Safety Of Battery System Research Articles

Kinetics study on inhibiting battery thermal runaway using an inorganic phase change material with a super high thermochemical storage capacity

Lithium-ion batteries are susceptible to fires and explosions due to thermal runaway, a serious safety hazard. This study explores the potential of using hydrated inorganic salt (TCM40) composite phase change materials to prevent thermal runaway in battery packs. TCM40 composites stand out due to their exceptional thermochemical heat storage capacity, which allows them to effectively absorb excess heat during runaway events. The research investigates how thermal conductivity, thermal storage capacity, and cell spacing influence the propagation of thermal runaway. The findings demonstrate that TCM40 composites, with a thermal storage density exceeding 1000 kJ/kg, are significantly more effective in preventing thermal runaway compared to traditional latent heat storage phase change materials with lower capacities. To gain a comprehensive understanding of thermal runaway mitigation, a combined thermal management model was developed. This model integrates a battery thermal runaway model with a kinetic model describing the decomposition of TCM40 composites. The analysis reveals that the high heat absorption capability of TCM40 composites minimizes heat transfer to neighboring cells during thermal runaway. Furthermore, the model provides valuable insights into the synergistic effects of thermal conductivity and heat storage capacity on runaway propagation. This knowledge can be directly applied to design safer battery packs, even for compact configurations where cell spacing is less than 2 mm. This study offers significant advancements in both thermal protection materials and design strategies for lithium-ion battery packs. These advancements have the potential to significantly improve battery system safety and minimize the risk of explosions.

A Data-Driven Approach for Battery System Safety Risk Evaluation Based on Real-World Electric Vehicle Operating Data

A Data-Driven Approach for Battery System Safety Risk Evaluation Based on Real-World Electric Vehicle Operating Data

A novel LFSSA-LSTM algorithm for the remaining useful life prediction of lithium-ion batteries in whole life cycle

The accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential to improve the safety of battery systems and optimize battery management strategies. Therefore, a new algorithm combining Sparrow search algorithm (SSA) improved by the Levy flight method and long short-term memory neural network (LSTM) is proposed in this paper. By extracting the National Aeronautics and Space Administration (NASA) battery aging datasets, the battery correlated health characteristics are obtained, and the deterioration trend of battery performance is effectively described. The sparrow search algorithm improved by the Levy flight method enhances the ability of the algorithm to jump out of the optimal solution. By using the improved algorithm, the hyper-parameter optimization problem of the long short-term memory network is solved, and the accuracy of the algorithm is improved. Finally, the probability density function is used to predict the remaining useful life of lithium-ion batteries, which effectively prevents the uncertainty caused by a single prediction result. Compared with long short-term memory neural network and the one optimized by a traditional sparrow search algorithm, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the proposed algorithm are kept at 1.77 %, 2.24 %, and 1.25 %, respectively. The results indicate that the algorithm has better accuracy and fitting performance.

Voltage-fault diagnosis for battery pack in electric vehicles using mutual information

The safety of battery system is compromised by the abusive operation and aging, potentially resulting in the abnormal voltage levels. Rapid detection and accurate diagnosis of voltage fault are crucial for ensuring the safety of battery packs. A battery voltage fault diagnosis method is proposed by using the mutual information in this work, which can identify faulty cells timely. Specifically, the voltage of battery pack in an electric vehicle is collected, and the mutual information of voltages between each paired-cells is calculated. The presence of faulty cells disturbs the original distribution of mutual information. Therefore, the Ryan Joiner (RJ) test is used to judge whether the normal distribution of calculated mutual information or not. The interquartile test is used to detect the outliers. The faulty cell is diagnosed by matching the cell corresponding to the outliers. The defaults resulting from an internal short circuit and mechanical vibration are analyzed and diagnosed. The method can avoid misdiagnosis caused by the mutual influence between different faults. Furthermore, the fault diagnosis capability of presented method is discussed under various states of aged cells. The experimental results verify the effectiveness, robustness, model-free, and easiest-to-implement of the presented method.

Experimental Analysis of Thermal Runaway Energy Release of an Automotive Li-Ion Battery Cell

Thermal runaway (TR) reactions of lithium ion cells pose a particular risk to the safety of battery systems. Beside the energy remaining in the cell body, particles and gases exiting the cell during the venting process cause major thermal load for adjacent cells and further system components [1]. In addition, high energy particles can act as an ignition source in the presence of ignitable venting gases and oxygen [2]. Therefore, measurements minimizing the risk of thermal propagation (TP) caused by venting ejecta material and the ignition of combustible gases are introduced on system level. For the dimensioning of the measures, the energy transported by venting particles and gases is identified as a crucial parameter. Depending on the amount of transported energy, the gas cooling distance as well as the complexity of the particle separation structure differ. Simulation methods are used to evaluate the effectiveness of such system measures. More information is needed regarding energy release to accurately predict the system behavior in simulations and to use them as a tool for design.Introducing a novel approach for TR tests enables the determination of the energy remaining in the cell body, separated from the energy transported by venting ejecta. The test setup combines a calorimeter combined with a cyclone separator placed inside an autoclave reactor. The TR energy release of 156 Ah prismatic lithium ion cells have been analyzed using the setup. Venting particles and gases transported a main part of the released energy out of the cell during the TR. Using an analytical approach, the energy transferred to the calorimeter is calculated. The obtained data can be used for validating simulation models for predicting the impact of venting reactions on TP.

(Invited) Battery System Safety Design Based on Studying the Runaway in Battery Cell

Lithium ion battery systems towards certain military application such as submarines and torpedoes are considered “fire critical applications”. Meaning a fire in the battery system on board a submarine is equal to a lost submarine with many crewmen dead. There have already been critical lithium ion battery fires on board submarines. In the united states a midget submarine were destroyed about fifteen years ago during the development phase of the submarine delaying the incorporation of lithium ion batteries in to marine platforms in the USA by about ten years even though no one were killed by the fire. Four years ago the Russian spy submarine Loshariks lithium ion battery system caught fire during the new battery systems maiden voyage. The fire started when the submarine were at a depth of about 200m below the surface of Barents ocean and 14 crewmen were killed almost all were high ranking officers. Too, be able to come up with a safe battery system design for the Swedish submarines we concluded that we need a strategy for the battery system design. We studied literature, conducted modelling work and carried out a lot of abuse test to understand what happens when a battery cell fails.What happens when a 21700 battery cell is overcharged? First the cell is heated to about 110-115°C and then the membrane between the anode and the cathode melts. In the next step the CIP opens up and hot gas is released at about 125 - 135°C. The cell self-heat itself, the temperature rises fast to about 190-200 ° C, then the gas caught fire, and a jet flame is formed with a temperature above 1100°C. A huge amount of hot gases mixed with sluggish particles is released from the top of the battery cell at high speed. The sluggish particles are black and look like a lava flow and when analysed they are found to be electric conductive. What is also interesting is that the lava particles slowly releases its heat to the surroundings, thus the particles kept at a high temperature for a relatively long-time. The surface of the battery cell reaches a peak temperature of about 300°C. This information is enough to start the battery module design work.Why is a round lithium battery cell the obvious choice for a safe high performing battery system? A runaway in round 21700 battery cell leads to axial expansion of 1-2mm the radial expiation is taken up by the design materials. A runaway in a prismatic or pouch cell battery the horizontal expansion can be several centimetres leading to an inefficient design to be able to take up the expansion of failing battery cell. To be able to do an efficient design to keep down the weight of the battery system the cells can be part of the mechanical design. When organizing the round battery cells symmetrically in a battery module each battery cell will work as springs in the right design material, thus be able to take up energy from an impact from an object. If the incoming object is a bullet at speed of 900m /s and a weight of 50 gram, the symmetrically organized battery cells will absorb the energy fast and only few of the battery cells in the module will be de destroyed and will start to burn.Finally yet importantly, the round battery cells are in most cases smaller than the prismatic battery cells for the same application thus contains less energy per battery cell.One battery cell can always burn, but a propagation in battery module or system is not an option for “Fire Critical Military Applications”.

Battery Fault Diagnosis Method Based on Online Least Squares Support Vector Machine

Battery fault diagnosis technology is crucial for the reliable functioning of battery systems. This research introduces an online least squares support vector machine method tailored for battery fault diagnosis. After examining battery fault types and gathering relevant data, this method creates a diagnostic model, effectively addressing small and sporadic fault data that is inadequately handled by conventional support vector machines. Recognizing that certain battery malfunctions evolve over time and are multifaceted, confidence intervals have been integrated into the diagnostic models, enhancing accuracy. Upon testing this model using empirical data, it demonstrated rapid diagnostic capabilities and outperformed other algorithms in identifying progressive faults, ensuring precise fault identification, minimizing false alarms, and bolstering battery system safety.

Extreme learning machine model with honey badger algorithm based state-of-charge estimation of lithium-ion battery

Accurate state-of-charge (SOC) detection was still a challenging task to complete due to complex battery dynamics and constantly changing external conditions. The formula for SOC was difficult to determine since external parameters including voltage, current, temperature, and battery arrangement were complex. Also the methods for estimating SOC that were already in use were not always appropriate for the same car operating in various road and climatic conditions. In all situations, the conventional methodologies did not deliver an accurate estimation performance. Here, a unique optimization-based Extreme Learning Machine (ELM) was created to accurately determine a battery's SOC and enhance the operation and safety of battery systems. A lithium ion battery was first created, and data on its current, voltage, SOC, capacity, duration, and discharge rate were gathered to produce a real-time dataset at several temperatures, including 00,250 and 450. The dataset underwent additional pre-processing to standardize the values and enhance the accuracy of the data. To determine the precise state of the battery, these pre-data were loaded into the ELM model. However, the performance of ELM was significantly influenced by the length of training and the number of neurons in a hidden layer. An advanced Honey Badger Optimization Algorithm (HBA) was used to choose the appropriate hidden neurons and increase the estimation accuracy in order to overcome this problem. The proposed SOC estimation model provides 97% accuracy in the FUDS drive cycle and 99% accuracy in the US06 drive cycle. The proposed model provides a well performance for estimating SOC in lithium-ion battery at various temperature, also the proposed model was fit for real time implementation.

Mechanical strain signal based early warning for failure of different prismatic lithium-ion batteries

Thermal runaway propagation (TRP) is the most challenging safety issue of lithium-ion battery systems. An early warning signal is promising for providing time to mitigate or prevent TRP and further disasters timely. This study investigates the strain characteristics of different format prismatic batteries during the TR/TRP processes, dividing the strain-changing trend into three stages according to the complex chemical composition interactions and TR features. Experimental results prove that the signal provides more than a 500s interval for a timely solution from the battery management system. Besides, the maximum strain increment (Δεmax)/capacity(Q)-Q equalization is quantitively analyzed. Moreover, Δεmax-Q and RI-Q equalizations are proposed to reveal the TR mechanical feature and guide the TR warning threshold definition of battery management systems (BMSs). Furthermore, the strain-changing trend and warning effect is also proved in TRP tests. TRP strain mechanism is unlocked from the mechanical deformation perspective. Battery deformation is opposite to the TRP direction, which guides accident analysis. This study proposes a cheap and reliable warning signal for an in-line configuration battery system, with only one strain gauge attached to the surface center of the first/last battery casing, which has more potential to guarantee the active safety of battery systems.

Health and lifespan prediction considering degradation patterns of lithium-ion batteries based on transferable attention neural network

With the continuous concern on the safety of battery systems, accurate and rapid assessment of battery degradation is essential for practical applications. In this paper, a transferable attention network model based on deep learning is developed to evaluate battery degradation, which can simultaneously predict state of health (SOH) and remaining useful life (RUL) for lithium-ion batteries. First, degradation patterns of the cells are identified by K-means clustering based on the difference of the cells at their early cycles. Secondly, the attention mechanisms are designed to suppress noises in extracted feature maps and trace the interaction between long- and short-term degradation modes. Thirdly, the common knowledge represented by the reference cells and the unique degradation features of the target cell are fused by transfer learning, then SOH and RUL prediction are realized through multi-task learning. Finally, a large-scale battery dataset containing different cycle conditions is used to verify the accuracy and generalization of the developed method. The results show that the developed method achieves accurate SOH and RUL prediction with the average root mean square error within 0.14% and six cycles, respectively.

Bi-directional non-isolated impedance converter for extra low-voltage battery system safety

Bi-directional non-isolated impedance converter for extra low-voltage battery system safety

Development of a Detailed 3D Finite Element Model for a Lithium-Ion Battery Subject to Abuse Loading

<div class="section abstract"><div class="htmlview paragraph">Lithium-ion batteries (LIBs) have been used as the main power source for Electric vehicles (EVs) in recent years. The mechanical behavior of LIBs subject to crush loading is crucial in assessing and improving the impact safety of battery systems and EVs. In this work, a detailed 3D finite element model for a commercial vehicle battery was built, in order to better understand battery failure behavior under various loading conditions. The model included the major components of a prismatic battery jellyroll, i.e., cathodes, anodes, and separators. The models for these components were validated against the corresponding material coupon tests (e.g., tension and compression). Then the components were integrated into the cell level model for simulation of jellyroll loading and damage behavior under three types of compressive indenter loading: (1) Flat-end punch, (2) Hemispherical punch and (3) Round-edge wedge. The comparisons showed reasonable agreement between modeling and experiments. With the validated numerical model, parametric studies were further performed to analyze the effect of separator anisotropy, to highlight its important role in the overall structural response of LIBs.</div></div>

Data-Driven State of Charge Estimation for Power Battery With Improved Extended Kalman Filter

Accurately monitoring battery state of charge (SOC) is essential for battery system safety. However, single and open-loop combination algorithms are mainly used for SOC estimation currently, which may have the problems of low accuracy and poor reliability. Here, a closed-loop combination algorithm with the variance-compensation extended Kalman filter (VCEKF) and back-propagation (BP) neural network is developed for SOC estimation. First, the second-order resistance–capacitance model is established, and the model’s parameters are identified by the forgetting factor recursive least square (FFRLS). Second, the extended Kalman filter (EKF), the variance compensation algorithm, and the BP neural network are merged under a closed-loop structure. Specifically, the variance compensation algorithm updates the process noise covariance of the EKF algorithm in real-time, while the BP neural network simultaneously provides compensation value to obtain the finally estimated SOC. Afterward, the proposed algorithm is testified under different driving schedules. Experimental results show that the accurate SOC estimation under different driving schedules is realized using the proposed algorithm and also illustrate a better performance than the commonly used open-loop algorithm and EKF-based algorithms.

Lithium Plating Detection Based on Electrochemical Impedance and Internal Resistance Analyses

Lithium plating, induced by fast charging and low-temperature charging, is one of the reasons for capacity fading and causes safety problems for lithium-ion batteries. Hence, reliable and effective non-destructive detection methods for lithium plating are needed. In this research, electrochemical impedance and internal resistance for batteries are measured during the rest period after charging. The results for lithium plating batteries and normal batteries are compared and analyzed. Lithium plating detection is realized with multiple indicators extracted from electrochemical impedance and internal resistance results. The effectiveness of the proposed detection methods is verified by the experiments conducted with commercial large-capacity batteries. The proposed methods have further potential to be used in battery management systems to realize online detection of lithium plating and improve the safety of battery systems.

No thermal runaway propagation optimization design of battery arrangement for cell-to-chassis technology

Innovative technology for electric vehicles has been developed in the past years, especially in the design of battery pack. Cell-to-pack (CTP) technology abandoned the conventional module structure and integrated the cell in the pack directly. The next generation Cell-to-chassis (CTC) technology will integrate the battery directly into the chassis fame. How to balance the integration considering both the safety and volume energy density is a challenging task. This paper uses the validated 3D model to investigate the conventional in-line configuration and new proposed brick configuration on thermal runaway propagation (TP) characterization. The in-line module occurs TP while the brick module doesn’t. The analysis of heat flux and heat energy flow among TR battery between adjacent normal batteries points out that the brick module has low peak heat flow and has more battery (heat capacity) to absorb heat, thus the brick can cease TP. In addition, the length of the brick module is optimized to improve the space utilization in CTC fame. Based on not adding thermal barriers, the volume energy density of brick configuration system decreases by less than 3% compare with in-line configuration system. This paper also proves that the structural design can improve the safety of battery system without adding cost.

Remaining useful life prediction for lithium-ion batteries in later period based on a fusion model

Lithium-ion batteries are broadly used in many fields. Accurate remaining useful life (RUL) prediction ensures the reliable operation and the safety of battery systems. However, no single model can realize long-term prediction for RUL with the reliable uncertainty management in the later period. To this end, a competitive model based on an improved autoregressive (AR) and particle filter (PF) model is proposed. Specifically, the similarity capacity series is creatively employed in the AR model, while the underlying capacity is introduced as a new approach for the parameter estimation of the observation equation in PF. Then, average weight is used to update the state equation and describe the future system states. After that, the RUL and its probability density function are obtained by PF again. The effectiveness and robustness are verified by the National Aeronautics and Space Administration (NASA) dataset. Results illustrate that the fusion model outperforms others and accurately predicts RUL with narrow uncertainty representation in the later period.

Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles

State of health (SOH) estimation is critical to the safety of battery systems in real-world electric vehicles. Accurate battery health status is difficult to be measured during dynamic and robust vehicular operation conditions. This paper proposes a novel SOH estimation model based on Catboost and interval capacity during the charging process. A year-long operation dataset of an electric taxi is derived with all charging segments separated to construct the research dataset. The charging patterns are analyzed, and the segments with rich aging information are extracted, then a general aging feature of interval capacity is extracted by incremental capacity analysis. Furthermore, comparison with the other six machine learning methods is conducted, and five inputs are determined through Pearson correlation analysis, including start charging state of charge (SOC), end charging SOC, mileage, temperature of probe, and current. The results show the Catboost-based model achieves the best accuracy, with the mean absolute percentage error and root mean squared error limited within 2.74% and 1.12%, respectively. More importantly, a battery aging evaluation strategy and its further research plan is proposed for the application in real-world electric vehicles.

Comprehensively analysis the failure evolution and safety evaluation of automotive lithium ion battery

In response to environmental pollution and the energy crisis, the number of electric vehicles (EV) has increased year by year. However, frequent EV accidents have pushed the safety of EVs to a new height of attention. The failure of lithium-ion batteries (LIBs) is the root of most accidents. Although many standards have been made, the battery system's safety still lacks scientific, comprehensive, and quantifiable assessment. Here, we innovatively put forward a comprehensive map of LIBs failure evolution combining battery tests and forward development. By analyzing the root cause of the EV fire through the Fault Tree Analysis (FTA), 20 basic events, 26 minimum cut sets, and 29 battery tests related to the accident were obtained. The result indicates that the low thermal stability materials and battery management system (BMS) failure to warn in time are the most important factors leading to EV fire. According to the test content, the battery tests are classified, and the Analytic Hierarchy Process (AHP) model of the battery test is established. By comparing the importance of every two tests with the judgment matrix, the weights of battery tests are obtained. Finally, combining with the fault tree branch structure, comprehensive forward development suggestions are put forward to improve the safety of EV.

Study on Influencing Factors of Consistency in Manufacturing Process of Vehicle Lithium‐Ion Battery Based on Correlation Coefficient and Multivariate Linear Regression Model

AbstractLithium‐ion battery manufacturing is a multiprocess serial system and the consistency of each single cell will affect the performance and safety of battery system after grouping. Therefore, optimizing the battery preparation process and improving the battery consistency have become the key technical issues in battery preparation process. In this study, the inconsistency of finished batteries caused by manufacturing process is analyzed from three processes: electrode preparation, battery assembly, and liquid injection and formation and the structure–activity relationship between manufacturing process data and battery consistency is studied. Taking the actual capacity, self‐discharge rate, and internal resistance of single cell as target variables, the correlation coefficient matrix between original process variables and target variables is constructed by using correlation analysis method and the correlation index between process variables and target variables is obtained. The multiple linear regression model is established by stepwise regression method and the weight of original process variables affecting battery consistency is deduced. The results show that the data of multiple process links have a high influence weight on the actual capacity and self‐discharge rate of batteries, which provide a theoretical model for the key strategies of improving quality consistency in lithium‐ion battery industry.

Development of Cell Selection and Screening Method and Its Validation Process for the Multi-Combination Battery System

A commercial-off-the-shelf (COTS) cylindrical 18650 Li-Ion cells have been proven to be extremely reliable power sources from many electronic applications for long periods and adopted to the EV applications. The cylindrical cells have been considered a reliable power source for the aerospace battery system, thanks to their high energy and power density. However, some electronic applications' field failure histories and some test programs have indicated that batteries built with cylindrical 18650 cells in multi-cell configurations (series and parallel) have experienced safety issues under various test conditions. Another requirement of the aerospace battery is a long-life cycle under harsh operating conditions. A tiny potential risky outlier cell, which cannot be identified and detected during the cell manufacturing process, can severely affect the multi-combination battery system's safety and reliability, like the EV, ESS, and Aerospace battery system. This paper studied a cell selection and screening method and developed a validation process to evaluate the efficiency and reliability of the cell selection and screening process. A cell selection method includes visual inspection, mass and dimension check, some electrical tests (OCV, CCV, AC Impedance), capacity measurement, and cell level safety test according to widely accepted cell safety standards and battery industrial best practices of the lithium-ion battery. The cell screening method and procedure followed the process and criteria on the NASA technical document (Specification for Acceptance Testing Commercial Lithium-Ion Cells). This program adopted the 6-sigma concept on the data analysis of the cell selection and screening process, which rejects the outlier cells of the 3-sigma level of each test item. The validation process was designed with two test conditions: 1) cycle test on the regular operation of the Human-spaceflight's mission operating conditions and 2) accelerate test condition of the maximum (most severe) cycle condition of the cell makers specification document. The regular operation cycle test was designed to evaluate the screening process's efficiency, whether the 3-sigma level screening is acceptable to the performance of the battery system using all passed cell samples from the screening. The regular cycle test selects four types of 4S string battery groups from different combinations of the cell samples. And it was performed according to the Human-spaceflight's mission operating conditions to validate the 4S can meet the required cycle condition. On the other hand, the accelerated cycle test was designed to identify the difference of cell degradation and capacity decay between the two types of cell combinations by the cycle test. One group use cells randomly selected samples from the non-screen cell, and the other group used selected cells from passed group cell as a control sample. Both 4S strings were cycled at the accelerate test condition, the maximum (most severe) cycle condition of the cell makers specification document. The battery capacity decay and the cell's internal resistance changes by DC pulse technics were checked regularly every 100 cycles during the cycle test.The performance of both 4S was almost similar until 200 cycles. However, the non-screen 4S string's capacity decay speed was much faster and reach death after the 500 cycles, while the screened 4S runs without any severe capacity decline until 800 cycles. EIS (Electrochemical Impedance Spectroscopy) technique was applied to each cell of the 4S string. The DPA (destructive Physical Analysis) and electron microscope analysis were used to identify the electrode's degradation degree and study the cell failure mechanism during the accelerated cycle tests. The DPA and electron microscope analysis (SEM-EDS) identified that gradual damage of the cathode electrode material of the non-screened 4S string during the accelerated cycle test was the leading cause of the cell capacity decay. This research discusses that non-screened 4S string may have different characteristic cell among the four cells in the 4 S string compare to the screened 4S string. That can initiate the stress to the cell and then increase the cell internal resistance, following enhanced cell heat generation and eventually getting the cell imbalance. The cell imbalance and heat generation get the cell getting worse after 200 cycles and eventually kill the cell and 4S sting. This research concludes that the used cell screening process is effective and beneficial as a cell screening process for the multi-cell battery system, especially for the long-term cycle-life battery system, like EV, ESS, and aerospace applications. Figure 1