Modeling and SOC estimation of lithium iron phosphate battery considering capacity loss
© The Author(s) 2018
Received: 3 June 2017
Accepted: 23 January 2018
Published: 27 February 2018
Modeling and state of charge (SOC) estimation of Lithium cells are crucial techniques of the lithium battery management system. The modeling is extremely complicated as the operating status of lithium battery is affected by temperature, current, cycle number, discharge depth and other factors. This paper studies the modeling of lithium iron phosphate battery based on the Thevenin’s equivalent circuit and a method to identify the open circuit voltage, resistance and capacitance in the model is proposed. To improve the accuracy of the lithium battery model, a capacity estimation algorithm considering the capacity loss during the battery’s life cycle. In addition, this paper solves the SOC estimation issue of the lithium battery caused by the uncertain noise using the extended Kalman filtering (EKF) algorithm. A simulation model of actual lithium batteries is designed in Matlab/Simulink and the simulation results verify the accuracy of the model under different operating modes.
Wind power generation has been developing rapidly in recent years for being pollution-free and sustainable [1–4]. However, wind power curtailment has become a prominent problem due to the constraints imposed by power dispatch and wind power’s fluctuation and unpredictability. Energy storage is an effective means to solve the wind power curtailment problem as it can dynamically absorbs and releases energy. It also realizes the temporal transition of power and energy to effectively eliminate wind power curtailment caused by the system’s poor peak regulation ability.
Electrochemical energy storage exemplified by lithium battery has been applied in renewable power generation for its high controllability, modularity, energy density and conversion efficiency . Multiple lithium battery energy storage demonstration projects have been conducted throughout China, including Zhangbei County in Zhangjiakou of Hebei Province (14 MW/63WMh lithium phosphate battery system), Baoqing energy storage station in Shenzhen (4 MW/16MWh lithium iron phosphate battery system) etc. To promote the development and application of lithium battery technology, the main task is to develop safe, low-cost and long-life lithium ion battery energy storages .
Researches on the modeling, control, and capacity allocation of lithium battery energy storage systems have been reported. In terms of energy storage modeling, a battery is composed of positive electrode, negative electrode and electrolyte. Its charge and discharge are electrochemical process and its voltage and current as well as the resistance of the active materials inside are affected by polarization, temperature and other factors [7–10]. The lithium battery will age and lose capacity due to on-going charge and discharge in its life cycle, and therefore, the capacity assessment on lithium battery is necessary and conducive to the adjustment of its operating status in due time. As battery energy storage is generally expensive, it is thus a key issue to establish an effective battery model to analyze the technical and economic characteristics of energy storage system in new energy application.
In , a simplified constant power model is adopted which considers capacity limit but the influence of relevant parameters are neglected. Such simplified model is incapable of effectively verifying the application results of energy storage. In , a Thevenin’s equivalent circuit model is used but it results in significant errors due to the negligence of the influence of the state of charge (SOC) on model parameters. The modeling methods in [13–15] present the corresponding numerical relationship between the open circuit voltage, the resistance, the capacitance and the SOC. However, methods for estimating SOC are not included. In , a corresponding spatial model based on the equivalent circuit model of lithium iron battery is proposed where the model parameters are estimated using least square method with variable forgetting factors. However, all the above-mentioned models fail to consider the capacity loss during the battery’s life cycle. In [17, 18], the cycle life of high-power lithium iron phosphate battery is studied. Experiment results indicate that battery aging leads to significant impedance amplification and capacity attenuation during the battery’s life cycle. Therefore, it is necessary to monitor the battery capacity to avoid damages caused by over charge and discharge.
In this paper, the state equations based on the equivalent circuit model of lithium iron phosphate battery are established. The rest of this paper is organized as follows. Section 2 describes the modeling of lithium iron phosphate battery based on the Thevenin’s equivalent circuit. In Section 3, experimental results under constant current and no-load charging and discharging are provided to analyze the resistance and capacitance in the model under different SOC conditions. Capacity loss and available capacity based on different charging and discharging depths are also discussed. And the methods section extended Kalman filtering algorithm (EKF) to estimate the SOC of lithium battery caused by uncertain noise and verify the feasibility of the method. A simulation model of actual lithium batteries is developed using Matlab/Simulink in Section 4 and Section 5. Finally, Section 6 draws the conclusion.
2 Equivalent circuit of lithium iron phosphate battery
Battery energy storage is difficult to be mathematically modeled in detail with conventional physical models as it is an electrochemical reaction process. The accuracy and applicability of the model need to be balanced. Simple models are unable to reflect batteries’ characteristics while detailed models may significantly complicate the solution and application of control strategies. The equivalent circuit modeling is adopted for most of existing systems based on the dynamic characteristics and external characteristic performance of the batteries.
The external characteristics based equivalent circuit modeling is a simple and effective way for electrochemical battery modeling. The equivalent circuit model constructs a circuit network with voltage source, capacitance, resistance, inductance and other electrical components to simulate a battery’s external transient and steady-state characteristics. It has been applied widely in electrical engineering for its simplicity as the parameters can be accurately identify. It is convenient to integrate multiple elements and is suitable for mathematical analysis.
According to the equivalent circuit model, the left and right circuit networks are coupled and connected by SOC. The state equation indicates that the battery’s output voltage is determined by both open circuit voltage and polarization voltage, whereas the polarization voltage is directly related to its corresponding resistance, capacitance and current. The fundamental part of battery modeling is to estimate the available capacity (Cuse), SOC, open circuit voltage, resistance and capacitance of the battery in real time.
3.1 Identification of parameters related to battery model
Based on Section 2, the parameters of the equivalent circuit model of lithium battery vary with load and external condition as its operating status is affected by discharge depth, cycle number, capacity loss and etc. Therefore, a more reliable model needs be developed by comparing experimental measurements and off-line modeling to establish the relationships between different parameters.
The state of charge (SOC) is the most important influence factor among all the parameters of the resistance-capacitance model. Thus, determining the relationship between impedance parameters and SOC under the battery’s standard operation is the primary part of resistance-capacitance modeling. U oc and SOC of lithium battery have a stable relationship under normal operation conditions and is not affected by temperature. Thus, U oc can be considered to be solely determined by SOC and their relationship can be acquired with a fitting function.
The R and C values under different states can be acquired by conducting spline interpolation.
3.2 Assessment of battery’s available capacity
A battery has a limited service life. Because of the continuous charge and discharge during the battery’s life cycle, the lithium iron loss and active material attenuation in the lithium iron phosphate battery could cause irreversible capacity loss which directly affects the battery’s service life. A real-time capacity assessment on the battery can facilitate the correct recognition of the battery’s real-time status and the prediction on the battery’s status at certain time point in the future.
SOH varies from 0 to 100%, reflecting the battery’s health status and indicates the aging degree. A battery would have lost its functions and cannot perform charge and discharge when SOH is reduced to 20 to 30% .
3.3 State estimation on SOC with EKF algorithm
According to the previous sections, SOC is an important parameter influencing the safe and reliable operation of lithium battery, and accurate SOC estimation can facilitate the real-time adjustment of control strategy by operators.
The Kalman filtering algorithm is composed of state equation, output equation and the statistical characteristics of system process noise and observation noise. The required states or parameters are calculated according to the system’s state equation and output equation. This algorithm can perform optimal minimum variance estimation on SOC and facilitate the prediction and estimation on battery at a certain moment in the future. The conventional Kalman filtering algorithm is a state equation with a linear system while the extended Kalman filtering algorithm (EFK) is required for nonlinear models such as battery. This paper adopts EKF to conduct estimation on the battery’s real-time SOC state with the following procedure.
In Fig. 5, k|k-1 and k-1|k-1 refer to the results of the previous state prediction and the optimal results of the previous moment, respectively. P(k), Q(k) and R(k) correspond to the covariances of X(k), W(k) and V(k).
The data is collected from experiments on domestic lithium iron phosphate batteries with a nominal capacity of 40 AH and a nominal voltage of 3.2 V. The parameters related to the model are identified in combination with the previous sections and the modeling is performed in Matlab/Simulink to compare the output changes between 500 and 1000 circles. Meanwhile, the SOC is estimated with EKF under certain current and voltage for verification.
It is shown that significant errors could occur in the adjacent period of the SOC estimation without considering capacity loss. It can also be seen that the batteries have significant output voltage variation under different SOCs which poses seriously challenges to the accuracy of the modeling and output voltage estimation.
Voltage errors between the actual and estimated values with and without considering capacity loss
Voltage error without considering capacity loss
Voltage error considering capacity loss
As the battery energy storage system presents “random” charge and discharge characteristics during application, the battery’s current may change significantly. In such cases, the conventional Ah counting method can result in significant errors while the extended Kalman filtering algorithm is a better choice. A more accurate SOC can be obtained quickly based on the established model according to the measured current and voltage.
Considering the capacity loss during the battery’s life cycle significantly improves the estimation accuracy on the real-time operation status and facilitates the adjustment of related states.
The extended Kalman filtering algorithm for SOC estimation and system discretization can be used for both computer programming and the establishment of energy management system.
This work is supported in part by Open Fund of State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (DGB51201700424), Industrial Innovation of Jilin Province Development and Reform Commission (2017C017-2), and Jilin Provincial “13th Five-Year Plan” Science and Technology Project( 88).
JL contributed to the conception of the study and manuscript submission as a corresponding author. FG and GY contributed significantly to analysis and manuscript preparation. TZ revised the manuscript. JL helped perform the study analysis with constructive discussions and senior professional advice. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Sahu, B. K., Hiloidhari, M., & Baruah, D. C. (2013). Global trend in wind power with special focus on the top five wind power producing countries. Renewable & Sustainable Energy Reviews, 19(1), 348–359.View ArticleGoogle Scholar
- Li, H., Eseye, A. T., Zhang, J., et al. (2017). Optimal energy management for industrial microgrids with high-penetration renewables. Protection & Control of Modern Power Systems, 2(1), 12.View ArticleGoogle Scholar
- Bo, L., Zhijia, H., & Hao, J. (2016). Wind power status and development trends. Journal of Northeast Electric Power University, 36(02), 7–13.Google Scholar
- Xiaoming, Y., Shijie, C., & Jinyu, W. (2013). Prospects analysis of energy storage application in grid integration of large-scale wind power. Automation of Electric Power Systems, 37(1), 14–18.Google Scholar
- China Industrial Association of Power Sources. (2016). China Energy Storage Industry Report (2016)[R]. Shenzhen: CIBF.Google Scholar
- Yi, L., Guojun, T., & Xiaoqun, H. (2017). Optimized battery model based adaptive sigma Kalman filter for state of charge estimation. Transactions of China Electrotechnical Society, 32(2), 108–118.Google Scholar
- Xikun, C., & Dong, S. (2016). Research on lithium-ion battery modeling and model parameter identification methods. Proceedings of the CSEE, 36(22), 6254–6261.Google Scholar
- Chen, Q., Zhao, X., & Gan, D. (2017). Active-reactive scheduling of active distribution system considering interactive load and battery storage. Protection & Control of Modern Power Systems, 2(1), 29.View ArticleGoogle Scholar
- Cuiping, L., Pujia, C., Junhui, L., et al. (2017). Review on reactive voltage control methods for large-scale distributed PV integrated grid. Journal of Northeast Electric Power University, 37(02), 82–88.Google Scholar
- Xiaoyu, L., Chunbo, Z., Guo, W., et al. (2016). Online parameter estimation of a simplified impedance spectroscopy model based on the fractional joint Kalman filter for life PO4 battery. Transactions of China Electrotechnical Society, 31(24), 141–149.Google Scholar
- Yang, B., Jingmei, Y., Yingkai, Z., et al. (2017). A real-time rain flow algorithm and its application to state of health modeling for LiCoO2 lithium-ion batteries. Proceedings of the CSEE, 37(12), 3627–3635.Google Scholar
- Xikun, C., Dong, S., & Xiaohu, C. (2015). Modeling and state of charge robust estimation for lithium-ion batteries. Transactions of China Electrotechnical Society, 30(15), 141–147.Google Scholar
- Li, X., Huang, Y., Huang, J., et al. (2014). Modeling and control strategy of battery energy storage system for primary frequency regulation, International Conference on Power System Technology (pp. 543–549). IEEE.Google Scholar
- Junhui, L., Lian, J., Cuiping, L., et al. (2017). Control strategy designed for converter of super capacitor energy storage system. Journal of Northeast Electric Power University, 37(04), 32–38.Google Scholar
- Roscher, M. A., Assfalg, J., & Bohlen, O. S. (2011). Detection of utilizable capacity deterioration in battery systems. IEEE Transactions on Vehicular Technology, 60(1), 98–103.View ArticleGoogle Scholar
- Wei, S., Jiuchun, J., Yanru, Z., et al. (2015). Capacity fading and degradation mechanism of A123 battery. Power System Technology, 39(4), 899–903.Google Scholar
- Jinlong, Z., Wei, T., Duankai, L., et al. (2017). Rate capacity modeling and state of charge estimation of LiFePO4 battery. Transactions of China Electrotechnical Society, 32(7), 215–222.Google Scholar
- Zhou, X., Zhang, B., Zhao, H., et al. (2013). State of charge estimation based on improved Li-ion battery model using extended Kalman filter[C], IEEE 8th Conference on Industrial Electronics and Applications (pp. 607–612). IEEE.Google Scholar