| Abstract: |
SuperCapacitor (SC) is one solution as Energy Storage System with batteries, fuel cells and flywheels, each of them have specifics properties depending on their use. In Electric Vehicle (EV), they can be used along the main battery to benefit from their complementary characteristics. As technology continues to advance, exploiting their high power density, wide operating temperature range and long cycle life, their use in various industries and applications is likely to grow.
Recently, Supercapacitors have undergone various improvements which make it possible to extend their use, thus causing a resurgence of interest in the study of SCs. In the case of EV, the SC is part of a Management Systems (MS) to optimize energy usage, extend supercapacitor lifespan, and enhance overall system performance. For the MS, it is necessary to provide SOx (x=charge, health, energy) informations based only on a few measurements (voltage, current). To prevent overheating, voltage pikes or abnormalities, the MS need real-time monitoring, then an estimation of useful parameters. Each online method has advantages and disadvantages depending on the objectives. Among these, we can find observer-based, filtering-based, data-driven techniques. The observer-based methods achieve high accuracy and have good computational efficiency, which makes them suitable for embedded implementation in MSs.
Notable results are obtained by the use of an UKF, GESO or hybrid observer. To avoid a calculation overload, they are obtained either with very simple models and fairly simplistic current profiles.
For model-based techniques, quality and complexity of the model are crucial points. From a computational efficiency and implementation point of view, an Equivalent Circuit Model model is suitable. The simplest model failed to reflect some important phenomena (self-discharge, diffusion behaviour) but is enough in an ageing goal with stepwise current profile (Magarotto et al, “A New Observer Design for Aging Detection of Supercapacitors”, JDSMC 2019). It turned out to be a better alternative than the Spectroscopy method.
To better reflect EV reality, new current profile (from NEDC or WLTP norms) must be adopted. This is quite rare in observer-based research work, because of the strong dynamics of the input and the resulting difficulties, even more considering a larger sampling time. The use of an inter-sampled observer-predictor, with the aid of Lyapunov theory, has shown, in a different context (Magarotto et al, “A new sampled-data observer design for bioreactors”, CODIT22), that it could provide solutions to this kind of problem. Applying it to Supercapacitors, recent results were obtained with real data but with simple model (Magarotto et al, “Sampled-data observer for supercapacitor parameters estimation under NEDC cycles”, CODIT23).
Our current work focuses on an extension to multi-branch models. First results give good estimates of voltages with a gain tuning for the trade-off between sampling–time and performances. Our actual objective is to design an hybrid exponentially convergent observer to achieve the same quality of parameters estimation with NEDC/WLTP profiles while respecting the minimum core consumption (increasing sampling period).
Finally, they are still many challenges to overcome: one must explore robustness (to measurement noise) and the use of a diffusion model which lead to a natural extension of observers for PDE. |