State of health estimation of the LiFePO4 power battery based on the forgetting factor recursive Total Least Squares and the temperature correction

Wu, Muyao; Wang, Li; Wu, J.

doi:10.1016/j.energy.2023.128437

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…The effectiveness and accuracy of the proposed method are confirmed via simulation. Wu et al [143] proposed a novel LS-based algorithm to estimate SoH for LiFePO 4 batteries. A temperature correction method was also proposed to avoid ambient temperature influence on SoH accuracy.…”

Section: Filter-based Methodsmentioning

confidence: 99%

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

Di Luca,

Di Blasio,

Gimelli

et al. 2023

Energies

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The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions.

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Section: Filter-based Methodsmentioning

confidence: 99%

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

Di Luca,

Di Blasio,

Gimelli

et al. 2023

Energies

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show abstract

“…Supervised learning techniques, especially regression algorithms such as Least Squares Regression, [147] Gaussian Process Regression, [148] Support Vector Regression, [149] and k-nearest Neighbor Regression [150] are used to find suitable functional relationships to predict the battery capacity level from input signals such as voltage, current, and temperature. Gaussian Process Regression (GPR) is a non-parametric regression method that models the probability distribution of data, providing estimates of uncertainty.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

Yang,

Zhang,

Wang

et al. 2024

Carbon Neutralization

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The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.

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“…[4][5][6] The SOH estimation of lithium-ion batteries is one of the core tasks of the battery management system. [7][8][9] SOH cannot be measured directly, 10,11 but it can be estimated. There are two main common SOH estimation methods, one is based on an electrochemical model or equivalent circuit model, and the other is based on a data-driven approach.…”

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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State of health estimation of the LiFePO4 power battery based on the forgetting factor recursive Total Least Squares and the temperature correction

Cited by 6 publications

References 28 publications

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

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

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