Data-driven state estimation of large-scale battery systems

The system states of a grid-connected battery energy storage system (BESS), state of charge (SOC) and state of health (SOH), are essential for its control to trade energy and provide services such as frequency response. There is significant work in estimating these states at cell-level, however, for a large-scale BESS these methods have not been examined before to see whether they can scale. There are a number of challenges with large BESS that need to be considered. The first is that these often contain 10,000-100,000s of cells where interconnections make the system more complex. The second is that estimation methods rely on accurate and reliable measurements of voltage and current, for large BESS where the range of the sensors is larger the errors will be higher. This thesis also considers the real-world scenario where data granularity, accuracy and quality is variable.

In this work it is shown how cell-level state estimation techniques can be utilised on large-scale BESSs using experimental data from a 2MW, 1MWh BESS. The results show how a Dual Sigma Point Kalman Filter (DSPKF) SOC estimation can provide improved accuracy over the integrated commercial battery management system SOC estimation. It is then demonstrated how the DSPKF parameters can be tuned by a genetic algorithm to simplify selection to generalise the application of the method for different BESSs. Using system round-trip efficiency (RTE) measurements, validation on the accuracy of the methodology is provided.

This thesis also proposes how the improved SOC estimation can be combined with a total least-squares (TLS) method for capacity estimation to less than 1% error. To achieve this an approach is presented for data selection that is required to minimize the error. Finally, parameters of the equivalent circuit model (ECM) of BESSs are estimated in the weight filter of the DSPKF and the results are validated by a voltage simulation process. Throughout the thesis online system state estimation is demonstrated using both designed test and real-world operational data where the BESS has provided contracted frequency response services to the GB National Grid.