SOC estimation and fault diagnosis framework of battery based on multi-model fusion modeling

Li, Jiabo; Min, Yong; Ma, Xiaoqian; Wang, Qiao; Wang, Yan

doi:10.1016/j.est.2023.107296

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…[18][19][20] However, these methods tend to be more complex to implement and may have longer estimation times. 21 Data-driven methods for SOC estimation usually make use of neural networks, 22 support vector machines, deep learning, and other data-driven techniques. 23,24 They train models to directly map the relationship between various parameters like voltage, current, and temperature measured during battery operation to the SOC, 25 without the need for mathematical battery models.…”

Section: Motivations and Technical Challengesmentioning

confidence: 99%

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

Zou,

Wang,

Hai

et al. 2024

J. Electrochem. Soc.

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With the rapid development of electric vehicles and green energy sources, the use of backpropagation neural network (BPNN) to precisely estimate the state of charge (SOC) in lithium-ion batteries has become a popular research topic. However, traditionally BPNN has low prediction accuracy and large output fluctuations. To address the shortcomings of BPNN, self-adaptive flower pollination algorithm (SFPA) was proposed to optimize the initial weights and thresholds of BPNN, and an output sliding average window (OSAW) strategy is proposed to smooth SOC outputs in this research, which SOC estimation method is named SFPA-BP-OSAW. In addition, the performance of the newly-proposed method is compared with other common related algorithms under different working conditions to verify the effectiveness of SFPA-BP-OSAW. The experimental results show that the mean absolute error of SFPA-BP-OSAW is 0.771% and 0.897%, and the root mean square error is 0.236% and 0.37%, respectively, under HPPC and BBDST working conditions. Experimental data and error analysis show that the method proposed in this paper has fast convergence, high prediction accuracy, and curve smoothness.

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Section: Motivations and Technical Challengesmentioning

confidence: 99%

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

Zou,

Wang,

Hai

et al. 2024

J. Electrochem. Soc.

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show abstract

“…Li et al [122] proposed a model-based fault diagnosis algorithm to address challenges in real-time SOC estimation for LIBs using Coulomb counting. This algorithm effectively diagnoses three typical faults without extra measurements or prior battery knowledge.…”

Section: Coulomb Countingmentioning

confidence: 99%

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

Madani,

Ziebert,

Vahdatkhah

et al. 2024

Batteries

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In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring.

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“…However, the HPPC experiment has no fixed experimental content. [ 27–30 ] Wang et al. [ 31 ] investigated the effects of different pulse times, current multiples, and shelving times on battery modeling accuracy in the HPPC experiment.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium‑Ion Batteries Based on Extended Kalman Particle Filter and Orthogonal Optimized Battery Model

Shi,

Zhang,

et al. 2024

Advcd Theory and Sims

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State of charge (SOC) is a key state variable in lithium‑ion battery management system. The battery model and estimation algorithm are important factors that affect the accuracy of SOC estimation. In this paper, the optimization battery model is created by optimizing the hybrid power pulse characteristic (HPPC) parameter combination through orthogonal analysis. The simulation results demonstrate that optimizing the HPPC parameter combinations can improve battery modeling accuracy. Then, an extended Kalman particle filter (EKPF) algorithm is proposed by using the extended Kalman filter (EKF) algorithm as the density function of the particle filter (PF) algorithm. The EKPF algorithm is verified under the dynamic stress test and Beijing bus dynamic stress test conditions, the root mean absolute errors and root mean square errors in all cases are less than 1.5%. The experimental results show that the EKPF algorithm can combine the advantages of EKF and PF to estimate lithium‐ion battery SOC accurately.

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SOC estimation and fault diagnosis framework of battery based on multi-model fusion modeling

Cited by 10 publications

References 36 publications

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

State of Charge Estimation for Lithium‑Ion Batteries Based on Extended Kalman Particle Filter and Orthogonal Optimized Battery Model

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