Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries
By Michael Schimpe, Maik Naumann, Holger C. Hesse, Kandler A. Smith, Andreas Jossen. 2017
226 citations recorded by the source corpus.
For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately.For parameterization, a lifetime test study is conducted including storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1 % of the original cell capacity.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/22f31cd04608a2fa02b0c2ca0fd7ae6744bd7c61
How researchers see this paper
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In Automotive Engineering
- Kailong Liu
- Widanalage Dhammika Widanage
- David A. Howey
- Rui Xiong
- Hongwen He
- Xinhua Liu
- Shichun Yang
- Michael G. Pecht
In Electrical and Electronic Engineering
- Widanalage Dhammika Widanage
- Kailong Liu
- Xinhua Liu
- Shichun Yang
- Billy Wu
- Hongwen He
- Rui Xiong
- Michael G. Pecht
Citations in the corpus
- Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “Most of the existing studies model the battery cyclic and calendar aging independently, and combine the two to make predictions under a dynamic load profile [6,94,95].”
- Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, methodology): “Schimpe et al. [25] for instance presented a comprehensive lifetime study on a LFP-Gr cell, whereby the SOC dependant calendar ageing was coupled to an anode potential curve, Tafel equation and Arrhenius rate law approach.”
- The challenge and opportunity of battery lifetime prediction from field data (2021, background): “To be effective with field data, feature-based data-driven methods should demonstrate that extracted features are independent of instantaneous operating conditions such as C-rate, temperature, or SOC.”
- A Review on Temperature-Dependent Electrochemical Properties, Aging, and Performance of Lithium-Ion Cells (2020, methodology): “[178] proposed a model for the temperature and SOC-dependent cyclic aging and investigated the dominant cyclic aging mechanism separately.”
- Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries (2018, background): “Although lithium-ion batteries in practice work under changeable currents, temperatures, and SOCs, the capacity degradation trends are expected to be similar under different working conditions [32], [33].”