Editorial for modelling, monitoring and fault-tolerant control for complex systems

This is the editorial for the special issue entitled ``Modelling, Monitoring and Fault-Tolerant Control for Complex Systems'' published in the Open Automation and Control Systems Journal

Novel Degradation Diagnosis Algorithm for Solar Cell Modules by Taking into Account Electrical Characteristics and Ambient Factors

The Korean national policies have enforced to spread the RES (renewable energy sources) as one of the alternative solutions to solve the environmental pollution problems caused by global climate change. Under these circumstances, installations of solar cell systems are considered due to the global concern in eco-friendly and renewable solar energy sources. Nevertheless, electrical performance results and lifetime of solar cell modules installed outside of the buildings have been gradually reduced due to the varied outer ambient circumstances such as temperature, humidity, ultra-violet rays that may cause the reduction of solar cell modules efficiency. Therefore, to solve these issues, this study aims to present a diagnosis algorithm and also perform a diagnosis algorithm to reduce a condition of solar cell modules by taking into account ambient factors and the electrical performance which is energetically related to annual degradation of solar cell modules. Furthermore, this study focused on the implementation of the diagnosis system of solar cell modules with the proposed evaluation algorithm. In addition, the results show that the proposed evaluation and diagnosis system of solar cell modules are practical and useful tools for the improvement of operational efficiency in the solar cell system

Degradation and thermal performance of Li-ion batteries: implications for electric vehicles: Modelling the degradation and thermal performance of Li-ion batteries: implications for electric vehicles

Worldwide adoption of transport electrification creates a demand for optimised energy storage and control systems. This technology, despite being under strong development, has not yet reached its maturity and the electric vehicle (EV) performance envelope still needs improvement. This work investigates key challenges in battery management systems (BMS) and battery modelling for EVs, with a focus on ageing diagnostics, its effective inclusion within the BMS, and an accurate internal state estimation during high current applications i.e. fast charging. Entropy profiling was investigated as a new approach to tackle the challenge of battery degradation diagnosis. This method leverages the interpretation of temperature and concentration dependencies of cell voltage to provide insight into the morphological changes experienced during battery life. The study finds that entropy profiling can successfully track ageing markers in a complementary way to differential voltage analysis, making it a useful battery diagnostics tool. Even if degradation diagnosis is performed successfully, the inclusion of ageing information into a BMS is problematic. As an alternative, a periodic model parameter update is proposed here. The impact of this work was two-fold. Firstly, it highlights how the single particle model can accurately simulate both pristine and aged cell voltage responses with appropriate parameter updates. Secondly, it provides qualitative insight into the impact of ageing on model parameters, informing safety issues such as increased heat generation. The prediction of heat generation during fast charging is a significant concern when considering the safety and performance of batteries. To address this issue, a pseudo-3D thermal-continuum model was proposed and tested up to 10C. The results showed that the fast diffusion encountered in high-power cells allows for substantial model simplifications without compromising prediction accuracy

Determination of model parameters of PV modules using a low cost I-V tracer

Tracing current-voltage characteristics of photovoltaic modules in open field conditions is a very important task in degradation and fault diagnosis. Usually this is done by using expensive DC electronic loads, many times not suitable for outdoor tests. The health state of photovoltaic modules can be evaluated by monitoring the parameters of its exponential model, mainly the series resistance. This can be done by tracing the current-voltage characteristics in field conditions from which those model parameters can be extracted. This paper presents the determination of the parameters of the exponential model of photovoltaic modules from experimental current-voltage curves using a low cost I-V tracer developed in previous works

Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world

Application of a method to diagnose the source of performance degradation in MPC systems

Model Predictive Control systems may suffer from performance degradation mainly for two reasons: (i) external unmeasured disturbances are not estimated correctly, (ii) the (linear) dynamic model used by the MPC does not match (any longer) the actual process response. In this work we present the application of a method to detect when performance is not optimal, to diagnose the source of performance degradation and to propose appropriate corrections. In the simplest situation (i), optimal performance can be restored by recomputing the estimator parameters; in the other case (ii), re-identification becomes necessary. The method is based on analysis of the prediction error, i.e. the difference between the actual measured output and the corresponding model prediction, and uses three main tools: a statistical (whiteness) test on the prediction error sequence, a subspace identification method to detect the order of the input-to-prediction error system, and a nonlinear optimization algorithm to recompute optimal estimator parameters. We illustrate the effectiveness of the method on a large-scale rigorously simulated industrial process. Copyright © 2013, AIDIC Servizi S.r.l

Battery aging impedance spectroscopy and incremental capacity analysis

We analize electrochemical LiFePO4 cells with impedance spectroscopy and incremental capacity analysis in order to establish a correlation with capacity fade. We found that polarization and diffusion impeances increased with aging, but at different rates depending on the aging stage. This aging stage dependence was also found in ICA analyzes, where the lithium intercalation was investigated. A correlation with capacity decrease measured with Coulomb counting was established.Postprint (author's final draft