Bayesian Monte Carlo Algorithm predicting the end of useful performance with a probability density function of the time to reach the threshold value

The problem of state estimation must be considered in the context of the entire BMS. Certain applications may have restrictions on the types of data that can be collected. For example, a BMS in an electric vehicle can rely on frequent discharge data in order to make SOC estimations, where as a BMS in a standby power supply will be required to make state estimations off-line due to infrequent use. Therefore the type of sensors available to a BMS must be considered when developing a state estimation algorithm. In order for these algorithms to be used effectively they must interact with other subsystems of the BMS. If SOH monitoring is applied to individual cells in a multi-cell battery pack, then SOH can be used to determine when to perform cell balancing. If a voltage measurement largely disagrees with the modeled voltage in the SOC algorithm, then a fault condition could be triggered and the BMS should stop current flow through the battery. A high level schematic which outlines some of the interactions between subsystems of a BMS is shown below

High level BMS schematic