State of health prediction model based on internal resistance

Ji, Hao; Zhang, Wei; Pan, Xuhai; Hua, Min; Chung, Yi-Hong; Shu, Chi‐Min; Zhang, Lijing

doi:10.1002/er.5383

Cited by 37 publications

(16 citation statements)

References 29 publications

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“…Zhang, Miao, & Liu, 2017;Tang et al, 2019;Perez et al, 2018), polynomial (Micea, Ungurean, Cârstoiu, & Groza, 2011), sigmoid (Johnen et al, 2020) or a combination of these (Xing, Ma, Tsui, & Pecht, 2013). More complicated models also account for differences in C-rates and temperatures (Ji et al, 2020;Singh, Chen, Tan, & Huang, 2019). Other analytical model may be based on other relationships, such as a current-time constant (J.…”

Section: Empirical/analytical Modelsmentioning

confidence: 99%

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

Vanem¹,

Alnes

Lam³

2021

PHM_CONF

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Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. Furthermore, the various methods for data-driven diagnostics are categorized in a few overall approaches with quite different properties and requirements with respect to data for training and from the operational phase. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.

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Section: Empirical/analytical Modelsmentioning

confidence: 99%

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

Vanem¹,

Alnes

Lam³

2021

PHM_CONF

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show abstract

“…Data-driven methods are not required to understand the internal structure and working principles of the battery, and rely solely on the extraction of the corresponding aging characteristics of the battery that are inputted into the corresponding SOH estimator to obtain the health of the battery. As reported by existing studies, the common battery aging characteristics largely include capacity, internal resistance Ji et al (2020), Chen et al (2018), Hung et al (2014), battery cycle times Wognsen et al (2015), and the use of stacking pressure Cannarella and Arnold (2014), SEI impedance Zhang and Wang (2009), etc. Meng et al (2018) developed a new method for accurately estimating battery SOH using support vector machine (SVM) technology, which selects the sharp point of the voltage response curve to be the characteristic quantity of battery SOH.…”

Section: Introductionmentioning

confidence: 99%

On-Line Estimation Method of Lithium-Ion Battery Health Status Based on PSO-SVM

Zhang

et al. 2021

Front. Energy Res.

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Battery management system (BMS) refers to a critical electronic control unit in the power battery system of electric vehicles. It is capable of detecting and estimating battery status online, especially estimating state of charge (SOC) and state of health (SOH) accurately. Safe driving and battery life optimization are of high significance. As indicated from recent literature reports, most relevant studies on battery health estimation are offline estimation, and several problems emerged (e.g., long time-consuming, considerable calculation and unable to estimate online). Given this, the present study proposes an online estimation method of lithium-ion health based on particle swarm support vector machine algorithm. By exploiting the data of National Aeronautics and Space Administration (NASA) battery samples, this study explores the changing law of battery state of charge under different battery health. In addition, particle swarm algorithm is adopted to optimize the kernel function of the support vector machine for the joint estimation of battery SOC and SOH. As indicated from the tests (e.g., Dynamic Stress Test), it exhibits good adaptability and feasibility. This study also provides a certain reference for the application of BMS system in electric vehicle battery online detection and state estimation.

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“…Data‐driven methods have been widely used, including Neural Networks, 24 Bayesian Networks, 25 fusion algorithm, 26 and online sequential extreme learning machine method 27 . For the model‐based methods, different parameters are used, including the inner resistance, 28 dQ/dV curve, 29 charging characters curve, 30 and voltage stabilization 31 are studied.…”

Section: Introductionmentioning

confidence: 99%

The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods

et al. 2021

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Summary An accurate estimate of the battery state of charge and state of health is critical to ensure the lithium‐ion battery's efficiency and safety. The equivalent circuit model‐based methods and data‐driven models show the potential for robust estimation. However, the state of charge and state of health estimation system's performance with a parallel comparison has been rarely investigated. In this study, the performances of state of charge and state of health with equivalent circuit model‐based methods and data‐driven estimations are analyzed by different aged and capacity batteries through methods including extended Kalman filters, fully connected deep network with drop methods, and the combination (extended Kalman filters—fully connected deep network with drop methods). Besides the battery state of the voltage and current, the relationship between inner resistance, temperature, and capacity are also considered. Finally, a suggested method is promising for online state estimation of battery working at different temperatures and initial working state. The results indicate that the maximum state of charge estimation errors of the fully connected deep network with drop methods is 0.56% for the fully charged battery. Simultaneously, with an uncertain initial state of charge, the extended Kalman filter shows the lowest maximum state of charge estimation errors (1.4%). For the state of health estimation, the optimized method uses extended Kalman filters to do the monitor first; after 5 testing points, if the state of health drops to lower than 0.95, extended Kalman filters—fully connected deep network with drop methods is suggested. And finally, estimation errors for this method decreased from 30% to 2%.

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State of health prediction model based on internal resistance

Cited by 37 publications

References 29 publications

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

On-Line Estimation Method of Lithium-Ion Battery Health Status Based on PSO-SVM

The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods

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