Thermal Behavior Of Battery Research Articles

Algorithm-driven optimization of lithium-ion battery thermal modeling

Detailed modeling of battery thermal behaviour has high computational demand due to the presence of multi-scale and multi-physics phenomena. For battery module/pack level simulation, a simple and accurate battery heat generation estimation is urgently required. This paper investigates the optimization of thermal numerical modeling for cylindrical 21,700 lithium-ion batteries with a nominal capacity of 5 Ah. A 3-dimensional battery model was built in the Multiphysics simulation software, COMSOL. The heat source of the model adopts a commonly used heat generation model incorporating irreversible and reversible heat. Correction factors, as a function of the state of charge, were introduced to the calculation of irreversible heat item. The Particle Swarm Optimization (PSO) algorithm, written in MATLAB, was coupled with the COMSOL numerical model to minimize the prediction error by varying correction factors. Battery surface temperature data under the continuous discharge tests (0.5C–3.5C) and dynamic loads were experimentally obtained and used to validate the model. The simulation results of the unoptimized model showed a discrepancy of up to 5 °C with the experimental data. After optimization, the prediction error was reduced to less than 0.5 °C on average. The optimized model was applied to predict the thermal behaviour of a battery module (16 aged cells) using oil-based immersion cooling. The pristine battery module with coolant flow velocities of 0.01 m/s was chosen as the baseline. The results indicate that the aged battery modules with internal resistance of 50 mΩ and 75 mΩ require coolant flow velocities of 0.05 m/s and 0.12 m/s, respectively, to achieve the baseline temperature. The study highlights a high-precision and low-computational cost approach for heat generation calculation of lithium-ion batteries is provided, which contributes to the development of battery thermal management systems.

Thermal circuit model of prismatic lithium cell considering dynamic non-uniform characteristics during charging-discharging in energy storage

Current analytical and simulation models for lithium battery thermal behaviour encounter efficiency or accuracy challenges in energy storage applications. In this paper, an analytical thermal analysis approach for prismatic lithium cells considering dynamic non-uniform characteristics is proposed to calculate the dynamic temperature distribution. A thermal circuit model and analytical differential equation group were formulated to capture the dynamic non-uniform thermal characteristics during charging/discharging. A solving algorithm was provided to balance accuracy and efficiency by establishing an iterative mechanism over the modelling parameters. The proposed model and algorithm were applied to three commercial lithium cells. The experiments under various charge-discharge conditions verified the millisecond-level computational cost and less than 3 % calculation accuracy. The parameter calibration, iteration mechanism, and reproducible codes enhanced the engineering applicability. The performance of the approach suggested its excellent potential in battery thermal management for energy storage.

Simulation study on the interaction between the battery module and busbar under typical driving conditions of electric vehicles

Accurate simulation of the battery thermoelectric coupling characteristics is the key to the thermal design and thermal management. As the electrically connected component between the battery electrodes, the heat production and heat transfer of the busbars have a significant impact on the battery thermoelectric behavior. A small number of battery thermal management studies have begun to model busbars recently, but the thickness, length, and material of the busbars are diverse. There is a lack of perfect simulation analysis and optimization methods. There is an urgent need to conduct in-depth research on the influence of characteristic parameters of the busbars on the battery thermoelectric behavior. Based on the MSMD-NTGK model, this paper investigates the influence of the thickness, length, and material of the busbars on the battery thermoelectric behavior without considering the economic cost and differences in the battery structure design. In this paper, a simulation analysis method and an ideal improvement solution for the design of the busbars are proposed. This paper compares the changes in the battery thermal behavior before and after the improvement of the busbars under the constant-rate discharge process, FTP75 condition, NEDC condition and WLTC condition respectively. This study can improve the realism of the battery thermal behavior simulation, provide a reliable simulation analysis method and reference basis for the industrial design and optimization of the busbars, and further improve the reliability of the subsequent battery thermal management simulation.

Analysis of nanofluid flow and heat transfer behavior of Li-ion battery modules

The operating battery temperature significantly affects electric vehicle performance, reliability, and safety. Therefore, batteries need to keep within the operating temperature design. The 3D Eulerian model is applied to determine battery thermal behavior with five different flow directions of coolant throughout the battery pack jacket. The computational domain consists of sixty cylindrical Li-ion cells inserted into the cooling module socket with constant power input conditions. The predicted results are consistent with the experimental results, with an average error of 1.28%. Coolant-improved flow direction and thermophysical properties significantly affect the decreasing maximum operating temperature and temperature gradient across a cell. The highest temperatures of the battery module are 30.06 °C, 30.00 °C, 29.91 °C, 29.89 °C, and 29.49 °C for models II, IV, III, I, and V, respectively. In addition, for the maximum temperature gradient across a cell, models I, II, and III yield the highest value [0.42 °C], followed by models IV [0.40 °C] and model V [0.15 °C], respectively. The proposed battery nanofluid cooling pack can therefore optimize the thermal management system of the EV pack.

Modeling and optimization of micro heat pipe cooling battery thermal management system via deep learning and multi-objective genetic algorithms

Battery thermal management and electrochemical performance are critical for efficient and safe operation of battery pack. In this research, a multi-physics model considering the battery aging effect is developed for micro heat pipe battery thermal management system (MHP-BTMS). A novel multi-variables global optimization framework combining multi-physics modeling, deep learning and multi-objective optimization algorithms is established for optimizing the structural parameters of MHP-BTMS to improve battery thermal management and electrochemical performance simultaneously. It is found that MHP-BTMS fails to control the temperature of aged battery pack due to the higher heat generation caused by solid electrolyte interphase formation. After 1000 cycles, the maximum temperature and maximum temperature difference were increased by 3.32 K, 2.49 K, 2.04 K and 1.78 K, 1.46 K, 1.26 K, respectively. It is also found that the battery electrochemical performance during the cycling is highly related to battery thermal behaviors. MHP-BTMS with 0.004/s inlet velocity achieved the best performance in preventing SEI formation and battery aging effect, which was lower by 7.01 nm (SEI) and 1.65% (aging), 2.31 nm and 0.58% as compared to 0.002 and 0.003 m/s cases. Besides, MHP-BTMS with optimized inlet velocity, MHP arrangement and cold plate can improve cooling performance and electrochemical performance. Multi-variables global optimization can provide the optimal structure parameters of MHP-BTMS under the different combinations of weighted coefficients and optimization strategies to achieve the trade-off between battery thermal issues and electrochemical performance. In addition, it is demonstrated that the weighted coefficients and optimization strategies in this novel framework can be changed according to the actual needs in engineering applications.

Co-regulation of integrated thermal management based on refrigerant cooling for electric vehicles

As an essential part of an electric vehicle thermal management system, collaborative regulation of power battery thermal management and AC temperature is imperative to maximize the vehicle's driving range and energy utilization effectiveness. This work proposes a joint control pattern for the compressor (Com) and variable opening valves (VOVs) with a dual-evaporator system. Specifically, the compressor rotation speed is the dominant control variable, and the battery's thermal behavior and system COP are investigated. The analytic results demonstrate that when the system is under moderate and high thermal loads, the Com-VOVB-VOVC strategy can regulate the temperature of both the battery and AC temperature. Meanwhile, the goal of low energy consumption is optimized. Nevertheless, when the thermal load is low, the Com-VOVB strategy can attain steady temperature regulation for the dual-evaporator and decrease the complexity of the system control. In addition, a Map approach to the temperature regulation is presented, enhancing the effectiveness and dynamism of thermal management control. The common control method for an integrated thermal management system can effectively confront system thermal load changes and decrease energy consumption.

Battery Thermal Management System for Electric Vehicles

Electrical vehicles (EVs) as a result of their rapid evolution and growing popularity, zero-emission, and high tank-to-wheelefficiency. Though, some features, particularly those relating to battery performance, cost, lifetime, and protection, restrict the development of the electrical car. In order to operate at peak efficiency under various circumstances, battery management is therefore required. The BTMS is essential for controlling the thermal performance of the battery. The BTMS technologies include heating, air conditioning, liquid cooling, direct refrigerant cooling, phase change material (PCM) cooling, and thermoelectric cooling. Performance, weight, size, cost, dependability, safety, and energy consumption are trade-offs analyzed for these systems. According to the analysis the system is made up of two coolant loops, one refrigeration loop, and one cabin HVAC loop. The batteries, drivetrain, and cabin all contribute to the thermal burden. The model of these system is been built in the software MATLAB/SIMULINK. Based on the outcomes of the simulation, BTMS is crucial for regulating battery thermal behavior. Through the integration of the simulation model with battery thermal and ML models, next research might be more thorough and precise.

Computational Investigation on Phase Change Material based Hybrid Battery Thermal Management System for Electric Vehicle

The future of the automobile industry is moving towards the electrification of vehicles due to the increase in pollution and global warming. The thermal management of the battery is necessary to enhance the performance of the battery module at extreme operating conditions. Battery thermal management systems using phase change material can be used to control the temperature extremity and to achieve uniformity in temperature inside battery module. In this work, the conventional battery layout system is modified to induce the passive cooling with the use of PCM (paraffin wax) which has been placed around the periphery of each cell in the battery pack. Different thickness of PCM layer 2mm, 3mm and 4mm have been taken. This reduces the weight and cost compared to the existing PCM based BTMS. This also ensures improvise in the efficiency of the battery. The battery thermal behaviour is studied using ANSYS for various discharge rate condition (4C, 3C, 2C and 1C) with air assisted cooling system having an air flow rate of 4m/s. From the analysis, it is found that maximum temperature of a battery pack at 4C discharge rate without any BTMS is, about 60C. Elevated temperature of the battery pack covered with a PCM, for same discharge rate with same air flow rate for 2mm is 33.5C and for 3mm it is 34.4C and for 4mm it is 34.07C which falls under the optimal temperature. While adding expanded graphite to the PCM it is found that the temperature reduces to 30C for 2mm thickness PCM.

Investigation of battery thermal management system with considering effect of battery aging and nanofluids

In this research, a novel model considering electrochemistry, battery aging and heat transfer is developed for the design and optimization of battery thermal management system (BTMS) to ensure efficient and durable operation of batteries. The multiphysics behaviors in different working cycles of BTMSs are analyzed and compared. It is found that solid electrolyte interphase (SEI) formation inside the aged battery pack leads to the higher heat generation rate, which is the main reason that BTMSs only provide effective cooling performance in the initial working cycles but fail to control the battery temperature after 1000 cycles. Meanwhile, BTMS with water provides the lowest maximum temperature and temperature difference with the lowest pressure loss, which were 5.14 K, 4.33 K, 3.79 K and 3.94 K, 3.51 K, 3.2 K and 2772.7 Pa, 3980.9 Pa, 5271.8 Pa lower than those of BTMS with EO, were 2.17 K, 1.66 K, 1.37 K and 1.79 K, 1.43 K, 1.19 K and 544.4 Pa, 758.1 Pa, 984.3 Pa lower than those of BTMS with EG. In addition, BTMS with water also showed the best performance in controlling SEI formation and capacity fade, leading the highest average potential. Furthermore, dispersing nanoparticles into BTMSs can further enhance the cooling performance with a higher pressure loss, and BTMS with water-based nanofluid achieves the best performance. Besides, the cooling performance of BTMS increases with increasing volume fraction of nanoparticles, although the pressure loss is also higher. Nanoparticle shapes also have a significant effect on battery thermal behaviors and electrochemical performance. With brick-shaped nanoparticles, BTMS well cools the battery pack and reduces the battery capacity fade. For comparison, BTMS with spherical-shaped nanoparticles achieves the lowest pressure loss with providing favorable thermal manage for battery pack.

Thermal behavior of lithium-ion battery under variation of convective heat transfer coefficients, surrounding temperatures, and charging currents

In the transient state of the real-world environment, shedding light on the thermal behavior of Lithium-ion battery (LIB) has become crucial for strengthening the battery's safety. However, previous studies have not taken into account the concurrent dynamics of LIB parameters. This study employs an electrochemical thermal model to examine the thermal behavior of LIB when simultaneously varying the charging currents, surrounding temperatures, and heat transfer coefficients. The accuracy of the developed model is validated using experimental data of 18,650 LiCoO2/graphite LIB charged at 1C, 2C and 3C, as well as surrounding temperatures of 35, 50 and 60 °C, and convective heat transfer coefficients of 0, 4, 8, 12, 25, and 50 W/m2. K. The results show that the generated model and the experimental results agree well. The thermal behavior of the predicted model depicted three phases caused by battery warm-up, exothermic reaction and excessive build-up of heat from the two earlier stages. The simulation and experimental results had the highest relative error of less than 2.0% and the lowest root mean square error of 0.58 °C. As charging currents and surrounding temperatures rise concurrently, the battery surface temperature rises abruptly beyond safe limits. It is crucial to analyze how much convection would be necessary to maintain the battery's performance and overall health. Additionally, engineering/BMS/failure mitigation systems may benefit from understanding the curves' characteristics and the influence of convection, surrounding temperature and charging rates might, as by the time to peak temperature is reached the cell is already failed.

An electrochemical-thermal coupling model for prismatic lithium-ion batteries over wide temperature range

Accurate and rapid prediction of battery thermal behavior can provide accurate basis for making battery use plan, which is beneficial for alleviating the influence of thermal effect on battery life and safety. A fast numerical solution method of electrochemical-thermal coupling model for prismatic lithium-ion batteries is proposed in this work. Firstly, based on an existing electrochemical single particle model and one-dimensional thermal resistance network, analytical expression of thermal resistance network model in two-dimensional space is obtained by theoretical formula derivation. Then, to calibrate the model parameters at different temperatures, temperature range is divided into several intervals, and optimal parameter sets at different intervals are obtained by excitation response analysis method and multi-group particle swarm optimization algorithm. Finally, to realize the accurate numerical simulation of the model, a double adaptive extended Kalman filter method is applied to further optimize the parameters in real time. Seen from the simulation results over wide temperature range, mean absolute errors of voltage and temperature are less than 30 mV and 0.12 K at −5–55 °C respectively, which indicates the capability of the developed model for high-precision numerical simulation over temperature range for prismatic lithium-ion batteries.

(Digital Presentation) The Effects of Charging Rates on the Thermal Behavior of Lithium-Sulfur Batteries Via Comsol Simulation

Li-S battery, with Li metal as anode and sulfur as cathode, exhibits a theoretical capacity of 1667 mAh/g and outperforms Lithium-ion batteries (LIBs) in energy density, and promises great utilization in battery-based aircraft. Given its important role in the SEI interphase and safety concerns, the temperature distribution within cells is of great interest to the community these days. In this study, with thermal effects during charging/discharging process considered, a 3D homogeneous electrochemical model was introduced. What’s more, the effects of increasing charging rates from 1 C charging to 6 C charging of typical Li-S single layer pouch cells were probed via COMSOL simulation. It is found that the higher the charging rates, the higher the temperature within Li-S pouch cells. Meanwhile, for a given charging rate, the middle part of the electrodes exhibits the highest temperature increase due to its worst heat transfer in the core regions. All simulation results agree well with published experimental result, this 3D model can be further utilized by designers to assess temperature distribution within Li-S batteries and exaccerlate design for battery packages.

Enhanced optimization algorithm for the structural design of an air‐cooled battery pack considering battery lifespan and consistency

Electric scooters are increasingly popular for short-distance commuting. To improve the thermal safety, performance, and lifespan of their batteries, their heat needs to be managed. This study proposes a method for optimizing the air channels in a scooter battery pack. It includes an electro-thermal-degradation model for predicting the battery's electrical and thermal behaviors and capacity loss, a heat transfer model for predicting convective heat exchange between the battery and the air, and a genetic algorithm for structural optimization of an air-cooled battery thermal management system (BTMS). Unlike conventional optimization of a BTMS, the proposed algorithm aims to improve the electrical consistency, lifespan, and thermal safety of the battery via rapid global optimization of its air ducts. The optimization algorithm was tested on a 3P4S air-cooled battery pack from an electric scooter. It improved the pack's consistency of state of charge (SOC) and its lifespan by reducing its heat and temperature gradient. Under on-design conditions, the optimized air ducts reduced the maximum pack temperature by 0.45°C and the difference between the average temperatures of the cells in a branch to 15.9% that of the original pack. Moreover, the optimized air ducts decrease the SOC difference by 81.1% and improved the state of health by 0.03%. Hence, the proposed air duct optimization method can improve the pack's thermal performance, SOC distribution, and lifespan under off-design conditions.

A Comprehensive Study on Influence of Battery Thermal Behavior on Degradation and Consistency

An electro-thermal coupling model is established for a parallel-connected pack with cooling on the two ends of the stack of cells. Unlike the traditional battery model, this model can calculate the branch current in the pack and precisely describe the pack performance. Experiments are implemented and it is proved that the proposed model has a high prediction accuracy. Then the proposed model is applied to study the mechanism on how the pack temperature and its temperature difference affect its electrical consistency and lifespan. According to the results, the state of charge (SOC) homogeneity and state of health (SOH) of pack are related with the temperature variation of the pack and its temperature, respectively. Enhancing the cooling can reduce the pack temperature and extend the pack lifespan. The pack SOH fall reduces to 0.33%, when the air temperature and the battery initial falls to 10 °C and the heat transfer coefficient between the battery and the air increases to 220 W/( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{K}\cdot \text{m}^{2}$ </tex-math></inline-formula> ). However, this method produces a large temperature gradient, enlarges the branch current difference, and aggravates SOC inhomogeneity in the pack. When the heat-transfer coefficient increases from 5 to 220 W/( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{K}\cdot \text{m}^{2}$ </tex-math></inline-formula> ), the temperature variation inside the pack rises by 737.8%, making SOC difference go up by 892%. Moreover, the most suitable temperature difference for the SOC homogeneity is not traditional 5 °C, but rises with ambient temperature and temperature of cooling medium. It is the same for that of SOH. The research results also indicate that the control of pack temperature and that of temperature different are contradictory and they should be well balanced to achieve a good SOC homogeneity and long pack lifespan.

Hybrid data-based modeling for the prediction and diagnostics of Li-ion battery thermal behaviors

Lithium-ion battery (LIB) has been deployed for the electrification of the transport sector as a key strategy for climate change mitigation and adaptation. However, it has significant technical challenges such as thermal runaway, requiring a good understanding and accurate prediction of the LIB thermal behavior (heat generation rate). In this study, a novel hybrid approach using an Artificial Neural Network (ANN) is developed for predicting the heat generation rate with discharge current, output voltage, ambient temperature, cell surface temperature, and Depth of Discharge (DOD) as the feature vectors (inputs); where the DOD is estimated with an Extended Kalman Filter (EKF), and direct Coulomb Counting (CC) method, respectively. A shallow neural network utilizing the Marquette-Levenberg algorithm is designed and calibrated using over 8000 cases of the testing data. It is shown that the predicted heat generation rate of LIB agrees well with the experimental results with an accuracy of R > 0.995. Further potential of this hybrid data-based model is evaluated by simulating a thermal management system control and by introducing voltage and current sensor faults for diagnostic purposes. It is shown that, when compared to the experimental value, the relative error of the total heat output generated is less than 2% when there is no sensor fault, and greater than 50% and 25%, respectively, with an induced failure of the current and voltage sensor, demonstrating the ability to build accurate models relying solely on LIB discharge data for sensor diagnostics. This study highlights the combination of using battery thermal behavior with machine learning for real time battery system monitoring, controls and field diagnostics.

Experimental Analysis of Thermal Behavior of a Lithium-Ion Battery using Constant Voltage under Different Cooling Conditions

Experimental Analysis of Thermal Behavior of a Lithium-Ion Battery using Constant Voltage under Different Cooling Conditions

Numerical study of PCM thermal behavior of a novel PCM-heat pipe combined system for Li-ion battery thermal management

A three-dimensional numerical model is developed and validated against experimental data for a cylindrical lithium-ion battery module with a compact hybrid cooling system of phase change material (PCM) and heat pipes. This cooling system is particularly well-suited for small or medium-sized battery module or pack. The PCM process and its effect on battery thermal behavior are numerically investigated for cases based on different C-rate discharge processes (0.5C, 1C and 2C), different PCM properties (different mass fractions of expandable graphite) and different charge–discharge cycles of the battery module. The results indicate that the PCM liquid fraction distribution is not uniform during the discharge process, the PCM starts to melt from its outermost neighboring the batteries and its upper part gets more liquid due to natural advection of liquid PCM. The different melting rates within the PCM lead to slightly enlarged temperature difference between batteries. Increasing discharge C-rate further accentuates this temperature difference. A tested composite PCM of 12 wt% EG (expanded graphite) shows better heat dissipation performance than that of pure PCM, and consequently it is better suited for battery thermal management systems. The simulations for different charge–discharge cycles indicate that lowering C-rate and increasing recovery time can improve thermal performance of the battery module and thermal control is thus more effective.

Role of natural convection and battery arrangement for phase change material based battery thermal management unit

Phase change material (PCM) based battery thermal management (BTM) has been of increasing interest due to the passive concept with efficient cooling and temperature uniformity performance. Due to the inherent characteristics of solid-liquid phase change during operation, it is worth in-depth investigation of the influence of buoyancy driven natural convection (NC) on battery thermal behavior. Here, a typical PCM enclosed cylindrical battery unit is considered as the benchmark problem. The effects of battery arrangement and liquid PCM NC on melting process and thermal performance are investigated numerically based on rigorously validated model. It shows that excellent cooling performance could be achieved for battery with the function of latent heat storage of PCM, whether the unit is arranged vertically or horizontally. In addition, the horizontal arrangement (4.1 K) has a better temperature uniformity than the vertical arrangement (5.7 K), which can be ascribed to the varying melting rate of PCM. To further insight into the thermal behavior of BTM unit, the relationship between the melting rate and interface length associated with differential liquid fraction curve is proposed for the first time. When the liquid PCM is full of the upper half of the BTM unit, the length reaches its maximum value and after that the differential liquid fraction is significantly declined, indicating the poor heat transfer and thermal performance. The motivation is to not only explore the fundamental mechanism of passive phase change method, but also to serve as a basis for the design of more practical and efficient PCM based BTM in the future.

Investigation of Thermal Behavior of Cylindrical Lithium-Ion Batteries for Electric Vehicle

With growing concerns over climate change due to automotive emissions, fossil fuel depletion, and the increasing price of crude oil, electric vehicles have gained more interest as a mode of transportation. Various electric vehicles have been developed in recent years, including pure electric vehicles (EVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). One of the keen focus area in the ongoing development of EVs is their energy storage systems. Mostly lithium-ion batteries are being used as an energy storage system in EVs. Various complicated reaction occurs during charging/discharging of batteries and the thermal behavior of the batteries are coupled to these reactions. Electrochemical reactions affect the heat generation rate, and higher temperatures further increase the speed of the electrochemical reactions. Adverse operating temperatures can impact battery performance, degradation, and safety. Hence it is become very important to investigate the thermal behavior of the lithium-ion batteries. Thermal investigation of cylindrical lithium-ion batteries of different chemistry and shape factor is conducted for different charging/discharging rate and atmospheric conditions using numerical technique. The numerical technique includes the determination of surface temperature of battery by solving the energy balance equation using suitable numerical method. The accuracy of numerical techniques is validated using the experimental techniques. Thermal investigation conducted in this research can help in deciding the operating strategies of battery management system and further in designing the proper thermal management system.

Prediction of battery thermal behaviour in the presence of a constructal theory-based heat pipe (CBHP): A multiphysics model and pattern-based machine learning approach

This study investigates the thermal conductivity of a constructal theory-based heat pipe and presents the predction of a lithium-ion battery's thermal behaviour during charge and discharge by combining a special form of machine learning with a multiphysics numerical simulation. A series of multiple physical processes such as boiling, evaporation, and condensation were assumed to find the variable thermal conductivity of heat pipes. We used a combination of physics-informed machine learning and visual tracking method (pattern-based) to find the pattern of each feature, including temperature, for the first time. The findings reveal that a heat pipe design based on constructal theory can reduce the average and maximum temperatures of the battery by up to 13.43% and 27%, respectively, during the charge/discharge cycle. An approach based on constructal theory to the geometry of the heat pipe could reduce length (by up to 12%) without compromising efficiency. Additionally, by employing pattern-based machine learning (PBML), training time and transfer data were reduced significantly. Also, thermal conductivity could be predicted for heat pipes during charge/discharge cycles. The results of this study provide insight into adaptable thermal management systems for developing a new generation of compact battery packs