Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries

By Weihan Li, Decheng Cao, Dominik Jöst, Florian Ringbeck, Matthias Kuipers, Fabian Frie, Dirk Uwe Sauer. Applied Energy, 2020

193 citations recorded by the source corpus.

Accurate identification of physical parameters of a lithium-ion electrochemical model is of critical importance for next-generation battery management systems. The complexity of the electrochemical model increases the difficulty of the identification process, and hence the analysis of parameter identifiability is the cornerstone for accurate parameter identification. The overarching goal of this paper is to analyze the parameter sensitivity of an electrochemical model under both the charging process and real-world driving cycles. The boundaries for the sensitivity analysis of 26 physical parameters are determined with a systematic benchmarking of published parameters for lithium Nickel-Manganese-Cobalt-Oxide/graphite cells. In particular, the sensitivity of the parameters is analyzed not only for terminal voltage but also for essential states in an electrochemical model-based battery management system, e.g., cathode bulk state of charge, cathode surface state of charge and anode potential. The sensitivity matrices of the parameters under different C-rates and depth of discharge regions clearly show their different influences on capacity-related parameters and other parameters. Furthermore, the rankings of the normalized parameter sensitivity index provide us the identifiability of the parameters, as well as the influence of parameter inaccuracy on the main functionalities in an electrochemical model-based battery management system.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3ccddfbeafd4498ac54d11dfaa241dfb372807a5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, background): “Towards understanding this, Li et al. [20] investigated how identiﬁable these P2D models are by conducting a sensitivity study on a NMC-Gr cell by looking at 26 physical parameters.”
Online capacity estimation of lithium-ion batteries with deep long short-term memory networks (2021, background): “Models can be of several types, such as equivalent circuit models [3,10 – 16] or electrochemical models [17 – 24].”