A Co-Estimation Framework for State of Charge and Parameters of Lithium-Ion Battery With Robustness to Aging and Usage Conditions

Natella, Domenico; Onori, Simona; Vasca, Francesco

doi:10.1109/tie.2022.3194576

Cited by 13 publications

(3 citation statements)

References 37 publications

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“…All experiments were conducted on 5 Ah LG Chem M50LT 21700 cylindrical lithium-ion cells, which comprise an NMC cathode and graphite-silicon anode [20,21] and have previously been used in relevant publications related to EV BMSs [22,23]. Three cells were used for the tests, with each cell subjected to all procedures, The stabilisation rig maintained uniform and repeatable pressure applied from the electrical connections onto the cell, ensuring traceability across each test setup and each cell.…”

Section: Methodsmentioning

confidence: 99%

Advancing state estimation for lithium-ion batteries with hysteresis: systematic extended Kalman filter tuning

Knox,

Blyth,

Hales

2024

Preprint

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Knowledge of remaining battery charge is fundamental to electric vehicle deployment. Accurate measurements of state-of-charge (SOC) cannot be directly obtained, and estimation methods must be used instead. This requires both a good model of a battery and a well-designed state estimator. Here, hysteretic reduced-order battery models and adaptive extended Kalman filter estimators are shown to be highly effective, accurate SOC estimators. A battery model parameterisation framework is proposed, which enhances standardised methods to capture hysteresis effects. The hysteretic model is parameterised for three independent NMC811 lithium-ion cells and is shown to reduce voltage RMS error by 50% across 18-hour automotive drive-cycles. Parameterised models are used alongside an extended Kalman filter, which demonstrates the value of adaptive filter parameterisation schemes. When used alongside an extended Kalman filter, adaptive covariance matrices yield highly accurate SOC estimates, reducing SOC estimation error by 85%, compared to the industry standard battery model.

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Section: Methodsmentioning

confidence: 99%

Advancing state estimation for lithium-ion batteries with hysteresis: systematic extended Kalman filter tuning

Knox,

Blyth,

Hales

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…All experiments were conducted on 5 Ah LG Chem M50LT 21700 cylindrical lithium-ion cells, which comprise an NMC cathode and graphite-silicon anode 28,29 and have previously been used in relevant publications related to EV BMSs 30,31 . Three cells were used for the tests, with each cell subjected to all procedures, resulting in three complete datasets.…”

Section: Methodsmentioning

confidence: 99%

Advancing state estimation for lithium-ion batteries with hysteresis through systematic extended Kalman filter tuning

Knox,

Blyth,

Hales

2024

Sci Rep

View full text Add to dashboard Cite

Knowledge of remaining battery charge is fundamental to electric vehicle deployment. Accurate measurements of state-of-charge (SOC) cannot be obtained directly and estimation methods must be used instead. This requires both a good model of a battery and a well-designed state estimator. Here, hysteretic reduced-order battery models and adaptive extended Kalman filter estimators are shown to be highly effective, accurate predictors of SOC. A battery model parameterisation framework is proposed, which enhances standardised methods to capture hysteresis effects. The hysteretic model is parameterised for three independent NMC811 lithium-ion cells and is shown to reduce voltage RMS error by 50% across 18 h automotive drive-cycles. Parameterised models are used alongside an extended Kalman filter, which demonstrates the value of adaptive filter parameterisation schemes. When used alongside an extended Kalman filter, adaptive covariance matrices yield highly accurate SOC estimates, reducing SOC estimation error by 85%, compared to the industry standard battery model.

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“…The least-square filters include recursive least square (RLS) and weight recursive least square (WRLS), as well as non-recursive least square, which also supports iterative LS but is cost-effective in terms of memory saving and computation [71,72]. The Luenberger observer is a simple algorithm to determine a feedback gain which modifies SOC by comparing the model voltage and measured voltage [73]. In [74], battery state-of-health monitoring and remaining usable life (RUL) prediction were investigated using an enhanced particle filter (PF) technology.…”

Section: Reqmentioning

confidence: 99%

Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies

et al. 2022

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The online battery management system (BMS) is very critical for the safe and reliable operation of electric vehicles (EVs) and renewable energy storage applications. The primary responsibility of BMS is data assembly, state monitoring, state management, state safety, charging control, thermal management, and information management. The algorithm and control development for smooth and cost-effective functioning of online BMS is challenging research. The complexity, stability, cost, robustness, computational cost, and accuracy of BMS for Li-ion batteries (LiBs) can be enhanced through the development of algorithms. The model-based and non-model-based data-driven methods are the most suitable for developing algorithms and control for online BMS than other methods present in the literatures. The performance analysis of algorithms under different current, thermal, and load conditions have been investigated. The objective of this review is to advance the experimental design and control for online BMS. The comprehensive overview of present techniques, core issues, technical challenges, emerging trends, and future research opportunities for next-generation BMS is covered in this paper with experimental and simulation analysis.

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A Co-Estimation Framework for State of Charge and Parameters of Lithium-Ion Battery With Robustness to Aging and Usage Conditions

Cited by 13 publications

References 37 publications

Advancing state estimation for lithium-ion batteries with hysteresis: systematic extended Kalman filter tuning

Advancing state estimation for lithium-ion batteries with hysteresis: systematic extended Kalman filter tuning

Advancing state estimation for lithium-ion batteries with hysteresis through systematic extended Kalman filter tuning

Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies

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