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Physics informed Li-ion cell parameter estimation using characteristic response isolation
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AI
DOI：10.1016/j/est.2020.101962.png)
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摘要
En 中文
Next generation of Battery Management Systems (BMS) are moving towards implementing electrochemical models for Li-ion cell and pack state estimation modules, for better accuracy at a wide range of operating conditions. To enable this, the knowledge of geometric, kinetic and transport parameters used in electrochemical model of the cell become crucial for operation and performance of the BMS. However, most of these parameters are proprietary and are not provided by the battery supplier. Thus it becomes important for the BMS developer to obtain these properties for any new given cell in a quick, cost effective and non-destructive manner. Here, we propose development of an approach which uses Electrochemical Impedance Spectroscopy (EIS) and purpose designed cycling data from any given cell, in conjunction with a suite of electrochemical models, to estimate parameters of the cell. The sensitivities of the models to these parameters are established using Monte Carlo Simulation and the p-value calculation method. Then individual parameters are estimated using the models to which they show higher sensitivity. We call this mapping of parameters to responses of the cell as 'Characteristic Response Isolation'. A Particle Swarm based optimization scheme with Kalman correction is applied to estimate the parameters. It is seen that the use of multiple responses to estimate different sets of parameters leads to higher accuracy, lesser computation time compared to using only voltage error minimization. The results obtained through simulation show low error in the estimated parameters and good matching of the voltage and current profile. The same approach applied on data generated for commercial cell shows good accuracy in voltage estimation in normal operating regime.
Keyword:
Lithium ion battery
Parameter estimation
Electrochemical impedance spectroscopy (EIS)
Reduced order model (ROM)
Particle swarm optimization (PSO)
Sensitivity analysis
