A Genetic Algorithm and RNN-LSTM model for Remaining Battery Capacity Prediction

2021 ◽  
pp. 1-34
Author(s):  
Mukul Singh ◽  
Shrey Bansal ◽  
Vandana ◽  
Bijaya K. Panigrahi ◽  
Akhil Garg
Keyword(s):  
Genetic Algorithm ◽  
Short Term Memory ◽  
Support Vector ◽  
Battery Life ◽  
Proposed Model ◽  
Battery Capacity ◽  
Li Ion ◽  
Test Sets ◽  
Support Vector Regressor ◽  
Capacity Prediction

Abstract Li-ion batteries have diversified applications in everyday life. The temperature change, overcharging, over-discharging is playing critical roles in affecting battery life in a significant manner. In this paper, the deep learning-based method is applied for the prognostics of a single Li-ion battery. The proposed design uses a recurrent neural network variant, Long short term memory. The model's parameters are optimized through a Genetic Algorithm based parameter selector The method applies to a sequence of data values comprising of the voltage, the charge capacity, the current, and the temperature. The estimation of battery capacity is not only based on the current or defined state of the battery; instead, it is generated on the complete data profile. The robustness of the model is tested by comparing with techniques such as Support vector regressor, Kalman Filter, neural networks on normal and noisy test sets. The paper also proposes a feature selection and engineering scheme for battery capacity prediction. The proposed model outperforms the techniques available in literature with high generalization to noise and other perturbations. The model is independent of the section of charging curve used for prediction of battery capacity. Various experimentation has been conducted on the model and the results have been validated.

Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

2018 ◽  
Vol 141 (4) ◽  
Author(s):  
Qihong Feng ◽  
Ronghao Cui ◽  
Sen Wang ◽  
Jin Zhang ◽  
Zhe Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Diffusion Coefficient ◽  
Co2 Storage ◽  
Co2 Injection ◽  
Support Vector ◽  
Hybrid Technique ◽  
Machine Model ◽  
Proposed Model ◽  
Wide Range

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

scholarly journals A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Yu ◽  
Jianling Qu ◽  
Feng Gao ◽  
Yanping Tian
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Support Vector ◽  
Rolling Bearings ◽  
Hierarchical Algorithm ◽  
Bearing Fault Diagnosis ◽  
Rotary Machinery ◽  
Proposed Model ◽  
Series Of Experiments

Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.

scholarly journals Spatiotemporal Traffic Flow Prediction with KNN and LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xianglong Luo ◽  
Danyang Li ◽  
Yu Yang ◽  
Shengrui Zhang
Keyword(s):  
Support Vector Regression ◽  
Traffic Flow ◽  
Prediction Models ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

scholarly journals A Genetic Algorithm based Hybrid Deep Learning Approach for Heart Disease Prediction

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Khushbu Verma ◽  
Ankit Singh Bartwal ◽  
Mathura Prasad Thapliyal
Keyword(s):  
Genetic Algorithm ◽  
Heart Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Support Vector ◽  
Disease Prediction ◽  
The Past ◽  
Lifestyle Choices ◽  
Proposed Model ◽  
Tree Algorithms

People nowadays suffer from a variety of heart ailments as a result of the environment and their lifestyle choices. As a result, analyzing sickness at an early stage becomes a critical responsibility. Data mining uses disease data to uncover important knowledge. In this research paper, we employ the hybrid combination of a Genetic Algorithm based Feature selection and Ensemble Deep Neural Network Model for Heart Disease prediction. In this algorithm, we used a 0.04 learning rate and Adam optimizer was used for enhancement of the proposed model. The proposed algorithm has come to 98% accuracy of heart disease prediction, which is higher than the past approaches. Other exist models such as random forest, logistic regression, support vector machine, Decision tree algorithms have taken a higher time and give less accuracy compare to the proposed hybrid deep learning-based approach.

Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Nan Wei ◽  
Changjun Li ◽  
Chan Li ◽  
Hanyu Xie ◽  
Zhongwei Du ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Natural Gas ◽  
Support Vector Regression ◽  
Series Data ◽  
Support Vector ◽  
Selection Algorithm ◽  
Short Term ◽  
Proposed Model ◽  
Gas Consumption ◽  
Natural Gas Consumption

Forecasting of natural gas consumption has been essential for natural gas companies, customers, and governments. However, accurate forecasting of natural gas consumption is difficult, due to the cyclical change of the consumption and the complexity of the factors that influence the consumption. In this work, we constructed a hybrid artificial intelligence (AI) model to predict the short-term natural gas consumption and examine the effects of the factors in the consumption cycle. The proposed model combines factor selection algorithm (FSA), life genetic algorithm (LGA), and support vector regression (SVR), namely, as FSA-LGA-SVR. FSA is used to select factors automatically for different period based on correlation analysis. The LGA optimized SVR is utilized to provide the prediction of time series data. To avoid being trapped in local minima, the hyper-parameters of SVR are determined by LGA, which is enhanced due to newly added “learning” and “death” operations in conventional genetic algorithm. Additionally, in order to examine the effects of the factors in different period, we utilized the recent data of three big cities in Greece and divided the data into 12 subseries. The prediction results demonstrated that the proposed model can give a better performance of short-term natural gas consumption forecasting compared to the estimation value of existing models. Particularly, the mean absolute range normalized errors of the proposed model in Athens, Thessaloniki, and Larisa are 1.90%, 2.26%, and 2.12%, respectively.

scholarly journals Prediction of Corn and Sugar Prices Using Machine Learning, Econometrics, and Ensemble Models

2021 ◽  
Vol 9 (1) ◽  
pp. 31
Author(s):  
Roberto F. Silva ◽  
Bruna L. Barreira ◽  
Carlos E. Cugnasca
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
State Of The Art ◽  
Support Vector ◽  
Short Term ◽  
Corn Prices ◽  
Lower Variation ◽  
Test Sets ◽  
Long Short Term Memory ◽  
Machine Learning Models

This paper explores the use of several state-of-the-art machine learning models for predicting the daily prices of corn and sugar in Brazil in relation to the use of traditional econometrics models. The following models were implemented and compared: ARIMA, SARIMA, support vector regression (SVR), AdaBoost, and long short-term memory networks (LSTM). It was observed that, even though the prices time series for both products differ considerably, the models that presented the best results were obtained by: SVR, an ensemble of the SVR and LSTM models, an ensemble of the AdaBoost and SVR models, and an ensemble of the AdaBoost and LSTM models. The econometrics models presented the worst results for both products for all metrics considered. All models presented better results for predicting corn prices in relation to the sugar prices, which can be related mainly to its lower variation during the training and test sets. The methodology used can be implemented for other products.

Machine-Learning for Li-Ion Battery Capacity Prediction in Manufacturing Process

2021 ◽  
Vol MA2021-02 (3) ◽  
pp. 427-427
Author(s):  
Mona Faraji Niri ◽  
Kailong Liu ◽  
Geanina Apachitei ◽  
Luis Roman-Ramirez ◽  
Michael Lain ◽  
...  
Keyword(s):  
Machine Learning ◽  
Manufacturing Process ◽  
Li Ion Battery ◽  
Battery Capacity ◽  
Li Ion ◽  
Capacity Prediction

Optimal Equivalent Consumption Minimization Strategy for Plug-In Hybrid Electric Vehicle with Improved Genetic Algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 143-154
Author(s):  
Changyin Wei ◽  
◽  
Yong Chen ◽  
Xiuxiu Sun ◽  
Yue Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Management ◽  
Fuel Economy ◽  
Battery Life ◽  
Improved Genetic Algorithm ◽  
Life Index ◽  
Battery Capacity ◽  
Equivalent Factor ◽  
Equivalent Consumption Minimization Strategy

The equivalent consumption minimization strategy (ECMS) is a promising energy management approach to low-fuel economy with the outstanding features of high efficiency. In this article, an optimal ECMS by Improved Genetic Algorithm (IGA) is proposed. To this end, we improved the genetic algorithm (GA) from the coding method, initialization mode, and cross and mutation process. And based on the comprehensive energy consumption and Pontryagin’s minimum principle, the equivalent factor was derived. The IGA was used to optimize the equivalent factor. To evaluate the performance of the proposed energy management strategy (EMS), the average efficiency of the engine and the motor was analyzed in an urban area, high-speed area, and the whole area. The comprehensive fuel consumption was used as the energy consumption index, and the battery capacity loss under the transient conditions was amplified to 10 years as the evaluation battery life index. The simulation results show that under the New European Driving Cycle (NEDC), the proposed strategy improves the fuel economy and battery life index by 14.64% and 36.76%, respectively, compared with the rule-based EMS.

A genetic-based data mining model for customer relationship management in B2B ecommerce

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
AS Makinde ◽  
OR Vincent ◽  
ID Acheme ◽  
AT Akinwale
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Calibration Graph ◽  
Customer Relationship ◽  
Business Strategies ◽  
Support Vector ◽  
Proposed Model ◽  
Time Calibration

Customer Relationship Management (CRM) improves the responsiveness and understanding of business strategies among employees and helps to achieve better customer service. However, in Business-to-Business (B2B) eCommerce, CRM data are rarely analyzed across market segments or customer categories, and customer-to-business relationship also poses issues. Thus, making appropriate decisions in the CRM model is difficult. This study presents a modified B2B CRM using the Genetic algorithm and Data Mining Techniques to improve decision making. The model classifies consumers into consumers of Repeat and Shop-and-Go. Modified data mining C5.0 and the Genetic algorithm was employed to optimize rules generated by the decision tree algorithm. The findings showed that the proposed model allocates resources effectively to the most profitable customers’ decisions. The output metrics are machine time, calibration graph, and ROC curve. In comparison with the conventional C5.0, k-NN, and Support Vector Machine, the proposed model has greater accuracy of 89.3 percent. Keywords: Customer Relationship Management, B2B eCommerce, Genetic Algorithm, Data Mining

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