Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network

Hu, Panpan; Tang, Wendy; Li, C. H.; Mak, S. L.; Li, Chun-Yin; Lee, Chi Chung

doi:10.3390/en16145313

Cited by 15 publications

(3 citation statements)

References 42 publications

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“…Learning rate is the most important hyperparameter of LSTM neural networks, followed by network size, while momentum gradient has little effect on the final results [36]. In order to match the LSTM structure with the data characteristics of lithium-ion batteries, the PSO-LSTM prediction model was constructed.…”

Section: Principle Of Pso-lstm Modelmentioning

confidence: 99%

Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

Zhang,

Xu,

et al. 2023

WEVJ

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With the increasingly serious problem of environmental pollution, new energy vehicles have become a hot spot in today’s research. The lithium-ion battery has become the mainstream power battery of new energy vehicles as it has the advantages of long service life, high-rated voltage, low self-discharge rate, etc. The battery management system is the key part that ensures the efficient and safe operation of the vehicle as well as the long life of the power battery. The accurate estimation of the power battery state directly affects the whole vehicle’s performance. As a result, this paper established a lithium-ion battery charge state estimation model based on BP, PSO-BP and LSTM neural networks, which tried to combine the PSO algorithm with the LSTM algorithm. The particle swarm algorithm was utilized to obtain the optimal parameters of the model in the process of repetitive iteration so as to establish the PSO-LSTM prediction model. The superiority of the LSTM neural network model in SOC estimation was demonstrated by comparing the estimation accuracies of BP, PSO-BP and LSTM neural networks. The comparative analysis under constant flow conditions in the laboratory showed that the PSO-LSTM neural network predicts SOC more accurately than BP, PSO-BP and LSTM neural networks. The comparative analysis under DST and US06 operating conditions showed that the PSO-LSTM neural network has a greater prediction accuracy for SOC than the LSTM neural network.

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Section: Principle Of Pso-lstm Modelmentioning

confidence: 99%

Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

Zhang,

Xu,

et al. 2023

WEVJ

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“…Because SOC and capacity are not decoupled, the convergence of capacity estimation tend to be slow. Lai et al [16] proposed a data-driven method based on the NSSR-LSTM Neural Network to achieve the joint estimation of SOC and SOH for lithium-ion batteries. However, their generalization to untrained cases is usually weak.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of SOC and SOH for Lithium-Ion Batteries Based on Dual Adaptive Central Difference H-Infinity Filter

Sang,

Wu,

Yang

et al. 2024

Energies

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The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is crucial for the safe and reliable operation of battery systems. In order to overcome the practical problems of low accuracy, slow convergence and insufficient robustness in the existing joint estimation algorithms of SOC and SOH, a Dual Adaptive Central Difference H-Infinity Filter algorithm is proposed. Firstly, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for parameter identification, and an inner loop with multiple updates of the parameter estimation vector is added to improve the accuracy of parameter identification. Secondly, the capacity is selected as the characterization of SOH, and the open circuit voltage and capacity are used as the state variables for capacity estimation to improve its convergence speed. Meanwhile, considering the interaction between SOC and SOH, the state space equations of SOC and SOH estimation are established. Moreover, the proposed algorithm introduces a robust discrete H-infinity filter equation to improve the measurement update on the basis of the central differential Kalman filter with good accuracy, and combines the Sage–Husa adaptive filter to achieve the joint estimation of SOC and SOH. Finally, under Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) conditions, the SOC estimation errors are 0.5% and 0.63%, and the SOH maximum estimation errors are 0.73% and 0.86%, indicating that the proposed algorithm has higher accuracy compared to the traditional algorithm. The experimental results at different initial values of capacity and SOC demonstrate that the proposed algorithm showcases enhanced convergence speed and robustness.

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“…Ref. [46] utilized a nonlinear state space reconstruction-long short-term memory neural network for dual estimation of SOC and SOH on lithium-ion battery packs of electric vehicles. Their proposed model consists of two LSTM neural network estimators for SOC and SOH, respectively, and achieved an RMSE of within 1.3% for SOC estimation and 2.5% for SOH estimation.…”

Section: Introductionmentioning

confidence: 99%

Cross-Stitch Networks for Joint State of Charge and State of Health Online Estimation of Lithium-Ion Batteries

Yao,

Neupert,

Kowal

2024

Batteries

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As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into the battery systems. Among other critical functionalities, BMSs provide information about the key states of the batteries under usage, including state of charge (SOC) and state of health (SOH). This paper proposes a data-driven approach for the joint online estimation of SOC and SOH utilizing multi-task learning (MTL) approaches, particularly highlighting cross-stitch units and cross-stitch networks. The proposed model is able to achieve an accurate estimation of SOC and SOH in online applications with optimized information sharing and multi-scale implementation. Comprehensive results on training and testing of the model are presented. Possible improvements for future work are also discussed in the paper.

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Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network

Cited by 15 publications

References 42 publications

Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

Research on SOC Estimation Method for Lithium-Ion Batteries Based on Neural Network

Joint Estimation of SOC and SOH for Lithium-Ion Batteries Based on Dual Adaptive Central Difference H-Infinity Filter

Cross-Stitch Networks for Joint State of Charge and State of Health Online Estimation of Lithium-Ion Batteries

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