Lithium-ion Battery Pack Research Articles

An Early Micro Internal Short Circuit Fault Diagnosis Method Based on Accumulated Correlation Coefficient for Lithium-Ion Battery Pack

Early micro internal short circuit (ISC) fault diagnosis is crucial for the safe and reliable operation of lithium-ion batteries. In order to solve the problem that the early micro ISC fault is difficult to identify due to its weak fault characteristics, this paper proposes a fault diagnosis method based on the accumulated correlation coefficient. Specifically, the method uses the accumulated voltage value within the time window as the input feature, constructs an adjustment factor based on the distance difference of the accumulated voltage value to amplify the difference between the fault voltage correlation coefficient and the normal voltage correlation coefficient, and finally achieves the purpose of highlighting the faulty cell. The effectiveness and diagnostic capability of the proposed method are verified in experiments of short circuit faults of different severity. The results show that the proposed method can effectively identify and locate early micro ISC faults within 200 s, and improve the diagnostic capability up to 0.02 C short-circuit severity. In addition, a multi-level diagnostic warning mechanism can be established according to the decrease of the fault voltage correlation coefficient, so as to measure the severity of the fault and track the fault evolution process.

Numerical investigations on liquid cooling plate partially filled with porous medium for thermal management of lithium-ion battery pack

In order to address the thermal management of lithium-ion battery pack, a liquid cooling plate partially filled with porous medium is proposed and effects of filling ratio, filling position and segment number of porous medium on the maximum temperature and temperature difference of each cell in the pack and pressure drop of liquid cooling plate are investigated. Results suggest that the maximum temperature and temperature difference of each cell in the pack cooling by liquid cooling plate partially filled with porous medium are firstly decreased with the increase of filling ratio, reaching the minimum value at the filling ratio of 75 %, and finally increased with the filling ratio rising up to 100 %, but the pressure drop of the liquid cooling plate partially filled with porous medium is increased with the increase of filling ratio. However, positive effects of porous medium are weakened when the filling position is moving from microchannel downstream to microchannel upstream and segment number is increasing from one to three. This is because that the heat dissipation ability of liquid cooling plate at the microchannel downstream and upstream is affected by the filling ratio, filling position and segment number.

Exploring a preheating strategy for lithium-ion battery pack using graphene-enhanced microencapsulated phase change materials

Lithium-ion batteries (LIBs) are fundamental to the operation of electric vehicles due to their superior energy density and extended cycle life. However, the performance of LIBs deteriorates significantly in subzero temperatures. This study investigates the potential of graphene-enhanced microencapsulated phase change materials (G-MEPCM) as a solution to improve the preheating performance of LIBs in cold environments. Numerical analysis has been conducted to evaluate the thermal performance of the LIB/G-MEPCM system under varying conditions, including different graphene contents, ambient temperatures, heat transfer coefficients, and heating power levels. Results indicate that higher graphene content within MEPCM improves thermal uniformity and reduces internal temperature differences, with 4 wt% graphene content identified as optimal for achieving a balance between rapid heating and temperature uniformity. Besides, increasing the heat transfer coefficient decreases the average temperature and increases the temperature difference of the battery pack. On the contrary, increasing ambient temperature increases the average temperature and decreases the temperature difference of the battery pack. When ambient temperature equals to −25 °C, the temperature difference of battery pack is 21.4 % higher than that when ambient temperature equals to −10 °C. Furthermore, both the average temperature and temperature difference of the battery back increase with the increases of the heating power.

Lithium-Ion Battery Health Management and State of Charge (SOC) Estimation Using Adaptive Modelling Techniques

Effective health management and accurate state of charge (SOC) estimation are crucial for the safety and longevity of lithium-ion batteries (LIBs), particularly in electric vehicles. This paper presents a health management system (HMS) that continuously monitors a 4s2p LIB pack’s parameters—current, voltage, and temperature—to mitigate risks such as overcurrent and thermal runaway while ensuring balanced charge distribution between cells. An improved online battery model (IOBM) is developed to enhance SOC estimation accuracy. The system utilises forgetting factor recursive least squares (FFRLS) for real-time parameter updates, an adaptive nonlinear sliding mode observer (ANSMO) for SOC estimation, and a long short-term memory (LSTM) network to dynamically adjust capacity based on operating conditions. Validation using the urban dynamometer driving schedule (UDDS) test demonstrated high accuracy, with the proposed battery model achieving a root mean square error (RMSE) of 12.13 mV and the LSTM achieving an RMSE of 0.0118 Ah. Regular updates to the battery’s current capacity, along with the proposed IOBM, significantly improved SOC estimation performance, maintaining estimation errors within 1.08%.

Switched supercapacitor based active cell balancing in lithium-ion battery pack for low power EV applications

Switched supercapacitor based active cell balancing in lithium-ion battery pack for low power EV applications

Research on the heat dissipation performances of lithium-ion battery pack with liquid cooling system

Research on the heat dissipation performances of lithium-ion battery pack with liquid cooling system

A cell level design and analysis of lithium-ion battery packs

A cell level design and analysis of lithium-ion battery packs

Characteristics of Solidification Cracking for Ni-P Coated Cu-Al5052 Single-Mode Fiber Laser Welds

This study fundamentally examines the characteristics of solidification cracking in Cu-Al5052 single-mode fiber laser welding by utilizing uncoated and Ni-P coated Cu plates for the high durability busbar welding of pouch-type lithium-ion battery packs. Weld solidification cracking was confirmed regardless of Ni-P coating for both weld materials. However, there were differences in the regions of crack occurrence. Unlike the uncoated Cu-Al5052 welds where cracks occurred randomly within the fusion zone, for the Ni-P coated Cu-Al5052 welds, solidification cracks were concentrated near the faying surface of the upper and lower materials, particularly near the fusion line. The effect of the Ni and P components on the solidification cracking susceptibility, i.e., the welding solidification temperature range, was considered. Diffusion-controlled Scheil’s solidification calculations revealed that the mushy zone range enlarged with the use of a Ni coating layer. It was confirmed that an extremely wide mushy zone range of 1060 K was calculated at the solidification cracking region. Furthermore, P was also identified as an element that expands the solidification temperature range based on the Cu-P binary system. Consequently, during the laser welding of Cu-Al5052 dissimilar materials, the behavior of solidification cracking in the welds is influenced by the composition of the coating material and the incorporated composition within the weld fusion zone. Hence, it is necessary to select the optimal coating material and layer thickness considering weld solidification cracking susceptibility.

Early Internal Short Circuit Diagnosis for Lithium-Ion Battery Packs Based on Dynamic Time Warping of Incremental Capacity

Timely identification of early internal short circuit faults, commonly referred to as micro short circuits (MSCs), is essential yet poses significant challenges for the safe and reliable operation of lithium-ion battery (LIB) energy storage systems. This paper introduces an innovative diagnostic method for early internal short circuits in LIB packs, utilizing dynamic time warping (DTW) applied to incremental capacity (IC). Initially, the terminal voltages of all cells within the LIB pack are ordered at any moment to determine the median terminal voltage, which is then used to generate the median IC curve. This curve acts as a reference benchmark that represents the condition of healthy cells in the pack. Subsequently, the DTW algorithm is utilized to measure the similarity between each cell’s IC curve and the median IC curve. Cells exhibiting similarity scores that exceed a specified threshold are identified as having MSC faults. Lastly, for the cells diagnosed with MSC conditions, a method for estimating short-circuit resistance (SR) based on variations in maximum charging voltage is devised to quantitatively evaluate the severity and evolution of the MSC. Experimental findings reveal that the proposed method effectively identifies MSC cells in the LIB pack and estimates their SRs without the necessity of a battery model, thereby affirming the method’s validity.

Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques

One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics.

Optimization of Retired Lithium-Ion Battery Pack Reorganization and Recycling Using 3D Assessment Technology

Optimization of Retired Lithium-Ion Battery Pack Reorganization and Recycling Using 3D Assessment Technology

Thermal runaway evolution of a 4S4P lithium-ion battery pack induced by both overcharging and unilateral preheating

To clarify the thermal runaway characteristics of lithium-ion battery pack, this study has established a thermal runaway experimental platform based on actual power battery pack. A 4 in series and 4 in parallel battery pack was assembled using 86 Ah lithium iron phosphate batteries, and the experiment of thermal runaway induced by overcharging and unilateral preheating was carried out. The behavior and characteristics including the temperature change characteristics of each cell, the heat generated and transfer paths during thermal runaway propagation, the voltage changes of each serial module and the total voltage, flame evolution behavior, gas generation characteristics, debris, and mass loss were investigated. The research results show that module 1 was the first to experience thermal runaway due to preheating. The redistributed current caused the batteries in the remaining modules to rapidly generate heat. Subsequently, the heat transfer from module 1 triggered thermal runaway in modules 2, 3, and 4 in sequence. The entire flame combustion process lasted for 38 min, with the maximum temperature reaching 937.1 °C, resulting in thermal runaway in all batteries. The sequence of thermal runaway has been clarified, with the flame generated by the ignition of the battery casing, connecting tabs, and combustible gases emitted from the batteries serving as the primary paths for heat transfer and thermal radiation. The experimental results provide valuable insights into the thermal engineering issues of large-scale lithium-ion battery pack.

Optimization of thermal management performance of direct-cooled power battery based on backpropagation neural network and deep reinforcement learning

The batteries of new energy vehicles generate a large amount of heat when discharged at large multiplication rates, which can cause safety hazards if the heat is not eliminated in time. In this paper, a Swiss roll-type cooling belt is used to improve the thermal performance of lithium-ion battery packs. This paper selects six key parameters as optimization parameters, including the structure of Swiss roll-type cooling belt, coolant type, inlet coolant mass flow rate, inlet coolant gas volume fraction, battery discharge rate, and ambient temperature. Use neural network fitting and deep reinforcement learning to optimize these six parameters in order to achieve optimized cooling performance. The results show that after the cold plate has been structurally optimised and the inlet parameters have been optimised by deep reinforcement learning, the thermal performance has been significantly improved. Not only does the cold plate inlet require less mass flow, but the battery pack also performs better in terms of temperature difference.

Cooling lithium-ion batteries with silicon dioxide -water nanofluid: CFD analysis

Temperature is recognized to have a major impact on the safety, performance, and cycle life of lithium-ion batteries (LiBs). Since LiB cells are sensitive to temperature variations, even little variations can result in a reduction in performance or even cell failure. This work introduces a novel cooling system utilizing SiO2-Water Nanofluid and CFD analysis to enhance the thermal management of lithium-ion battery packs with varying silicon dioxide nanoparticle diameters. The results showed that SiO2 nanofluids with smaller nanoparticle diameters had higher average Nusselt numbers at all Reynolds numbers. This is because smaller nanoparticles have a larger surface area, which increases the collision rate of the nanoparticles with the fluid and thus enhances heat transfer. The increase in Nusselt number was found to be 2.8 %, 5.5 %, 11.6 %, and 22.6 % for nanoparticle sizes of 50, 40, 30, and 20 nm, respectively. The results also showed that for all particle sizes, the temperature of cell 4 equalled the inlet temperature at Re = 30,000. This is because cell 4 is located at the first column of the system and is oriented towards the entrance section, which results in a large temperature difference between the cell and the coolant. Cell 4, therefore, experiences a higher heat discharge to the coolant than the other cells. Overall, this study has shown that smaller nanoparticles and higher Reynolds numbers significantly improve the heat exchange capacity of LiB cells. This can lead to improved electrical properties and extended battery cell lifespan.

Degradation modeling of serial space lithium-ion battery pack based on online inconsistency representation parameters

Establishing an inconsistency-based degradation model for lithium-ion battery packs is crucial for suppressing the degradation of battery packs by optimizing the inconsistency. This paper proposes a method for modeling the degradation of serial space lithium-ion battery packs based on online inconsistency representation parameters. Firstly, the static inconsistency representation parameters are acquired online by quantifying the difference among voltage intervals of cells through Gaussian distribution, addressing the challenge of acquiring static inconsistency representation parameters online. Simultaneously, dynamic inconsistency representation parameters are serialized representations by quantifying the voltage differences of cells at multiple moments, better capturing the rapid evolutionary process of inconsistency. Secondly, a linear model is used to model the serialized parameters and degradation, simplifying the modeling process while achieving multi-source information fitting. Meanwhile, a nonlinear model is constructed to better fit the nonlinear degradation trend of the battery pack. Then, Kalman filter is utilized for model fusion to achieve complementary advantages and improve modeling accuracy. Cross-validation with laboratory battery pack data confirms that this method outperforms comparison approaches, achieving the mean absolute error and maximum error of less than 1.73 % and 3.31 %, respectively. The method proposed in this paper provides a basis for future work on battery pack life extension.

An Investigation into the Viability of Cell-Level Temperature Control in Lithium-Ion Battery Packs

Abstract This article focuses on the thermal management and temperature balancing of lithium-ion battery packs. As society transitions to relying more heavily on renewable energy, the need for energy storage rises considerably, as storage facilitates power regulation between these sources and the grid. Lithium-ion batteries are leading the market for energy storage options, but their properties are temperature sensitive, with thermal abuse resulting in shortened pack lifetime and possible safety issues. Current battery thermal management systems (BTMS) are implemented in a number of ways to ensure consistent and reliable operation. However, they are typically limited in architecture and restricted in ability to attend to temperature gradients. This work proposes a BTMS topology that permits control of the individual cooling received by a cell in a pack. First, an analysis is done using timescale separation to confirm that cell-level temperature control is capable of extending the lifetime of a pack as compared to pack-level control. The analysis is used to guide the gain tuning of a state feedback controller, which directs more cooling effort to cells of higher temperatures. Validation of the BTMS topology and control is performed through the simulation of a battery pack, with variations in total cooling power and resistance heterogeneity. The outcome of the validation studies indicates that the proposed BTMS configuration is better equipped to reduce temperature differences and extend pack life. This benefit increases as total input power increases, giving the controller more freedom to cool unhealthy cells while remaining within power constraints.

Detection of Impedance Inhomogeneity in Lithium-Ion Battery Packs Based on Local Outlier Factor

The inhomogeneity between cells is the main cause of failure and thermal runaway in Lithium-ion battery packs. Electrochemical Impedance Spectroscopy (EIS) is a non-destructive testing technique that can map the complex reaction processes inside the battery. It can detect and characterise battery anomalies and inconsistencies. This study proposes a method for detecting impedance inconsistencies in Lithium-ion batteries. The method involves conducting a battery EIS test and Distribution of Relaxation Times (DRT) analysis to extract characteristic frequency points in the full frequency band. These points are less affected by the State of Charge (SOC) and have a strong correlation with temperature, charge/discharge rate, and cycles. An anomaly detection characteristic impedance frequency of 136.2644 Hz was determined for a cell in a Lithium-ion battery pack. Single-frequency point impedance acquisition solves the problem of lengthy measurements and identification of anomalies throughout the frequency band. The experiment demonstrates a significant reduction in impedance measurement time, from 1.05 h to just 54 s. The LOF was used to identify anomalies in the EIS data at this characteristic frequency. The detection results were consistent with the actual conditions of the battery pack in the laboratory, which verifies the feasibility of this detection method. The LOF algorithm was chosen due to its superior performance in terms of FAR (False Alarm Rate), MAR (Missing Alarm Rate), and its fast anomaly identification time of only 0.1518 ms. The method does not involve complex mathematical models or parameter identification. This helps to achieve efficient anomaly identification and timely warning of single cells in the battery pack.

In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning

Battery management systems (BMSs) play a vital role in understanding battery performance under extreme conditions such as high C-rate testing, where rapid charge or discharge is applied to batteries. This study presents a novel BMS tailored for continuous monitoring, transmission, and storage of essential parameters such as voltage, current, and temperature in an NCA 18650 4S lithium-ion battery (LIB) pack during high C-rate testing. By incorporating deep learning, our BMS monitors external battery parameters and predicts LIB’s health in terms of discharge capacity. Two experiments were conducted: a static experiment to validate the functionality of BMS, and an in operando experiment on an electrically propelled vehicle to assess real-world performance under high C-rate abuse testing with vibration. It was found that the external surface temperatures peaked at 55 °C during in operando flight, which was higher than that during static testing. During testing, the deep learning capacity estimation algorithm detected a mean capacity deviation of 0.04 Ah, showing an accurate state of health (SOH) by predicting the capacity of the battery. Our BMS demonstrated effective data collection and predictive capabilities, mirroring real-world conditions during abuse testing.

Impact of Nickel Strip Configurations on Resistance and Voltage Drop in Lithium Ion Battery Packs

The impact of nickel strip designs on the resistance and voltage drop in lithium ion battery packs is examined in this study. In a series parallel battery pack configuration, the effectiveness of coated and pure nickel strips is assessed, with particular attention paid to how they influence voltage drop, internal resistance, and overall efficiency. Each of the 24 series and 3 parallel cells that make up the battery pack has an internal resistance of 6 mΩ. Two configurations are analyzed: one utilizing pure nickel strips and another with coated nickel strips. The resistivity, cross sectional area, and length of the material are used to compute the equivalent resistance of the nickel strips for each arrangement. Voltage dips at a load current of 50A are determined to compare the performance of both strip. The study also looks at the voltage drop at key locations in the battery pack, including particular bent strips. The findings show that the coated nickel design displays a larger resistance (0.237Ω) and voltage drop (11.735V) than the pure nickel configuration, which has a lower total resistance (0.048Ω) and voltage drop (2.82V). Evaluation of the voltage drop during charging is also done for charging currents of 6A and 10A, demonstrating that the pure nickel arrangement allows for more efficient charging. One of the main elements affecting battery pack performance is internal resistance, which has a direct impact on the system's voltage drop and overall energy efficiency. The thickness, width, resistivity, and number of parallel strips utilized in this nickel strip material all have a major effect on the battery pack's total resistance. Because of this, the nickel strip design can improve or worsen the pack's power delivery, particularly in high load scenarios.

Implementation strategy for launch and performance improvement of high throughput manufacturing inspection systems

Product technologies are changing rapidly in advanced automotive propulsion systems. These products are driving the need for new manufacturing processes and new inspection methods. To keep new propulsion systems affordable and ensure these new products are introduced with high quality, automotive manufacturers are seeking automated inspection solutions with low cost and near-zero error rates to inspect 100% of the items. In this paper, a progressive deployment strategy of a hybrid inspection system is presented and studied in the context of technology development and rapid deployment. It enabled us to begin with human inspection and gradually phase-in automated inspection technology, while almost never failing to identify a bad item. This strategy was applied successfully to inspect ultrasonic welds in lithium ion battery packs. At the time of this study, a 75% reduction in human inspection was achieved with prospects for further reduction. Actual results from the implementation of this strategy in production are presented. Recommendations are made regarding the most appropriate time to employ this strategy and how it could increase the use of advanced automated in-line inspection technologies.