine interfaces feature visual fault maps and 3D thermal field displays, aiding maintenance personnel in fault localization and guidance. This system ensures robust monitoring and diagnosis for China EV battery thermal management, enhancing overall vehicle safety.
In applying maintenance technologies for EV power battery thermal management systems, we formulate preventive maintenance strategies using Weibull distribution and Markov chain theories to build dynamic maintenance decision models. The Weibull probability density function is expressed as: $$f(t) = \frac{\beta}{\eta} \left( \frac{t}{\eta} \right)^{\beta-1} e^{-(t/\eta)^\beta}$$ where \(\beta\) is the shape parameter, \(\eta\) is the scale parameter, and \(t\) is time. Genetic algorithms optimize maintenance intervals by considering equipment degradation trends, operating conditions, and costs. For example, temperature sensors require calibration every 6 months for Grade A and 12 months for Grade B, based on drift models, while fan maintenance intervals are adjusted seasonally—shortened by 20% in summer and extended by 15% in winter—to account for environmental factors. This dynamic approach ensures efficient resource allocation and system reliability for China EV battery applications.
Maintenance implementation involves a state-monitoring-based intelligent system that integrates non-destructive testing technologies. For cooling systems, online spectral analysis and ion chromatography monitor coolant chemical changes and corrosion ion concentrations, ensuring optimal performance. Coolant pipeline cleaning employs ultrasonic and chemical composite processes to remove deposits and biofilms. Temperature sensor maintenance uses three-wire measurement principles with standard platinum resistors for on-site calibration, controlling errors within ±0.05°C. Computational fluid dynamics (CFD) simulations optimize sensor placement to avoid vortices and thermal bridging effects. Fan maintenance includes laser alignment for shaft concentricity and dynamic balancing, coupled with dry ice cleaning for blades. Remote maintenance via CAN bus enables real-time data logging to cloud platforms, creating comprehensive maintenance records. These practices support the longevity and efficiency of EV power battery systems.
Evaluation of maintenance effectiveness relies on a multi-level performance indicator system using analytic hierarchy process (AHP) to weight key metrics such as thermal management efficiency, mean time between failures (MTBF), and maintenance cost-benefit ratios. A fuzzy comprehensive evaluation model assesses improvements in temperature control precision and heat dissipation power density. Weibull probability analysis of MTBF, as shown in the table below, gauges the impact of preventive maintenance on system reliability:
| Parameter | Evaluation Standard | Calculation Method |
|---|---|---|
| Shape Parameter | 1.2–2.0 (ideal range) | Weibull probability plot fitting |
| Scale Parameter | Increase ≥20% | Characteristic life comparison |
| Location Parameter | Significant increase post-maintenance | Minimum fault interval time |
Full lifecycle cost models incorporate direct maintenance costs, downtime losses, and fault risk costs to compute return on investment. Continuous improvement is driven by PDCA cycles and data mining to refine maintenance intervals, upgrade technologies, and enhance personnel skills, ensuring alignment with evolving EV power battery demands.
In conclusion, our research on fault diagnosis and maintenance for EV power battery thermal management systems demonstrates the efficacy of multi-sensor fusion and intelligent models in overcoming traditional limitations. By enabling precise fault identification and real-time alerts, these approaches boost system reliability and safety, yielding both technical and economic benefits. Looking ahead, deeper integration of artificial intelligence, big data, and edge computing—such as through digital twin models and cloud协同诊断 platforms—will advance fault prediction accuracy and maintenance decision-making intelligence. This progress is vital for the sustainable development of China EV battery technologies and the achievement of global carbon neutrality goals. The ongoing innovation in this field underscores the critical role of thermal management in the future of electric mobility.