Novel SOH Estimation of Lithium-Ion Batteries for Real-Time Embedded Applications

Kamali, M. Adib; Caliwag, Angela C.; Lim, Wansu

doi:10.1109/les.2021.3078443

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…Model-based methods aim to develop a mechanistic model that describes the degradation behavior of the battery, and they mainly focus on developing an equivalent circuit model (ECM) and electrochemical model. The ECM simulates the charge-discharge characteristics of lithium-ion batteries by numerically expressing the electrical components, including the capacitors and resistors [11]. To some extent, this approach considers the aging mechanism and has the advantages of a simple structure and good dynamic response [6,12,13]; however, the accuracy of this approach is highly dependent on the fidelity of the ECM model.…”

Section: A Brief Review Of Existing Approachesmentioning

confidence: 99%

“…The loss function is the key to combining quantile regression and LightGBM. From Equations ( 10) and (11), the information about the loss function of LightGBM has been included in the first-order derivative g i and second-order derivative h i . Therefore, by substituting Equation ( 17) into Equations ( 10) and ( 11), the first-order derivative g i and second-order derivative h i can be obtained.…”

Section: Weighted Quantile Regression For Lightgbmmentioning

confidence: 99%

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A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries

Qin

Fan

et al. 2023

Energies

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High maintenance costs and safety risks due to lithium-ion battery degeneration have significantly and seriously restricted the application potential of batteries. Thus, this paper proposes an efficient calculation approach for state of health (SOH) estimation in lithium-ion batteries that can be implemented in battery management system (BMS) hardware. First, from the variables of the charge profile, only the complete voltage data is taken as the input to represent the complete aging characteristics of the batteries while limiting the computational complexity. Then, this paper combines the light gradient boosting machine (LightGBM) and weighted quantile regression (WQR) methods to learn a nonlinear mapping between the measurable characteristics and the SOH. A confidence interval is applied to quantify the uncertainty of the SOH estimate, and the model is called LightGBM-WQR. Finally, two public datasets are employed to verify the proposed approach. The proposed LightGBM-WQR model achieves high accuracy in its SOH estimation, and the average absolute error (MAE) of all cells is limited to 1.57%. In addition, the average computation time of the model is less than 0.8 ms for ten runs. This work shows that the model is effective and rapid in its SOH estimation. The SOH estimation model has also been tested on the edge computing module as a possible innovation to replace the BMS bearer computing function, which provides tentative solutions for online practical applications such as energy storage systems and electric vehicles.

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Section: A Brief Review Of Existing Approachesmentioning

confidence: 99%

Section: Weighted Quantile Regression For Lightgbmmentioning

confidence: 99%

A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries

Qin

Fan

et al. 2023

Energies

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“…In Equation (22), k (k) represents the gain matrix of estimation R 0 , and ε (k) is the residual. In Equation (24), u is the adjustable coefficient, where u = 0.5, and p is the length of the multi-innovation.…”

Section: Miukf Estimate Rmentioning

confidence: 99%

“…According to the FOM of a battery, the Ohm internal resistance R 0 directly affects the voltage by multiplying the current, thus affecting the SOC estimation. In addition, R 0 is also a key parameter for estimating the health status of lithium batteries [21,22]. Therefore, the real-time estimation of R 0 to update R 0 is bound to effectively reduce the voltage error and improve SOC estimation accuracy under the premise of offline parameter identification.…”

Section: Introductionmentioning

confidence: 99%

Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter

Xing

Ling

et al. 2022

Applied Sciences

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Accurate state-of-charge (SOC) estimation of lithium batteries is of great significance for electric vehicles. In this paper, a combined estimation method of multi-innovation unscented Kalman filter (MIUKF) and fractional order square root cubature Kalman filter (FSRCKF) for lithium batteries is proposed. Firstly, the adaptive genetic algorithm (AGA) is applied to carry out offline parameter identification for the fractional order model (FOM) of a lithium battery under the Dynamic Stress Test (DST). Then, battery SOC is estimated by FSRCKF, while the Ohm internal resistance R0 of the fractional order battery model is estimated and updated by MIUKF in real time. The results show that MIUKF-FSRCKF is better than FSRCKF, FCKF and SRCKF in estimating the SOC of lithium batteries under the Federal Urban Driving Schedule (FUDS), Beijing Dynamic Stress Test (BJDST) and US06 Highway Driving Schedule tests, especially when R0 is inaccurate.

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“…14 The internal resistance of the battery grows gradually as the battery ages and its capacity reduces. 15 The hardship of SOH estimation by the internal resistance approach is to extract the mapping relationship between SOH and internal resistance, in particular by considering SOC, temperature and multiplicity. 16 Furthermore, the extracted characteristic relationship is only available for a certain brand and model of battery and is by no means very widespread.…”

mentioning

confidence: 99%

Improved Particle Swarm Optimization-Extreme Learning Machine Modeling Strategies for the Accurate Lithium-ion Battery State of Health Estimation and High-adaptability Remaining Useful Life Prediction

Zhang

Wang

et al. 2022

J. Electrochem. Soc.

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To ensure the secure and stable operation of lithium-ion batteries, the state of health (SOH) and the remaining useful life (RUL) are the critical state parameters which must be estimated precisely. Here, a joint SOH and RUL estimation approach based on an improved particle swarm optimization extreme learning machine (PSO-ELM) is proposed. The approach adopts Pearson coefficients to screen multivariate information of the discharge process as health indicators and uses them as inputs to enable accurate estimation of SOH and RUL prediction of lithium-ion batteries on the basis of the PSO-ELM model. The validity of the model is demonstrated by the NASA lithium-ion battery data set: the maximum root mean square error (RMSE) of SOH estimation of the tested battery is 0.0033, the maximum RMSE of its RUL prediction is 0.0082, and the maximum absolute error of RUL prediction is one cycle number. In comparison with the prediction results of the traditional extreme learning machine, the proposed optimized model estimates the SOH of lithium-ion batteries and RUL with relatively high accuracy.

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Novel SOH Estimation of Lithium-Ion Batteries for Real-Time Embedded Applications

Cited by 13 publications

References 11 publications

A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries

A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries

Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter

Improved Particle Swarm Optimization-Extreme Learning Machine Modeling Strategies for the Accurate Lithium-ion Battery State of Health Estimation and High-adaptability Remaining Useful Life Prediction

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