Battery Temperature Research Articles

A novel bionic lotus leaf channel liquid cooling plate for enhanced thermal management of lithium-ion batteries

Battery Thermal Management Systems (BTMS) are pivotal in safeguarding the safety, efficiency, and lifespan of electric vehicles. In this study, we propose a bionic lotus leaf (BLL) channel liquid-cooled plate to mitigate temperature accumulation issues observed during lithium-ion battery (LIB) discharge. The primary objective is to enhance heat dissipation and achieve uniform temperature distribution within LIBs. We investigated the impact of different interior configurations and mass flow rates on individual lithium-ion battery (LIB) temperatures using single-variable analysis. Our findings indicate that higher inlet mass flow rates effectively lower the temperature increase in individual LIBs, albeit at the cost of inducing a significant pressure drop.The optimal parameter combination is determined through orthogonal analysis. Specifically, the channel angle (α) and inlet mass flow rate (M) are found to significantly influence the maximum temperature (Tmax) of LIBs. The number of channels (N) is optimized to enhance temperature distribution and reduce maximum temperature variation (ΔTmax) within LIBs. Furthermore, adjusting the channel width (D) substantially mitigated pressure drop (ΔP) in the BLL channel liquid cooling plate, thereby improving hydraulic pump efficiency. The identified optimal combination (M = 1.0 g s-1, N = 4, D = 2.5 mm, α = 90°) ensures that Tmax remains below 29.732 °C even during a 5C discharge, a critical requirement. Compared to a traditional serpentine channel with equivalent heat transfer area and inlet mass flow, the optimized BLL channel showed a decrease in Tmax by 0.776 °C, ΔTmax reduction by 2.445 °C, and ΔP reduced to only 43.44 % of that in the serpentine channel. The BLL channel proposed in this research can serve as a valuable reference for BTMS.

Field study on the temperature uniformity of containerized batteries using a two-phase liquid cooling system under mismatched conditions

The conventional liquid cooling system carries the risk of dew condensation and air cooling has poor thermal management performance for battery energy storage systems. To address these issues, a novel two-phase liquid cooling system was developed for containerized battery energy storage systems and tested in the field under mismatched conditions. The thermal management performance and safety during the charging and discharging processes were analyzed by investigating the main influencing factors, including battery temperature and cooling system characteristics (battery temperature variation, temperature uniformity, supply liquid temperature, cold plate temperature distribution, and compressor status). The results showed that the novel system can maintain a maximum cell temperature difference of less than 3°C at the rack level and less than 2°C at the pack level, resulting in a 60% reduction compared to the traditional air cooling system. Relative to a supply liquid temperature of 15∼25°C, a relatively high supply temperature of 20∼30°C for two-phase liquid cooling can not only reduce the running time of the compressor but also enhance its start-stop consistency. Compared to other studies, this novel system is reliable and safe for containerized energy storage batteries, even in capacity mismatch and direct return pipeline conditions.

Methods for faster estimation of the entropy profile of a lithium-ion battery: A comparison of accelerated potentiometry and the estimation of entropy through thermal signatures

The operating temperature of a lithium-ion battery (LIB) has strong effects on its electrochemical performance, safety, and lifetime. Reliable predictions of cell temperature and heat generation rate are required for the design and thermal management of battery systems. The entropy variation of a LIB has a significant influence on the heat generation and the cell temperature variation, especially at a lower C-rate. Since the entropy is a function of State-of-Charge (SoC), and may change during ageing, the entropy profile may as well be used as an indicator for battery State-of-Health diagnostics. The present experimental methods, such as potentiometric or calorimetric methods, for determining the entropy variation throughout a battery's full SoC window are time-consuming and require specialized equipment with good thermal insulation and instrumentation. Hence, faster and more convenient methods are needed. In this paper, we compare an accelerated potentiometric method with a new entropy characterization method where the entropy profile is extracted from the thermal signature of a LIB during charge and discharge at low C-rates. The accelerated potentiometric method aims to obtain the entropy from thermal cycling while the cell is still not completely relaxed to its stable OCV. The thermal signature method monitors the variations of a cell's outer surface temperature during a low constant current charge or discharge, from which the continuous variation of entropy with the SoC can be extracted. The obtained entropy profile was validated through a modeling approach that combines a P2D-electrochemical model for battery operation and a 3D-finite-element model for the thermal behavior during battery operation. Good agreement between the model predictions and experiments was achieved on the temporal variation of the cell's outer surface temperature.

Study on a novel thermal management system and heat recovery strategy of range extend electric vehicle

The integrated thermal management system (ITMS) can effectively improve the pure electric range of the vehicle. This work proposes a ITMS based on a super bottle. In this work, the power battery temperature is subdivided into five stages and a control strategy for heating the battery and cabin with dual heat sources in a low-temperature environment is developed. Then a numerical integrated simulation model of the ITMS is developed and experimentally verified in an environmental chamber. The experimental and simulation conditions used throughout the study are set to an ambient temperature of 0°C. At the same time, due to the better sampling of data under 40 km/h, a constant speed of 40 km/h is used. At 0 °C, through 660 s of 40 km/h conditions, the results show that the ITMS can heat the battery to 15 °C in about 20 minutes at a low temperature of 0°C, and heat the cabin to 22 °C in 10 min. The ITMS also improves battery warm-up efficiency by 16.7% and reduces energy consumption by 5.5% compared to the original system. The ITMS is 17% more expensive than the original system. In addition, the low-temperature electric range has been increased by 3.3 km, and the cost of increasing the range by integrating the thermal management components is approximately 28 ¥/km.

Optimizing Hybrid Active–Passive Thermal Management of Prismatic Li‐Ion Batteries Using Phase Change Materials and Porous‐Filled Mini‐Channels

ABSTRACTTackling climate change is crucial, and electrifying the vehicular transportation sector is essential to reduce greenhouse gas emissions. Lithium‐ion (Li‐ion) batteries are highly efficient for electric vehicles (EVs) but face challenges such as thermal management, risk of thermal runaway, and high costs of lithium and cobalt. Overcoming these challenges is vital for the widespread adoption of hybrid and EVs. To overcome this drawback, this article proposed a large‐kernel attention graph convolutional network (LKAGCN) with leaf in wind optimization algorithm (LWOA) named as LKAGCN‐LWOA technique, which enhances the thermal management of prismatic Li‐ion batteries by integrating both active and passive cooling techniques. The system incorporates phase change materials (PCMs) with porous‐filled mini‐channels to regulate battery temperature effectively. The LKAGCN analyze thermal properties, battery conditions, and PCM characteristics to predict and optimize the thermal behavior of the battery pack using LWOA. The proposed methods tune the parameters of the hybrid thermal management system, ensuring efficient thermal regulation and improved performance. The proposed method is compared to various existing methods such as convolutional neural network (CNN), Taguchi method, and Finite element model (FEM).

Bidirectional circulating preheating method harnessing engine and battery waste heat reuse in hybrid electric vehicles

In hybrid electric vehicles (HEVs), both the engine and power batteries experience reduced performance at low temperatures, necessitating the consumption of significant energy for preheating. The bidirectional preheating system method, which for the first time applies waste thermal energy to preheat both the engine and power battery in HEVs, utilizes the engine’s residual thermal energy to preheat the power battery and the thermal energy from the power battery to preheat the engine. To implement this method, a numerical model of the engine radiator and power battery waste heat is first established, and the temperature rise characteristics and temperature distribution of the waste heat system are simulated and analyzed. This analysis reveals the heat transfer law of the engine radiator and power battery waste heat. An automatic bidirectional thermal control device based on waste heat reuse is designed. The device utilizes a multi-stage series thermal energy transfer system with phase change materials (PCM) as the medium. A low-temperature experiment is conducted to assess the integration of heating and cooling between the engine and power battery. The results indicate that this method effectively utilizes the engine’s waste heat for both heating and cooling purposes. Specifically, a heat exchanger is used to preheat the power battery using the engine’s cooling residual heat, maintaining the internal battery temperature at 30 °C. Simultaneously, the residual heat from the power battery’s cooling system is circulated back to the engine body, preheating the engine coolant to 42 °C. Compared with existing HEVs, it can save more than 90% of the preheating energy consumption of the engine and power battery. This method can reduce power battery and engine warm-up time by more than 50%, while reducing emissions by more than 40% during engine cold start. This approach effectively addresses the challenge of low-temperature preheating for both power batteries and engines by utilizing cooling residual heat bidirectionally, thereby maximizing energy utilization efficiency in HEV systems and optimizing both battery and engine performance.

Power Battery Temperature Prediction Based on Charging Strategy Classification and Improved Adaptive GA-BP

Power Battery Temperature Prediction Based on Charging Strategy Classification and Improved Adaptive GA-BP

Natural convection characteristics of novel immersion liquid applied to battery thermal management in static mode

Battery thermal management (BTM) based on immersion liquid is a novel and promising technology due to its excellent thermal performance. However, the natural convection characteristics of immersion liquid are crucial yet often neglected for the design of immersion-based BTM. In this study, a typical BTM unit and module are designed utilizing a multi-component oil as the immersion liquid. To characterize the effect of natural convection, a rigorously validated model coupled with heat transfer and natural convection is developed. For the BTM unit, the impact of varying space occupied by immersion liquid is investigated. The increase in the thickness of the immersion liquid progressively enhances the natural convection effect. The Grashof number in the vertical interlayer of finite space helps assess the strength of natural convection of the BTM unit. Furthermore, a comparison of the BTM module with and without natural convection is performed. The results show that the natural convection effect enhances heat dissipation but reduces thermal uniformity in static mode. When the thickness of immersion liquid is 10 mm, the natural convection effect results in a decrease of 1.4 °C in the average battery temperature. Meanwhile, the battery temperature difference increases from 1.27 °C to 3.14 °C, and the average temperature of the terminals and busbars increases by >1 °C. This study can provide a foundation and reference for the BTM design based on immersion liquid.

Study on Thermal Runaway Behavior and Early Warning Algorithm of Ternary Lithium Battery Pack Under Preload Force

Overcharging is a primary cause of thermal runaway in ternary lithium‐ion batteries, often leading to serious safety incidents. Early detection of thermal runaway during overcharging is therefore critical. This study investigates a 5 Ah ternary lithium battery pack, applying appropriate preload force to simulate real‐world conditions. Various overcharge experiments are conducted under different conditions, and changes in battery voltage, temperature, and expansion force are thoroughly analyzed. The results indicate that under the same initial conditions, higher charging rates accelerate the temperature rise in the lithium battery. Additionally, the internal gas generation rate increases, causing a faster rise in edge pressure and leading to earlier battery cracking. Building on these findings, a three‐level early warning algorithm is developed, which comprehensively considers voltage, temperature, and expansion force changes. Experimental validation demonstrates that this algorithm can accurately identify the current stage of thermal runaway and detect the transition to the third warning stage 604 s before complete failure, thus providing critical protection for the safe operation of the battery pack. This study offers valuable guidance for enhancing the monitoring and early warning capabilities of battery management systems.

Structural optimization and battery temperature prediction of battery thermal management system based on machine learning

Lithium-ion batteries significantly extend the driving range for electric motorcycles. The battery thermal management system (BTMS) is critical for achieving optimal battery performance. Moreover, precise battery temperature prediction is essential for efficient thermal management. Therefore, a battery thermal management system integrating air and phase change material (PCM) cooling is proposed. Initially, the impact of PCM height, PCM thickness, and air velocity on battery temperature is analyzed. Subsequently, with cost minimization as the objective and ensuring that the maximum battery temperature remains below a threshold, the Black Kite Algorithm (BKA) is employed to optimize the BTMS structure. Finally, a BKA-Convolutional Neural Network (CNN)-Self Attention (SA) model is introduced for battery temperature prediction. The results indicate that increasing the thickness of the PCM and air velocity facilitates battery heat dissipation but with diminishing marginal effects. An increase in PCM height enhances battery cooling at low air velocities but becomes detrimental at high air velocities. The optimized PCM height is 35 mm, resulting in a cost of 0.073 USD for the BTMS per battery. Additionally, the BKA-CNN-SA model achieved a maximum error of 0.45 °C on the validation set and accurately predicted battery temperature changes before and after PCM melting.

Battery temperature estimation at wide C-rates using the LSTM model based on polarization characteristics

Data-driven based approaches have achieved significant results in estimating battery temperature. Nevertheless, the present challenge emanates from the dearth of theoretical guidance governing the training strategy of the model, leading to an inefficient training process and constrained accuracy, especially under specific conditions. In this paper, a Long Short-Term Memory (LSTM) neural network based on polarization characteristics is proposed to estimate the battery discharge temperature. Firstly, the LSTM temperature estimation model is established and the hyperparameters are optimized by Genetic Algorithm (GA). The results show that the accuracy of the traditional model is less satisfactory, with the Maximum Error (ME) of 3.79 °C. Subsequently, the voltage and polarization heat production characteristics of the battery are analyzed under various discharge conditions. It is found that the heat production of the battery is smaller at 0–1.5C, and the heat production characteristics are similar at medium-rate, but the change of heat production is significant when C-rate exceeds 3C. Finally, a LSTM temperature estimation model based on polarization characteristics is proposed, which divides the datasets according to heat production characteristics. Further, a strategy for predicting temperatures in later stages based on limited data from the current condition is also proposed, this mothed combines with the transfer learning method to rapidly develop models for different high-rate conditions. The ME of the model on the test set is 1.1 °C, and the training time is reduced by 32.93 s. The results show that the LSTM temperature estimation model based on polarization characteristics has higher accuracy and training efficiency than the traditional LSTM temperature estimation model.

High-uniformity liquid-cooling network designing approach for energy storage systems by graph-coupled genetic algorithm

Electrochemical battery energy storage stations have been widely used in power grid systems and other fields. Controlling the temperature of numerous batteries in the energy storage station to be uniform and appropriate is crucial for their safe and efficient operation. Thus, effective thermal management is required. In this work, an approach for rapid and efficient design of the liquid cooling system for the stations was proposed. A hydraulic solution model for the liquid-cooling network was established based on graph theory principles, and the genetic algorithm was employed for automatic system optimization to achieve high cost-effectiveness and ideal temperature distribution. Experiments were conducted to validate the accuracy of the hydraulic model and evaluate the efficacy of the optimization strategy. The flow rate calculated from the hydraulic model agreed well with the experiments, with a discrepancy smaller than 3.5 %, and the uniformity of flow rates was improved to as high as 70 % after the optimization. Furthermore, the applicability of this approach was also confirmed across various sizes of energy storage systems.

Investigation and optimization of battery thermal management system based on composite phase change material and variable wall liquid cooling plate

To mitigate the risk of thermal runaway in lithium-ion batteries, an efficient battery thermal management system (BTMS) assumes paramount importance. A BTMS based on composite phase change material (CPCM) and variable wall liquid cooling plate (LCP) is proposed in this research. The numerical model of the BTMS was established and experimentally validated. The influence of the wall of LCP on battery temperature was investigated, and the efficiency of phase change material (EOP) index was proposed to assess the efficacy of CPCM. The genetic algorithm was employed to optimize the structure of the CPCM, and the influence of flow rate on the maximum temperature of the battery pack was studied. The results demonstrate a reduction of 1.81 °C in the maximum temperature of the battery pack upon implementation of the variable wall LCP. The optimized EOP achieves a value of 0.07 °C/g, resulting in a temperature difference of 0.56 °C. Furthermore, maintaining the maximum temperature of the battery pack below 40 °C only requires a water flow rate greater than 0.89 g/s. These results can serve as a valuable reference for the development of battery thermal management systems utilizing CPCM and liquid-cooling.

Battery Thermal Management System in Electric Two-Wheelers

This work documents the design of a battery thermal management system for an electric vehicle in which a liquid cooling system was designed for a 24V Li-ion battery pack. Then, a thermal simulation was performed by using one-dimensional heat transfer modelling using Solidworks. This model also consists of the design of a heater, pump and evaporator to determine the total energy consumption of BTMS. The results indicate that the battery temperature is maintained between 20-30 °???? for various driving profiles with a maximum energy consumption of 0.3%.This work documents the design of a battery thermal management system for an electric vehicle in which a liquid cooling system was designed for a 24V Li-ion battery pack. Then, a thermal simulation was performed by using one-dimensional heat transfer modelling using Solidworks. This model also consists of the design of a heater, pump and evaporator to determine the total energy consumption of BTMS. The results indicate that the battery temperature is maintained between 20-30 °???? for various driving profiles with a maximum energy consumption of 0.3%.

Simulation study on heat dissipation of a prismatic power battery considers anisotropic thermal conductivity in air cooling system

Heat accumulation could be occurred due to low thermal conductivity in one direction inside the power battery, which may lead to the decrease charging performance and potential thermal runaway. In this study, the impact of the anisotropic thermal conductivity (kx, ky, kz) on the temperature of the battery was analyzed. The anisotropic thermal conductivity was divided into the flow direction, thickness direction and spanwise conductivity. Then, the variation regularityof surface temperature distribution and heat flux are revealed by changing the anisotropic thermal conductivity. The results show that the increase of kx improves the heat dissipation efficiency of the upstream battery by 24 %. The higher thermal conductivity allows additional heat to be transferred from upstream to downstream along the direction of flow. The increase of kx makes the temperature difference only about 3 K, which improves the temperature uniformity of the battery. In addition, the impact of kx on the battery heat dissipation process is more dominant than ky and kz. However, there is an upper limit to the impact of ky on the battery temperature. When the ky exceeds 10 times the value of kx, the maximum temperature and temperature uniformity of the battery are almost unaffected by the change of ky. Meanwhile, the increase of kz has the minor effect on the temperature distribution of the battery surface. The anisotropic thermal conductivity is adjusted to reduce heat accumulation in the battery and improve the temperature uniformity. This work is beneficial to the engineering application of battery thermal management technology.

Thermal management analysis with different PCMs in Sodium-Ion batteries

The advent of modern technology has led to a significant increase in the utilisation of rechargeable secondary batteries. Despite the sustainability of lithium-ion (Li-ion) batteries, the limited availability of lithium has driven research into alternative energy storage technologies. Sodium-ion (Na-ion) batteries, as a potential alternative to Li-ion batteries, have emerged as a highly promising contender. However, for these batteries to become industrially viable, certain properties such as energy and power density need to be enhanced. To address this issue, batteries have been a focus of research, and battery thermal management systems have been developed. These systems aim to evaluate battery performance, which is strongly influenced by temperature. Effective thermal management ensures that the battery operates within the optimal temperature range. This study models a pouch-type battery and evaluates the effects of phase changing materials (PCM) on battery temperature control. Comparative analysis is conducted using different PCMs to understand their impact. The study analyses the battery's response to specific temperature ranges and assesses how different PCMs affect battery cooling performance. The results provide critical insights for ensuring efficient battery operation. Furthermore, this research supports the broader adoption of Na-ion batteries in industrial applications and contributes to the development of sustainable energy storage systems.

Analysis of the Effect of Motor Waste Heat Recovery on the Temperature and Driving Range of Electric Heavy Truck Batteries

In some scenarios, electric heavy-duty trucks with battery swapping mode (ETBSm) are more cost-effective than battery charging mode. The viability of battery swapping stations is contingent upon the operational requirements and range capabilities of the ETBSm. Low temperatures have the effect of reducing the range of the ETBSm, thereby creating difficulties for battery swapping. This article proposes the use of motor waste heat recovery (MWHR) to heat batteries, which would improve range. A number of subsystem models have been established, including the ETBSm, battery, motor, and thermal management system (TMS). The calibration of battery temperature and motor efficiency is achieved with a model error of less than 5%. Comparison of performance, such as temperature, energy consumption, and range, when using only positive temperature coefficient (PTC) heating and when using both PTC heating and motor waste heat. The results indicate a 15% increase in the rate of rise in battery temperature and a 10.64 kW·h reduction in energy consumption under Chinese heavy-duty vehicle commercial vehicle test cycle (CHTC) conditions. Then, the motor waste heat percentage, energy consumption, and range are analyzed at different ambient temperatures. At an ambient temperature of −20 °C, −10 °C, and 0 °C, the percentage of the motor waste heat is 32.1%, 35%, and 40.5%; when 75% of the state of charge (SOC) is consumed, the range is improved by 6.55%, 4.37%, and 4.49%. Additionally, the effect of the PTC heater on temperature characteristics and power consumption is investigated by changing the target temperature of the coolant at the battery inlet. In accordance with the stipulated conditions of an ambient temperature of −20 °C and a target coolant temperature of 40 °C at the battery inlet, the simulation results indicated a battery temperature rise rate of 0.85 °C/min, accompanied by a PTC power consumption of 15.6 kW·h. This study demonstrates that as the ambient temperature increases, the utilization of motor waste heat becomes more effective in reducing PTC heating power consumption. At the lowest ambient temperature tested, the greatest improvement in driving range is observed. It is important to note that while an increase in the target heating temperature of the PTC helps to raise the battery temperature more rapidly, this is accompanied by a higher energy consumption. This article provides a reference for the ETBSm with MWHR.

Paraffin/graphite/boron nitride composite as a novel phase change material for rapid heat absorption in battery thermal management technology

Phase change material (PCM) cooling is widely employed in the thermal management of compact battery packs. Thermal protection triggered by rapid temperature rise shortens the effective discharge time in high-rate discharge, making it essential to prepare PCM that rapidly absorbs heat at high temperatures. However, research on the thermal absorption performance and insulating properties of PCM at high-rate discharge remains limited. Herein, we innovatively propose a thermally conductive and insulating PCM with a wide phase transition temperature to address the overheating issue in batteries at 7 C, 8 C and 9 C discharge rates. The C236 consists of paraffin (PA236), graphite and boron nitride with a ratio of 6: 2: 2, which achieves outstanding thermal performance with a latent heat of up to 138.0 J/g and a high-volume resistivity of 1.6 × 108 Ω·m. It is found that at a discharge rate of 7 C, the C236 with a thickness of 3 mm effectively maintains the temperature below 80 °C. At discharge rates of 8 C and 9 C, the PCM significantly reduces the battery temperature by 26.9 % and 32.5 %, respectively, highlighting the rapid heat absorption capability at high temperatures. Furthermore, after four cycles at a 9 C discharge rate, the temperature differential of the battery remains below 5 °C. The low-cost PCM provides a new experimental reference for battery thermal management in high-rate discharge, with broad application prospects.

Integrated battery thermal management and CO2 control in electric buses using recirculated air and waste heat recovery

In recent years, electric vehicles (EVs) have significantly progressed due to their environmental protection and energy-saving advantages. However, maintaining indoor thermal comfort and effective battery thermal management are critical considerations for EVs. Unlike conventional vehicles, EVs cannot use waste heat and depend on heat pump systems for winter heating and summer cooling. This research proposes a novel system to reduce the energy consumption of heat pump systems by using recirculated air while addressing the challenges of battery temperature management and maintaining safe CO2 concentrations in the cabin. The primary objectives of this research are threefold: first, to ensure passenger comfort through an integrated approach that includes cabin CO2 concentration safety; second, to recover heat from recirculated cabin air to save energy and control battery temperature; and third, to comprehensively analyze the performance of the Electric Vehicle Thermal Management System (EVTMS) using precise Computational Fluid Dynamics (CFD) analysis and mathematical modeling. This study will significantly enhance the understanding of energy consumption related to CO2 concentration control; an aspect previously overlooked in the literature. It will develop a battery temperature management strategy that leverages bus exhaust heat. These contributions will aid in achieving both passenger comfort and battery stability in electric buses, providing practical insights for the design and optimization of thermal management systems for future electric buses.

Bio-based eutectic composite phase change materials with enhanced thermal conductivity and excellent shape stabilization for battery thermal management

Organic phase change materials (PCMs) are commonly used for battery thermal management. Organic petroleum-based PCMs such as paraffin have an adverse effect on environment. Fatty acids as environmentally friendly bio-based PCMs, have enormous potential in sustainable development strategies. Nevertheless, the application of fatty acids in battery thermal management (BTMS) is restricted by the leakage of liquid PCM, poor thermal performance and the improper phase change temperature. We proposed a novel bio-based eutectic composite phase change materials (CPCM) with enhanced thermal conductivity and excellent shape-stabilization, composed of ethylene-vinyl acetate (EVA), Aluminum nitride (AlN), and the eutectic PCM of Lauric acid (LA) and Stearic acid (SA). Significantly, the screened eutectic PCM of LA-SA possesses a suitable phase change temperature(37.03 °C) corresponding to the battery operating temperature, and long-term thermal cycling stability of up to 100 cycles. And the cross-linked structure of EVA effectively encapsulated the eutectic PCM, whose mass only lost below 4% after being heated at 80 °C for 24 h. Simultaneously, the introduction of AlN can greatly improve their heat transfer ability and mechanical properties. LA-SA/EVA/AlN composites with 5 wt% AlN has adequate latent heat of 107.94 J.g−1 and high thermal conductivity of 0.726 W.(m·K)−1 as well as with low flexural strength of 1.72 MPa. Additionally, the proposed CPCM with 5 wt% AlN achieved the maximum temperature of battery below 45 °C during 4C discharging test. It suggested that the CPCM incorporated with AlN is capable effective cooling battery and dissipate energy inside PCM rapidly.