Unknown Input Observer for Battery Open Circuit Voltage Estimation: an LMI Approach

Dewangga, Bobby Rian; Herdjunanto, Samiadji; Cahyadi, Adha Imam

doi:10.1109/iciteed.2018.8534889

Cited by 3 publications

(6 citation statements)

References 9 publications

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“…Rank (C * D) = rank (D) is required for the system to be observable where (C,A) is an observable pair. The structure of the UIO for the abovementioned (2) linear system can be described as follows [38]. The structure of such UIO is shown in Figure 5 [39].…”

Section: Observer Designmentioning

confidence: 99%

“…For the estimated state vector, x(t) convergence to x(t), the UIO parameters are determined by formulating into LMI. UIO state estimation error can be noted as follows [38],…”

Section: Observer Designmentioning

confidence: 99%

“…where e and I are the state estimation error and the identity matrix, respectively. Hence, state estimation error dynamics can be given as stated below [38]:…”

Section: Observer Designmentioning

confidence: 99%

“…To ensure that the system is independent from the unknown input and converges, the following conditions must be satisfied [38],…”

Section: Observer Designmentioning

confidence: 99%

“…(CD) T and Y is an arbitrary matrix [38]. For notational convenience, Equation ( 17) is rewritten as…”

Section: Observer Designmentioning

confidence: 99%

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Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer

2022

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The anode state of charge (SOC) and degradation information pertaining to lithium-ion batteries (LIBs) is crucial for understanding battery degradation over time. This information about each cell in a battery pack can help prolong the battery pack’s life cycle. Because of the limited observability, estimating the anode state and capacity fade is difficult. This task is even more challenging for the cells in a battery pack, as the current through the individual cell is not constant when cells are connected in parallel. Considering these challenges, this paper presents a novel method to set up three-electrode cells by using the battery’s casing as a reference electrode for building a three-electrode battery pack. This work is a continuation of the authors’ previous research. An unknown input observer (UIO) is employed to estimate the anode SOC of an individual battery in the battery pack. To ensure the stability of a defined Lyapunov function, the UIO parameter matrices are expressed as a linear matrix inequality (LMI). The anode SOC of a lithium nickel manganese cobalt oxide (NMC) battery is estimated by using the standard graphite potential (SGP) and state of lithiation (SOL) characteristic curve. The anode capacity is then calculated by using the total charge transferred in a charging cycle and the estimated SOC of the anode. The degradation of the battery is then evaluated by comparing the capacity fading of the anode to the total charge carried to the cell. The proposed method can estimate the anode SOC and capacity fade of an individual battery in a battery pack, which can monitor the degradation of the individual batteries and the battery pack in real time. By using the proposed method, we can identify the over-degraded batteries in the pack for remaining useful life analysis on the battery.

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Section: Observer Designmentioning

confidence: 99%

“…For the estimated state vector, x(t) convergence to x(t), the UIO parameters are determined by formulating into LMI. UIO state estimation error can be noted as follows [38],…”

Section: Observer Designmentioning

confidence: 99%

“…where e and I are the state estimation error and the identity matrix, respectively. Hence, state estimation error dynamics can be given as stated below [38]:…”

Section: Observer Designmentioning

confidence: 99%

“…To ensure that the system is independent from the unknown input and converges, the following conditions must be satisfied [38],…”

Section: Observer Designmentioning

confidence: 99%

“…(CD) T and Y is an arbitrary matrix [38]. For notational convenience, Equation ( 17) is rewritten as…”

Section: Observer Designmentioning

confidence: 99%

See 3 more Smart Citations

Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer

2022

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Increasing Battery Management System Resilience Following Identification of Sensor Anomalies Using Unknown Input Observer

Obrien,

Trevizan,

Rao

2024

2024 IEEE Electrical Energy Storage Application and Technologies Conference (EESAT)

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A Fast Prediction of Open-Circuit Voltage and a Capacity Estimation Method of a Lithium-Ion Battery Based on a BP Neural Network

et al. 2022

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The battery is an important part of pure electric vehicles and hybrid electric vehicles, and its state and parameter estimation has always been a big problem. To determine the available energy stored in a battery, it is necessary to know the current state-of-charge (SOC) and the capacity of the battery. For the determination of the battery SOC and capacity, it is generally estimated according to the Electromotive Force (EMF) of the battery, which is the open-circuit-voltage (OCV) of the battery in a stable state. An off-line battery SOC and capacity estimation method for lithium-ion batteries is proposed in this paper. The BP neural network with a high accuracy is trained in the case of sufficient data with the new neural network intelligent algorithm, and the OCV can be accurately predicted in a short time. The model training requires a large amount of data, so different experiments were designed and carried out. Based on the experimental data, the feasibility of this method is verified. The results show that the neural network model can accurately predict the OCV, and the error of capacity estimation is controlled within 3%. The mentioned method was also carried out in a real vehicle by using its cloud data, and the capacity estimation can be easily realized while limiting inaccuracy to less than 5%.

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Unknown Input Observer for Battery Open Circuit Voltage Estimation: an LMI Approach

Cited by 3 publications

References 9 publications

Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer

Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer

Increasing Battery Management System Resilience Following Identification of Sensor Anomalies Using Unknown Input Observer

A Fast Prediction of Open-Circuit Voltage and a Capacity Estimation Method of a Lithium-Ion Battery Based on a BP Neural Network

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