Monitoring and Diagnosis of Manufacturing Systems Using Timed Coloured Petri Nets

Novel fault modelling and integration method were applied in the case when the faultless operation of the system was modelled by a high-level, coloured Petri net. In order to achieve realistic investigations, a timed coloured Petri net model of the system was constructed, where faults can occur in the manufacturing lines. The faultless and fault containing models were implemented in CPNTools both for non-timed and timed cases. The resulted model was investigated both via simulation and using the occurrence graph. For efficient analysis of the occurrence graph a software module called OGAnalyser was developed

Temperature Dependent Parameter Estimation of Electrical Vehicle Batteries

Parameter estimation of electrical vehicle batteries in the presence of temperature effect is addressed in this work. A simple parametric temperature dependent battery model is used for this purpose where the temperature dependence is described by static relationships. A two-step method is used that includes a parameter estimation step of the key parameters at different temperatures followed by a static optimization step that determines the temperature coefficients of the corresponding parameters. It was found that the temperature dependent parameter characteristics can be reliably estimated from charging profiles only. The proposed method can be used as a computationally effective way of determining the key battery parameters at a given temperature from their actual estimated values and from their previously determined static temperature dependence. The proposed parameter estimation method was verified by simulation experiments on a more complex battery model that also describes the detailed dynamic thermal behavior of the battery

Design of Experiments for Battery Aging Estimation

Li-ion batteries are widely used in EV applications and are imposed to several aging effects during their lifetime. Since battery health cannot be measured directly, information about its health can be obtained by iteratively re-estimating the parameters of the model describing its dynamical behavior. The optimal design of experiments is investigated in this paper. The proposed method applies families of input signals (PRBS current and constant current-constant voltage (CC-CV) signals) to the batteries to estimate the key aging factors. Simulation experiments have been used to analyze the statistical properties of the estimators as a function of the design parameters of the input signal families. The results show that the CC-CV charging-discharging cycle has the possibility to gain the most information out of the battery model parameter estimation

An Optimized Fuzzy Controlled Charging System for Lithium-Ion Batteries Using a Genetic Algorithm

Fast charging is an attractive way of charging batteries; however, it may result in an undesired degradation of battery performance and lifetime because of the increase in battery temperature during fast charge. In this paper we propose a simple optimized fuzzy controller that is responsible for the regulation of the charging current of a battery charging system. The basis of the method is a simple dynamic equivalent circuit type model of the Li-ion battery that takes into account the temperature dependency of the model parameters, too. Since there is a tradeoff between the charging speed determined by the value of the charging current and the increase in temperature of the battery, the proposed fuzzy controller is applied for controlling the charging current as a function of the temperature. The controller is optimized using a genetic algorithm to ensure a jointly minimal charging time and battery temperature increase during the charging. The control method is adaptive in the sense that we use parameter estimation of an underlying dynamic battery model to adapt to the actual status of the battery after each charging. The performance and properties of the proposed optimized charging control system are evaluated using a simulation case study. The evaluation was performed in terms of the charge profiles, using the fitness values of the individuals, and in terms of the charge performance on the actual battery. The proposed method has been evaluated compared to the conventional contant current-constant voltage methods. We have found that the proposed GA-fuzzy controller gives a slightly better performance in charging time while significantly decreasing the temperature increase

An Optimized Fuzzy Controlled Charging System for Lithium-Ion Batteries Using a Genetic Algorithm

Fast charging is an attractive way of charging batteries; however, it may result in an undesired degradation of battery performance and lifetime because of the increase in battery temperature during fast charge. In this paper we propose a simple optimized fuzzy controller that is responsible for the regulation of the charging current of a battery charging system. The basis of the method is a simple dynamic equivalent circuit type model of the Li-ion battery that takes into account the temperature dependency of the model parameters, too. Since there is a tradeoff between the charging speed determined by the value of the charging current and the increase in temperature of the battery, the proposed fuzzy controller is applied for controlling the charging current as a function of the temperature. The controller is optimized using a genetic algorithm to ensure a jointly minimal charging time and battery temperature increase during the charging. The control method is adaptive in the sense that we use parameter estimation of an underlying dynamic battery model to adapt to the actual status of the battery after each charging. The performance and properties of the proposed optimized charging control system are evaluated using a simulation case study. The evaluation was performed in terms of the charge profiles, using the fitness values of the individuals, and in terms of the charge performance on the actual battery. The proposed method has been evaluated compared to the conventional contant current-constant voltage methods. We have found that the proposed GA-fuzzy controller gives a slightly better performance in charging time while significantly decreasing the temperature increase

Diagnosis of Technological Systems Based on the Structural Decomposition of their Coloured Petri Net Model

Diagnosing faults during the operation of a system is an essential task when investigating technological systems. In this paper, a new online fault identification method is proposed which is based on the occurrence graph of the coloured Petri net model of the system. The model is able to simulate the normal and faulty operations of the system given in the form of event lists, so called traces. The diagnosis is based on the search for deviations between the traces of the normal and the actual operations. In the case of complex technological systems, the occurrence graph can contain hundreds of nodes; therefore, the computational effort and searching-time increase significantly. Our proposed structural decomposition method can manage these demands so it has a crucial impact on the practical application of diagnostic processes. The main idea of our method is that the complex systems can be decomposed into technological units. Therefore, the diagnosis can be done by components separately and the diagnostic result of a unit can be used for the diagnosis of the other units connected to it. Because of the structural decomposition, the diagnosis has to be performed on much smaller occurrence graphs but the effect of faults in previous units is taken into account. The proposed method is illustrated by a simple case study