An integrated approach to battery health monitoring using bayesian regression and state estimation

The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of relevance vector machine (RVM), and to state estimation via particle filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of-life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures. Batteries, composed of multiple electrochemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the support vector machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.