Estimation of Li-ion Battery State of Health based on Multilayer Perceptron: as an EV Application

Kim, Jungsoo; Yu, Jungwook; Kim, Minho; Kim, Kwangrae; Han, Soohee

doi:10.1016/j.ifacol.2018.11.734

Cited by 50 publications

(25 citation statements)

References 15 publications

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“…To further evaluate the performance of the proposed GRU/LSTM algorithms, we performed a set of extensive comparisons with other state of the art battery SOH estimation methods: second order polynomial regression (“Poly2”), 4 exponential regression (“Exp”), and multilayer perceptron (“MLP”) 27 . For the aforementioned methods, we used their online versions.…”

Section: Resultsmentioning

confidence: 99%

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

2020

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Summary Online state of health (SOH) prediction of lithium‐ion batteries remains a very important problem in assessing the safety and reliability of battery‐powered systems. Deep learning techniques based on recurrent neural networks with memory, such as the long short‐term memory (LSTM) and gated recurrent unit (GRU), have very promising advantages, when compared to other SOH estimation algorithms. This work addresses the battery SOH prediction based on GRU. A complete BMS is presented along with the internal structure and configuration parameters. The neural network was highly optimized by adaptive moment estimation (Adam) algorithm. Experimental data show very good estimation results for different temperature values, not only at room value. Comparisons performed against other relevant estimation methods highlight the performance of the recursive neural network algorithms such as GRU and LSTM, with the exception of the battery regeneration points. Compared to LSTM, the GRU algorithm gives slightly higher estimation errors, but within similar prediction error range, while needing significantly fewer parameters (about 25% fewer), thus making it a very suitable candidate for embedded implementations.

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Section: Resultsmentioning

confidence: 99%

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

2020

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“…Similarly for studies of LIBs, a number of data-driven estimation models for Figure 1), comprising a pair of resistors R e , R ct , accounting for the resistance of the electrolyte and current collector foils, capacitor C dl for charge transfer effects and electrical double-layers, and a Warburg impedance Z W element representing diffusion. 35 the battery SoC, 15,39,40 SoH 12,[15][16][17][18]41 and RUL have been developed. 12,42,43 Many of these studies focus on online estimation of SoH by capacity, with the prevailing approach involving following the evolution of the current, terminal voltage and partial capacity curves of the battery with time as applied by previous works.…”

Section: Machine Learning For Soh Estimationmentioning

confidence: 99%

“…For example, data-driven approaches have been applied with success in the related area of bearing fault diagnosis and prognostics, 37,38 where it is often impractical to develop precise health degradation models to predict their RUL. Similarly for studies of LIBs, a number of data-driven estimation models for the battery SoC, 15,39,40 SoH 12,15–18,41 and RUL have been developed. 12,42,43 Many of these studies focus on online estimation of SoH by capacity, with the prevailing approach involving following the evolution of the current, terminal voltage and partial capacity curves of the battery with time as applied by previous works.…”

Section: Background and Related Workmentioning

confidence: 99%

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

Rastegarpanah

Hathaway

Ahmeid

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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There is growing interest in recycling and re-use of electric vehicle batteries owing to their growing market share and use of high-value materials such as cobalt and nickel. To inform the subsequent applications at battery end of life, it is necessary to quantify their state of health. This study proposes an estimation scheme for the state of health of high-power lithium-ion batteries based on extraction of parameters from impedance data of 13 Nissan Leaf 2011 battery modules modelled by a modified Randles equivalent circuit model. Using the extracted parameters as predictors for the state of health, a baseline single hidden layer neural network was evaluated by root mean square and peak state of health prediction errors and refined using a Gaussian process optimisation procedure. The optimised neural network predicted state of health with a root mean square error of (1.729 ± 0.147)%, which is shown to be competitive with some of the most performant existing neural network–based state of health estimation schemes, and is expected to outperform the baseline model with ∼50 training samples. The use of equivalent circuit model parameters enables more in-depth analysis of the battery degradation state than many similar neural network–based schemes while maintaining similar accuracy despite a reduced dataset, while there is demonstrated potential for measurement times to be reduced to as little as 30 s with frequency targeting of the impedance measurements.

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“…A multi-layer perception-based neural network model was developed to estimate SOH of a battery by extracting their parameters using ECM. In addition, this method considered battery data collected from discrete life span to predict SOH accurately [37].…”

Section: Data-driven Approach For State-of-health (Soh) Estimationmentioning

confidence: 99%

State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition

Venugopal

Vigneswaran

2019

Energies

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In electric vehicles (EVs), battery management systems (BMS) carry out various functions for effective utilization of stored energy in lithium-ion batteries (LIBs). Among numerous functions performed by the BMS, estimating the state of health (SOH) is an essential and challenging task to be accomplished at regular intervals. Accurate estimation of SOH ensures battery reliability by computing remaining lifetime and forecasting its failure conditions to avoid battery risk. Accurate estimation of SOH is challenging, due to uncertain operating conditions of EVs and complex non-linear electrochemical characteristics demonstrated by LIBs. In most of the existing studies, standard charge/discharge patterns with numerous assumptions are considered to accelerate the battery ageing process. However, such patterns and assumptions fail to reflect the real world operating condition of EV batteries, which is not appropriate for BMS of EVs. In contrast, this research work proposes a unique SOH estimation approach, using an independently recurrent neural network (IndRNN) in a more realistic manner by adopting the dynamic load profile condition of EVs. This research work illustrates a deep learning-based data-driven approach to estimate SOH by analyzing their historical data collected from LIBs. The IndRNN is adapted due to its ability to capture complex non-linear characteristics of batteries by eliminating the gradient problem and allowing the neural network to learn long-term dependencies among the capacity degradations. Experimental results indicate that the IndRNN based model is able to predict a battery’s SOH accurately with root mean square error (RMSE) reduced to 1.33% and mean absolute error (MAE) reduced to 1.14%. The maximum error (MAX) produced by IndRNN throughout the testing process is 2.5943% which is well below the acceptable SOH error range of ±5% for EVs. In addition, to demonstrate effectiveness of the IndRNN attained results are compared with other well-known recurrent neural network (RNN) architectures such as long short-term memory (LSTM) and gated recurrent unit (GRU). From the comparison of results, it is clearly evident that IndRNN outperformed other RNN architectures with the highest SOH accuracy rate.

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Estimation of Li-ion Battery State of Health based on Multilayer Perceptron: as an EV Application

Cited by 50 publications

References 15 publications

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition

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