The state of the art approaches to estimate the state of health (SOH) and state of function (SOF) of lithium Ion batteries

Balagopal, Bharat; Chow, Mo-Yuen

doi:10.1109/indin.2015.7281923

Cited by 45 publications

(28 citation statements)

References 25 publications

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“…There is more consensus about the state-of-charge definition, which is usually understood as an amount of charge, which is available to be extracted from the actual battery capacity [7,26]. Sometimes, the nominal capacity term is used instead of the actual capacity [27]. However, that might lead to an incorrect assumption of neglecting the capacity change during the battery life.…”

Section: State Definitionsmentioning

confidence: 99%

“…Furthermore, the impedance was accommodated as an another indicator for the SOH [15,26] and it is also related to the maximum available power of the battery, as the impedance growth contributes a lot to the power fade. However, other factors might also be considered for the SOH estimation [27], as for example the porosity of the electrodes [31] or the terminal voltage [32].…”

Section: State Definitionsmentioning

confidence: 99%

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Concurrent Real-Time Estimation of State of Health and Maximum Available Power in Lithium-Sulfur Batteries

et al. 2018

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Lithium-sulfur (Li-S) batteries are an emerging energy storage technology with higher performance than lithium-ion batteries in terms of specific capacity and energy density. However, several scientific and technological gaps need to be filled before Li-S batteries will penetrate the market at a large scale. One such gap, which is tackled in this paper, is represented by the estimation of state-of-health (SOH). Li-S batteries exhibit a complex behaviour due to their inherent mechanisms, which requires a special tailoring of the already literature-available state-of-charge (SOC) and SOH estimation algorithms. In this work, a model of SOH based on capacity fade and power fade has been proposed and incorporated in a state estimator using dual extended Kalman filters has been used to simultaneously estimate Li-S SOC and SOH. The dual extended Kalman filter’s internal estimates of equivalent circuit network parameters have also been used to the estimate maximum available power of the battery at any specified instant. The proposed estimators have been successfully applied to both fresh and aged Li-S pouch cells, showing that they can accurately track accurately the battery SOC, SOH, and power, providing that initial conditions are suitable. However, the estimation of the Li-S battery cells’ capacity fade is shown to be more complex, because the practical available capacity varies highly with the applied current rates and the dynamics of the mission profile.

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Section: State Definitionsmentioning

confidence: 99%

Section: State Definitionsmentioning

confidence: 99%

Concurrent Real-Time Estimation of State of Health and Maximum Available Power in Lithium-Sulfur Batteries

et al. 2018

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“…In addition, when the loss of lithium in the activated carbon occurs, it causes self-discharge and capacity degradation. External factors include overcharging, overdischarging, operating temperature, inappropriate charge and discharge cycles, and high charge and discharge rate [10], [11], [12]. It becomes quite difficult to estimate the SOH of the battery since these unpredictable factors affect the health of the battery.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries

Cui

Joe

2021

IEEE Access

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A proper battery management system (BMS) plays a vital role in ensuring the safety and reliability of electric vehicles (EVs) and other electronic products. Accurate State-of-Health (SOH) estimation of Lithium-ion (Li-ion) batteries is a key factor in a BMS. It is difficult to determine SOH because of the complexity of the electrochemical reactions within the battery. To improve the accuracy of SOH estimation, a dynamic spatial-temporal attention-based gated recurrent unit (DSTA-GRU) model is proposed in this paper. First, we extract six features from the battery's charging and discharging processes that can reflect the aging degree of the battery to some extent. Second, this paper proposes a model to combine spatial attention and temporal attention that can not only consider the effects of states at different time step on the results, but also consider the effects of different features in the space domain. Third, the proposed model is trained and tested on NASA battery datasets and compared with other conventional models. Experiments carried on these data sets demonstrate that our model achieves higher accuracy than other conventional models. INDEX TERMS Lithium-ion battery, state of health, dynamic spatial attention, temporal attention, gated recurrent unit

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“…Some of the internal factors includes battery material, calendar ageing and increase in internal resistance. The external factors are operating temperature, uncertain driving condition, overcharging/discharging, high charge/discharge rate and improper charge/discharge cycling [8,9]. Due to many unknown and unpredictable factors influencing the health of the battery, estimating battery SOH becomes quite challenging.…”

Section: Introductionmentioning

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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The state of the art approaches to estimate the state of health (SOH) and state of function (SOF) of lithium Ion batteries

Cited by 45 publications

References 25 publications

Concurrent Real-Time Estimation of State of Health and Maximum Available Power in Lithium-Sulfur Batteries

Concurrent Real-Time Estimation of State of Health and Maximum Available Power in Lithium-Sulfur Batteries

A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries

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

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