Luenberger Observer for Lithium Battery State-of-Charge Estimation

One of the main concerns regarding energy storage systems during their normal operation is the possibility to perform an accurate state-of-charge estimation. This cannot be done by simple ampere-hour counting, unless drift correction means are put in place to avoid accumulation of measurement errors over time. In this paper, a state-of-charge estimation algorithm is widely analysed and tested on a nickel manganese cobalt oxide (NMC) lithium cell. The procedure consists of the utilisation of an equivalent electrical network battery model and the implementation of a Luenberger technique for a runtime correction, from the measure of battery’s voltage and current. Although application of Luenberger-style estimation is not new in literature for application to batteries, new expressions of battery model parameters and more detailed simulations are shown, to imply much higher estimation accuracy than in the past. After setting the model parameters, different test cycles have been considered, to evaluate the robustness of the proposed technique

Enhanced Kalman Filter-Based Identification of a Fuel Cell Circuit Model in Impedance Spectroscopy Tests

Model parameters identification plays an important role in enhancing the currently available diagnosis techniques for fuel cells (e.g. electrochemical impedance spectroscopy). In this work, the dual Kalman filter (DKF) has been used for the parametric identification of a Randles circuit model. The fuel cell has been stimulated with typical EIS input signals, and the results of the identification have been validated by using the impedance spectra produced by the Fouquet impedance model. The obtained results allow to infer a functional relation between the filter settings and the input signal, thus enabling the possibility of detecting faults by inspecting the deviation of model parameters

Critical Review of Ageing Mechanisms and State of Health Estimation Methods for Battery Performance

International audienceBattery Management System (BMS) is an essential component for lithium-ion battery-based devices. It provides a variety of functionalities that help to improve the overall lifespan of the battery, including states estimation algorithms. An accurate state of health is one of the essential features an advanced BMS provides, in order to track long-term performance and ensure reliable operation of the battery. This paper aims to provide a structured review of ageing mechanisms that affect the battery performance and also some of the most relevant algorithms that were used in the recent literature to predict the actual health of the battery. Finally, based on the different approaches that were studied, evaluation criteria are proposed to help judge the performance of State Of Health (SOH) estimation algorithm

An improved PSO for flexible parameters identification of lithium cells equivalent circuit models

Nowadays, the equivalent circuit approach is one of the most used methods for modeling electrochemical cells. The main advantage consists in the beneficial trade-off between accuracy and complexity that makes these models very suitable for the State of Charge (SoC) estimation task. However, parameters identification could be difficult to perform, requiring very long and specific tests upon the cell. Thus, a more flexible identification procedure based on an improved Particle Swarm Optimization that does not require specific and time consuming measurements is proposed and validated. The results show that the proposed method achieves a robust parameters identification, resulting in very accurate performances both in the model accuracy and in the SoC estimation task

Report on Lithium-Ion Battery Ageing Tests

Lithium-ion battery ageing modelling and prediction is one of the most relevant topics in the energy storage research field. The development and assessment of reliable solutions are not straightforward, because of the necessity to acquire information on the cell ageing processes by employing very time-consuming tests. During these tests the cells are subjected to different profiles, usually based on the repetition of several charge/discharge cycles, in order to reproduce the ageing effects in laboratory. This paper aims at accelerating the advancement in this research field by discussing a dataset containing three different ageing tests and making it available to be used by other research groups. The tests are accurately described and a preliminary analysis of the obtained results is carried out

Report on Lithium-ion Battery Ageing Tests

Lithium-ion battery ageing modelling and prediction is one of the most relevant topics in the energy storage research field. The development and assessment of reliable solutions are not straightforward, because of the necessity to acquire information on the cell ageing processes by employing very time-consuming tests. During these tests the cells are subjected to different profiles, usually based on the repetition of several charge/discharge cycles, in order to reproduce the ageing effects in laboratory. This paper aims at accelerating the advancement in this research field by discussing a dataset containing three different ageing tests and making it available to be used by other research groups. The tests are accurately described and a preliminary analysis of the obtained results is carried out