Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model

Ali, Muhammad Umair; Kallu, Karam Dad; Masood, Haris; Niazi, Kamran Ali Khan; Alvi, Muhammad Junaid; Ghafoor, Usman; Zafar, Amad

doi:10.1016/j.isci.2021.103286

Cited by 13 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) It introduces the gating mechanism, so it can maintain the continuity of gradients and effectively solve the problem of gradient disappearance or explosion. (3) By relying on multiple structures and parameters to control the flow of information, it can not only reduce the risk of underfitting but also be robust to noise [ 47 , 48 ].…”

Section: Methodology Equationmentioning

confidence: 99%

Do multisource data matter for NGP prediction? Evidence from the G-LSTM model

Hao,

Shang,

Yuan

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Methodology Equationmentioning

confidence: 99%

Do multisource data matter for NGP prediction? Evidence from the G-LSTM model

Hao,

Shang,

Yuan

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Li-ion battery is a highly nonlinear and complex electrochemical system, which significantly impacts its health under dynamic operating conditions. SoC, SoH, SoL, and RUL are the different parameters primarily used to predict the health of Li-ion batteries [ 40 , 41 ]. SoH is one of the essential components of the BHP system [ 42 ].…”

Section: State Of Health Of Lithium-ion Batterymentioning

confidence: 99%

A Hybrid Data-Driven Approach for Multistep Ahead Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries

Ali

Zafar

Masood

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

In this paper, a novel multistep ahead predictor based upon a fusion of kernel recursive least square (KRLS) and Gaussian process regression (GPR) is proposed for the accurate prediction of the state of health (SoH) and remaining useful life (RUL) of lithium-ion batteries. The empirical mode decomposition is utilized to divide the battery capacity into local regeneration (intrinsic mode functions) and global degradation (residual). The KRLS and GPR submodels are employed to track the residual and intrinsic mode functions. For RUL, the KRLS predicted residual signal is utilized. The online available experimental battery aging data are used for the evaluation of the proposed model. The comparison analysis with other methodologies (i.e., GPR, KRLS, empirical mode decomposition with GPR, and empirical mode decomposition with KRLS) reveals the distinctiveness and superiority of the proposed approach. For 1-step ahead prediction, the proposed method tracks the trajectory with the root mean square error (RMSE) of 0.2299, and the increase of only 0.2243 RMSE is noted for 30-step ahead prediction. The RUL prediction using residual signal shows an increase of 3 to 5% in accuracy. This proposed methodology is a prospective approach for an efficient battery health prognostic.

show abstract

“…It adapts and dynamically updates the model depending on time series data, lowering model complexity and computation time. Since its inception, it has become a popular approach for online prediction due to its natural recursive shape, being an extension of the traditional RLS algorithm via the kernel method [65]. The ARMAX model is used in this work to represent the prediction linearly.…”

Section: Kernel Recursive Least Square Modelmentioning

confidence: 99%

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

2022

View full text Add to dashboard Cite

Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots.

show abstract

Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model

Cited by 13 publications

References 31 publications

Do multisource data matter for NGP prediction? Evidence from the G-LSTM model

Do multisource data matter for NGP prediction? Evidence from the G-LSTM model

A Hybrid Data-Driven Approach for Multistep Ahead Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

Contact Info

Product

Resources

About