A novel back propagation neural network-dual extended Kalman filter method for state-of-charge and state-of-health co-estimation of lithium-ion batteries based on limited memory least square algorithm

Wang, Chao; Wang, Shunli; Zhou, Jinzhi; Qiao, Jialu; Yang, Xiao Guang; Xie, Yanxin

doi:10.1016/j.est.2022.106563

Cited by 30 publications

(9 citation statements)

References 51 publications

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“…Here, SOC (t) represents the SOC at time t. When SOC = 0% holds, the terminal voltage of the battery is a 0 and a 1 is derived from a 0 and the value of V oc (t) at SOC = 100%. The relation in (16) proves that the SOC estimate is equivalent to the OCV estimate. Moreover, the SOC-OCV correlation cannot be the same for all types of batteries.…”

Section: Exploration Of Open Circuit Voltagementioning

confidence: 85%

“…Afterwards, the correlation of the battery is detected through the conducting of charging and discharging. Moreover, in contrast to the lithiumion battery, the lead-acid battery has a linear correlation within SOC-OCV, which is demonstrated by (16).…”

Section: Exploration Of Open Circuit Voltagementioning

confidence: 97%

“…However, owing to highly non-linear nature of the battery, a reliable SOC estimation is challenging. Currently, various methods are utilized for the purpose of SOC estimation, especially for lithium-ion batteries, such as open circuit voltage (OCV) [15], ampere-hour (AH) integration [16], neural network and model-based methods.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

Monirul

Pan

Ruby

et al. 2023

IET Control Theory & Appl

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The battery management system (BMS) is a crucial component of electric vehicles (EVs) owing to its sustainable operation. To ensure optimal performance of the BMS, state of charge (SOC) of the equipped battery is required to be effectively and accurately estimated. In this paper, the authors consider high‐order equivalent circuit model (ECM) to capture the dynamic characteristics of lithium‐ion batteries, which are connected in series with internal resistance by 2‐RC networks. The parameters of the RC network are determined by mathematically solving the working conditions of the two states. Moreover, the parameters of the battery can be derived by hybrid pulse power characterization (HPPC) tests. Then, based on the open‐circuit voltage, the proposed feedback‐based extended Kalman filtering (FEKF) algorithm is established. The parameters from the simulation have shown that the highest error is 0.0306 V, the optimal knowledge of which can improve the SOC estimation approach remarkably and can provide a reference value. Afterwards, the non‐linear predicting and corrective techniques are applied to the experiment in the extended calculation process. The original error is reduced by the FEKF algorithm, where the maximum and average errors are 0.0298 and 0.0240 V, respectively. Consequently, the established high‐order ECM utilizing the FEKF algorithm may provide SOC estimation with an error of 1.5% or less, resulting in superb performance from the lithium‐ion battery pack.

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Section: Exploration Of Open Circuit Voltagementioning

confidence: 85%

Section: Exploration Of Open Circuit Voltagementioning

confidence: 97%

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Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

Monirul

Pan

Ruby

et al. 2023

IET Control Theory & Appl

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“…18 On the basis of establishing the second-order equivalent circuit model, Wang et al proposed a double extended Kalman filter method to realize the collaborative estimation of the charge state and health state of lithiumion batteries, which had high accuracy and good robustness. 19 The data-driven method mainly extracts feature data that can characterize the battery SOH from the historical data of lithium-ion batteries, and then uses data-driven algorithms to estimate SOH based on the extracted feature data. [20][21][22] Peng et al proposed a lithium-ion battery health state estimation method based on multi-health feature extraction and improved long short-term memory (LSTM) neural network.…”

mentioning

confidence: 99%

State-of-Health Estimation of Lithium-ion Batteries Based on Singular Value Decomposition and an Improved Group Method of Data Handling

Li,

Bai,

Yan

et al. 2024

J. Electrochem. Soc.

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Lithium-ion batteries are complex electrochemical systems, and the degradation of their state-of-health (SOH) is a nonlinear process. Accurate SOH estimation is critical to lithium-ion battery life and safety. We used a data-driven approach to study SOH estimation of lithium-ion batteries. First, we used the singular value decomposition method to extract features from the battery charging history data. Second, the particle swarm (PSO) algorithm was used to optimize the parameter configuration of the group method of data handling (GMDH). Finally, the SOH estimation wa completed using the optimized GMDH. The results show that the proposed PSO-GMDH estimation model maintains an error within 0.89% for estimating its subsequent SOH using historical data of a certain battery, and maintains an error within 0.5% for estimating the SOH of another battery of the same model using historical data of multiple batteries. At the same time, the results also show that the PSO-GMDH estimation model has higher estimation accuracy than the GMDH model without parameter optimization.

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“…2,3 Nevertheless, the function of lithium-ion batteries is influenced by the environment and operating conditions, posing potential failure risks. [4][5][6] In this regard, a battery management system (BMS) is required to monitor the battery status and ensure that it runs safely and efficiently. [7][8][9] State of charge (SOC) estimation of batteries is one of the most significant and demanding functions in the BMS.…”

mentioning

confidence: 99%

Improved Feature Decoupling Transfer Network Modeling based on Singular Value Decomposition for SOC Estimation in Energy-Storage Lithium-ion Batteries

Liu

Wang

Liang

et al. 2023

J. Electrochem. Soc.

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Deep learning (DL) methods are applied extensively in the field of state of charge (SOC) estimation, which require training data and test data to have similar distribution. Discrepancies in data distribution arising from the complexity and diversity of lithium-ion batteries under operational conditions in practice, as well as the difficulty in obtaining data labels, make it enormously challenging to access sufficient battery data to train a specific deep estimator. Aiming to improve the performance of cross-domain SOC estimation for lithium-ion batteries, a model for SOC estimation which combines transfer learning with singular value decomposition (SVD) is proposed. To begin, a gated recurrent unit network is employed to avail the nonlinear dynamic characteristics of the battery from the source and target domains. Then, the features are decoupled by using SVD method to extract task-relevant, important and minor information in the network. Further, the amount of transferred information over the source network to the target network is automatically tuned by the maximum mean discrepancy to determine the different degrees of similarity in domain, and the cosine discrepancy to measure the discrepancy on the same domain, which achieves the optimized performance of the target network.

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A novel back propagation neural network-dual extended Kalman filter method for state-of-charge and state-of-health co-estimation of lithium-ion batteries based on limited memory least square algorithm

Cited by 30 publications

References 51 publications

Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

State-of-Health Estimation of Lithium-ion Batteries Based on Singular Value Decomposition and an Improved Group Method of Data Handling

Improved Feature Decoupling Transfer Network Modeling based on Singular Value Decomposition for SOC Estimation in Energy-Storage Lithium-ion Batteries

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