scholarly journals Modelling Lithium-Ion Battery Ageing in Electric Vehicle Applications—Calendar and Cycling Ageing Combination Effects

Batteries ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 14 ◽  
Author(s):  
Eduardo Redondo-Iglesias ◽  
Pascal Venet ◽  
Serge Pelissier
Keyword(s):  
Lithium Ion Batteries ◽  
Electric Vehicle ◽  
Reaction Rate ◽  
Lithium Ion ◽  
Capacity Fade ◽  
Lithium Ion Cells ◽  
Rest Periods ◽  
Cost Modelling ◽  
Original Approach ◽  
Model Battery

Battery ageing is an important issue in e-mobility applications. The performance degradation of lithium-ion batteries has a strong influence on electric vehicles’ range and cost. Modelling capacity fade of lithium-ion batteries is not simple: many ageing mechanisms can exist and interact. Because calendar and cycling ageings are not additive, a major challenge is to model battery ageing in applications where the combination of cycling and rest periods are variable as, for example, in the electric vehicle application. In this work, an original approach to capacity fade modelling based on the formulation of reaction rate of a two-step reaction is proposed. A simple but effective model is obtained: based on only two differential equations and seven parameters, it can reproduce the capacity evolution of lithium-ion cells subjected to cycling profiles similar to those found in electric vehicle applications.

scholarly journals Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 723
Author(s):  
Saurabh Saxena ◽  
Darius Roman ◽  
Valentin Robu ◽  
David Flynn ◽  
Michael Pecht
Keyword(s):  
Machine Learning ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Consumer Electronics ◽  
Stress Factors ◽  
Test Time ◽  
Capacity Fade ◽  
Maximum Acceleration ◽  
Accelerated Degradation ◽  
Qualification Testing

Lithium-ion batteries power numerous systems from consumer electronics to electric vehicles, and thus undergo qualification testing for degradation assessment prior to deployment. Qualification testing involves repeated charge–discharge operation of the batteries, which can take more than three months if subjected to 500 cycles at a C-rate of 0.5C. Accelerated degradation testing can be used to reduce extensive test time, but its application requires a careful selection of stress factors. To address this challenge, this study identifies and ranks stress factors in terms of their effects on battery degradation (capacity fade) using half-fractional design of experiments and machine learning. Two case studies are presented involving 96 lithium-ion batteries from two different manufacturers, tested under five different stress factors. Results show that neither the individual (main) effects nor the two-way interaction effects of charge C-rate and depth of discharge rank in the top three significant stress factors for the capacity fade in lithium-ion batteries, while temperature in the form of either individual or interaction effect provides the maximum acceleration.

Experimental Evaluation of the Effect of Cycle Profile on the Durability of Commercial Lithium Ion Power Cells

2018 ◽  
Vol 16 (1) ◽  
Author(s):  
K. N. Radhakrishnan ◽  
T. Coupar ◽  
D. J. Nelson ◽  
M. W. Ellis
Keyword(s):  
Lithium Ion ◽  
Critical Factor ◽  
Internal Resistance ◽  
Wave Profile ◽  
Total Charge ◽  
Capacity Fade ◽  
Square Wave ◽  
Lithium Ion Cells ◽  
Discharge Profile ◽  
Testing Protocols

The effect of the charge/discharge profile on battery durability is a critical factor for the application of batteries and for the design of appropriate battery testing protocols. In this work, commercial high-power prismatic lithium ion cells for hybrid electric vehicles (HEVs) were cycled using a pulse-heavy profile and a simple square-wave profile to investigate the effect of cycle profile on battery durability. The pulse-heavy profile was designed to simulate on-road conditions for a typical HEV, while the simplified square-wave profile was designed to have the same total charge throughput, but with lower peak currents. The 5 Ah batteries were cycled for 100 kAh with periodic performance tests to monitor the state of the batteries. Results indicate that, for the batteries tested, the capacity fade for the two profiles was very similar and was 11±0.5% compared to beginning of life (BOL). The change in internal resistance of the batteries during testing was also monitored and found to increase 21% and 12% compared to BOL for the pulse-heavy and square-wave profiles, respectively. The results suggest that simplified testing protocols using square-wave cycling may provide adequate insight into capacity fade behavior for more complex hybrid vehicle drive cycles.

Screening of retired lithium-ion batteries using incremental capacity charging curve based residual capacity estimation method for facilitating sustainable circular lithium-ion battery system

2021 ◽  
pp. 1-16
Author(s):  
Honglei Li ◽  
Liang Cong ◽  
Huazheng Ma ◽  
Weiwei Liu ◽  
Yelin Deng ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Capacity Fade ◽  
Characteristic Parameters ◽  
Capacity Estimation ◽  
Capacity Curve ◽  
Residual Capacity ◽  
Remaining Capacity ◽  
Incremental Capacity

Abstract The rapidly growing deployment of lithium-ion batteries in electric vehicles is associated with a great waste of natural resource and environmental pollution caused by manufacturing and disposal. Repurposing the retired lithium-ion batteries can extend their useful life, creating environmental and economic benefits. However, the residual capacity of retired lithium-ion batteries is unknown and can be drastically different owing to various working history and calendar life. The main objective of this paper is to develop a fast and accurate capacity estimation method to classify the retired batteries by the remaining capacity. The hybrid technique of adaptive genetic algorithm and back propagation neural network is developed to estimate battery remaining capacity using the training set comprised of the selected characteristic parameters of incremental capacity curve of battery charging. Also, the paper investigated the correlation between characteristic parameters with capacity fade. The results show that capacity estimation errors of the proposed neural network are within 3%. Peak intensity of the incremental capacity curve has strong correlation with capacity fade. The findings also show that the translation of peak of the incremental capacity curve is strongly related with internal resistance.

Three-Parameter Modeling of Nonlinear Capacity Fade for Lithium-Ion Batteries at Various Cycling Conditions

2017 ◽  
Vol 164 (12) ◽  
pp. A2767-A2776 ◽  
Author(s):  
Je-Feng Li ◽  
Yang-Shan Lin ◽  
Chi-Hao Lin ◽  
Kuo-Ching Chen
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Capacity Fade ◽  
Nonlinear Capacity
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