A novel aging characteristics-based feature engineering for battery state of health estimation

Wang, Jinyu; Zhang, Caiping; Zhang, Linjing; Su, Xiaojia; Zhang, Weige; Li, Xu; Du, Jingcai

doi:10.1016/j.energy.2023.127169

Cited by 14 publications

(5 citation statements)

References 38 publications

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“…15 Currently, the SOH of batteries is commonly quantified using capacity and impedance. 11 Capacity is a critical measure of a battery's ability to store and release electrical energy. In contrast, impedance, as a measure of the internal resistance, is a crucial parameter in assessing the battery's power performance.…”

Section: Battery Degradation Datasets and Methodsmentioning

confidence: 99%

“…Nevertheless, accurately assessing the SOH of a battery is crucial for predicting its operational performance, developing effective maintenance strategies, and ensuring the safe and stable operation of the battery system. 11 Currently, various advanced algorithms and models have been employed for estimating battery SOH, including data-driven methods, physical model-based approaches, and hybrid models that integrate multiple techniques.…”

mentioning

confidence: 99%

“…Datadriven methods require a sufficient amount of high-quality data to train the model. 11,13 Additionally, accurately extracting feature parameters that are highly correlated with battery capacity degradation is a crucial factor that affects prediction accuracy. 9,18,19 Piao et al 9 proposed a new health feature based on the IC curve and validated its effectiveness using the Pearson correlation coefficient.…”

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confidence: 99%

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A Novel State-of-Health Prediction and Assessment Strategies for High-Capacity Mining Lithium-Ion Batteries Based on Multi-Indicator

Feng,

Cai,

Zhan

et al. 2024

J. Electrochem. Soc.

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Accurately assessing battery state of health (SOH) is crucial for ensuring the safety of lithium-ion batteries. However, current SOH evaluation methods suffer from inconsistent criteria and limited accuracy in prediction models. This paper introduces a novel SOH prediction and assessment strategy that relies on multiple indicators to address these challenges. First, multifaceted health factors are extracted based on charge cycle data, including battery charging time, incremental capacity, and dV/dt curve. Subsequently, a support vector regression model optimized by the sparrow search algorithm is proposed to predict SOH. The results show that MAE, RMSE, and MAPE are less than 0.037%, 0.047%, and 0.04%, respectively. Meanwhile, the Kalman filtering method is used to identify the second-order RC model online, and the relative SOH curves are obtained by defining the SOH through the internal resistance. Finally, by analyzing the effects of capacity and internal resistance changes on SOH, a new strategy for SOH assessment is proposed, which considers various factors and selects an appropriate judgment mechanism according to the characteristics exhibited by the battery at different life stages. The strategy is more conservative and reliable, providing a solid guarantee for the safe operation of mining equipment.

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Section: Battery Degradation Datasets and Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Novel State-of-Health Prediction and Assessment Strategies for High-Capacity Mining Lithium-Ion Batteries Based on Multi-Indicator

Feng,

Cai,

Zhan

et al. 2024

J. Electrochem. Soc.

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show abstract

“…However, the IC curve generally requires constant current (CC) charging and discharging at a low current. Furthermore, other researchers have focused on digging out features from voltage ans temperature data measured during battery charging and discharging, such as charging time/capacity in a fixed voltage interval or the amount of voltage/capacity change in a fixed period time [29][30][31]. Jianfang Jia et al [32] extracted the time of the CC phase from the voltage profile as a health factor to capture the battery capacity degradation process response.…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Engineering-Based SOH Estimation Method for Lithium-Ion Battery with Downgraded Laboratory Data

Wang,

Zhang,

Meng

et al. 2024

Batteries

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Accurate estimation of lithium-ion battery state of health (SOH) can effectively improve the operational safety of electric vehicles and optimize the battery operation strategy. However, previous SOH estimation algorithms developed based on high-precision laboratory data have ignored the discrepancies between field and laboratory data, leading to difficulties in field application. Therefore, aiming to bridge the gap between the lab-developed models and the field operational data, this paper presents a feature engineering-based SOH estimation method with downgraded laboratory battery data, applicable to real vehicles under different operating conditions. Firstly, a data processing pipeline is proposed to downgrade laboratory data to operational fleet-level data. The six key features are extracted on the partial ranges to capture the battery’s aging state. Finally, three machine learning (ML) algorithms for easy online deployment are employed for SOH assessment. The results show that the hybrid feature set performs well and has high accuracy in SOH estimation for downgraded data, with a minimum root mean square error (RMSE) of 0.36%. Only three mechanism features derived from the incremental capacity curve can still provide a proper assessment, with a minimum RMSE of 0.44%. Voltage-based features can assist in evaluating battery state, improving accuracy by up to 20%.

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“…Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs), drones, and portable electronic devices due to their advantages of high energy density, high charging efficiency, wide operating temperature range, and long cycle life [1,2]. LIBs are pivotal in powering EVs and determining their performance and reliability [3].…”

Section: Introductionmentioning

confidence: 99%

An Improved LSTNet Approach for State-of-Health Estimation of Automotive Lithium-Ion Battery

Ping

Miao

et al. 2023

Electronics

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Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) is one of the pivotal technologies to ensure the safe and dependable operation of electric vehicles (EVs). To tackle the challenges related to the intricate preprocessing procedures and extensive data prerequisites of conventional SOH estimation approaches, this paper proposes an improved LSTNet network model. Firstly, the discharged battery sequence data are divided into long-term and short-term sequences. A spatially convolutional long short-term memory network (ConvLSTM) is then introduced to extract multidimensional capacity features. Next, an autoregressive (AR) component is employed to enhance the model’s robustness while incorporating a shortcut connection structure to enhance its convergence speed. Finally, the results of the linear and nonlinear components are fused to make predictive judgments. Experimental comparisons on two datasets are conducted in this study to demonstrate that the method fits the electric capacity recession curve well, even without the preprocessing step. For the data of four NASA batteries, the maximum root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the prediction results were maintained at 0.65%, 0.58%, and 0.435% when the proportion of the training set was 40%, which effectively validates the model’s feasibility and accuracy.

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A novel aging characteristics-based feature engineering for battery state of health estimation

Cited by 14 publications

References 38 publications

A Novel State-of-Health Prediction and Assessment Strategies for High-Capacity Mining Lithium-Ion Batteries Based on Multi-Indicator

A Novel State-of-Health Prediction and Assessment Strategies for High-Capacity Mining Lithium-Ion Batteries Based on Multi-Indicator

A Novel Feature Engineering-Based SOH Estimation Method for Lithium-Ion Battery with Downgraded Laboratory Data

An Improved LSTNet Approach for State-of-Health Estimation of Automotive Lithium-Ion Battery

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