Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation

Energy ◽  
2021 ◽  
Vol 215 ◽  
pp. 119078 ◽  
Author(s):  
Lin Chen ◽  
Huimin Wang ◽  
Bohao Liu ◽  
Yijue Wang ◽  
Yunhui Ding ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Error Compensation ◽  
State Model ◽  
State Of Health ◽  
Learning Machine ◽  
Degradation State

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.

scholarly journals Multivariable nonlinear predictive control of a clinker sintering system at different working states by combining artificial neural network and autoregressive exogenous

2020 ◽  
Vol 12 (1) ◽  
pp. 168781401989650
Author(s):  
Meiqi Wang ◽  
Enli Chen ◽  
Pengfei Liu ◽  
Wenwu Guo
Keyword(s):  
Steady State ◽  
Dynamic Model ◽  
Extreme Learning Machine ◽  
Dynamic Models ◽  
Good Control ◽  
State Model ◽  
Cement Clinker ◽  
Control Performance ◽  
Steady State Model ◽  
Learning Machine

The clinker sintering system is widely controlled manually in the factory, and there is a large divergence between a linearized control model and the nonlinear rotary kiln system, so the controlled variables cannot be calculated accurately. To accommodate the multivariable and nonlinear features of cement clinker sintering systems, steady-state model and dynamic models are established using extreme learning machine and autoregressive exogenous models. The steady-state model is used to describe steady-state nonlinear relations, and the dynamic model is used to describe the dynamic characteristics of the sintering system. By obtaining the system gains based on the steady-state model, the parameters of the dynamic model are rectified online to conform to the system gain. Thus, a dynamic model named extreme learning machine-autoregressive exogenous is proposed, which can describe the nonlinear dynamic features of a sintering system. The results show that, compared with the autoregressive exogenous model, the extreme learning machine-autoregressive exogenous model has good control performance on the multivariable and nonlinear system and can reduce computing resource requirements during the online running. In addition, fluctuations of NOx and O2 concentrations decreases, again demonstrating good control performance of an actual clinker sintering system using the extreme learning machine-autoregressive exogenous model.

scholarly journals Quality prediction modeling for sintered ores based on mechanism models of sintering and extreme learning machine based error compensation

2018 ◽  
Vol 38 ◽  
pp. 02002
Author(s):  
Wu Tiebin ◽  
Liu Yunlian ◽  
Li Xinjun ◽  
Yu Yi ◽  
Zhang Bin
Keyword(s):  
Extreme Learning Machine ◽  
Error Compensation ◽  
High Accuracy ◽  
Industrial Applications ◽  
Total Iron ◽  
Quality Prediction ◽  
Sintering Process ◽  
Chemical Reaction Mechanism ◽  
Learning Machine ◽  
Weighted Error

Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.

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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