Online capacity estimation of lithium-ion batteries with deep long short-term memory networks
By Weihan Li, N. Sengupta, Philipp Dechent, David A. Howey, Anuradha M. Annaswamy, Dirk Uwe Sauer. Journal of Power Sources, 2021
309 citations recorded by the source corpus.
Abstract There is an increasing demand for modern diagnostic systems for batteries under real-world operation, specifically for the estimation of their state of health, for example, via their remaining capacity. The online estimation of the capacity of a cell is challenging due to the dynamic nature of cell aging and the limited variety of inputs available from a cell under operation. The scope of this work is the development of a data-driven capacity estimation model for cells under real-world working conditions with recurrent neural networks having long short-term memory capability. Voltage-time sensor data from the partial constant current phase charging curve is used as input, reflecting input availability in the real world. The network achieves a best-case mean absolute percentage error of 0.76% and is extremely robust while handling input noise. It also has the ability to handle variations in the length of the input time series and can generate a viable estimation even with an incomplete collection of input due to sensor errors. The model validation with several scenarios is done in a local embedded device, highlighting the use case of such models in future battery management systems.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/2a6ed4805229e34cf7144059d387f815ac743f0b
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Citations in the corpus
- State-of-Health Estimation of Lithium-Ion Batteries by Fusing an Open Circuit Voltage Model and Incremental Capacity Analysis (2022, background): “, C/20 or C/25) to approximate the measurable terminal voltage toward the open circuit voltage (OCV), which is unfortunately impossible to guarantee in practice [35].”