In the context of global energy transition towards carbon neutrality, the rapid expansion of renewable energy sources like wind and solar power has become a pivotal strategy. However, the inherent intermittency and volatility of these sources pose significant challenges to grid stability, driving an urgent demand for efficient energy storage solutions. Among various technologies, electrochemical energy storage, particularly lithium-ion battery systems, has emerged as a dominant force due to its high energy density, long cycle life, and declining costs. As of recent reports, lithium-ion batteries account for over 90% of the installed electrochemical storage capacity worldwide, underscoring their critical role in balancing power supply and demand. Nonetheless, the safety of large-scale lithium-ion battery energy storage stations has become a pressing concern, with frequent incidents of thermal runaway and fires highlighting the need for advanced monitoring and management systems. In this article, I will delve into the research progress on internal temperature detection and estimation for lithium-ion batteries, a key parameter for early thermal runaway warning and operational safety.
The internal temperature of a lithium-ion battery is a crucial indicator of its operational state, often diverging significantly from surface measurements. During thermal runaway events, studies have shown that internal temperatures can exceed surface temperatures by hundreds of degrees Celsius, with external temperature responses lagging behind internal changes. This discrepancy underscores the limitation of relying solely on surface-mounted sensors in Battery Management Systems (BMS), which may lead to delayed warnings and increased safety risks. Accurate internal temperature monitoring is thus essential for predicting battery health, optimizing thermal management, and preventing catastrophic failures. In this review, I will explore three primary approaches: internal temperature measurement via embedded sensors, internal temperature estimation based on Electrochemical Impedance Spectroscopy (EIS), and internal temperature prediction using machine learning algorithms. I will analyze their principles, applications, advantages, and limitations, and provide future perspectives to guide further research and development in this field.
To understand the importance of internal temperature monitoring, it is essential to first examine the microstructural changes and thermal runaway mechanisms in lithium-ion batteries under elevated temperatures. The internal temperature rise in a lithium-ion battery triggers a cascade of exothermic reactions and material degradations that can lead to thermal runaway. At the microscopic level, key components such as the cathode, anode, electrolyte, and separator undergo significant transformations. For instance, the cathode may experience phase transitions and oxygen release, while the anode suffers from Solid Electrolyte Interphase (SEI) layer decomposition and lithium dendrite growth. The electrolyte can decompose, producing flammable gases, and the separator may melt, causing internal short circuits. These processes are often interdependent, leading to a chain reaction that accelerates heat generation. The thermal runaway process can be divided into stages, each characterized by specific critical temperatures and observable parameters, as summarized in Table 1.
| Component | Microstructural Changes | Macroscopic Manifestations |
|---|---|---|
| Cathode | Phase transformation, oxygen release, metal dissolution | Capacity fade, gas generation, thermal runaway |
| Anode | SEI reconstruction, lithium plating, particle cracking | Increased impedance, short-circuit risk |
| Electrolyte | Decomposition and gas evolution, acid corrosion | Swelling, increased internal resistance, thermal runaway |
| Separator | Melting and shrinkage, pore closure | Internal short circuit, ion transport blockage |
Mathematically, the heat generation within a lithium-ion battery can be described by the energy balance equation, which accounts for irreversible heat from overpotentials and reversible heat from entropy changes. The general form is:
$$ q = I(E – U) + I T \frac{\partial U}{\partial T} $$
where \( q \) is the heat generation rate per unit volume, \( I \) is the current, \( E \) is the terminal voltage, \( U \) is the open-circuit voltage, and \( T \) is the absolute temperature. This equation highlights the dependency of heat generation on internal electrochemical states, which are influenced by temperature. As temperature rises, reaction rates increase, often following the Arrhenius law:
$$ k = A \exp\left(-\frac{E_a}{RT}\right) $$
where \( k \) is the rate constant, \( A \) is the pre-exponential factor, \( E_a \) is the activation energy, and \( R \) is the universal gas constant. This relationship underscores why internal temperature monitoring is vital for predicting reaction kinetics and preventing thermal runaway.
Internal Temperature Measurement via Embedded Sensors
Embedding temperature sensors directly into lithium-ion batteries offers a direct and accurate means of monitoring internal thermal states. This approach involves placing miniature sensors within the battery cell to measure temperature in situ, overcoming the lag and inaccuracy associated with surface measurements. Various sensor technologies have been developed for this purpose, including Fiber Bragg Grating (FBG) sensors, Liquid Metal Microsensors (LMM), and Flexible Thin Film Sensors (FTFS). These sensors are favored for their small size, high sensitivity, chemical resistance, and immunity to electromagnetic interference, making them suitable for the harsh internal environment of a lithium-ion battery.
The working principle of embedded sensors varies by type. For example, FBG sensors rely on the modulation of Bragg wavelength due to temperature-induced changes in fiber grating period and refractive index. The reflected wavelength shift \( \Delta \lambda_B \) is linearly related to temperature change \( \Delta T \) as:
$$ \Delta \lambda_B = \lambda_B (\alpha + \xi) \Delta T $$
where \( \lambda_B \) is the Bragg wavelength, \( \alpha \) is the thermal expansion coefficient, and \( \xi \) is the thermo-optic coefficient. This allows precise temperature measurement by detecting wavelength variations. Similarly, other sensors use resistive or capacitive changes correlated with temperature.
In practice, researchers have successfully integrated sensors into commercial battery formats like 18650 cylindrical cells and pouch cells. For instance, compact FBG sensors have been inserted into 18650 batteries to monitor internal temperature and pressure during thermal runaway, providing real-time data for safety assessment. In pouch cells, flexible sensors embedded near electrodes have enabled continuous temperature tracking during charge-discharge cycles, revealing heat generation patterns and identifying hotspot regions. Studies show that such integration can be achieved with minimal impact on battery performance, such as capacity retention and impedance stability, demonstrating the feasibility of embedded sensing.
However, this method faces several challenges. Embedding sensors can compromise the structural integrity of the lithium-ion battery, potentially introducing defects that affect longevity and safety. The manufacturing process becomes more complex and costly, requiring precise alignment and sealing to prevent electrolyte leakage. Sensors must also endure corrosive environments and mechanical stresses over many cycles, necessitating robust materials and designs. Additionally, thermal lag between the sensor and surrounding materials can cause measurement delays, and the small size of sensors may limit spatial resolution. Despite these issues, embedded sensors provide invaluable direct data, especially for research and high-stakes applications where accurate internal temperature knowledge is critical.
| Sensor Type | Principle | Advantages | Disadvantages |
|---|---|---|---|
| Fiber Bragg Grating (FBG) | Wavelength shift due to temperature/strain | High accuracy, multiplexing capability, immune to EMI | Fragility, complex interrogation setup |
| Liquid Metal Microsensor (LMM) | Resistance change with temperature | Flexibility, good thermal contact | Potential leakage, limited temperature range |
| Flexible Thin Film Sensor (FTFS) | Thermoresistive or thermocouple effect | Conformable, low profile, easy integration | Sensitivity to mechanical damage, calibration drift |
Internal Temperature Estimation Based on Electrochemical Impedance Spectroscopy (EIS)
Electrochemical Impedance Spectroscopy (EIS) is a non-invasive technique that estimates internal temperature by analyzing the impedance response of a lithium-ion battery to sinusoidal excitations across a frequency range. EIS provides insights into electrochemical processes such as charge transfer, diffusion, and interfacial reactions, which are temperature-dependent. By identifying impedance features that correlate strongly with temperature but weakly with State of Charge (SOC) and State of Health (SOH), one can develop models for internal temperature estimation without physically penetrating the battery.
The fundamental principle of EIS involves applying a small-amplitude AC voltage \( V(t) = V_0 \sin(\omega t) \) and measuring the resulting current \( I(t) = I_0 \sin(\omega t + \phi) \), where \( \omega \) is the angular frequency and \( \phi \) is the phase shift. The complex impedance \( Z(\omega) \) is calculated as:
$$ Z(\omega) = \frac{V(\omega)}{I(\omega)} = Z_{\text{re}} + j Z_{\text{im}} $$
where \( Z_{\text{re}} \) and \( Z_{\text{im}} \) are the real and imaginary parts, respectively. The impedance spectrum, often plotted as a Nyquist or Bode plot, reveals characteristic frequency regions corresponding to different electrochemical phenomena. For temperature estimation, specific frequency points where impedance parameters (e.g., phase angle, magnitude) show monotonic relationships with temperature are selected. For example, the phase angle \( \phi \) at a particular frequency \( f^* \) might follow a linear model:
$$ \phi(f^*) = a T + b $$
where \( a \) and \( b \) are coefficients determined empirically, and \( T \) is the internal temperature. More advanced models incorporate multiple frequencies or use Arrhenius-type equations to capture nonlinearities.
Research has demonstrated the effectiveness of EIS-based temperature estimation. Studies on LiFePO4 and NCM lithium-ion batteries have achieved estimation errors within ±1.5°C across temperature ranges from -10°C to 55°C. Techniques include using impedance phase shift under equilibrium conditions, or compensating for non-equilibrium effects during dynamic operations. For instance, a method combining phase shift and relaxation time has been proposed to account for transient states in electric vehicle applications. Additionally, machine learning algorithms like Support Vector Regression (SVR) have been applied to EIS data, further improving accuracy by handling complex multi-frequency relationships.
Despite its promise, EIS-based estimation has limitations. The impedance spectrum of a lithium-ion battery can be influenced by factors beyond temperature, such as aging, SOC, and current history, complicating the extraction of pure temperature signals. Measurement requires specialized equipment and careful calibration, and the process can be time-consuming, posing challenges for real-time implementation in BMS. Furthermore, the optimal frequency points may vary with battery chemistry and design, necessitating customization for each lithium-ion battery type. Nevertheless, EIS remains a powerful tool for non-destructive internal temperature monitoring, particularly in laboratory settings and for diagnostic purposes.
To illustrate the correlation, consider the temperature dependence of the charge transfer resistance \( R_{ct} \), which often appears in the mid-frequency range of the EIS spectrum. According to Arrhenius behavior, \( R_{ct} \) can be expressed as:
$$ R_{ct} = R_0 \exp\left(\frac{E_a}{RT}\right) $$
where \( R_0 \) is a pre-exponential factor. By linearizing, we get:
$$ \ln(R_{ct}) = \ln(R_0) + \frac{E_a}{R} \cdot \frac{1}{T} $$
Thus, plotting \( \ln(R_{ct}) \) versus \( 1/T \) yields a straight line, allowing temperature estimation from measured \( R_{ct} \). This approach has been used in various studies to track internal temperature changes in lithium-ion batteries.
Internal Temperature Prediction Using Machine Learning Algorithms
Machine learning algorithms offer a data-driven approach for predicting the internal temperature of lithium-ion batteries by learning patterns from historical operational data. These methods leverage features such as current, voltage, surface temperature, and environmental conditions to estimate internal thermal states without direct measurement. Techniques like Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF), and Long Short-Term Memory (LSTM) networks have been applied, demonstrating high accuracy and adaptability to complex battery behaviors.
The workflow for machine learning-based temperature prediction typically involves data collection, preprocessing, feature engineering, model selection, training, and evaluation. For a lithium-ion battery, input features might include time-series data of current \( I(t) \), voltage \( V(t) \), and surface temperature \( T_s(t) \), while the target output is the internal temperature \( T_i(t) \). Feature engineering may extract statistical measures (e.g., moving averages, variances), frequency-domain components via Fourier transform, or physics-informed features like estimated heat generation rates. The model is trained on datasets collected under various operating conditions to capture the underlying thermoelectrochemical relationships.
For example, a deep learning model combining 1D convolutional layers and LSTM units can effectively process sequential data to predict internal temperature. The model might learn a mapping function \( f \) such that:
$$ T_i(t+1) = f(I(t), V(t), T_s(t), \ldots; \theta) $$
where \( \theta \) represents the model parameters optimized during training. Physics-informed neural networks (PINNs) integrate differential equations governing battery thermal behavior into the loss function, enhancing predictive robustness. Studies have shown that such models can achieve prediction errors below 1.0°C in terms of Mean Absolute Error (MAE), even under dynamic driving cycles like UDDS.
Machine learning approaches excel in handling nonlinearities and multi-variable interactions inherent in lithium-ion battery systems. They can adapt to different battery chemistries and formats through retraining, and once deployed, offer real-time predictions suitable for BMS integration. However, these methods require large, high-quality datasets for training, which can be expensive and time-consuming to acquire. There is also a risk of overfitting, where models perform well on training data but poorly on unseen data, necessitating careful validation. Additionally, the computational complexity of some algorithms may challenge resource-constrained embedded systems, though lightweight models like gradient boosting or simplified neural networks can mitigate this.
To quantify performance, common evaluation metrics include MAE, Root Mean Square Error (RMSE), and the coefficient of determination \( R^2 \). For instance, a well-trained model might achieve:
$$ \text{MAE} = \frac{1}{n} \sum_{k=1}^n |T_i^{\text{pred}}(k) – T_i^{\text{true}}(k)| < 1.0^\circ\text{C} $$
and \( R^2 \) close to 1, indicating high predictive accuracy. These metrics are crucial for comparing different algorithms and ensuring reliability in practical applications for lithium-ion battery management.
| Algorithm | Key Characteristics | Typical Accuracy (MAE) | Challenges |
|---|---|---|---|
| Artificial Neural Network (ANN) | Multi-layer perceptron, nonlinear mapping | 0.5–2.0°C | Requires large data, prone to overfitting |
| Support Vector Machine (SVM) | Kernel-based, good for small datasets | 0.8–2.5°C | Sensitive to hyperparameters, scalability issues |
| Random Forest (RF) | Ensemble of decision trees, robust | 0.7–2.0°C | Less interpretable, memory-intensive |
| Long Short-Term Memory (LSTM) | Recurrent network, captures temporal dependencies | 0.4–1.5°C | High computational cost, complex training |
Comparative Analysis and Integrated Perspectives
Each of the three methods—embedded sensors, EIS-based estimation, and machine learning prediction—has distinct advantages and drawbacks for monitoring the internal temperature of lithium-ion batteries. Embedded sensors provide direct, high-fidelity measurements but are invasive and costly. EIS offers non-destructive estimation with insights into electrochemical states but requires complex instrumentation and may lack real-time capability. Machine learning enables non-invasive, adaptive predictions using operational data but depends heavily on data quality and computational resources. A holistic approach might combine these techniques; for example, using embedded sensors to collect training data for machine learning models, or using EIS to validate predictions in critical scenarios. Future BMS could integrate multi-method frameworks to enhance reliability and safety for lithium-ion battery systems.
The choice of method often depends on the application context. For research and development of new lithium-ion battery designs, embedded sensors are invaluable for understanding internal thermal phenomena. In electric vehicles, where real-time monitoring is essential, machine learning models trained on fleet data could provide continuous internal temperature estimates without additional hardware. For stationary energy storage, periodic EIS testing might suffice for health assessment and temperature calibration. Ultimately, advancing these technologies requires addressing their limitations through material science, signal processing, and algorithm optimization.
Conclusions and Future Outlook
In conclusion, internal temperature monitoring is paramount for ensuring the safety and performance of lithium-ion batteries, especially as their deployment scales in energy storage and electric vehicles. This review has examined three prominent approaches: embedded temperature sensors, Electrochemical Impedance Spectroscopy (EIS)-based estimation, and machine learning prediction. Embedded sensors deliver accurate in-situ measurements but pose challenges related to battery integrity and cost. EIS provides a non-invasive means to infer temperature from electrochemical responses, though it can be affected by operational variables and requires sophisticated analysis. Machine learning algorithms leverage data-driven models to predict temperature with high accuracy, yet they necessitate extensive datasets and computational power.
Looking ahead, several research directions hold promise. First, the development of miniaturized, wireless, and self-powered embedded sensors could mitigate invasion issues and enable large-scale deployment in lithium-ion battery packs. Second, advancing EIS techniques towards faster, single-frequency measurements could facilitate real-time temperature estimation in BMS. Third, hybrid models that fuse physics-based equations with machine learning (e.g., PINNs) may improve prediction robustness and generalizability across diverse lithium-ion battery types and conditions. Additionally, standardization of testing protocols and data sharing could accelerate progress in machine learning applications. Ultimately, integrating these methods into smart BMS with adaptive thermal management strategies will be key to preventing thermal runaway and extending the lifespan of lithium-ion batteries.
As the demand for lithium-ion batteries continues to grow, innovations in internal temperature detection and estimation will play a critical role in enabling safe, efficient, and reliable energy storage solutions. By overcoming current limitations and embracing interdisciplinary approaches, researchers and engineers can pave the way for next-generation battery technologies that meet the challenges of a sustainable energy future.