Heat Transfer and Thermal Stress in a Lithium-Ion Battery

2010 ◽  
Author(s):  
Wei Wu ◽  
Xinran Xiao ◽  
Danghe Shi
Keyword(s):  
Heat Transfer ◽  
Thermal Stress ◽  
Lithium Ion ◽  
Scale Model ◽  
Heat Sources ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Multi Scale ◽  
Induced Stress ◽  
Li Ion

This paper presents a finite element based multi-scale model for a lithium-ion (Li-ion) battery cell. The model considers multi-physics including battery kinetics, diffusion, thermal and stress analysis. In battery thermal analysis, the heat source is critical. In this model, both resistive and entropic heating were considered. Simulations were carried out for a LiC6/LiPF6/LiyMn2O4 cell under a discharge-charge cycle. The heat generations due to these two heat sources were compared. The thermal stress was computed and compared with the intercalation stress for individual battery components. Within the electrode particles, the thermal induced stress was negligible. In the separator, however, the thermal induced stress was comparable or even higher than the stress caused by intercalation deformation of the electrode particles.

Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew
Keyword(s):  
Real Time ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
Time Estimation ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Thermal Dynamics ◽  
Estimation Scheme ◽  
Real Time Estimation ◽  
Li Ion

This paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical–thermal dynamics emerging from the fact that the lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding-mode observer and an unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: (1) estimation of cell-capacity and (2) tracking the capacity loss due to degradation mechanisms such as lithium plating. These capabilities are possible since the two-electrode model needs not be reduced further to a single-electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of measurement noise and parametric uncertainties is also included in the simulation results to evaluate the performance of the proposed scheme.

Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning

2015 ◽  
Author(s):  
Chao Hu ◽  
Gaurav Jain ◽  
Craig Schmidt ◽  
Carrie Strief ◽  
Melani Sullivan
Keyword(s):  
Medical Device ◽  
Bayesian Learning ◽  
Lithium Ion ◽  
Scale Model ◽  
Sparse Bayesian Learning ◽  
Li Ion Battery ◽  
Implantable Medical Device ◽  
Charge Voltage ◽  
Battery Capacity ◽  
Li Ion

Lithium-ion (Li-ion) rechargeable batteries are used as one of the major energy storage components for implantable medical devices. Reliability of Li-ion batteries used in these devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, patients and physicians. To ensure a Li-ion battery operates reliably, it is important to develop health monitoring techniques that accurately estimate the capacity of the battery throughout its life-time. This paper presents a sparse Bayesian learning method that utilizes the charge voltage and current measurements to estimate the capacity of a Li-ion battery used in an implantable medical device. Relevance Vector Machine (RVM) is employed as a probabilistic kernel regression method to learn the complex dependency of the battery capacity on the characteristic features that are extracted from the charge voltage and current measurements. Owing to the sparsity property of RVM, the proposed method generates a reduced-scale regression model that consumes only a small fraction of the CPU time required by a full-scale model, which makes online capacity estimation computationally efficient. 10 years’ continuous cycling data and post-explant cycling data obtained from Li-ion prismatic cells are used to verify the performance of the proposed method.

scholarly journals A New All-Solid-State Lithium-Ion Battery Working without a Separator in an Electrolyte

2018 ◽  
Author(s):  
Seonggyu Cho ◽  
Shinho Kim ◽  
Wonho Kim ◽  
Seok Kim ◽  
Sungsook Ahn
Keyword(s):  
Solid State ◽  
Ionic Conductivity ◽  
Solid Electrolyte ◽  
System Development ◽  
Lithium Ion ◽  
Internal Resistance ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Metal Anode ◽  
Li Ion

Considering the safety issues of Li ion batteries, all-solid-state polymer electrolyte has been one of the promising solutions. In this point, achieving a Li ion conductivity in the solid state electrolytes comparable to liquid electrolytes (>1 mS/cm) is particularly challenging. Employment of polyethylene oxide (PEO) solid electrolyte has not been not enough in this point due to high crystallinity. In this study, hybrid solid electrolyte (HSE) systems are designed with Li1.3Al0.3Ti0.7(PO4)3(LATP), PEO and Lithium hexafluorophosphate (LiPF6) or Lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Hybrid solid cathode (HSC) is also designed using LATP, PEO and lithium cobalt oxide (LiCoO2, LCO)—lithium manganese oxide (LiMn2O4, LMO). The designed HSE system displays 3.0 × 10−4 S/cm (55 ℃) and 1.8 × 10−3 S/cm (23 ℃) with an electrochemical stability as of 6.0 V without any separation layer introduction. Li metal (anode)/HSE/HSC cell in this study displays initial charge capacity as of 123.4/102.7 mAh/g (55 ℃) and 73/57 mAh/g (25 °C). To these systems, Succinonitrile (SN) has been incorporated as a plasticizer for practical secondary Li ion battery system development to enhance ionic conductivity. The incorporated SN effectively increases the ionic conductivity without any leakage and short-circuits even under broken cell condition. The developed system also overcomes the typical disadvantages of internal resistance induced by Ti ion reduction. In this study, optimized ionic conductivity and low internal resistance inside the Li ion battery cell have been obtained, which suggests a new possibility in the secondary Li ion battery development.

scholarly journals Lithium-ion battery models: a comparative study and a model-based powerline communication

2017 ◽  
Vol 15 ◽  
pp. 83-91 ◽  
Author(s):  
Fida Saidani ◽  
Franz X. Hutter ◽  
Rares-George Scurtu ◽  
Wolfgang Braunwarth ◽  
Joachim N. Burghartz
Keyword(s):  
Real Time ◽  
Equivalent Circuit ◽  
Lithium Ion ◽  
Complex Model ◽  
Physical Models ◽  
Chemical System ◽  
Li Ion Battery ◽  
Battery Cell ◽  
Powerline Communication ◽  
Li Ion

Abstract. In this work, various Lithium-ion (Li-ion) battery models are evaluated according to their accuracy, complexity and physical interpretability. An initial classification into physical, empirical and abstract models is introduced. Also known as white, black and grey boxes, respectively, the nature and characteristics of these model types are compared. Since the Li-ion battery cell is a thermo-electro-chemical system, the models are either in the thermal or in the electrochemical state-space. Physical models attempt to capture key features of the physical process inside the cell. Empirical models describe the system with empirical parameters offering poor analytical, whereas abstract models provide an alternative representation. In addition, a model selection guideline is proposed based on applications and design requirements. A complex model with a detailed analytical insight is of use for battery designers but impractical for real-time applications and in situ diagnosis. In automotive applications, an abstract model reproducing the battery behavior in an equivalent but more practical form, mainly as an equivalent circuit diagram, is recommended for the purpose of battery management. As a general rule, a trade-off should be reached between the high fidelity and the computational feasibility. Especially if the model is embedded in a real-time monitoring unit such as a microprocessor or a FPGA, the calculation time and memory requirements rise dramatically with a higher number of parameters. Moreover, examples of equivalent circuit models of Lithium-ion batteries are covered. Equivalent circuit topologies are introduced and compared according to the previously introduced criteria. An experimental sequence to model a 20 Ah cell is presented and the results are used for the purposes of powerline communication.

A Diagnostic Scheme for Detection, Isolation and Estimation of Electrochemical Faults in Lithium-Ion Cells

2015 ◽  
Author(s):  
Satadru Dey ◽  
Beshah Ayalew
Keyword(s):  
Sliding Mode ◽  
Direct Method ◽  
Lithium Ion ◽  
Battery Management ◽  
Li Ion Battery ◽  
Modeling Framework ◽  
Battery Cell ◽  
Safety And Reliability ◽  
Li Ion ◽  
Sliding Mode Observers

Improvement of the safety and reliability of the Lithium-ion (Li-ion) battery operation is one of the key tasks for advanced Battery Management Systems (BMSs). It is critical for BMSs to be able to diagnose battery electrochemical faults that can potentially lead to catastrophic failures. In this paper, an observer-based fault diagnosis scheme is presented that can detect, isolate and estimate some internal electrochemical faults. The scheme uses a reduced-order electrochemical-thermal model for a Li-ion battery cell. The paper first presents a modeling framework where the electrochemical faults are modeled as parametric faults. Then, multiple sliding mode observers are incorporated in the diagnostic scheme. The design and selection of the observer gains as well as the convergence of the observers are verified theoretically via Lyapunov’s direct method. Finally, the performance of the observer-based diagnostic scheme is illustrated via simulation studies.

Physics-Based State of Health Estimation of Lithium-Ion Battery Using Sequential Experimental Design

2018 ◽  
Author(s):  
Yu Hui Lui ◽  
Meng Li ◽  
Mohammadkazem Sadoughi ◽  
Chao Hu ◽  
Shan Hu
Keyword(s):  
Process Model ◽  
Design Space ◽  
Sequential Sampling ◽  
Lithium Ion ◽  
State Of Health ◽  
Li Ion Battery ◽  
Simulation Experiments ◽  
Battery Cell ◽  
Sampling Process ◽  
Li Ion

State of health (SOH) estimation is a critical yet challenging task due to the complex degradation process of lithium-ion (Li-ion) battery. This paper proposes to combine physics-based modeling of Li-ion battery and sequential design of simulation experiments to build an accurate SOH estimator in a computationally efficient manner. A novel sequential backward optimization process is adopted to build a multivariate Gaussian process model that quantifies three degradation modes in a Li-ion battery cell: loss of lithium inventory and losses of active materials in the positive and negative electrodes. The sequential process for the design of simulation experiments is realized via the use of an acquisition function, the maximization of which gives rise to a new sample point in the design space for the next experiment. The acquisition function achieves an optimal balance between exploration of new regions in the design space with high prediction uncertainty and exploitation of challenging regions with high response nonlinearity. The preliminary results from COMSOL Multiphysics degradation scenario simulations show that the SOH estimator designed with the sequential sampling process can provide faster error decay in degradation estimation when compared to that without the sequential sampling process.

Enhancing Co/Co2VO4 Li-ion Battery Anode Performances via 2D-2D Heterostructure Engineering

Nanoscale ◽  
2021 ◽  
Author(s):  
Kun Wang ◽  
Yongyuan Hu ◽  
Jian Pei ◽  
Fengyang Jing ◽  
Zhongzheng Qin ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Anode Material ◽  
Output Voltage ◽  
High Capacity ◽  
Lithium Ion ◽  
Li Ion Battery ◽  
Li Ion ◽  
Battery Anode

High capacity Co2VO4 becomes a potential anode material for lithium ion batteries (LIBs) benefiting from its lower output voltage during cycling than other cobalt vanadates. However, the application of this...

Sign in / Sign up

Export Citation Format

Share Document