In this paper, we propose an online estimation method for the internal voltages of a three-branch super-capacitor model. The main goal is to provide a fast and accurate estimation of these voltages, which are crucial for assessing the state of charge (SOC) for the management system. To achieve this, we design a decoupled continuous nonlinear observer, whose outputs are discretized using a zero-order hold (ZOH). Simulation results demonstrate that the observer delivers high-performance estimation, even under fast dynamic input conditions, such as those encountered in WLTP cycles.

In this paper, based on a two-branch model, an online internal voltages estimation method of a supercapacitor is proposed. The goal is to provide a fast and accurate online estimation of such voltages as they are useful for estimating its State Of Health (SOH) and/or State Of Charge (SOC). An hybrid nonlinear observer is designed to estimate voltages using sampled measurements. This kind of observer uses a correction term in the prediction step with the aim of performing with large sampling period, then it is suitable for efficient online estimation. Simulation results highlight good performances of the observer in online parameters estimation in the case of fast dynamics inputs under NEDC and WLTP cycles.

The aim of this paper is to propose a novel sampled-data high-gain observed-based control for a class of nonlinear systems. More precisely, we consider the case where we have a Zero-Order-Hold (ZOH) device at the input and sampled output. Sufficient conditions ensuring the stability of the overall closed-loop are derived by using a suitable Lyapunov functional.

In this paper, a parameters estimation method of a supercapacitor is proposed through NEDC cycles. The goal is to provide a fast and good online estimation of key parameters (resistance and capacitance) as they are useful for estimating of its State Of Charge (SOF) and State Of Health (SOH). A new continuous-discrete nonlinear observer is designed to estimate, both states and parameters using sampled measurements. This observer is proved to be globally exponentially convergent by a sufficient condition about the sampling time. Experimental results highlight good performances of the observer in online parameters estimation.

In this paper, an inter-sampled observer-predictor is designed for a class of system which is characterized by sampled data measurements. The main drawback of numerous approaches lead to small or limited sampled time period, which is an important point in industrial processes. To overcome this difficulty, we propose a modified inter-sampled observer-predictor which ensures an exponential convergence and gives the ability to push the sampled time period to its limit. Comparing to other observer structures, our design is char-acterized by its simplicity because we only need a tuning gain. Our main contribution is to show that our modified observer allows to widen the sampling period as much as possible, while guaranteeing the exponential convergence of our observer. The effectiveness of our proposed approach is shown through numerical simulations which have been performed on a well-known bioreactor example.