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.

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 Development of Life-cycle Detection and Extending Function for Reusing Automotive Lithium-ion Battery – Verified in 52V LiMnNiCoO2 Platform

2020 ◽  
Author(s):  
Wu-Yang Sean ◽  
Ana Pacheco
Keyword(s):  
Life Cycle ◽  
Lithium Ion Battery ◽  
Management System ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Cycle Detection

Abstract For reusing automotive lithium-ion battery, an in-house battery management system is developed. To overcome the issues of life cycle and capacity of reused battery, an online function of estimating battery’s internal resistance and open-circuit voltage based on adaptive control theory are applied for monitoring life cycle and remained capacity of battery pack simultaneously. Furthermore, ultracapacitor is integrated in management system for sharing peak current to prolong life span of reused battery pack. The discharging ratio of ultracapacitor is adjusted manually under Pulse-Width-Modulation signal in battery management system. In case study in 52V LiMnNiCoO2 platform, results of estimated open-circuit voltage and internal resistances converge into stable values within 600(s). These two parameters provide precise estimation for electrical capacity and life cycle. It also shows constrained voltage drop both in the cases of 25% to 75% of ultracapacitors discharging ratio compared with single battery. Consequently, the Life-cycle detection and extending functions integrated in battery management system as a total solution for reused battery are established and verified.

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 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.

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 An Open Circuit Voltage Model Fusion Method for State of Charge Estimation of Lithium-Ion Batteries

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1797
Author(s):  
Quanqing Yu ◽  
Changjiang Wan ◽  
Junfu Li ◽  
Lixin E ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Open Circuit Voltage ◽  
Stress Test ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Battery Management System ◽  
Fusion Method ◽  
Battery Management ◽  
Model Fusion ◽  
Cubature Kalman Filter

The mapping between open circuit voltage (OCV) and state of charge (SOC) is critical to the lithium-ion battery management system (BMS) for electric vehicles. In order to solve the poor accuracy in the local SOC range of most OCV models, an OCV model fusion method for SOC estimation is proposed. According to the characteristics of the experimental OCV–SOC curve, the method divides SOC interval (0, 100%) into several sub-intervals, and respectively fits the OCV curve segments in each sub-interval to obtain a corresponding number of OCV sub-models with local high precision. After that, the OCV sub-models are fused through the continuous weight function to obtain fusional OCV model. Regarding the OCV curve obtained from low-current OCV test as the criterion, the fusional OCV models of LiNiMnCoO2 (NMC) and LiFePO4 (LFP) are compared separately with the conventional OCV models. The comparison shows great fitting accuracy of the fusional OCV model. Furthermore, the adaptive cubature Kalman filter (ACKF) is utilized to estimate SOC and capacity under a dynamic stress test (DST) at different temperatures. The experimental results show that the fusional OCV model can effectively track the performance of the OCV–SOC curve model.

scholarly journals A Novel Adaptive State of Charge Iterative Calculation Method by Using the Improved Unscented Kalman Filtering Algorithm for the Lithium Ion Battery Packs

2018 ◽  
Author(s):  
Shun-Li Wang ◽  
Carlos Fernandez ◽  
Chun-Mei Yu ◽  
Chuan-Yun Zou ◽  
James Coffie-Ken
Keyword(s):  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Battery Management ◽  
Iterative Calculation ◽  
Calculation Process ◽  
State Of Charge Estimation

The state of charge estimation is an important part of the battery management system, the estimation accuracy of which seriously affects the working performance of the lithium ion battery pack. The unscented Kalman filter algorithm has been developed and applied to the iterative calculation process. When it is used to estimate the SOC value, there is a rounding error in the numerical calculation. When the sigma point is sampled in the next round, an imaginary number appears, resulting in the estimation failure. In order to improve the estimation accuracy, an improved adaptive square root - unscented Kalman filter method is introduced which combines the QR decomposition in the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The error remains about 1.60% under the condition of drastic voltage and current changes. The conclusion of this experiment can provide a theoretical basis of the state of charge estimation in the battery management of the lithium ion battery pack.

scholarly journals Development of Enhancing Battery Management for Reusing Automotive Lithium-Ion Battery

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3306 ◽  
Author(s):  
Wen-Poo Yuan ◽  
Se-Min Jeong ◽  
Wu-Yang Sean ◽  
Yi-Hsien Chiang
Keyword(s):  
Adaptive Control ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Open Circuit ◽  
Life Extension ◽  
Duty Ratio ◽  
Battery Management System ◽  
Battery Management ◽  
Control Approach ◽  
Two Parameters

In this study, a battery management system (BMS) is developed for reused lithium-ion battery (RLIB). Additional enhancing functions of battery management are established, i.e., estimation of life-sensitized parameters and life extension. Life-sensitizing parameters mainly include open-circuit voltage (OCV) and internal resistances (IRs). They are sensitized parameters individually relative to state of charge (SOC) and state of health (SOH). For estimating these two parameters, an adaptive control scheme is implemented in BMS. This online adaptive control approach has been extensively applied to nonlinear systems with uncertainties. In two experiments, OCV and IRs of reused battery packs are accurately extracted from working voltage and discharge current. An offline numerical model using a schematic method is applied to verify the applicability and efficiency of this proposed online scheme. Furthermore, a solution of actively extending life by using an ultracapacitor to share peak power of RLIB through adjusting duty ratio is also proposed. It is shown that this enhancing battery management for RLIB can properly estimate OCV and IRs, and actively extend the life of the RLIB in two experiments.

A novel voltage matching-adaptive extended Kalman filtering construction method for the state of charge prediction of lithium-ion batteries

2021 ◽  
pp. 014233122110294
Author(s):  
Weihao Shi ◽  
Shunli Wang ◽  
Lili Xia ◽  
Peng Yu ◽  
Bowen Li
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Kalman Filtering ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
The State ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Extended Kalman Filtering

Accurately estimating the state of charge of lithium-ion batteries is of great significance to the development of the new energy industry. This research proposes a method for estimating the state of charge of lithium-ion batteries based on a voltage matching-adaptive extended Kalman filtering algorithm. The voltage matching part and the first-order resistance-capacitance (RC) part is combined into a new equivalent circuit model. This model improves the accuracy of voltage simulation at different charging and discharging stages through segment matching. Model-based adaptive extended Kalman filter algorithm adds a noise correction factor to adaptively correct the influence of noise on the estimation process and improve the estimation accuracy. The forgetting factor is introduced to improve the real-time performance of the algorithm. To verify the reliability of the model and algorithm, a multi-condition experiment is carried out on the lithium-ion battery. The verification results show that the simulation error of the circuit model to the working state of the lithium-ion battery is less than 0.0487V. The improved algorithm can accurately estimate the state of charge of lithium-ion batteries, the estimation accuracy of the discharge stage is 98.34%, and the estimation accuracy of the charging stage is 97.75%.

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