Investigation of Mechanical Integrity of Prismatic Lithium-Ion Batteries With Various State of Charge

2020 ◽  
Vol 18 (3) ◽  
Author(s):  
Tao Wang ◽  
Xiaoping Chen ◽  
Gang Chen ◽  
Hongbo Ji ◽  
Ling Li ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Lithium Ion Batteries ◽  
Electric Vehicle ◽  
Lithium Ion ◽  
State Of Charge ◽  
Finite Element Models ◽  
Similar Response ◽  
Loading Direction ◽  
Safety Design ◽  
Curve Profile

Abstract The crush safety of lithium-ion batteries (LIBs) has recently become one of the hottest topics as the electric vehicle (EV) market is growing rapidly. In this study, mechanical properties of prismatic lithium-ion batteries under compression loading are investigated. Batteries with different values of state of charge (SOC) under different loading directions are compared. Results show LIB cells with different SOCs under same loading direction exhibit similar response curve profile, however, the load capacities of LIBs can be influenced by SOCs. The stiffness and stress have approximately linear relationship with the SOC values. Based on experimental results, finite element models are established which can predict the mechanical properties of prismatic LIBs. The proposed models can be utilized to evaluate the crush behaviors of LIBs, providing guidance for electric vehicle safety design.

Lithium-Ion Battery Cell Health Monitoring Using Vibration Diagnostic Test

2013 ◽  
Author(s):  
Huan L. Pham ◽  
J. Eric Dietz ◽  
Douglas E. Adams ◽  
Nathan D. Sharp
Keyword(s):  
Mechanical Properties ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Diagnostic Test ◽  
Excitation Frequency ◽  
High Capacity ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Battery Cell

With their superior advantages of high capacity and low percentage of self-discharge, lithium-ion batteries have become the most popular choice for power storage in electric vehicles. Due to the increased potential for long life of lithium-ion batteries in vehicle applications, manufacturers are pursuing methodologies to increase the reliability of their batteries. This research project is focused on utilizing non-destructive vibration diagnostic testing methods to monitor changes in the physical properties of the lithium-ion battery electrodes, which dictate the states of charge (SOC) and states of health (SOH) of the battery cell. When the battery cell is cycled, matter is transported from one electrode to another which causes mechanical properties such as thickness, mass, stiffness of the electrodes inside a battery cell to change at different states of charge; therefore, the detection of these changes will serve to determine the state of charge of the battery cell. As mass and stiffness of the electrodes change during charge and discharge, they will respond to the excitation input differently. An automated vibration diagnostic test is developed to characterize the state of charge of a lithium-ion battery cell by measuring the amplitude and phase of the kinematic response as a function of excitation frequency at different states of charge of the battery cell and at different times in the life of the cell. Also, the mechanical properties of the electrodes at different states of charge are obtained by direct measurements to develop a first-principles frequency response model for the battery cell. The correlation between the vibration test results and the model will be used to determine the state of charge of the cell.

Joint Estimation of State of Charge and Capacity of Lithium-ion Batteries in Electric Vehicle

2021 ◽  
Author(s):  
Yuehui Wang ◽  
Tao Wei ◽  
Denggao Huang ◽  
Xu Wang ◽  
Zhongwen Zhu ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Electric Vehicle ◽  
Lithium Ion ◽  
State Of Charge ◽  
Joint Estimation

scholarly journals A Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 121 ◽  
Author(s):  
Xintian Liu ◽  
Xuhui Deng ◽  
Yao He ◽  
Xinxin Zheng ◽  
Guojian Zeng
Keyword(s):  
Particle Filter ◽  
Lithium Ion Batteries ◽  
Electric Vehicle ◽  
Smart Cities ◽  
Estimation Method ◽  
Lithium Ion ◽  
State Of Charge ◽  
Dynamic State ◽  
Battery Management System ◽  
Advantages And Disadvantages

With the increasing environmental concerns, plug-in electric vehicles will eventually become the main transportation tools in future smart cities. As a key component and the main power source, lithium-ion batteries have been an important object of research studies. In order to efficiently control electric vehicle powertrains, the state of charge (SOC) of lithium-ion batteries must be accurately estimated by the battery management system. This paper aims to provide a more accurate dynamic SOC estimation method for lithium-ion batteries. A dynamic Thevenin model with variable parameters affected by the temperature and SOC is established to model the battery. An unscented Kalman particle filter (UPF) algorithm is proposed based on the unscented Kalman filter (UKF) algorithm and the particle filter (PF) algorithm to generate nonlinear particle filter according to the advantages and disadvantages of various commonly used filtering algorithms. The simulation results show that the unscented Kalman particle filter algorithm based on the dynamic Thevenin model can predict the SOC in real time and it also has strong robustness against noises.

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

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