scholarly journals Implementation of Coulomb Counting Method for Estimating the State of Charge of Lithium-Ion Battery

2021 ◽  
pp. 1-8
Author(s):  
Kevin C. Ndeche ◽  
Stella O. Ezeonu
Keyword(s):  
Lithium Ion Battery ◽  
Coulombic Efficiency ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Battery Management System ◽  
Counting Method ◽  
Voltage Measurement ◽  
Initial State ◽  
Battery Capacity

An accurate estimate of the state of charge, which describes the remaining percentage of a battery’s capacity, has been an important and ever existing problem since the invention of the electrochemical cell. State of charge estimation is one of the important function of a battery management system which ensures the safe, efficient and reliable operation of a battery. In this paper, the coulomb counting method is implemented for the estimation of the state of charge of lithium-ion battery. The hardware comprises an Arduino based platform for control and data processing, and a 16-bit analog to digital converter for current and voltage measurement. The embedded algorithm initializes with a self-calibration phase, during which the battery capacity, coulombic efficiency and initial state of charge are evaluated. The initial state of charge is determined at the fully charged state (100% state of charge) or the fully discharged state (0% state of charge). The cumulative error of this method was addressed by routine recalibration of the capacity, coulombic efficiency and state of charge at the fully-charged and fully-discharged states. The algorithm was validated by charging/discharging a lithium-ion battery through fifty complete cycles and evaluating the error in the estimated state of charge. The result shows a mean absolute error of 0.35% in the estimated state of charge during the test. Further analysis, considering prolonged battery operation without parameters recalibration, suggests that error in the coulombic efficiency term contributes the most to the increasing error in the estimated state of charge with each cycle.

scholarly journals Estimation of lithium-ion battery state-of-charge using an extended kalman filter

2021 ◽  
Vol 10 (4) ◽  
pp. 1759-1768
Author(s):  
Mouhssine Lagraoui ◽  
Ali Nejmi ◽  
Hassan Rayhane ◽  
Abderrahim Taouni
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Ohmic Loss ◽  
Battery Management System ◽  
Transfer Resistance

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

scholarly journals Applying Set Membership Strategy in State of Charge Estimation for Lithium-ion Battery

2022 ◽  
Vol 21 ◽  
pp. 1-19
Author(s):  
Wang Jianhong ◽  
Ricardo A. Ramirez-Mendoza
Keyword(s):  
State Estimation ◽  
State Space ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
External Noise ◽  
The State ◽  
State Of Charge ◽  
Battery Management System ◽  
Set Membership ◽  
State Of Charge Estimation

As state of charge is one important variable to monitor the later battery management system, and as traditional Kalman filter can be used to estimate the state of charge for Lithium-ion battery on basis of probability distribution on external noise. To relax this strict assumption on external noise, set membership strategy is proposed to achieve our goal in case of unknown but bounded noise. External noise with unknown but bounded is more realistic than white noise. After equivalent circuit model is used to describe the Lithium-ion battery charging and discharging properties, one state space equation is constructed to regard state of charge as its state variable. Based on state space model about state of charge, two kinds of set membership strategies are put forth to achieve the state estimation, which corresponds to state of charge estimation. Due to external noise is bounded, i.e. external noise is in a set, we construct interval and ellipsoid estimation for state estimation respectively in case of external noise is assumed in an interval or ellipsoid. Then midpoint of interval or center of the ellipsoid are chosen as the final value for state of charge estimation. Finally, one simulation example confirms our theoretical results.

An Improved Test Method of LiFePO4/Graphene Hybrid Cathode Lithium-Ion Battery and the State of Charge Estimation

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Meiying Li ◽  
Zhiping Guo ◽  
Yuan Li ◽  
Wenliang Wu
Keyword(s):  
Lithium Ion Battery ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Least Square ◽  
Test Method ◽  
Battery Management System ◽  
Battery Management

Abstract The state of charge (SoC) of the battery is a typical characterization of the operating state of the battery and criterion for the battery management system (BMS) control strategy, which must be evaluated precisely. The establishment of an accurate algorithm of SoC estimation is of great significance for BMS, which can help the driver judge the endurance mileage of electric vehicle (EV) correctly. In this paper, a second-order resistor-capacity (RC) equivalent circuit model is selected to characterize the electrical characteristics based on the electrochemical model of the LiFePO4/graphene (LFP/G) hybrid cathode lithium-ion battery. Moreover, seven open circuit voltage (OCV) models are compared and the best one of them is used to simulate the dynamic characteristics of the battery. It is worth mentioning that an improved test method is proposed, which is combined with least square for parameters identification. In addition, the extended Kalman filter (EKF) algorithm is selected to estimate the SoC during the charging and discharging processes. The simulation results show that the EKF algorithm has the higher accuracy and rapidity than the KF algorithm.

Performance Evaluation of an Extended Kalman Filter for State Estimation of a Pseudo-2D Thermal-Electrochemical Lithium-Ion Battery Model

2015 ◽  
Author(s):  
Shi Zhao ◽  
Adrien M. Bizeray ◽  
Stephen R. Duncan ◽  
David A. Howey
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
Low Noise ◽  
Particle Model ◽  
Battery Management System ◽  
Battery Management ◽  
Voltage Measurement

Fast and accurate state estimation is one of the major challenges for designing an advanced battery management system based on high-fidelity physics-based model. This paper evaluates the performance of a modified extended Kalman filter (EKF) for on-line state estimation of a pseudo-2D thermal-electrochemical model of a lithium-ion battery under a highly dynamic load with 16C peak current. The EKF estimation on the full model is shown to be significantly more accurate (< 1% error on SOC) than that on the single-particle model (10% error on SOC). The efficiency of the EKF can be improved by reducing the order of the discretised model while maintaining a high level of accuracy. It is also shown that low noise level in the voltage measurement is critical for accurate state estimation.

A Novel Method for Estimating State of Charge of Lithium Ion Battery Packs

2010 ◽  
Vol 152-153 ◽  
pp. 428-435 ◽  
Author(s):  
Yuan Liao ◽  
Ju Hua Huang ◽  
Qun Zeng
Keyword(s):  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Methods ◽  
Counting Method ◽  
Soc Estimation ◽  
Battery Packs ◽  
Novel Method ◽  
Road Data

In this paper a novel method for estimating state of charge (SOC) of lithium ion battery packs in battery electric vehicle (BEV), based on state of health (SOH) determination is presented. SOH provides information on aging of battery packs and it declines with repeated charging and discharging cycles of battery packs, so SOC estimation depends considerably on the value of SOH. Previously used SOC estimation methods are not satisfactory as they haven’t given enough attention to the decline of SOH. Therefore a novel SOC estimation method based on SOH determination is introduced in this paper; trying to compensate the deficiency for lack of attention to SOH. Real time road data are used to compare the performance of the conventionally often used Ah counting method which doesn’t give any consideration to SOH with the performance of the proposed SOC estimation method, and better results are obtained by the proposed method in comparison with the conventional method.

scholarly journals An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6366
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Silun Peng ◽  
Feng Xiao ◽  
Yulong Shao ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Network Model ◽  
Working Conditions ◽  
Prediction Accuracy ◽  
Lithium Ion ◽  
State Of Charge ◽  
Battery Management System ◽  
Soc Estimation ◽  
Highly Nonlinear ◽  
Gated Recurrent Unit

An accurate state-of-charge (SOC) can not only provide a safe and reliable guarantee for the entirety of equipment but also extend the service life of the battery pack. Given that the chemical reaction inside the lithium-ion battery is a highly nonlinear dynamic system, obtaining an accurate SOC for the battery management system is very challenging. This paper proposed a gated recurrent unit recurrent neural network model with activation function layers (GRU-ATL) to estimate battery SOC. The model used deep learning technology to establish the nonlinear relationship between current, voltage, and temperature measurement signals and battery SOC. Then the online SOC estimation was carried out on different testing sets using the trained model. The experiments in this paper showed that the GRU-ATL network model could realize online SOC estimation under different working conditions without relying on an accurate battery model. Compared with the gated recurrent unit recurrent neural (GRU) network model and long short-term memory (LSTM) network model, the GRU-ATL network model had more stable and accurate SOC prediction performance. When the measurement data contained noise, the experimental results showed that the SOC prediction accuracy of GRU-ATL model was 0.1–0.4% higher than the GRU model and 0.3–0.7% higher than the LSTM model. The mean absolute error (MAE) of SOC predicted by the GRU-ATL model was stable in the range of 0.7–1.4%, and root mean square error (RMSE) was stable between 1.2–1.9%. The model still had high prediction accuracy and robustness, which could meet the SOC estimation in complex vehicle working conditions.

scholarly journals A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3383 ◽  
Author(s):  
Woo-Yong Kim ◽  
Pyeong-Yeon Lee ◽  
Jonghoon Kim ◽  
Kyung-Soo Kim
Keyword(s):  
Lithium Ion Battery ◽  
Nonlinear Model ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Battery Cell ◽  
Model Based ◽  
State Of Charge Estimation

This paper presents a nonlinear-model-based observer for the state of charge estimation of a lithium-ion battery cell that always exhibits a nonlinear relationship between the state of charge and the open-circuit voltage. The proposed nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies. The proposed method has the following advantages: (1) The observability condition of the proposed nonlinear-model-based observer is derived regardless of the shape of the open circuit voltage curve, and (2) because the terminal voltage is contained in the state vector, the proposed model and its observer are insensitive to sensor noise. A series of experiments using an INR 18650 25R battery cell are performed, and it is shown that the proposed method produces convincing results for the state of charge estimation compared to conventional SOC estimation methods.

scholarly journals High-Precision Monitoring of Volume Change of Commercial Lithium-Ion Batteries by Using Strain Gauges

2020 ◽  
Vol 12 (2) ◽  
pp. 557 ◽  
Author(s):  
Lisa K. Willenberg ◽  
Philipp Dechent ◽  
Georg Fuchs ◽  
Dirk Uwe Sauer ◽  
Egbert Figgemeier
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Volume Expansion ◽  
Characteristic Curve ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Strain Gauges ◽  
Curve Shape ◽  
Diameter Change

This paper proposes a testing method that allows the monitoring of the development of volume expansion of lithium-ion batteries. The overall goal is to demonstrate the impact of the volume expansion on battery ageing. The following findings are achieved: First, the characteristic curve shape of the diameter change depended on the state-of-charge and the load direction of the battery. The characteristic curve shape consisted of three areas. Second, the characteristic curve shape of the diameter change changed over ageing. Whereas the state-of-charge dependent geometric alterations were of a reversible nature. An irreversible effect over the lifetime of the cell was observed. Third, an s-shaped course of the diameter change indicated two different ageing effects that led to the diameter change variation. Both reversible and irreversible expansion increased with ageing. Fourth, a direct correlation between the diameter change and the capacity loss of this particular lithium-ion battery was observed. Fifth, computer tomography (CT) measurements showed deformation of the jelly roll and post-mortem analysis showed the formation of a covering layer and the increase in the thickness of the anode. Sixth, reproducibility and temperature stability of the strain gauges were shown. Overall, this paper provides the basis for a stable and reproducible method for volume expansion analysis applied and established by the investigation of a state-of-the-art lithium-ion battery cell. This enables the study of volume expansion and its impact on capacity and cell death.

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