A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

Movassagh, Kiarash; Raihan, Arif; Balasingam, Balakumar; Pattipati, Krishna R.

doi:10.3390/en14144074

Cited by 96 publications

(15 citation statements)

References 49 publications

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“…Even though CC is widely adopted [3,18,19], in [20,21], the authors described four types of error sources that can compromise the accuracy of the SoC prediction. These can be summarized in: (i) current measurement error; (ii) error due to the approximation of current integration with respect to time; (iii) uncertainty in the knowledge of battery capacity; and the (iv) error (drift) in timing oscillator.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the same approach, three types of error are summarized also for voltagebased (or OCV-based) models [21]: (i) OCV-SOC modeling error; (ii) voltage-drop modeling error; (iii) voltage measurement error.…”

Section: Introductionmentioning

confidence: 99%

“…The RNN applies the state equation (Equation ( 20)) reading in input the cell current I in , the dynamic voltage contribution evaluation at the previous step and the output generated by the NNs T dyn and R dyn , namely the vectors R dyn and τ dyn . The RNN has an output layer defined by the RC weights ω dyn (see (21)). The ω dyn training allows to better tune the RC voltage contribution, especially in case more RC filters are considered.…”

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confidence: 99%

“…C di f f s,i represents the concentration difference between the surface and the volume average value of the active particle which is function of the ion pore wall flux of the cell, and therefore of the density current. Finally, the transfer function related to C sur f s,i has been found and tested after applying a proper simplification and truncation to the first-order to reduce the computational cost (see Equation (21) in [34]):…”

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confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2021

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“…Methods based on the open circuit voltage, terminal voltage, internal resistance and Coulomb Counting (CC) are commonly used. However, these methods require regular calibration due to error propagation related to changes in the internal characteristics of the battery due to aging, inaccuracies in the assumed initial conditions, and measurement biases [6]. Temperature and mechanical measurements of a cell have also been taken into consideration to improve the battery management and therefore states estimation [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

et al. 2021

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Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than 2% over the full operating range of SoC along with an accurate estimation of SoH.

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State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

2022

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Lithium‐ion batteries (LIBs) are currently the most widely used energy storage technologies in electric vehicles (EVs) due to their higher power density, greater energy density, longer life, fast charging capabilities, and low discharge rate. The brain of rechargeable batteries is referred to be the battery management system (BMS). The operation of the battery is monitored & regulated by an electronic system called BMS. Most significantly, it prevents the battery from exceeding its safety limits. One of the important parameters of BMS is the state of charge (SOC); it determines the battery's remaining capacity of accessible stored energy, battery lifespan, and cell balancing. As a result, an accurate SOC calculation is essential for BMS, and it is used for safer operations that avoid overcharging and discharging for the optimum battery life. The primary ambition of this article is to find an accurate and robust SOC estimation based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques. RMS error, average error, minimum error, and maximum error are used to compare the EKF method and UKF method. Two resistor‐capacitor (2RC) equivalent circuit model has been considered for the dynamic performance of the battery. The waveforms are displayed, and errors are simulated for SOC estimate using the EKF and UKF algorithms, taking the same initial SOC into consideration at a time. The UKF algorithm outperforms the EKF approach in terms of dynamic results when comparing the two methods.

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A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

Cited by 96 publications

References 49 publications

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

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

Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

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