Evaluation of battery modules state for electric vehicle using artificial neural network and experimental validation

Artificial neural network (ANN) models have been used in the past to model surface roughness in manufacturing processes. Specifically, different parameters influence surface roughness in fused filament fabrication (FFF) processes. In addition, the characteristics of the networks have a direct impact on the performance of the models. In this work, a study about the use of ANN to model surface roughness in FFF processes is presented. The main objective of the paper is discovering how key ANN parameters (specifically, the number of neurons, the training algorithm, and the percentage of training and validation datasets) affect the accuracy of surface roughness predictions. To address this question, 125 3D printing experiments were conducted changing orientation angle, layer height and printing temperature, and measuring average roughness Ra as response. A multilayer perceptron neural network model with backpropagation algorithm was used. The study evaluates the effect of three ANN parameters: (1) number of neurons in the hidden layer (4, 5, 6 or 7), (2) training algorithm (Levenberg–Marquardt, Resilient Backpropagation or Scaled Conjugate Gradient), and (3) data splitting ratios (70%–15%–15% vs. 55%–15%–30%). Mean Absolute Error (MAE) was used as the performance metric. The Resilient Backpropagation algorithm, 7 neurons, and using 55% of training data yielded the best predictive performance, minimizing the MAE. Additionally, the impact of the dataset size on prediction accuracy was analysed. It was observed that the performance of the ANN gets worse as the number of datasets is reduced, emphasizing the importance of having sufficient data. This study will help to select appropriate values for the printing parameters in FFF processes, as well as to define the characteristics of the ANN to be used to model surface roughness.

Corrosion inhibitor is often opted as a corrosion protection method for various industries worldwide. The development of eco-friendly corrosion inhibitor has become a trending concern due to the various environmental regulations impose by several countries. However, a laboratory testing would be such a tedious, costly and time-consuming process. Therefore, artificial neural network (ANN) has been used extensively to predict the verdict based on the experimental values. In this study, 3-layered Multilayer Perceptron (MLP) models were developed with 3 inputs (Electrochemical Impedance Spectroscopy, Ω.cm2), (Potentiodynamic polarization, A/cm2), (weight loss, %), and one output (corrosion rate, mm.yr−1). The data were divided into three parts; 70%, 15%, and 15% for model development, model validation and model testing, respectively. Three training algorithms were tested during the model development, including the Levenberg-Marquadt (LM), Bayesian Regularization (BR), and Scale Conjugate Gradient (SCG). Results revealed that the best MLP models during the development were using neuron number 4 (r = 0.99272), 6 (r = 0.99155), and 2 (r = 0.98624) for LM, BR and SCG, respectively. Among the three training algorithms, LM is opted as the best training algorithm for the corrosion rate prediction which executed high correlation coefficient (R) values during development (R = 0.99272), validation (R = 0.99905), and testing (R = 0.97908). These findings will be an essential tool for the model development with the sole purposes of predicting the corrosion rate in line to ensure the exact time for repair and maintenance schedule.

This paper presents an Artificial Neural Network (ANN) design for a chaotic generator, and the training performances for a three layer ANN architecture with different number of hidden neurons. Chaotic systems can be synchronized and used for secure communication. Chaotic systems such as Lorenz attractor, Rossler attractor and Chen's system are generally implemented directly based on their definitions represented by a unique group of ordinary differential equations (ODEs). An feed forward ANN can be trained using the output values of a chaotic system. The training process is carried out on a computer and the weights are generated for all neurons in an ANN architecture. These weights are then used for a trained ANN architecture model to generate the expected output for the target chaotic system. The complexity of the ANN architecture defines the implementation cost and speed. Therefore it is beneficial to use less number of hidden neurons to achieve the target training performance. Lorenz attractor has its significance in studying chaotic systems and is used as the design subject in this paper. The 3-layer ANN has one input, one hidden and one output layer. The ANN architecture with 1 to 16 hidden neurons is designed and trained respectively using MATLAB Neural Network Toolbox with three training algorithms: Levenberg-Marquardt, Scaled Conjugate Gradient algorithm and Bayesian Regulation. The optimized ANN architecture can be used to improve the efficiency of the fixed-point implementation on an Field Programmable Gates Array (FPGA) device.

Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling

Introduction Atmospheric corrosion is the main cause of metallic heritage degradation when they are exposed to relative humidity, temperature cycle and air contaminants. Corrosivity maps are one of the preventive strategies for atmospheric corrosion mitigation, but they do not provide enough information about corrosion mechanism, besides that long experimentation periods are required [1]. Moreover, electrochemical techniques are alternatives to speed up results, but their relationship with constantly changing environmental variables is difficult to control.Currently, Artificial Intelligence has been introduced in corrosion engineering studies, because of its capability to make predictions based on big data. Also, experimental data of Electrochemical Impedance Spectroscopy had been used in Artificial Neural Network training to obtain Nyquist Diagrams [2]. The objective of this work is to obtain computational models of Artificial Neural Networks to predict atmospheric corrosion in Bronze with nanostructured patina with SiO2, in marine environments. Method Environmental parameters were taken from database previously reported [3,4], and from Civil Protection Secretariat of Veracruz. Whilst electrochemical evaluation consists in Polarization Resistance and Electrochemical Impedance Spectroscopy; they were carried out in a Gamry Interface 1000 Potentiostat with a Saturated Calomel Reference Electrode (SCE) and a graphite bar as counter electrode in an agar gelled cell. Meanwhile, working electrode was bronze with nanostructured patina with SiO2. Two patina were prepared, the first with CuSO4 solution (0.015 mol L-1) and the second applying a CuNO3 solution (20% wt).The 13 variables used in the database for Artificial Neural Networks (ANN) training were: exposure time, chloride concentration, sulfur compounds concentration, relative humidity, precipitation level, windspeed, temperature, nanocoating presence, corrosion potential, corrosion rate, frequency, real and imaginary component of impedance. The values of each variable were treated with measures of central tendency and dispersion, as well as correlation matrix and histograms, which allowed to find a relationship between variables, and choosing the inputs for ANN. The prediction models consisted in 5, 9, 10 and 12 neurons in the input layer (R5E, R9E, R10E and R12E, respectively), a hidden layer, and Zima as only neuron in the output layer. The number of neurons in hidden layer varied from 1 to 8 until the highest coefficient of determination was achieved. The computational model was performed using the toolbox in MATLAB with a feedforward ANN. It is worth mention that the hidden and output layers used a hyperbolic tangent and a linear transfer function, respectively, and a Levenberg-Marquardt algorithm as training function was used in the models. Results and Conclusions Figure 1 presents the coefficient of determination (R2) and root mean squared error (RMSE) with different ANN architectures. In the four models, the highest value was reached with 8 neurons in the hidden layer. Then, the linear regressions for R5E, R9E, R10E and R12E models and 8 neurons in the input layer were compared in Figure 2. In that sense, the Nyquist Diagrams in Figure 3 were simulated with the R9E and R12E models. Also, they were compared with experimental Nyquist Diagrams for Bronze with patina of CuSO4 and CuNO3, with and without SiO2 nanoparticles, at 56 days of exposure in marine atmospheres. The simulations demonstrated how an ANN model with 12 neurons in the input layer, 8 neurons in the hidden layer and 1 neuron in the output layer can be used as a corrosion prediction model of bronze with nanostructured patina with SiO2.

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

Selection of the most relevant features for training of an artificial neural network (ANN) is of significant importance affecting largely the complexity and accuracy of the trained network. Moreover, deciding the topology and architecture of an ANN including the training algorithm, number of hidden layers, activation functions and neurons in each layer is challenging for the effectiveness of an ANN to solve a particular problem. In this paper, we present a novel strategy to result into an optimized topological architecture for an ANN and selection method for the most important experimental features available for training. The proposed method is found to efficiently design and model flows in solar collectors and subsequently estimating pressure loss coefficients for flows in the tee junctions vastly used in solar collectors with high accuracy. Experimental results demonstrate that the number of features used in training of ANN can be reduced up to 85% by identifying the features upon which the pressure loss coefficients’ estimation shows strong dependency. Performance analysis of various training algorithms has been carried out with variation in hidden layers, activation functions and neurons that results into minimum mean square error leading to minimal percent relative error.

Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium)

This methodology employs Artificial Neural Networks (ANN) to predict the compressive strength of 3D printed alkali-activated geopolymers, focusing on materials containing fly ash (FA). Data for training is gathered through an extensive literature review, encompassing parameters relevant to FA-containing geopolymers. After meticulous curation, a dataset of 53 experimental records, including mix ratios, chemical data, and 28-day compressive strength, is assembled. ANN architecture determination involves trial and error, considering different neurons and hidden layers, and various training algorithms. Results reveal that Levenberg Marquardt (LM) models consistently exhibit lower Mean Square Error (MSE), signifying superior accuracy. The best-performing LM model (7-11-1) outperforms Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) models.The ANN achieves optimal validation performance offering a reliable framework for predicting 3D-printed material strength and enhancing construction material performance through understanding input parameter interplay.

Online implementation of an adaptive calibration technique for displacement measurement using LVDT

In this study, the malachite green adsorption process using Amberlite IRC-748 and Diaion CR-11 resins was modelled by artificial neural network method. In the model created for this study, adsorbent dosage, initial malachite green concentration and contact time parameters, which are the independent variables of the adsorption process, were used as input. Adsorption percentage values, which are the dependent variables of the adsorption process, were obtained as output. Mean squared error (MSE) and determination coefficient (R2) values were obtained from the models created using thirty-one experimental data for adsorption of malachite green with Amberlite IRC-748 and thirty-eight experimental data for adsorption with Diaion CR-11. By evaluating these values together, the most appropriate training algorithm, transfer function in the hidden layer and the number of neurons in the hidden layer were defined. Accordingly, for both Amberlite IRC-748 and Diaion CR-11 resins, the optimum training algorithm was determined as Levenberg-Marquardt back-propagation and the optimum hidden layer transfer function as tan sigmoid. The optimum number of neurons in the hidden layer was identified as 13 for Amberlite IRC-748 and 12 for Diaion CR11. The MSE, R2all and R2test values of the models produced with the optimum parameters were obtained as 0.000261, 0.9972, 0.9903 for Amberlite IRC-748 and 0.000482, 0.9932, 0.9931 for Diaion CR11, respectively.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

A LiFePO4 battery pack capacity estimation approach considering in-parallel cell safety in electric vehicles

Thermal and electrical performance assessments of lithium-ion battery modules for an electric vehicle under actual drive cycles

Methane number (MN) is a critical gas quality parameter for gaseous-fueled engines. It is a measure of knock resistance for gaseous fuels, as is the octane number for gasoline. Therefore, a priori knowledge of the MN of gaseous fuel is important to avoid any structural damage to the engine due to knock. In the present study, a model was developed to predict the MN of gaseous fuels using an artificial neural network (ANN). The model utilized measured MNs of 1202 different gaseous fuel compositions, out of which 90% of the data (randomly) was used to train the ANN model using the Levenberg–Marquardt algorithm. In order to obtain the best performance, the number of neurons in the hidden layer and the transfer function of the hidden and output layers were changed. The ANN model incorporating hyperbolic tangent sigmoid function in the hidden layer with 53 neurons, and linear function in the output layer, showed the best performance – with mean square error and correlation coefficient of 0.055 and 1, respectively. The MNs of the remaining 10% of the data were determined using the ANN model, and were compared with those determined by the AVL (Anstalt für Verbrennungskraftmaschinen List) model. The model was able to predict MN accurately (R = 0.999).