Fault detection of new and aged lithium-ion battery cells in electric vehicles

The main objective of this research is to develop a fault detection and isolation (FDI) methodologies for Cylla-Haase polymerization reactor, and implement the developed methods to the nonlinear simulation model of the proposed reactor to evaluate the effectiveness of FDI methods. The first part of this research focus of this chapter is to understand the nonlinear dynamic behaviour of the Chylla-Haase polymerization reactor. In this part, the mathematical model of the proposed reactor is described. The Simulink model of the proposed reactor is set up using Simulink/MATLAB. The design of Simulink model is developed based on a set of ordinary differential equations that describe the dynamic behaviour of the proposed polymerization reactor. An independent radial basis function neural networks (RBFNN) are developed and employed here for an on-line diagnosis of actuator and sensor faults. In this research, a robust fault detection and isolation (FDI) scheme is developed for open-loop exothermic semi-batch polymerization reactor described by Chylla-Haase. The independent (RBFNN) is employed here when the system is subjected to system uncertainties and disturbances. Two different techniques to employ RBF neural networks are investigated. Firstly, an independent neural network is used to model the reactor dynamics and generate residuals. Secondly, an additional RBF neural network is developed as a classifier to isolate faults from the generated residuals. In the third part of this research, a robust fault detection and isolation (FDI) scheme is developed to monitor the Chylla-Haase polymerization reactor, when it is under the cascade PI control. This part is really challenging task as the controller output cannot be designed when the reactor is under closed-loop control, and the control action will correct small changes of the states caused by faults. The proposed FDI strategy employed a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. In this research, an independent MLP neural network is implemented here to generate residuals for detection task. And another RBF is applied for isolation task performing as a classifier. The fault diagnosis scheme is developed for a Chylla-Haase reactor under open-loop and closed-loop control system. The comparison between these two neural network architectures (MPL and RBF) are shown that RBF configuration trained by (RLS) algorithm have several advantages. The first one is greater efficiency in finding optimal weights for field strength prediction in complex dynamic systems. The RBF configuration is less complex network that results in faster convergence. The training algorithms (RLs and ROLS) that used for training RBFNN in chapter (4) and (5) have proven to be efficient, which results in significant faster computer time in comparison to back-propagation one. Another fault diagnosis (FD) scheme is developed in this research for an exothermic semi-batch polymerization reactor. The scheme includes two parts: the first part is to generate residual using an extended Kalman filter (EKF), and the second part is the decision making to report fault using a standardized hypothesis of statistical tests. The FD simulation results are presented to demonstrate the effectiveness of the proposed method. In the lase section of this research, a robust fault diagnosis scheme for abrupt and incipient faults in nonlinear dynamic system. A general framework is developed for model-based fault detection and diagnosis using on-line approximators and adaptation/learning schemes. In this framework, neural network models constitute an important class of on-line approximators. The changes in the system dynamics due to fault are modelled as nonlinear functions of the state, while the time profile of the fault is assumed to be exponentially developing. The changes in the system dynamics are monitored by an on-line approximation model, which is used for detecting the failures. A systematic procedure for constructing nonlinear estimation algorithm is developed, and a stable learning scheme is derived using Lyapunov theory. Simulation studies are used to illustrate the results and to show the effectiveness of the fault diagnosis methodology. Finally, the success of the proposed fault diagnosis methods illustrates the potential of the application of an independent RBFNN, an independent MLP, an Extended kalman filter and an adaptive nonlinear observer based FD, to chemical reactors.

The battery critical functions such as State-of-Charge (SoC) and State-of-Health (SoH) estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee the battery performance, safety and life. This paper presents a systematic model-based fault diagnosis scheme for a battery cell to detect current or voltage sensor faults. The battery model is developed based on the equivalent circuit technique. For the diagnostic scheme implementation, the extended Kalman filter (EKF) is used to estimate the terminal voltage of battery cell, and the residual carrying fault information is then generated by comparing the measured and estimated voltage. Further, the residual is evaluated by a statistical inference method that determines the presence of a fault. To highlight the importance of battery sensor fault diagnosis, the effects of sensors faults on battery SoC estimation and possible influences are analyzed. Finally, the effectiveness of the proposed diagnostic scheme is experimentally validated, and the results show that the current or voltage sensor fault can be accurately detected.

In this paper, an adaptive observer is proposed to detect voltage sensor fault and state of charge fault in a battery management system of an electric vehicle considering the ageing effect. Aging mechanism in lithium-ion batteries will lead to a decrease in capacity and a rise in resistance of the whole cell. In designing the observers, calendar and cycle ageing effects on the capacity and resistance of the battery cell are modeled as unknown parameters, and adaptive observers will update these estimating ageing parameters to have a more accurate fault detection for aged batteries. Some Simulations have been conducted on a Li-ion cell to demonstrate the performance of the proposed approach.

A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

Fault detection and diagnosis strategy based on AMT (Automatic Mechanical Transmission) system trajectory is studied. Hybrid automata is used to build healthy AMT system model. Then fault definition of AMT system is analyzed from the point of system trajectory and fault diagnosability of AMT hybrid system is studied based on AMT system's fault model and fault threshold function. According to AMT system model trajectories and control cycle, AMT hybrid system's behavior is analyzed and classified into three types: steady-state system behavior, discrete event trigger behavior and physical system response behavior. At last AMT system's fault detection and diagnosis strategy is proposed based on behavior types. The fault diagnosis strategy with the diagnostic algorithm is transplanted to the real car platform and a large number of vehicle mileage prove the strategy's correctness and real-time.

In this paper the problem of adaptive state observer synthesis for linear time-varying SISO (single-input-single-output) dynamical system with partially unknown parameters was considered. It is assumed that the input signal and output variable of the system are measurable. It is also assumed that the state matrix of the plant contains known variables and unknown constants when the input matrix (vector) is unknown. Observer synthesis is based on GPEBO (generalized parameter estimation based observer) method proposed in [1]. Observer synthesis provides preliminary parametrization of the initial system and its conversion to a linear regression model with further unknown parameters identification. For identification of the unknown constant parameters classical estimation algorithm — least squares method with forgetting factor — was used. This approach works well in cases, when the known regressor is " frequency poor" (i.e. the regressor spectrum contains r/2 harmonics, where r is a value of the unknown parameters) or does not meet PE (persistent excitation) condition. To illustrate performance of the proposed method, an example is provided in this paper. A time-varying second-order plant with four unknown parameters was considered. Parametrization of the initial dynamical model was made. A linear static regression with six unknown parameters (including unknown state initial conditions vector) was obtained. An adaptive observer was synthesized and the simulation results were provided to illustrate the purpose reached. The main difference with the results, that were published earlier in [2], is the new assumption that not only does the state matrix of the linear time-varying system contain unknown parameters, but input matrix (vector) contains unknown constant coefficients.

A unified framework of fault detection and diagnosis based on fractional-order chaos system

Utilizing the organic Rankine cycle (ORC) for waste heat recovery is an important energy conversion method. Some faults may occur in the ORC in actual operation, but few studies have focused on the fault detection and diagnosis of the whole ORC system. Fault detection detects whether a fault occurs in the system and fault diagnosis diagnoses where the fault is. This paper investigated a fault detection and diagnosis scheme of the ORC system for waste heat recovery based on machine learning. First, a thermodynamic ORC model was established. Three kinds of faults (expander fault, pump fault, and heat exchanger fault) and three kinds of algorithms [logistic regression, softmax regression, and support vector machines (SVMs)] were described. The data of four major important faults (fouling fault of the evaporator and of the condenser, looseness of the mechanical moving parts in the expander, and blocking of the pump) were generated from the thermodynamic ORC model and used to train the fault detection and diagnosis schemes. To evaluate the accuracy of the fault detection and diagnosis schemes, a set of experimental data was employed to test the schemes. The accuracy scores of fault detection using logistic regression and support vector machines were 77.42% and 96.77%, respectively. The accuracy scores of fault diagnosis using softmax regression and SVM were 91.78% and 94.52%, respectively. The test times of fault diagnosis using softmax regression and SVM were 0.0099 and 0.0085 s, respectively. The results demonstrated that machine learning–based fault detection and diagnosis schemes for the ORC have high accuracy and immediacy. Therefore, the proposed schemes are promising tools for fault detection and diagnosis of the ORC system for waste heat recovery.

One of the key issues in the design of fault detection and diagnosis (FDD) schemes for hydraulic systems is the effect of model uncertainties such as severe parametric uncertainties and unmodeled dynamics on their performance. This paper presents the application of a nonlinear model based adaptive robust observer (ARO) to the fault detection and diagnosis of some common faults that occur in hydraulic systems. The ARO presented in this paper is designed by explicitly taking into account the nonlinear system dynamics. Some robust filter structures are designed to attenuate the effect of model uncertainties and controlled online parameter adaptation helps in reducing the extent of model uncertainty and in increasing the sensitivity of the fault detection scheme to help in the detection of incipient failure. The state and parameter estimates are continuously monitored to detect any off-nominal system behavior even in the presence of model uncertainty. Typical faults in hydraulic cylinders like sensor failure, fluid contamination, and lack of sufficient supply pressure are considered in this paper. Simulation results on the swing-arm of a three degree of freedom hydraulic robot are presented to demonstrate the effectiveness of the proposed scheme.

Fault detection and diagnosis for industrial systems has been an important field of research during the past years. Among these systems, the Tennessee Eastman process is extensively used as a realistic benchmark to test and compare different fault detection and diagnosis strategies. In this context, data-driven approach has been widely applied for fault detection and diagnosis of the Tennessee Eastman process, by exploiting the massive amount of available measurement data. However, only few published works had attempted to deal with the dynamic behavior of the whole system including the mixing zone, circulating pumps, the reactor, the separator, the stripper, and so on, because of the difficulty of modeling physical phenomena that may occur in such complex system. In this article, an accurate model of the Tennessee Eastman process, properly tailored for fault detection and diagnosis purposes, is provided. This model shows better fault detection and diagnosis performances than all the others proposed in the literature and gives better or comparable results with the data-driven approaches. This work uses the bond graph methodology to systematically develop computational and graphical model. This methodology provides a physical understanding of the system and a description of its dynamic behavior. The bond graph model is then used for monitoring purposes by generating formal fault indicators, called residuals, and algorithms for fault detection and diagnosis. Hence, abnormal situations are detected by supervising the residuals’ evolution and faults are isolated using the nature of the violated residuals. Therefore, the dynamic model of the Tennessee Eastman process can now be used as a basis to achieve accurately different analysis through the causal and structural features of the bond graph tool.

Due to the rapidly increasing demand for electric vehicles, the need for battery cells is also increasing considerably. However, the production of battery cells requires enormous amounts of energy, which is expensive and produces greenhouse gas emissions. Here, by combining data from literature and from own research, we analyse how much energy lithium-ion battery (LIB) and post lithium-ion battery (PLIB) cell production requires on cell and macro-economic levels, currently and in the future (until 2040). On the cell level, we find that PLIB cells require less energy than LIB cells per produced cell energy. On the macro-economic level, we find that the energy consumption for the global production of LIB and PLIB cells will be 130,000 GWh if no measures are taken. Yet, it is possible to optimize future production and save up to 66% of this energy demand.

In this paper, a robust fault detection and diagnosis scheme using neural state space models has been developed for a class of nonlinear systems. The neural state space models are adopted to estimate the modeling uncertainties in the states and outputs of the system. Subsequently, a residual is generated to identify the characteristics of the fault. Moreover, the robustness, sensitivity and stability properties of the proposed fault detection and diagnosis scheme are rigorously derived. Finally, the neural state space model based fault detection and diagnosis scheme is applied to a satellite attitude control system and the simulation results demonstrated its good performance.

One of the most critical components of a robotic system is the actuator, which undergoes a lot of wear and tear and may lead to its failure. In order to monitor such a system, we propose a neural network-based fault detection and diagnosis scheme for actuator failures in robotic manipulators. A single detection and diagnostic observer is utilized for online failure assessment and the weights of the failure online approximators are adaptively updated using Lyapunov re-design methods. The fault detection scheme is implemented for a SCARA manipulator and simulation results are presented.

Many fault detection and diagnosis schemes are based on the concept of comparing the plant output with a model in order to generate residues. A fault is deemed to have occurred if the residue exceeds a predetermined threshold. Unfortunately, the practical usefulness of model-based fault detection schemes is limited because of the difficulty in acquiring sufficiently rich experimental data to identify an accurate model of the system characteristics. This paper aims at developing a generic neurofuzzy model-based strategy for detecting broken rotor bars, which is one of the most common type of faults that may occur in a squirrel-cage induction motor. A neurofuzzy model that captures the generic characteristics of a class of asynchronous motor is the key component of the proposed approach. It is identified using data generated by a simulation model that is constructed using information on the name plate of the motor. Customization for individual motors is then carried out by selecting the threshold for fault detection via an empirical steady-state torque-speed curve. Since data obtained from a practical motor are used to select the threshold and not to build a complete model, the objective of reducing the amount of experimental input-output data required to design a model-based fault detector may be realized. Experimental results are presented to demonstrate the viability of the proposed fault detection scheme.

The task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.