As the global energy structure undergoes transformation and environmental awareness grows, the new energy vehicle (NEV) industry is developing rapidly. The lithium-ion battery, serving as its core power source, faces severe thermal management safety challenges. The heat generated during charge and discharge cycles, if not dissipated promptly, significantly increases the risk of thermal runaway. Statistics indicate that approximately 45% of NEV fire incidents globally over the past five years are associated with lithium-ion battery thermal management system (TMS) issues. Typically, the optimal operating temperature range for lithium-ion batteries is 15–35 °C. Temperatures outside this range, either too high or too low, lead to performance degradation and potential safety hazards. The thermal management system, being the key system for regulating battery temperature, directly impacts vehicle safety, driving range, and battery lifespan. Therefore, an in-depth study of the composition, design, common faults, and resolution strategies for the NEV lithium-ion battery TMS is of paramount importance for advancing NEV technology.
The thermal management of lithium-ion batteries is a critical subsystem within the broader battery management system (BMS). It is responsible for maintaining the battery pack within its optimal temperature window, ensuring performance, safety, and longevity. An efficient TMS must handle heat dissipation during high-load operation and fast charging, as well as provide heating in cold climates to preserve battery capacity and power output. This article, from my perspective as a practitioner in the field, will elaborate on the system architecture, delve into common failure modes and diagnostic techniques, and discuss optimization strategies for these complex systems.
Overview of the Lithium-ion Battery Thermal Management System
The Lithium-ion Battery Thermal Management System (TMS) is a vital component of the NEV powertrain. Its primary function is to precisely control the temperature distribution and rate of change within the battery pack, thereby sustaining the battery group in its optimal working state to guarantee both performance and safety. Traditional TMS designs are typically based on three fundamental cooling methods: air-cooling, liquid-cooling, and phase-change material (PCM) cooling. With continuous technological advancement, modern NEV TMS has evolved into an integrated, intelligent system that combines temperature monitoring, early warning, cooling, heating, and sophisticated control logic. Under typical operating conditions, a well-designed TMS ensures the battery operates within the 15–35 °C range. This not only effectively suppresses the formation of temperature gradients and slows the rate of performance degradation but also provides necessary temperature regulation in extreme environments, offering comprehensive protection for the efficient operation of the power battery.
System Composition and Design
System Architecture
The NEV lithium-ion battery TMS is composed of four major subsystems: the heat exchange unit, the fluid circulation system, the temperature sensor network, and the control management module. The interaction and data flow between these components are orchestrated by the central battery management system.
- Heat Exchange Unit: This consists of cold plates, heat pipes, or cooling fins attached to the battery cell or module surfaces. They are responsible for the rapid conduction and collection of heat from the cells.
- Fluid Circulation System: This includes the coolant pump, heater (e.g., PTC), expansion tank, and connecting pipes, forming a closed loop for heat transfer.
- Temperature Sensor Network: A distributed array of sensors, typically Negative Temperature Coefficient (NTC) thermistors or thermocouples, placed at key locations within the battery pack for real-time temperature data acquisition.
- Control Management Module: This is often an integral part of the BMS. It executes temperature control strategies based on data from the sensor network and communicates with the vehicle’s central controller via the CAN bus.
The table below summarizes the key components and their functions:
| Subsystem | Key Components | Primary Function |
|---|---|---|
| Heat Exchange | Cold plates, Heat pipes, Fins | Extract heat from battery cells |
| Fluid Circulation | Pump, Heater, Coolant, Pipes, Reservoir | Transport thermal energy to/from the pack |
| Sensor Network | NTC Thermistors, Thermocouples | Real-time temperature monitoring |
| Control Module (within BMS) | Microcontroller, Drivers, Communication Interface | Process data, execute control algorithms, manage actuators |
Cooling and Heating System Design
The design of cooling and heating systems requires a holistic consideration of heat dissipation efficiency, energy consumption balance, and structural complexity.
1. Cooling Systems: Liquid cooling systems, with their superior heat transfer coefficients (typically 5–10 times that of air-cooling), are widely used in high-power-density battery packs. The design key lies in optimizing the shape and layout of the cooling channels. Using Computational Fluid Dynamics (CFD) simulation, a combination of serpentine and parallel flow channels can be designed to maintain moderate flow resistance while controlling the maximum temperature difference (ΔTmax) within the pack to under 3 °C. The heat removal rate (Q) can be approximated by:
$$ Q = \dot{m} \cdot c_p \cdot \Delta T_{coolant} $$
where $\dot{m}$ is the coolant mass flow rate, $c_p$ is the specific heat capacity of the coolant, and $\Delta T_{coolant}$ is the temperature rise of the coolant across the battery pack.
While more complex than air-cooling, liquid cooling offers significantly improved temperature uniformity and heat dissipation efficiency, making it particularly suitable for fast-charging and high-power output scenarios.
2. Heating Systems: These primarily employ PTC (Positive Temperature Coefficient) electric heaters or heat pump technology. PTC heaters offer rapid response but have higher energy consumption. Heat pumps, with a Coefficient of Performance (COP) typically between 2.5 and 3.5, are more energy-efficient but increase system complexity. In low-temperature environments, the preheating control strategy significantly impacts battery life. Research suggests that a gradual heating path (0.2–0.3 °C/min) is more effective in reducing internal battery stress and minimizing energy loss compared to rapid heating methods.
| Method | Principle | Advantages | Disadvantages | Typical Application |
|---|---|---|---|---|
| Air Cooling | Forced convection using air | Simple, low cost, lightweight | Low cooling capacity, poor uniformity, noise | Low-power, cost-sensitive vehicles |
| Liquid Cooling | Circulating coolant through cold plates | High cooling capacity, excellent temperature uniformity | Complex, heavier, risk of leakage | Most mainstream EVs and PHEVs |
| PCM Cooling | Absorbing heat via material phase change | Passive, high latent heat, good for peak loads | Limited total heat absorption, weight, cost | Often used as a supplement to active systems |
| PTC Heating | Electrical resistance heating | Fast response, simple control | High energy consumption, reduces driving range | Widely used for cabin and battery heating |
| Heat Pump | Reverse refrigeration cycle | High energy efficiency (COP>1) | Complex system, higher cost, less effective at very low temps | Premium EVs for extended range in cold weather |
Control Unit and Sensor Layout Design
The design of the control unit and sensor layout directly determines the monitoring accuracy and control effectiveness of the TMS. The control unit, typically a 32-bit microprocessor-based embedded system within the BMS, integrates temperature data acquisition, algorithm processing, and actuator driving functions. It communicates with the vehicle’s powertrain management system via CAN bus.
Sensor layout should follow the principle of combining “key point coverage with uniform distribution.” Since temperature gradients are usually most pronounced between the edge and center regions of the battery pack, sensors should be strategically placed in these areas. A cost-effective ratio in a standard battery module is to configure one temperature sensor for every 25–30 cells. To enhance monitoring, infrared thermal imaging can be introduced as a non-contact supplement to traditional sensors, providing a comprehensive view of the battery surface temperature field and supporting early fault identification. The sensor data fusion and state estimation within the battery management system are crucial for reliable operation.
Common Faults in the Thermal Management System
Common Fault Types
1. Poor Heat Dissipation Leading to Overheating: This is a prevalent fault, characterized by local or overall battery pack temperature exceeding the safety threshold (often 55 °C) without effective reduction. Causes include clogged cooling channels, coolant leakage or insufficient flow, and degraded heat exchanger efficiency. Long-term operation leads to accumulation of impurities and scale, reducing flow area and cooling efficiency. Degradation of coolant additives can lower its thermal conductivity by 15–20%, exacerbating conditions and potentially triggering a thermal runaway chain reaction.
2. Control System Response Lag or Failure: This manifests when the control system fails to adjust promptly or at all in response to abnormal temperature fluctuations. Root causes often lie in defective controller algorithms, actuator failure, or electrical connection faults. Improper parameter configuration can cause control lag during complex driving or rapid charge/discharge cycles. Aging components, electromagnetic interference (EMI), or poor connector contact can interrupt or corrupt command transmission, leading to a loss of effective temperature control.
3. Sensor Faults Causing Monitoring Inaccuracy: This leads to abnormal temperature signal fluctuations, deviations from actual values, or complete signal loss, resulting in erroneous control decisions. Fault types include signal drift, open/short circuits, and sticky failures. Environmental humidity and EMI can cause thermistor characteristic shift, creating deviations of 5–15 °C. Aging wiring or oxidised contacts can interrupt signals. Sensor “sticking” prevents response to temperature changes, which is particularly dangerous during fast charging as it delays early warning of thermal runaway.
| Fault Category | Specific Manifestations | Primary Causes | Potential Impact on Battery |
|---|---|---|---|
| Heat Dissipation | High pack/segment temperature, slow cooldown | Clogged channels, low coolant, pump failure, fan failure | Accelerated aging, capacity fade, thermal runaway risk |
| Control System | No actuator response, erratic cooling/heating | BMS software bug, controller hardware fault, CAN comms error | Temperature excursions, reduced performance, safety hazard |
| Sensors | Fixed/implausible reading, signal noise, data loss | Sensor aging, wiring damage, poor contact, EMI | Incorrect BMS decisions, disabled thermal control, missed warnings |
| Fluid System | Coolant leak, low pressure, pump noise | Seal failure, hose crack, pump bearing wear, air ingress | Localized overheating, system shutdown, component damage |
Fault Diagnosis Methods
Fault diagnosis for the TMS primarily relies on two technical approaches: data-driven and model-driven methods, both implemented within the diagnostic functions of the battery management system.
Data-Driven Methods: These involve collecting vast amounts of system operational data to build a fault pattern recognition library. Machine learning algorithms, such as Support Vector Machines (SVM) or Random Forests, are applied to extract features from temperature time-series data. Faults are identified by comparing deviations in key indicators like temperature rise rate, temperature uniformity, and cooling response time against standard healthy patterns. Research shows this method can improve fault detection rates by over 35% compared to traditional threshold-based methods.
Model-Driven Methods: These involve establishing a mathematical model of the battery’s thermal behavior. State estimation techniques like Kalman Filters are used to assess the system state in real-time. A fault alarm is triggered when the observed values deviate from the model predictions beyond a confidence interval. Hybrid approaches combining digital twin technology create a real-time comparison between the actual battery temperature field and the simulated field, precisely locating fault type and position to guide maintenance.
The residual $r(t)$ between measured temperature $T_m(t)$ and model-predicted temperature $T_p(t)$ is a key diagnostic signal:
$$ r(t) = T_m(t) – T_p(t) $$
A persistent deviation of $|r(t)|$ beyond a defined threshold $\epsilon$ indicates a potential fault in the sensor or the cooling performance.
Real-Time Fault Monitoring System and Early Warning Mechanism
The real-time monitoring and early warning mechanism is built upon a distributed sensor network and a multi-level warning strategy, forming a closed-loop “Monitor-Analyze-Warn-Respond” safety management system managed by the BMS. A high-precision sensor network transmits data via a high-speed bus (e.g., CAN-FD with rates up to 5 Mbps) to the battery management system. Data sampling frequency is typically 1–10 Hz, but can be increased to 20 Hz during high-risk scenarios like fast charging to capture transient fluctuations.
The warning mechanism employs a three-tiered hierarchy:
- Mild Warning (e.g., T > 40°C or ΔT > 5°C): Triggers forced activation of the cooling system at a higher rate.
- Moderate Warning (e.g., T > 45°C or ΔT > 8°C): Limits charge/discharge power and issues a visual/audible alert to the driver.
- Severe Warning (e.g., T > 50°C or dT/dt > 1°C/s): Triggers emergency safety protocols, which may include forced disconnection of the high-voltage circuit and isolation of the affected battery module.
Fault Resolution and System Optimization Strategies
Fault Response and Maintenance Procedures
1. Emergency Response and Repair for Cooling System Faults: The principle is “isolate risk first, diagnose and repair later.” Upon detecting an anomaly, the BMS must immediately limit power and enter a safe mode. The repair process involves pressure and flow tests to locate the fault, followed by system purging and flushing to remove debris and air. For coolant leaks, fluorescent dye tracing can pinpoint the leak for targeted seal or pipe replacement. For degraded radiators, deep cleaning or core replacement is necessary, accompanied by fan functionality checks. Post-repair validation through pressure and thermal load testing is essential.
2. Control Unit and Software Fault Troubleshooting and Updates: A systematic diagnostic flow is required: communication bus signal analysis, diagnostic trouble code (DTC) reading, and power integrity verification. Technicians use specialized tools to analyze CAN bus data streams, checking for signal anomalies. Controller supply voltage stability (e.g., 4.75–5.25 V) and grounding must be verified. For software faults, parameter misconfigurations are resolved through recalibration, while program defects require firmware updates via OTA or offline programming.
3. Sensor Failure Handling: The procedure involves fault confirmation, replacement, and calibration. Faulty sensors are identified by comparing data with neighboring sensors or using a reference thermometer. During replacement, electrostatic discharge (ESD) protection is critical. The new sensor must be installed in the original position with good thermal contact, often using thermal paste. Calibration is paramount. For NTC sensors, a two-point calibration at 25°C and 45°C is common, adjusting correction coefficients to achieve an error within ±0.5°C. For high-precision applications, a multi-point calibration over a range (e.g., 5–55°C) with piecewise linear or curve-fitting establishes a correction function.
System Optimization and Control Strategy Improvement
Optimization efforts focus on three areas: improving temperature uniformity, reducing energy consumption, and optimizing response speed, all orchestrated by an advanced battery management system.
1. Thermal Design Optimization: Using CFD simulation and thermal field analysis to optimize cooling channel layout can reduce the internal temperature difference (ΔTmax) from 5–8°C in traditional designs to 2–3°C, significantly extending battery life. The design goal is to minimize the maximum temperature $T_{max}$ and the standard deviation $\sigma_T$ of temperatures across all cells $i$:
$$ \text{Minimize: } \Phi = \alpha \cdot T_{max} + \beta \cdot \sigma_T, \quad \text{where } \sigma_T = \sqrt{\frac{1}{N-1} \sum_{i=1}^{N} (T_i – \bar{T})^2} $$
where $\alpha$ and $\beta$ are weighting factors, $N$ is the number of cells, and $\bar{T}$ is the average temperature.
2. Energy-Efficient Actuation: Employing variable-speed pumps and intelligent thermal control valves enables on-demand coolant flow distribution, reducing system energy consumption by 15–20% while maintaining cooling performance.
3. Advanced Control Strategies: Upgrading from traditional Proportional-Integral-Derivative (PID) control to Model Predictive Control (MPC) can reduce temperature fluctuation amplitude from ±8.5°C to ±4.0°C. MPC solves a finite-horizon optimization problem at each time step $k$:
$$ \min_{u} \sum_{j=0}^{H_p-1} \| T(k+j|k) – T_{ref} \|^2_Q + \sum_{j=0}^{H_c-1} \| \Delta u(k+j|k) \|^2_R $$
subject to system dynamics and actuator constraints, where $H_p$ is the prediction horizon, $H_c$ is the control horizon, and $Q, R$ are weighting matrices. Furthermore, a zonal cooling control strategy, where different parts of the battery pack receive tailored cooling, can control temperature fluctuations within ±2.5°C, enhancing stability and longevity.
| Optimization Area | Specific Technique | Key Mechanism | Expected Benefit |
|---|---|---|---|
| Thermal Uniformity | CFD-optimized channel design, Zonal cooling | Improves heat distribution, targets hot spots | ΔTmax < 3°C, extended cycle life by ~20% |
| Energy Efficiency | Variable-speed pumps, Smart valves, Heat pumps | Matches cooling/heating output to real-time demand | 15-25% reduction in TMS auxiliary energy consumption |
| Control Intelligence | Model Predictive Control (MPC), AI-based prediction | Proactive control based on model and predictions | Faster response, smaller temperature overshoot, improved stability |
| Fault Tolerance | Redundant sensors, Model-based diagnostics | Detects and compensates for failures | Increased system reliability and safety |
Conclusion
As battery technology evolves, the lithium-ion battery thermal management system is advancing from passive cooling toward active, intelligent regulation. This effectively confines battery temperature fluctuations within an ideal range, significantly enhancing energy utilization efficiency and safety. Looking ahead, Artificial Intelligence (AI) will see widespread application within the TMS, integrated deeply with the battery management system. By analyzing historical vehicle usage data and driving patterns, AI can predict thermal load trends and preemptively adjust cooling strategies, substantially reducing the amplitude of battery temperature fluctuations. The integration of big data analytics will enable fleet-wide learning and optimization of thermal management parameters. Furthermore, the intelligence and integration level of the TMS will be markedly improved. Emerging technologies like direct cooling with dielectric fluids and integrated thermal runaway propagation barriers will offer robust solutions for managing large-capacity battery packs and mitigating the risk of catastrophic thermal events, paving the way for safer, more efficient, and longer-lasting electric vehicles.