SOC prediction of Lithium-Ion Battery using Extended Kalman Filter

Khalil, Ihsan Ullah; Ahsan, Mati Ullah; Ullah, Irshad; Adnan, Arbab; Khan, Naveed Ahmed; Nawaz, Shah

doi:10.1109/raee.2018.8706898

Cited by 19 publications

(6 citation statements)

References 4 publications

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“…The average maximum electric field strengths of transmission lines' six and four cross-arm towers of transmission lines were 14.33-15.70 kV/cm and 12.29-15.90 kV/cm, respectively. By comparing the corona loss and resistance loss of transmission lines under the optimal and the worst phase sequence arrangements in the project involving 750 kV four-circuit transmission lines on the same tower, it was found that the annual average CL was 25% to 47% of the resistance loss [93,94]. Moreover, the maximum corona loss from these transmission lines was between 107% and 215% of the resistance loss.…”

Section: Future Research Directionsmentioning

confidence: 99%

Historical Review of Advancements in Insulated Cross-Arm Technology

et al. 2022

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High-voltage transmission technology has advanced quickly with the overall development and increased use of renewable energy. More demands on the insulating system are made when high-voltage power systems evolve. One of the significant factors is the sharp rise in population density, which led to the high demand for electricity. Right-of-way infringement is a problem that frequently occurs these days. Transmission is done over a rated capacity; as a result, the transmission line heats up, the insulation ages, and the electric field becomes distorted. The insulating system is prone to fail too soon when the operating voltage inverses or when there is a significant temperature differential. Environmentally friendly insulating materials have received much attention recently. A synergistic optimisation of heat resistance properties, mechanical properties, and dielectric properties must be accomplished before these materials can be used in high-voltage transmission systems. They must also withstand harsh electrical and thermal shocks such as overvoltage and short-circuit faults. One of the developments that has become a popular research topic is the constantly evolving tower design. This review article presents advancements in cross-arm technology in high-voltage transmission systems to elaborate on the limitations and contributions of different research work.

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Section: Future Research Directionsmentioning

confidence: 99%

Historical Review of Advancements in Insulated Cross-Arm Technology

et al. 2022

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“…Finally, other advanced estimators such as particle filter and Bayesian estimator could give more accurate results, but they are expensive. [27][28][29][30] The EKF algorithm is as follows: State-space model:…”

Section: Algorithms and Implementationmentioning

confidence: 99%

“…The KF can perform many tasks and could be used widely in many applications. [29][30][31] For the estimation of the parameters, the system state is not the desired quantity to estimate, but some other parameters are related. Generally, to evaluate a parameter, it should be included in the state-space model similar to the next.…”

Section: State and Parameter Estimationmentioning

confidence: 99%

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State of charge estimation of a Li‐ion battery based on extended Kalman filtering and sensor bias

et al. 2020

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The growing usage of electric vehicles (EVs) has led to significant advancements in batteries' technology. State of charge (SOC) estimation is an essential function of the battery management system-the heart of EVs and Kalman filtering is a standard SOC estimation method. Because of the non-uniformities in tuning and testing scenarios, it is challenging to quantify SOC estimation algorithms' performance. A SOC estimation algorithm is developed in this work, extended Kalman filter (EKF), and tested for variable scenarios like adding sensor noise and bias to terminal voltage and current and varying state and parameter initializations. Also, a dual EKF is implemented to estimate the sensor voltage and current bias and compared it against the state EKF to estimate SOC. Finally, a comparative study has been introduced to decide which algorithm represents the most accurate estimation for the battery parameters, and it was found that the dual EKF gave the best results. K E Y W O R D S dual extended Kalman filter, extended Kalman filter, Kalman filter, lithium-ion batteries, sensor bias, state of charge, unsupervised learning tools

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“…In order to design more effective BMSs, non-linear KFs are usually supported with accurate prediction models impacting on the computational costs of the procedure as discussed in [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells

et al. 2021

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Battery Management System (BMS) design for Lithium-ion batteries State of Charge (SoC) prediction has a crucial role in Electric Vehicles (EVs) and smart grids development. The need to design compact, light and fast devices requires finding a suitable trade-off between effectiveness and efficiency. In the literature, it is well emphasized that the application of electrochemical-based methods such as the Pseudo-Two-Dimensional (P2D) model is computationally prohibitive and requires significant simplifications. Conversely, plain Equivalent Circuit Models (ECM) are too simple and unable to represent the cell dynamics. The application of an Ensemble Neural Network (ENN) as Equivalent Neural Network Circuit (ENNC) emerged as a promising solution able to synthesize expressive and computationally efficient models. Indeed, with the support of a suitable dataset, an ENN can be configured to represent a given ECM, modeling each lumped parameter through an assigned Neural Network (NN). Accordingly, the ENNC system is able to keep a physical description of the battery cell while approximating the non-linear dynamic of each component. The paper proposes a novel ENNC battery named Physical Inspired-Equivalent Neural Network Circuit (PI-ENNC) whose ensemble architecture relies on a fractional-order Extended Single Particle (ESP) Lithium-ion cell formulation. The PI-ENNC is designed to approximate the ESP transfer functions referred to the ohmic effects, the electrolyte diffusion and the non-uniform charge distribution in the cell. The proposed model has been tested with three publicly available datasets, investigating the model behavior according to two different training strategies and with different input configurations. In order to prove its effectiveness, results have been compared with a simpler version proposed in a previous work. Results highlight the effectiveness of PI-ENNC in SoC prediction, underlining the importance of designing an ENN architecture that leverages on equations and constraints that reflect the physical phenomena of the cell.

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SOC prediction of Lithium-Ion Battery using Extended Kalman Filter

Cited by 19 publications

References 4 publications

Historical Review of Advancements in Insulated Cross-Arm Technology

Historical Review of Advancements in Insulated Cross-Arm Technology

State of charge estimation of a Li‐ion battery based on extended Kalman filtering and sensor bias

A Physically Inspired Equivalent Neural Network Circuit Model for SoC Estimation of Electrochemical Cells

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