A Bayesian approach for Li-Ion battery capacity fade modeling and cycles to failure prognostics

Guo, Jian; Li, Zhaojun Steven; Pecht, Michael

doi:10.1016/j.jpowsour.2015.01.164

Cited by 115 publications

(46 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A statistical model is developed by ANOVA of the numerical data in a full factorial design framework [35][36][37][38][39]. ANOVA is primarily designed for the analysis of experimental data.…”

Section: Statistical Analysis Of Design Variablesmentioning

confidence: 99%

Electrochemical-Thermal Modelling and Optimisation of Lithium-Ion Battery Design Parameters Using Analysis of Variance

2017

View full text Add to dashboard Cite

A 1D electrochemical-thermal model of an electrode pair of a lithium ion battery is developed in Comsol Multiphysics. The mathematical model is validated against the literature data for a 10 Ah lithium phosphate (LFP) pouch cell operating under 1 C to 5 C electrical load at 25 • C ambient temperature. The validated model is used to conduct statistical analysis of the most influential parameters that dictate cell performance; i.e., particle radius (r p ); electrode thickness (L pos ); volume fraction of the active material (ε s,pos ) and C-rate; and their interaction on the two main responses; namely; specific energy and specific power. To achieve an optimised window for energy and power within the defined range of design variables; the range of variation of the variables is determined based on literature data and includes: r p : 30-100 nm; L pos : 20-100 µm; ε s,pos : 0.3-0.7; C-rate: 1-5. By investigating the main effect and the interaction effect of the design variables on energy and power; it is observed that the optimum energy can be achieved when (r p < 40 nm); (75 µm < L pos < 100 µm); (0.4 < ε s,pos < 0.6) and while the C-rate is below 4C. Conversely; the optimum power is achieved for a thin electrode (L pos < 30 µm); with high porosity and high C-rate (5 C).

show abstract

Section: Statistical Analysis Of Design Variablesmentioning

confidence: 99%

Electrochemical-Thermal Modelling and Optimisation of Lithium-Ion Battery Design Parameters Using Analysis of Variance

2017

View full text Add to dashboard Cite

show abstract

“…And the battery capacity fade model based on ambient temperature is given as equation (10). Set T = 293 and 313 K, we can get the capacity fade model for the experiment results.…”

Section: Experiments and Model Parameters Identificationmentioning

confidence: 99%

Battery module capacity fade model based on cell voltage inconsistency and probability distribution

Zhang

Yang

et al. 2017

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Cell voltage inconsistency of battery module is correlated with cell capacity fading inconsistency caused by uneven temperature or improper charge/discharge rate, so it is essential to study on cell voltage inconsistency when establishing a battery module capacity fade model. An accelerated life experiment is conducted on 12-series (12S) LiMn 2 O 4 battery. The evaluation index of the voltage inconsistency is given, and the evolution of the voltage of the 12S battery module is obtained. Furthermore, a model of capacity fade of this 12S battery is established based on cell voltage inconsistency, which is extended to any-series battery by means of probability distribution. Based on this extended model, the relationship between the number of cells and the life of the battery is obtained.

show abstract

“…Panchal et al had completed some degradation tests of batteries by using real world drive cycles collected from an electric vehicle, and further analyzed the impact of various discharge rates on electrical performance of the battery [17,18]. Especially, Pecht and his team performed many degradation tests for lithium-ion battery and obtained large amounts of degradation data, and then achieve a lot of valuable results depending on it [3,19,20]. However, there are still numerous problems needing to be further investigated in the future.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena

Zhang

et al. 2017

Energies

View full text Add to dashboard Cite

Abstract:A lithium-Ion battery is a typical degradation product, and its performance will deteriorate over time. In its degradation process, regeneration phenomena have been frequently encountered, which affect both the degradation state and rate. In this paper, we focus on how to build the degradation model and estimate the lifetime. Toward this end, we first propose a multi-phase stochastic degradation model with random jumps based on the Wiener process, where the multi-phase model and random jumps at the changing point are used to describe the variation of degradation rate and state caused by regeneration phenomena accordingly. Owing to the complex structure and random variables, the traditional Maximum Likelihood Estimation (MLE) is not suitable for the proposed model. In this case, we treat these random variables as latent parameters, and then develop an approach for model identification based on expectation conditional maximum (ECM) algorithm. Moreover, depending on the proposed model, how to estimate the lifetime with fixed changing point is presented via the time-space transformation technique, and the approximate analytical solution is derived. Finally, a numerical simulation and a practical case are provided for illustration.

show abstract

A Bayesian approach for Li-Ion battery capacity fade modeling and cycles to failure prognostics

Cited by 115 publications

References 29 publications

Electrochemical-Thermal Modelling and Optimisation of Lithium-Ion Battery Design Parameters Using Analysis of Variance

Electrochemical-Thermal Modelling and Optimisation of Lithium-Ion Battery Design Parameters Using Analysis of Variance

Battery module capacity fade model based on cell voltage inconsistency and probability distribution

A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena

Contact Info

Product

Resources

About