Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine

Energy ◽  
2018 ◽  
Vol 160 ◽  
pp. 466-477 ◽  
Author(s):  
Haihong Pan ◽  
Zhiqiang Lü ◽  
Huimin Wang ◽  
Haiyan Wei ◽  
Lin Chen
Keyword(s):  
Extreme Learning Machine ◽  
Estimation Method ◽  
Health Indicators ◽  
State Of Health ◽  
Online Estimation ◽  
Learning Machine ◽  
Multiple Health

Lithium-ion battery state of health estimation based on improved deep extreme learning machine

2021 ◽  
pp. 1-12
Author(s):  
Yu Zhang ◽  
Wanwan Zeng ◽  
Chun Chang ◽  
Qiyue Wang ◽  
Si Xu
Keyword(s):  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Time Interval ◽  
State Of Health ◽  
Discharge Data ◽  
Ergodic Method ◽  
Discharge Temperature ◽  
Learning Machine

Abstract Accurate estimation of the state of health (SOH) is an important guarantee for safe and reliable battery operation. In this paper, an online method based on indirect health features (IHF) and sparrow search algorithm fused with deep extreme learning machine (SSA-DELM) of lithium-ion batteries is proposed to estimate SOH. Firstly, the temperature and voltage curves in the battery discharge data are acquired, and the optimal intervals are obtained by ergodic method. Discharge temperature difference at equal time intervals (DTD-ETI) and discharge time interval with equal voltage difference (DTI-EVD) are extracted as IHF. Then, the input weights and hidden layer thresholds of the DELM algorithm are optimized using SSA, and the SSA-DELM model is applied to the estimation of battery's SOH. Finally, the established model is experimentally validated using the battery data, and the results show that the method has high prediction accuracy, strong algorithmic stability and good adaptability.

Sensorless effective wind speed estimation method based on unknown input disturbance observer and extreme learning machine

Energy ◽  
2019 ◽  
Vol 186 ◽  
pp. 115790 ◽  
Author(s):  
Xiaofei Deng ◽  
Jian Yang ◽  
Yao Sun ◽  
Dongran Song ◽  
Xiaoyan Xiang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Extreme Learning Machine ◽  
Disturbance Observer ◽  
Estimation Method ◽  
Unknown Input ◽  
Speed Estimation ◽  
Input Disturbance ◽  
Learning Machine

scholarly journals A Real-Time BOD Estimation Method in Wastewater Treatment Process Based on an Optimized Extreme Learning Machine

2019 ◽  
Author(s):  
Ping Yu ◽  
Jie Cao ◽  
Veeriah Jegatheesan ◽  
Xianjun Du
Keyword(s):  
Wastewater Treatment ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Measurement Data ◽  
Estimation Method ◽  
Cuckoo Search ◽  
Bp Network ◽  
Learning Machine ◽  
Hidden Layer

It is difficult to capture the real-time online measurement data for biochemical oxygen demand (BOD) in wastewater treatment processes. An optimized extreme learning machine (ELM) based on an improved cuckoo search algorithm (ICS) is proposed in this paper for the design of soft BOD measurement model. In ICS-ELM, the input weights matrices of the extreme learning machine (ELM) and the threshold of the hidden layer are encoded as the cuckoo's nest locations. The best input weights matrices and threshold are obtained by using the strong global search ability of improved cuckoo search (ICS) algorithm. The optimal results can be used to improve the precision of forecasting based on less number of neurons of the hidden layer in ELM. Simulation results show that the soft sensor model has good real-time performance, high prediction accuracy and stronger generalization performance for BOD measurement of the effluent quality compared to other modeling methods such as back propagation (BP) network in most cases.

scholarly journals A Systematic Error Compensation Method Based on an Optimized Extreme Learning Machine for Star Sensor Image Centroid Estimation

2019 ◽  
Vol 9 (22) ◽  
pp. 4751
Author(s):  
Xin Wei ◽  
Desheng Wen ◽  
Zongxi Song ◽  
Jiangbo Xi ◽  
Weikang Zhang ◽  
...  
Keyword(s):  
Systematic Error ◽  
Extreme Learning Machine ◽  
Estimation Method ◽  
Bat Algorithm ◽  
Location Estimation ◽  
Field Analysis ◽  
Star Sensor ◽  
Centroid Estimation ◽  
Learning Machine ◽  
Centroid Location

As an important error in star centroid location estimation, the systematic error greatly restricts the accuracy of the three-axis attitude supplied by a star sensor. In this paper, an analytical study about the behavior of the systematic error in the center of mass (CoM) centroid estimation method under different Gaussian widths of starlight energy distribution is presented by means of frequency field analysis and numerical simulations. Subsequently, an optimized extreme learning machine (ELM) based on the bat algorithm (BA) is adopted to predict the systematic error of the actual star centroid position and then compensate the systematic error from the CoM method. In the BA-ELM model, the input weights matrix and hidden layer biases parameters are encoded as microbat’s locations and optimized by utilizing the strong global search capacity of BA, which significantly improves the performance of ELM in terms of prediction accuracy. The simulation result indicates that our method can reduce the systematic error to less than 3.0 × 10−7 pixels, and its compensation accuracy is two or three orders of magnitude higher than that of other methods for estimating a star centroid location under a 3 × 3 pixel sampling window.

scholarly journals Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2243
Author(s):  
Ethelbert Ezemobi ◽  
Andrea Tonoli ◽  
Mario Silvagni
Keyword(s):  
Extreme Learning Machine ◽  
Load Condition ◽  
Machine Learning Algorithms ◽  
Constant Current ◽  
Large Set ◽  
State Of Health ◽  
Estimation Model ◽  
Discrete Variation ◽  
Parallel Layer ◽  
Learning Machine

The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 μs in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications.

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