As the installed capacity of energy storage power stations continues to expand, the thermal safety of large-scale lithium-ion battery systems has become one of the most critical technical challenges. In this dissertation, I focus on two interrelated aspects of energy storage battery systems: the design and optimization of an air-cooled thermal management structure for a large-format battery cabinet, and the development of a reliable internal short circuit (ISC) triggering method for safety validation. The study is based on a 280 Ah LiFePO₄ energy storage battery and a smaller commercial NCM111 pouch/square cell for ISC experiments. Through experimental parameter measurement, finite element simulation, and structured trigger experiments, I propose a centralized air cooling architecture and a hybrid internal short-circuit trigger combining shape memory alloy (SMA) and phase change material (PCM). The results show that the optimized thermal management system can control the maximum temperature and module temperature difference effectively, and the proposed ISC trigger method achieves a stable triggering rate of 83.3%, which is significantly higher than existing methods. The findings contribute to the standardization of energy storage battery safety testing and the improvement of thermal management performance.
1. Introduction
With the global transition towards renewable energy, the deployment of energy storage batteries has grown rapidly in recent years. Lithium-ion batteries are the most widely used energy storage medium due to their high energy density, long cycle life, and relatively low self-discharge rate. However, the thermal behaviour of an energy storage battery system directly affects its performance, lifetime, and safety. The optimal operating temperature range for most lithium-ion energy storage batteries is between 15 °C and 40 °C, with a maximum allowable temperature difference of 5 °C within a module. Exceeding these limits can lead to accelerated capacity degradation, internal resistance increase, and in severe cases, thermal runaway.
Thermal management is therefore a key enabling technology for energy storage battery systems. Among various cooling methods, air cooling remains the most widely used in commercial energy storage cabinets due to its low cost, simplicity, and reliability. However, conventional split-type thermal management systems often suffer from complex ducting, redundant wiring, and uneven airflow distribution. To address these drawbacks, I designed a centralized forced-air cooling system for a 129 kWh LiFePO₄ energy storage battery cabinet. The system uses parallel ventilation paths with an optimized inlet geometry to improve temperature uniformity.
In parallel with thermal management research, I observed abnormal heat generation and voltage drop in a battery during experimental validation, which was attributed to an internal short circuit caused by mechanical abuse. This observation highlighted the urgent need for reliable ISC triggering methods in safety regulations. Existing methods such as nail penetration, indentation, and external short circuits do not accurately represent the internal failure modes of lithium-ion batteries. More advanced techniques using PCM or SMA have been developed, but they suffer from poor repeatability and low triggering success rates. Therefore, I designed a new internal short-circuit trigger element that integrates both SMA and PCM, aiming to provide a stable and controllable conductive path between the positive and negative electrodes.
2. Experimental Parameter Measurement and Thermal Model Construction
2.1 Test Platform and Battery Parameters
I used a 280 Ah prismatic LiFePO₄ energy storage battery as the primary research object. The basic specifications are listed in Table 1.
| Parameter | Value |
|---|---|
| Weight (kg) | 5.42 ± 0.30 |
| Dimensions (L×W×H) (mm) | 173.7 × 72 × 207.5 |
| Nominal capacity (Ah) | 280 |
| Nominal voltage (V) | 3.2 |
| Charge cutoff voltage (V) | 3.65 |
| Discharge cutoff voltage (V) | 2.5 |
| Charging operating temperature (°C) | 0 to 45 |
| Discharging operating temperature (°C) | -20 to 60 |
| Standard charge/discharge rate | 0.5C |
The test platform consisted of a NEWARE 300A battery cycler, a temperature chamber, a data acquisition system, and thermocouples. The measurement environment was controlled at 25 °C unless specified otherwise. The following tests were performed: capacity test, HPPC internal resistance test, open circuit voltage (OCV) test, entropy coefficient test, and thermal property tests.
2.2 Capacity and Internal Resistance
The capacity test was performed with a CCCV charging protocol: constant current at 0.5C to 3.65 V, then constant voltage until the current dropped to 0.05C. After a 3-hour rest, the cell was discharged at 0.5C to 2.5 V. The measured capacity was 287.5 Ah. Three cells were tested and the results showed good consistency, with a maximum deviation of 0.71% in internal resistance and 0.28% in OCV.
The internal resistance was measured using the HPPC method. The test procedure is summarized in Table 2.
| Step | Operation | Condition | Stop criterion |
|---|---|---|---|
| 1 | Rest | — | 5 min |
| 2 | Constant current discharge | Test current | 10 s |
| 3 | Rest | — | 40 s |
| 4 | Constant current charge | Test current | 10 s |
| 5 | Rest | — | 5 min |
| 6 | Discharge | 0.5C | 10% SOC |
| 7 | Rest | — | 3 h |
| 8 | Loop steps 2-7 | — | 10 cycles |
| 9 | CC charge | 0.5C | 3.65 V |
| 10 | CV charge | 3.65 V | 0.05C |
The charging ohmic resistance \(R_{cha\_ohm}\), polarization resistance \(R_{cha\_pol}\), and total resistance \(R_{cha\_tot}\) are calculated as:
$$
R_{cha\_ohm} = \frac{U_B – U_A}{I_{cha}}
$$
$$
R_{cha\_pol} = \frac{U_C – U_B}{I_{cha}}
$$
$$
R_{cha\_tot} = R_{cha\_ohm} + R_{cha\_pol}
$$
where \(U_A\) is the open circuit voltage before the pulse, \(U_B\) is the instantaneous voltage at the beginning of the pulse, and \(U_C\) is the voltage at the end of the pulse. The measured internal resistance as a function of SOC is shown in Figure 1 (not reproduced here). The internal resistance is highest at low SOC, particularly below 10% SOC.
2.3 Entropy Coefficient
The entropy coefficient \(dU_{ocv}/dT\) is a critical parameter for quantifying reversible heat generation during charge and discharge. I used the potentiometric method: the battery was equilibrated at different temperatures (55 °C, 25 °C, 0 °C, -20 °C, and back to 25 °C), and the OCV was recorded at each temperature after sufficient rest. The entropy coefficient was derived from the linear regression of OCV versus temperature. The test was performed at every 10% SOC step. The results are shown in Figure 2 (omitted).
The reversible heat generation rate can be expressed as:
$$
Q_{rev} = I T_{amb} \frac{dU_{ocv}}{dT}
$$
where \(I\) is the current, \(T_{amb}\) is the ambient temperature, and \(dU_{ocv}/dT\) is the entropy coefficient.
2.4 Specific Heat Capacity and Thermal Conductivity
I used the local heating method with an accelerated rate calorimeter (ARC) to measure the specific heat capacity and thermal conductivity. A constant-power heating pad was sandwiched between two identical battery cells, and the whole assembly was wrapped in insulation and placed in the ARC. The temperature gradient was measured after reaching steady state. The measured specific heat capacity was 1033 J/(kg·°C). The in-plane thermal conductivity was 23.40 W/(m·K), while the through-plane thermal conductivity was only 1.15 W/(m·K). This anisotropic behaviour is due to the layered structure of the jellyroll.
2.5 Heat Generation and Heat Transfer Models
For the heat generation model, I adopted the Bernardi equation:
$$
Q = I\left(U_{ocv} – U_t\right) + I T \frac{dU_{ocv}}{dT}
$$
In a simplified form using internal resistance \(R\), this becomes:
$$
Q = I^2 R + I T_{amb} \frac{dU_{ocv}}{dT}
$$
The first term represents irreversible Joule heat, and the second term represents reversible reaction heat. The energy balance equation for a battery cell is:
$$
C_p M \frac{dT}{dt} = I^2 R + I T \frac{dU_{ocv}}{dT} – hA(T – T_{amb}) – Q_{cool}
$$
where \(C_p\) is specific heat capacity, \(M\) is mass, \(h\) is convective heat transfer coefficient, \(A\) is surface area, and \(Q_{cool}\) is the cooling power.
The three-dimensional heat conduction equation is:
$$
\rho C_p \frac{\partial T}{\partial t} = \frac{\partial}{\partial x}\left(\lambda_x \frac{\partial T}{\partial x}\right) + \frac{\partial}{\partial y}\left(\lambda_y \frac{\partial T}{\partial y}\right) + \frac{\partial}{\partial z}\left(\lambda_z \frac{\partial T}{\partial z}\right) + Q
$$
with initial condition \(T(x,y,z,0) = T_{amb}\).
2.6 Model Validation
I built a three-dimensional finite element model of a single cell in Fluent and performed a 0.85P constant power discharge simulation. Thermocouples were attached to six representative locations on the cell surface. The comparison between simulation and experiment is shown in the following table.
| Measurement point | Average error (%) | Maximum error (%) |
|---|---|---|
| T1 | 1.6 | 2.30 |
| T2 | 1.8 | 2.44 |
| T3 | 1.7 | 2.38 |
| T4 | 1.9 | 2.41 |
| T5 | 1.5 | 2.22 |
| T6 | 1.8 | 2.35 |
The average temperature error was 1.8%, and the maximum error was 2.44%. This confirms the accuracy of the electro-thermal model. Interestingly, during the validation experiment on another battery, I observed an abnormal voltage drop and sudden temperature rise during rest. This phenomenon was later identified as a mechanical abuse-induced internal short circuit, which motivated the second part of this research.
3. Optimization of Battery Cabinet Thermal Management Structure
3.1 Cabinet Design
I designed a centralized air-cooled thermal management system for a 129 kWh LiFePO₄ energy storage battery cabinet. The cabinet consists of six vertically arranged modules, labelled Module 1 to Module 6 from bottom to top. Each module contains 24 cells connected in series, with a nominal energy of 21.5 kWh per module. The cells are arranged in a parallel-flow channel configuration. The cooling air enters from the bottom plenum and is distributed to each module through vertical ducts. The simplified physical model is shown in Figure 4 (not reproduced here).
The computational domain was discretized with polyhedral meshes using Fluent Meshing. The total mesh count was 4.05 million, with local refinement at the inlet and outlet and three prism layers at the fluid-solid interface. The standard \(k-\epsilon\) turbulence model was used for airflow simulation. Convergence criteria were set to \(10^{-5}\) for all residuals.
3.2 Initial Simulation Results
The initial simulation was performed at an inlet airflow velocity of 5 m/s and an inlet air temperature of 25 °C. The battery cabinet was discharged at 0.85P constant power. After the discharge, the maximum temperature in the cabinet reached 41.17 °C, and the maximum module temperature difference was 7.89 °C. The temperature distribution was highly uneven: Modules 1 and 2 had relatively uniform temperature fields, while Modules 3 to 6 showed significant heat accumulation near the inlet side. The velocity distribution analysis indicated that the airflow velocity difference within Module 5 was as high as 9.22 m/s, whereas the most uniform module had a velocity difference of only 3.31 m/s.
The main cause of this non-uniformity was the right-angle inlet geometry, which forced the incoming air to maintain its horizontal momentum instead of entering vertically into the module. To improve the flow distribution, I compared four inlet geometries: right-angle, single-slope, double-slope, and rounded (fillet) designs.
3.3 Inlet Geometry Optimization
The simulation results for the three optimized geometries are summarized in Table 3.
| Inlet geometry | Module with best uniformity | Maximum velocity difference (m/s) | Improvement vs. right-angle |
|---|---|---|---|
| Right-angle (original) | Module 1 | 3.31 | — |
| Single-slope | Module 1 | 2.98 | 10% |
| Double-slope | Module 3 | 2.76 | 16.6% |
| Rounded (fillet) | Module 3 | 2.56 | 22.7% |
Among these, the rounded inlet produced the most uniform airflow distribution. The radius of the rounded geometry had a minor effect on the tangential and normal velocity components, but the arc angle played a decisive role. The airflow uniformity improves as the arc angle approaches \(\pi/2\) (i.e. a quarter circle). With the rounded inlet, the maximum airflow velocity difference across all modules decreased from 9.22 m/s to 3.07 m/s, a reduction of 66.7%.
Although the module internals were improved, Modules 3 to 6 still suffered from high radial velocity in the inlet duct. This was caused by the bend upstream of the module inlet. The air flowing through a bend is pushed towards the outer radius by centrifugal force. Since the module inlets were located on the outer side of the bend, the airflow entering the modules had a high horizontal component. To solve this problem, I changed the direction of the main inlet duct so that the module inlets were on the inner side of the bend.
3.4 Final Optimized Structure and Temperature Simulation
After changing the inlet duct orientation, the velocity distribution became much more uniform. The maximum velocity difference among all modules was reduced to 3.07 m/s (for Module 2), while the best module (Module 3) had a maximum velocity difference of only 2.56 m/s. The temperature field after a complete 0.85P discharge is shown in Figure 5 (omitted). The maximum cabinet temperature decreased to 38.86 °C, and the maximum module temperature difference was limited to 6 °C. Compared with the original structure, the maximum temperature was reduced by 2.31 °C, and the maximum temperature difference was reduced by 2 °C.
3.5 Parametric Study Using Orthogonal Simulations
To quantify the effects of inlet velocity and inlet temperature on cooling performance, I designed an orthogonal simulation matrix with four velocity levels (4, 5, 6, 7 m/s) and three temperature levels (23, 24, 25 °C), resulting in 12 simulation cases. The cell initial temperature and ambient temperature were set to 25 °C. The simulation results are summarized in Table 4 and Table 5.
| Velocity / Temperature | 23 °C | 24 °C | 25 °C |
|---|---|---|---|
| 4 m/s | 37.8 | 38.3 | 38.8 |
| 5 m/s | 37.2 | 37.7 | 38.2 |
| 6 m/s | 36.7 | 37.2 | 37.7 |
| 7 m/s | 36.3 | 36.8 | 37.3 |
| Velocity / Temperature | 23 °C | 24 °C | 25 °C |
|---|---|---|---|
| 4 m/s | 5.3 | 5.1 | 4.9 |
| 5 m/s | 5.6 | 5.4 | 5.2 |
| 6 m/s | 5.7 | 5.5 | 5.3 |
| 7 m/s | 5.5 | 5.3 | 5.1 |
The following trends were observed:
- For every 1 °C decrease in inlet air temperature, the maximum temperature decreased by 0.51 °C on average.
- For every 1 °C decrease in inlet air temperature, the maximum module temperature difference increased by approximately 0.24 °C.
- For every 1 m/s increase in airflow velocity, the maximum battery temperature decreased by 0.61 °C on average.
- Increasing the velocity initially increased the temperature difference slightly because the flow field became less uniform, but at higher velocities (above 6 m/s), the temperature difference began to decrease due to enhanced heat transfer.
These findings provide important guidelines for the design and control of air-cooled thermal management systems for energy storage batteries.
4. Internal Short Circuit In-Situ Triggering Method
4.1 Internal Short Circuit Types and Existing Trigger Methods
Internal short circuit is a major safety risk for lithium-ion energy storage batteries. It occurs when the positive and negative electrodes are electrically connected inside the cell, leading to localized current flow, heat generation, and potentially thermal runaway. There are four types of internal short circuits in a lithium-ion cell, as listed in Table 6.
| Type | Positive side | Negative side | Typical resistance | Hazard level |
|---|---|---|---|---|
| Positive-Negative | Positive material | Negative material | High (>100 Ω) | Low |
| Copper-Positive | Positive material | Copper foil | Medium (≈60 Ω) | Low |
| Aluminum-Copper | Aluminum foil | Copper foil | Very low (mΩ) | Medium (fusing effect) |
| Aluminum-Negative | Aluminum foil | Negative material | Low (≈10 Ω or less) | High (thermal runaway) |
Existing safety regulations, such as GB/T 31486, GB 38031, IEC 62660, and UL 1642, lack standardized internal short circuit test methods. The nail penetration and crush tests are poorly repeatable and do not accurately reproduce internal failure modes. The phase change material (PCM) method developed by NREL and the shape memory alloy (SMA) method developed by Tsinghua University have been widely cited, but both suffer from low success rates and poor reproducibility. In my preliminary tests, the PCM method achieved a stable triggering rate of only 16.7%, while the SMA method had a success rate of less than 30%.
4.2 Design of the Hybrid SMA-PCM Trigger Element
To overcome these limitations, I developed a new internal short circuit trigger element that combines the advantages of SMA and PCM. The element consists of a 0.2 mm thick nickel-titanium shape memory alloy disc with a diameter of 8 mm. The disc is processed to memorize a dome-shaped deformation at a transition temperature of 70–80 °C. Both surfaces of the disc are uniformly coated with a 0.1 mm layer of 48# refined low-temperature paraffin wax (phase change temperature around 48 °C). The total thickness of the coated element is approximately 0.4 mm. The working principle is illustrated in Figure 8 (not reproduced): during heating, the PCM melts first; above the SMA transition temperature, the SMA disc deforms and pushes the molten wax away, establishing a direct conductive path between the adjacent electrode foils or materials.
This design ensures that the conductive channel remains stable after triggering because the single-way shape memory effect retains the deformed shape even after cooling. The molten PCM is guided away from the contact area by the deforming SMA, preventing re-blocking of the short circuit path.
4.3 Fabrication and Implantation of the Trigger Element
The SMA discs were wire-cut from a NiTi alloy sheet and shape-memory-treated using a low-temperature annealing process. The discs were annealed at 800 °C, deformed into a dome shape at room temperature, and then aged at 250 °C for 3 minutes to fix the shape. After cleaning, the discs were coated with paraffin using a spin coater at 4500 r/min and 50 °C. SEM observation confirmed uniform coating thickness of approximately 0.1 mm on each side.
For implantation, I used a commercial 37 Ah prismatic cell with NCM111 cathode and graphite anode. The cell was fully discharged to 2 V before disassembly. The disassembly was performed in a dry room using a rotary cutting tool to thin the upper casing along two circular grooves, similar to opening a tin can. The jellyroll was then extracted and carefully unrolled. A circular hole with a diameter of 8 mm was punched in the separator at the desired location. Depending on the target ISC type, electrode material was scraped away to expose the underlying foil. The trigger element was then placed into the separator hole. Finally, the jellyroll was re-rolled, placed in a new casing, filled with a small amount of electrolyte, and sealed with adhesive tape and high-temperature inorganic glue.
After implantation, the batteries were rested for 48 hours and monitored for voltage stability. Only batteries with stable voltage were used for further tests.
4.4 Post-Implantation Validation
I verified the implant quality using X-ray CT scanning. The CT images confirmed that the element remained in its intended position between the electrode layers without any displacement during reassembly. I also performed capacity and leakage current tests on three cells: a control cell (disassembled and reassembled without any element, NoISC#1), a pristine cell (NoISC#2), and a cell with the implanted ISC element (ISC). The results are shown in Table 7.
| Cell | Capacity (Ah) | Leakage current (mA) |
|---|---|---|
| NoISC#1 (reassembled control) | 36.55 | 2.286 |
| NoISC#2 (pristine) | 36.70 | 2.341 |
| ISC (with trigger element) | 36.84 | 2.059 |
The maximum capacity deviation was 0.29 Ah, which is less than 1%. The leakage current of the implanted cell was within the normal range of the controls. These results confirm that the implanted trigger element does not affect the normal performance of the energy storage battery when not triggered.
5. Experimental Validation of the ISC Triggering Method
5.1 Triggering Positive-Negative ISC
I prepared a cell with a positive-negative type ISC risk (ISC#1) by implanting the hybrid element into a separator hole without exposing any current collector foil. The cell was charged to 100% SOC, and the triggering experiment was carried out in an explosion-proof box. The cell was clamped between stainless steel plates with mica sheets for insulation. A 120 W heater pad was attached to the cell surface on the side of the ISC element. The triggering process was monitored by thermocouples and a Keithley 2461 source meter.
During heating, the leakage current remained low until the SMA reached its transformation temperature. The entire triggering process could be divided into four stages: (I) initial self-discharge; (II) heating and triggering; (III) cooling after heater shutdown; and (IV) stable ISC leakage current. The final stable leakage current after 24 hours was 28.8 mA. Using the equivalent circuit model:
$$
\frac{1}{R_{ISC}} = \frac{1}{R_{leak}} – \frac{1}{R_{leak0}}
$$
where \(R_{leak0} = 1.90 \text{ k}\Omega\) is the self-discharge resistance of the reassembled cell, and \(R_{leak}\) is the measured total leakage resistance. I obtained an equivalent ISC resistance of 176 Ω, which is consistent with the characteristic of a positive-negative internal short circuit.
5.2 Triggering Copper-Positive ISC
For the copper-positive type, the separator hole was made over a region where the anode material was locally scraped to expose the copper foil. The element was placed between the copper foil and the positive electrode. The resulting cell (ISC#2) showed a stable leakage current of 73.4 mA after triggering, with an equivalent ISC resistance of 58.3 Ω. The voltage drop during triggering is shown in Figure 13 (omitted). This resistance is lower than that of the positive-negative type, leading to higher local Joule heating, which produced slight burn marks and electrolyte dry-out observed after disassembly.
5.3 Triggering Aluminum-Copper ISC
For the aluminum-copper type, both the positive and negative active materials were scraped away to expose the aluminum foil and copper foil. The element was placed between the two foils (ISC#3). During triggering, the voltage dropped only slightly and then recovered. After 24 hours, no large sustained leakage current was measured. The disassembly revealed severe localized ablation of the electrode materials. This phenomenon is attributed to the fusing effect: the initial short circuit current is extremely high due to the very low resistance of the aluminum-copper contact. The resulting intense Joule heat instantly melts a surrounding region, creating a solidified insulative layer that interrupts the short circuit path. This is a self-extinguishing internal short circuit, but it still causes local damage to the energy storage battery.
5.4 Triggering Aluminum-Negative ISC
I prepared two cells (ISC#4 and ISC#5) with aluminum-negative type ISC by exposing the aluminum foil beneath the positive material and placing the element between the aluminum foil and the negative electrode. These cells exhibited two distinct evolution modes:
- Non-runaway mode (ISC#4): The voltage dropped suddenly by 0.4 V at about 203 s, and the surface temperature jumped from 86.3 °C to 102.6 °C within one second. After the heater was turned off, the cell continued to self-heat to approximately 300 °C near the ISC region but did not enter thermal runaway. The internal average temperature remained below the onset threshold of 235.6 °C measured by ARC. The cell gradually self-discharged over more than an hour until the stored energy was depleted.
- Runaway mode (ISC#5): The ISC triggered at 107 s, the voltage collapsed to 0 V within seconds, and the recorded maximum temperature reached 523 °C, leading to full thermal runaway.
The difference between the two modes is determined by whether the growing short circuit path is maintained or interrupted. The non-runaway mode is consistent with the behaviour of the mysteriously dropped battery in Chapter 2, confirming the validity of my trigger method in reproducing realistic energy storage battery failure modes.
5.5 Disassembly Analysis
After each triggering test, the cells were fully discharged and dismantled for visual inspection. The results are summarized as follows:
| ISC type | Observed damage | Key mechanisms |
|---|---|---|
| Positive-Negative | No visible damage, no electrode detachment | High resistance limits current and heat generation |
| Copper-Positive | Mild burn marks, electrolyte dry-out around the short circuit region | Moderate resistance generates localized heat |
| Aluminum-Copper | Severe localized ablation, solidified fuzzy layer blocking the short path | Extreme current causes fusing and self-extinguishing |
| Aluminum-Negative | Large-area multi-layer ablation, electrode material peeling | Low resistance leads to continuous high current and heat accumulation |
5.6 Triggering Success Rate and Repeatability Comparison
To evaluate the success rate and repeatability of the proposed hybrid method, I compared it with the traditional NREL PCM method. A total of 24 pouch cells (4 Ah, NCM811) were used. Twelve cells were implanted with my hybrid elements (E1–E12) and twelve with the NREL PCM elements (R1–R12). All cells were fully discharged to near 0% SOC to ensure safety during triggering. The cells were heated with a 30 W heater pad until the surface temperature reached 90 °C. After cooling, the cells were rested for 24 hours, and the residual voltage and leakage current were recorded.
| Cell | Voltage at trigger (V) | Voltage after 24 h (V) | Leakage current (A) | Equivalent ISC resistance (kΩ) |
|---|---|---|---|---|
| E1 | 3.2167 | 0.0955 | 0.0305 | 0.1117 |
| E2 | 3.0479 | 0.0552 | 0.0367 | 0.0869 |
| E3 | 3.2451 | 0.0294 | 0.0312 | 0.1100 |
| E4 | 3.3004 | 0.0026 | 0.0354 | 0.0982 |
| E5 | 3.2627 | 0.2877 | 0.0313 | 0.1101 |
| E6 | 3.2989 | 3.2897 | — | — (not triggered) |
| E7 | 3.3529 | 3.3687 | — | — (not triggered) |
| E8 | 3.4018 | 0.0203 | 0.0362 | 0.0988 |
| E9 | 3.0381 | 0.1418 | 0.0276 | 0.1168 |
| E10 | 3.4307 | 0.2911 | 0.0313 | 0.1164 |
| E11 | 3.3079 | 0.1258 | 0.0312 | 0.1124 |
| E12 | 3.4047 | 0.2357 | 0.0330 | 0.1090 |
| Cell | Voltage at trigger (V) | Voltage after 24 h (V) | Leakage current (A) | Equivalent ISC resistance (kΩ) |
|---|---|---|---|---|
| R1 | 3.4246 | 3.3035 | Not measurable | — |
| R2 | 3.4298 | 3.4301 | — | — |
| R3 | 3.4250 | 3.4256 | — | — |
| R4 | 3.4333 | 3.4168 | — | — |
| R5 | 3.4252 | 0.0352 | 0.0467 | 0.0762 |
| R6 | 3.4245 | 0.0314 | 0.0275 | 0.1334 |
| R7 | 3.4320 | 3.4283 | — | — |
| R8 | 3.4362 | 3.4362 | — | — |
| R9 | 3.4350 | 3.4357 | — | — |
| R10 | 3.2976 | 3.2929 | — | — |
| R11 | 3.2769 | 3.0164 | 0.0069 | 0.6379 |
| R12 | 3.3152 | 3.2553 | Not measurable | — |
The results are summarized as follows:
| Method | Stable triggering rate | Unstable triggering rate | Total triggering rate |
|---|---|---|---|
| Hybrid SMA-PCM method | 83.3% (10/12) | 0% | 83.3% |
| NREL PCM method | 16.7% (2/12) | 25% (3/12) | 41.7% |
For the hybrid method, the stable triggering rate was 83.3%, with all successfully triggered cells exhibiting a leakage current of approximately 30 mA and an equivalent ISC resistance of about 100 Ω, which matches the expected positive-negative ISC characteristics. The excellent consistency in the measured resistance values demonstrates the high repeatability of the proposed method. In contrast, the NREL PCM method had a stable triggering rate of only 16.7%, and the triggered cells showed a wide spread of ISC resistance values, implying poor reproducibility.
6. Conclusion
In this dissertation, I have systematically investigated the thermal management and safety key technologies for energy storage batteries. The main conclusions are as follows:
- I established a comprehensive experimental platform to measure the thermal and electrical parameters of a 280 Ah LiFePO₄ energy storage battery, including capacity, internal resistance, entropy coefficient, specific heat capacity, and thermal conductivity. Based on these parameters, a coupled electro-thermal model was developed and validated with a maximum error of 2.44%, confirming its suitability for simulating energy storage battery thermal behaviour.
- I designed a centralized air-cooled thermal management system for a 129 kWh energy storage battery cabinet. Through comparative simulations, I found that the rounded inlet geometry significantly improves airflow and temperature uniformity. The optimized system reduced the maximum airflow velocity difference by 66.7% and the maximum temperature difference by 25.3% compared with the right-angle inlet. The maximum battery temperature was controlled below 39 °C, and the module temperature difference was kept within 6 °C. The parametric study revealed that lowering the inlet temperature by 1 °C reduces the maximum battery temperature by 0.51 °C but increases the temperature difference by 0.24 °C; increasing the airflow velocity by 1 m/s reduces the maximum temperature by 0.61 °C.
- I developed a novel internal short circuit trigger method for energy storage batteries by integrating shape memory alloy and phase change material. The trigger element is a double-sided paraffin-coated NiTi SMA disc. Implantation does not affect the cell’s capacity or leakage current when not triggered, as verified by CT and electrical measurements.
- The hybrid trigger method successfully induced all four types of internal short circuits in 37 Ah prismatic cells. I observed and explained two evolution modes of aluminum-negative ISC. Disassembly analysis revealed the fusing effect in aluminum-copper ISC and the severe damage caused by aluminum-negative ISC. In the success rate comparison, the proposed method achieved a 83.3% stable triggering rate with excellent repeatability, far exceeding the 16.7% rate of the NREL PCM method. This method provides a reliable and standardized approach for testing the safety of energy storage batteries.
These findings contribute to the advancement of thermal management design and safety testing for large-scale lithium-ion energy storage battery systems. Future work could explore the integration of liquid cooling for higher energy density cabinets and further optimize the ISC trigger element for different cell formats and materials.