Battery (electricity)

Lithium cobalt oxide

Identifiability

Control (management)

Engineering

State of charge

Lithium-ion battery

Electrochemistry

Materials science

Cathode

Power (physics)

Computer science

Physical chemistry

Thermodynamics

Physics

Electronic engineering

Artificial intelligence

Sensitivity (control systems)

Chemistry

Electrode

Control theory (sociology)

Machine learning

Electrical engineering

Applied Energy

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.

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