2020 >

## Signal Processing Techniques in Condition Monitoring and Control of Power Electronics Converters and Battery System

Maher Al-Greer- **Watch Now** - Duration: 38:31

There are several fundamental challenges in condition monitoring, fault detection, and control of power electronics converters and Li-Ion batteries. These are linked to the computation complexity of the signal processing algorithms, suitability for on-line and real-time implementation, cost of implementation, ability to deal with rapid real-time changes, etc.

Advanced signal processing algorithms have been found to provide superior condition monitoring, system identification, and state estimation solutions to tackle the aforementioned issues with fast dynamic performance, cost-effective solution, high accuracy prediction, good tracking ability to system changes.

This talk offers basic theory and some recent advances of innovative signal processing algorithms for condition monitoring and control of power converters and batteries. The talk covers different system identification and parameter estimation of power converters, adaptive signal processing algorithms for on-line modelling and estimation of power converters, parametric/ non-parametric system identification, remaining useful prediction, and sate of health estimation of Lithium-Ion batteries. This talk is intended for researchers and engineers in the area of signal processing and artificial intelligence to explore new applications of signal processing algorithms in power electronics applications and battery management systems, and for postgraduate students in these fields.

**Leonard**

**Maher**Speaker

We are working currently on the cell level, not a module or pack level. 80% capacity means the battery is at end of life in EVS application.

**Leonard**

For the BMS model, how many charge cycles where used to develop the statistical model? How many failures where observed? How many different Cells and packs? Was an N sample size calculator used to determine the difference in measurements (mean)?

**Maher**Speaker

it is until the end of life of the battery. As I mentioned we have not developed a statistical model we use deep learning here.

**Leonard**

for example 3 on non-parametric system. it appears that both current and voltage is measured. I was not clear on if this was used for control or just for health monitoring. You mention statistical methods. Is this ML for prediction of health? or just classical statistics then algorithmic processing?

**Maher**Speaker

In Example 3, yes we measured both for voltage and current and estimate the impedance. This only used for junction temperature prediction. In example 4, we only use ML but there is work that has also been done using statistical signal processing.

**Leonard**

on example one and #2 what is being measured?

output voltage only? or output voltage and current?

**Maher**Speaker

Hi Leonard,

In both examples we measured voltage only; however, it is possible to measure current as well but you have to estimate the parameters of the current-control model (transfer function).

BMS: to develop the statistical model for health and end of life (EOL) were the batteries tested to failure? what constituted EOL? From a system perspective could it tell if and which individual cell was failing? if so, how was it done for parallel and serial BMS systems?