Internal short circuit evaluation and corresponding failure mode analysis for lithium-ion batteries

Internal short circuit(ISC)is the major failure problem for the safe application of lithium-ion batteries,especially for the batteries with high energy density.However,how to quantify the hazard aroused by the ISC,and what kinds of ISC will lead to thermal runaway are still unclear.This paper investigates the thermal-electrical coupled behaviors of ISC,using batteries with Li(Ni_(1/3)CO_(1/3)Mn_(1/3))O_(2) cathode and composite separator.The electrochemical impedance spectroscopy of customized battery that has no LiPF6 salt is utilized to standardize the resistance of ISC.Furthermore,this paper compares the thermal-electrical coupled behaviors of the above four types of ISC at different states-of-charge.There is an area expansion phenomenon for the aluminum-anode type of ISC.The expansion effect of the failure area directly links to the melting and collapse of separator,and plays an important role in further evolution of thermal runaway.This work provides guidance to the development of the ISC models,detection algorithms,and correlated countermeasures.

Data-driven short circuit resistance estimation in battery safety issues

Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.

An Online Adaptive Internal Short Circuit Detection Method of Lithium-Ion Battery

Internal short circuit(ISC)is a critical cause for the dangerous thermal runaway of lithium-ion battery(LIB);thus,the accurate early-stage detection of the ISC failure is critical to improving the safety of electric vehicles.In this paper,a model-based and self-diagnostic method for online ISC detection of LIB is proposed using the measured load current and terminal voltage.An equivalent circuit model is built to describe the characteristics of ISC cell.A discrete-time regression model is formulated for the faulty cell model through the system transfer function,based on which the electrical model parameters are adapted online to keep the model accurate.Furthermore,an online ISC detection method is exploited by incorporating an extended Kalman filter-based state of charge estimator,an abnormal charge depletion-based ISC current estimator,and an ISC resistance estimator based on the recursive least squares method with variant forgetting factor.The proposed method shows a self-diagnostic merit relying on the single-cell measurements,which makes it free from the extra uncertainty caused by other cells in the system.Experimental results suggest that the online parameterized model can accurately predict the voltage dynamics of LIB.The proposed diagnostic method can accurately identify the ISC resistance online,thereby contributing to the early-stage detection of ISC fault in the LIB.

Internal Short Circuit Detection for Parallel-Connected Battery Cells Using Convolutional Neural Network

Reliable and timely detection of an internal short circuit(ISC)in lithium-ion batteries is important to ensure safe and efficient operation.This paper investigates ISC detection of parallel-connected battery cells by considering cell non-uniformity and sensor limitation(i.e.,no independent current sensors for individual cells in a parallel string).To characterize ISC-related signatures in battery string responses,an electro-thermal model of parallel-connected battery cells is first established that explicitly captures ISC.By analyzing the data generated from the electro-thermal model,the distribution of surface tem-perature among individual cells within the battery string is identified as an indicator for ISC detection under the constraints of sensor limitations.A convolutional neural network(CNN)is then designed to estimate the ISC resistance by using the cell surface temperature and the total capacity of the string as inputs.Based on the estimated ISC resistance from CNN,the strings are classified as faulty or non-faulty to guide the examination or replacement of the battery.The algorithm is evaluated in the presence of signal noises in terms of accuracy,false alarm rate,and missed detection rate,verifying the effectiveness and robustness of the proposed approach.

Thermal runaway evolution of a 280 Ah lithium-ion battery with LiFePO_(4) as the cathode for different heat transfer modes constructed by mechanical abuse

Lithium iron phosphate batteries have been increasingly utilized in recent years because their higher safety performance can improve the increasing trend of recurring thermal runaway accidents.However,the safety performance and mechanism of high-capacity lithium iron phosphate batteries under internal short-circuit challenges remain to be explored.This work analyzes the thermal runaway evolution of high-capacity LiFePO_(4) batteries under different internal heat transfer modes,which are controlled by different penetration modes.Two penetration cases involving complete penetration and incomplete penetration were detected during the test,and two modes were performed incorporating nails that either remained or were removed after penetration to comprehensively reveal the thermal runaway mechanism.A theoretical model of microcircuits and internal heat conduction is also established.The results indicated three thermal runaway evolution processes for high-capacity batteries,which corresponded to the experimental results of thermal equilibrium,single thermal runaway,and two thermal runaway events.The difference in heat distribution in the three phenomena is determined based on the microstructure and material structure near the pinhole.By controlling the heat dissipation conditions,the time interval between two thermal runaway events can be delayed from 558 to 1417 s,accompanied by a decrease in the concentration of in-situ gas production during the second thermal runaway event.

On-board Diagnosis of Soft Short Circuit Fault in Lithium-ion Battery Packs for Electric Vehicles Using an Extended Kalman Filter

The safety of lithium-ion batteries in electric vehicles(EVs)is attracting more attention.To ensure battery safety,early detection is necessary of a soft short circuit(SC)which may evolve into severe SC faults,leading to fire or thermal runaway.This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter(EKF)for on-board applications in EVs.In the proposed method,the EKF is used to estimate the state of charge(SOC)of the faulty cell by adjusting a gain matrix based on real-time measured voltages.The SOC difference between the estimated SOC and the calculated SOC through coulomb counting for the faulty cell is employed to detect soft SC faults,and the soft SC resistance values are further identified to indicate the degree of fault severity.Soft SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance values.The experimental data are acquired to validate the proposed soft SC fault diagnosis method.The results show that the proposed method is effective and robust in quickly detecting a soft SC fault and accurately estimating soft SC resistance.

Novel Lightweight and Protective Battery System Based on Mechanical Metamaterials

The challenges facing electric vehicles with respect to driving range and safety make the design of a lightweight and safe battery pack a critical issue.This study proposes a multifunctional structural battery system comprising cylindrical battery cells and a surrounding lightweight lattice metamaterial.The lattice density distribution was optimized via topological optimization to minimize stress on the battery during compression.Surrounding a single 18650 cylindrical battery cell,non-uniform lattices were designed featuring areas of increased density in an X-shaped pattern and then fabricated by additive manufacturing using stainless steel powders.Compression testing of the assembled structural battery system revealed that the stronger lattice units in the X-shaped lattice pattern resisted deformation and helped delay the emergence of a battery short circuit.Specifically,the short circuit of the structural battery based on a variable-density patterned lattice was∼166%later than that with a uniform-density lattice.Finite element simulation results for structural battery systems comprising nine battery cells indicate that superior battery protection is achieved in specially packed batteries via non-uniform lattices with an interconnected network of stronger lattices.The proposed structural battery systems featuring non-uniform lattices will shed light on the next generation of lightweight and impact-resistant electric vehicle designs.