Lifetime estimation of lithium-ion batteries for stationary energy storage systems

Abstract

With the continuing transition to renewable inherently intermittent energy sources like solar- and wind power, electrical energy storage will become progressively more important to manage energy production and demand. A key technology in this area is Li-ion batteries. To operate these batteries efficiently, there is a need for monitoring of the current battery state, including parameters such as state of charge and state of health, to ensure that adequate safety and performance is maintained. Furthermore, such monitoring is a step towards the possibility of the optimization of battery usage such as to maximize battery lifetime and/or return on investment. Unfortunately, possible online measurements during actual operation of a lithium-ion battery are typically limited to current, voltage and possibly temperature, meaning that direct measurement of battery status is not feasible. To overcome this, battery modeling and various regression methods may be used. Several of the most common regression algorithms suggested for estimation of battery state of charge and state of health are based on Kalman filtering. While these methods have shown great promise, there currently exist no thorough analysis of the impact of so-called filter tuning on the effectiveness of these algorithms in Li-ion battery monitoring applications, particularly for state of health estimation. In addition, the effects of only adjusting the cell capacity model parameter for aging effects, a relatively common approach in the literature, on overall state of health estimation accuracy is also in need of investigation. In this work, two different Kalman filtering methods intended for state of charge estimation: the extended Kalman filter and the extended adaptive Kalman filter, as well as three intended for state of health estimation: the dual extended Kalman filer, the enhanced state vector extended Kalman filer, and the single weight dual extended Kalman filer, are compared from accuracy, performance, filter tuning and practical usability standpoints. All algorithms were used with the same simple one resistor-capacitor equivalent circuit battery model. The Li-ion battery data used for battery model development and simulations of filtering algorithm performance was the “Randomized Battery Usage Data Set” obtained from the NASA Prognostics Center of Excellence.
It is found that both state of charge estimators perform similarly in terms of accuracy of state of charge estimation with regards to reference values, easily outperforming the common Coulomb counting approach in terms of precision, robustness and flexibility. The adaptive filter, while computationally more demanding, required less tuning of filter parameters relative to the extended Kalman filter to achieve comparable performance and might therefore be advantageous from a robustness and usability perspective. Amongst the state of health estimators, the enhanced state vector approach was found to be most robust to initialization and was also least taxing computationally. The single weight filter could be made to achieve comparable results with careful, if time consuming, filter tuning. The full dual extended Kalman filter has the advantage of estimating not only the cell capacity but also the internal resistance parameters. This comes at the price of slow performance and time consuming filter tuning, involving 17 parameters. It is however shown that long-term state of health estimation is superior using this approach, likely due to the online adjustment of internal resistance parameters. This allows the dual extended Kalman filter to accurately estimate the SoH over a full test representing more than a full conventional battery lifetime. The viability of only adjusting the capacity in online monitoring approaches therefore appears questionable. Overall the importance of filter tuning is found to be substantial, especially for cases of very uncertain starting battery states and characteristics.