I am a Ph.D. Scholar working in the area of Control System Theory IIIT Guwahati, India.
State of charge of Li-ion battery for the application of electric vehicle is estimated.
Due to the requirement of highly efficient and lower pollution, vehicle technology is shifting from conventional fuel based vehicles to electric vehicles (EVs). EVs mainly consist of three parts: internal combustion engine, electric motor and battery management system (BMS). The amount of motor power depends on maximum available battery charge/discharge power. Hence, reliable battery management system is essential for EVs. BMS is also responsible for controlling and monitoring various parameters in EVs. The battery is a vital part of BMS and needs to be operated carefully for improved performance, cost effectiveness, increased security, operator’s comfortability and extended lifespan. The available power in a battery can be directly obtained from state of charge (SoC) information, and hence it is imperative to get its value accurately for the best performance of EVs.
State of charge (SoC) of a battery is defined as the ratio of its remaining capacity (Ah) to its nominal capacity (Ah). It is just like a fuel gauge for battery. The unit of SoC is percentage (0% = empty; 100% = full). Knowing the amount of energy remained left in a battery compared to the energy it had when fully charged gives us an indication of how much longer a battery will work properly before it needs recharging. It is one of the most critical parameters of the battery, which gives the cell behaviour and execution. However, due to highly non-linear nature of battery, it is a very challenging task to all the researchers and manufacturers to get the value of SoC accurately. It is calculated from BMS by the cell voltage, temperature and the information like polarisation effect, etc. An alternate form to measure the same is the depth of discharge (DoD) and is broadly the inverse of SoC (100% = empty; 0% = full). Generally, SoC is used to discuss the current state of a battery in use, while DoD is often seen when considering the lifetime of the battery after repeated use. Control of the SoC is a primary function of the BMS.
Since SoC is an internal state of the battery and is not available for direct measurement, it should be estimated with the help of physical parameter measurements such as voltage and current at the terminal of the battery. Overcharging or over-discharging is prevented by the accurate knowledge of SoC which can be dangerous for the reliable operation of the battery. Hence, the current requirement is to estimate the SoC of battery accurately.
Abstract: An effective method to estimate the integrated state of charge (SOC) value for the lithium-ion battery (LIB) pack is proposed, because of its capacity state estimation needs in the high-power energy supply applications, which is calculated by using the improved extended Kalman filter (EKF) method together with the one order equivalent circuit model (ECM) to evaluate its remaining available power state. It is realized by the comprehensive estimation together with the discharging and charging maintenance (DCM) process, implying an accurate remaining power estimation with low computational calculation demand. The battery maintenance and test system (BMTS) equipment for the aerial LIB pack is developed, which is based on the proposed SOC estimation method. Experimental results show that, it can estimate SOC value of the LIB pack effectively. The BMTS equipment has the advantages of high detection accuracy and stability and can guarantee its power-supply reliability. The SOC estimation method is realized on it, the results of which are compared with the conventional SOC estimation method. The estimation has been done with an accuracy rate of 95% and has an absolute root mean square error (RMSE) of 1.33% and an absolute maximum error of 4.95%. This novel method can provide reliable technical support for the LIB power supply application, which plays a core role in promoting its power supply applications.
Pub.: 16 Dec '16, Pinned: 31 Aug '17
Abstract: This paper presents a state of charge (SOC) estimation method based on fractional order sliding mode observer (SMO) for lithium-ion batteries. A fractional order RC equivalent circuit model (FORCECM) is firstly constructed to describe the charging and discharging dynamic characteristics of the battery. Then, based on the differential equations of the FORCECM, fractional order SMOs for SOC, polarization voltage and terminal voltage estimation are designed. After that, convergence of the proposed observers is analyzed by Lyapunov’s stability theory method. The framework of the designed observer system is simple and easy to implement. The SMOs can overcome the uncertainties of parameters, modeling and measurement errors, and present good robustness. Simulation results show that the presented estimation method is effective, and the designed observers have good performance.
Pub.: 15 Oct '16, Pinned: 31 Aug '17
Abstract: In the field of state of charge (SOC) estimation, the Kalman filter has been widely used for many years, although its performance strongly depends on the accuracy of the battery model as well as the noise covariance. The Kalman gain determines the confidence coefficient of the battery model by adjusting the weight of open circuit voltage (OCV) correction, and has a strong correlation with the measurement noise covariance (R). In this paper, the online identification method is applied to acquire the real model parameters under different operation conditions. A criterion based on the OCV error is proposed to evaluate the reliability of online parameters. Besides, the equivalent circuit model produces an intrinsic model error which is dependent on the load current, and the property that a high battery current or a large current change induces a large model error can be observed. Based on the above prior knowledge, a fuzzy model is established to compensate the model error through updating R. Combining the positive strategy (i.e., online identification) and negative strategy (i.e., fuzzy model), a more reliable and robust SOC estimation algorithm is proposed. The experiment results verify the proposed reliability criterion and SOC estimation method under various conditions for LiFePO4 batteries.
Pub.: 21 Jun '16, Pinned: 31 Aug '17
Abstract: Coulomb counting method is a convenient and straightforward approach for estimating the state‐of‐charge (SOC) of lithium‐ion batteries. Without interrupting the power supply, the remaining capacities of them in an electric vehicle (EV) can be calculated by integrating the current leaving and entering the batteries. The main drawbacks of this method are the cumulative errors and the time‐varying coulombic efficiency, which always lead to inaccurate estimations. To deal with this problem, a least‐squares based coulomb counting method is proposed. With the proposed method, the coulombic losses can be compensated by charging/discharging coulombic efficiency η and the measurement drift can be amended with a morbid efficiency matrix. The experimental results demonstrated that the proposed method is effective and convenient. Copyright © 2016 John Wiley & Sons, Ltd.
Pub.: 29 Mar '16, Pinned: 31 Aug '17
Abstract: Since the main power source of hybrid electric vehicle(HEV) is supplied by the power battery, the predicted performance of power battery, especially the state-of-charge(SOC) estimation has attracted great attention in the area of HEV. However, the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected. A variable structure extended kalman filter(VSEKF)-based estimation method, which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition, is presented. First, the general lower-order battery equivalent circuit model(GLM), which includes column accumulation model, open circuit voltage model and the SOC output model, is established, and the off-line and online model parameters are calculated with hybrid pulse power characteristics(HPPC) test data. Next, a VSEKF estimation method of SOC, which integrates the ampere-hour(Ah) integration method and the extended Kalman filter(EKF) method, is executed with different adaptive weighting coefficients, which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes. According to the experimental analysis, the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV. The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method. In Summary, the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system, which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method. The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.
Pub.: 05 Mar '16, Pinned: 31 Aug '17
Abstract: Reliable online estimation of state of charge (SOC) and capacity is critically important for the battery management system (BMS). This paper presents a multi-timescale method for dual estimation of SOC and capacity with an online identified battery model. The model parameter estimator and the dual estimator are fully decoupled and executed with different timescales to improve the model accuracy and stability. Specifically, the model parameters are online adapted with the vector-type recursive least squares (VRLS) to address the different variation rates of them. Based on the online adapted battery model, the Kalman filter (KF)-based SOC estimator and RLS-based capacity estimator are formulated and integrated in the form of dual estimation. Experimental results suggest that the proposed method estimates the model parameters, SOC, and capacity in real time with fast convergence and high accuracy. Experiments on both lithium-ion battery and vanadium redox flow battery (VRB) verify the generality of the proposed method on multiple battery chemistries. The proposed method is also compared with other existing methods on the computational cost to reveal its superiority for practical application.
Pub.: 17 Feb '17, Pinned: 31 Aug '17
Abstract: Accurate state of charge (SOC) estimation is very crucial to guarantee the safety and reliability of lithium-ion batteries, especially for those used in electric vehicles. Since the SOC is unmeasurable and nonlinearly varies with factors (e.g., current rate, battery degeneration, ambient temperature and measurement noise), a reliable and robust algorithm for SOC estimation is expected. In this paper, an optimal adaptive gain nonlinear observer (OAGNO) for SOC estimation is proposed. The particle swarm optimization (PSO) algorithm is employed to optimize parameters of the adaptive gain nonlinear observer (AGNO). A combined error is presented as the fitness function to evaluate the search performance of the PSO algorithm. To perform the PSO-based parameter optimization of the AGNO, a combined dynamic loading profile consisting of the Federal Urban Driving Schedule, the New European Driving Cycle and the Dynamic Stress Test is developed. The proposed approach is verified by experiments performed on Panasonic NCR18650PF lithium-ion batteries and compared with different parametric AGNOs. Experimental results indicate that the proposed OAGNO is helpful to improve the accuracy of battery SOC estimation compared with the non-optimal AGNO methods. Additionally, the OAGNO approach is robust against initial SOC error, current noise and different driving cycles.
Pub.: 24 Dec '16, Pinned: 31 Aug '17
Abstract: The accurate online state estimation for some types of nonlinear singularly perturbed systems is challenging due to extensive computational requirements, ill-conditioned gains and/or convergence issues. This paper proposes a multi-time-scale estimation algorithm for a class of nonlinear systems with coupled fast and slow dynamics. Based on a boundary-layer model and a reduced model, a multi-time-scale estimator is proposed in which the design parameter sets can be tuned in different time-scales. Stability property of the estimation errors is analytically characterized by adopting a deterministic version of extended Kalman filter (EKF). This proposed algorithm is applied to estimator design for the state-of-charge (SOC) and state-of-health (SOH) in a lithium-ion battery using the developed reduced order battery models. Simulation results on a high fidelity lithium-ion battery model demonstrate that the observer is effective in estimating SOC and SOH despite a range of common errors due to model order reductions, linearisation, initialisation and noisy measurement.
Pub.: 17 Oct '16, Pinned: 31 Aug '17
Abstract: A state-of-charge (SOC) versus open-circuit-voltage (OCV) model developed for batteries should preferably be simple, especially for real-time SOC estimation. It should also be capable of representing different types of lithium-ion batteries (LIBs), regardless of temperature change and battery degradation. It must therefore be generic, robust and adaptive, in addition to being accurate. These challenges have now been addressed by proposing a generalized SOC-OCV model for representing a few most widely used LIBs. The model is developed from analyzing electrochemical processes of the LIBs, before arriving at the sum of a logarithmic, a linear and an exponential function with six parameters. Values for these parameters are determined by a nonlinear estimation algorithm, which progressively shows that only four parameters need to be updated in real time. The remaining two parameters can be kept constant, regardless of temperature change and aging. Fitting errors demonstrated with different types of LIBs have been found to be within 0.5%. The proposed model is thus accurate, and can be flexibly applied to different LIBs, as verified by hardware-in-the-loop simulation designed for real-time SOC estimation.
Pub.: 01 Nov '16, Pinned: 31 Aug '17
Abstract: Publication date: 28 February 2017 Source:Journal of Power Sources, Volume 342 Author(s): Mohammed Farag, Matthias Fleckenstein, Saeid Habibi Model-order reduction and minimization of the CPU run-time while maintaining the model accuracy are critical requirements for real-time implementation of lithium-ion electrochemical battery models. In this paper, an isothermal, continuous, piecewise-linear, electrode-average model is developed by using an optimal knot placement technique. The proposed model reduces the univariate nonlinear function of the electrode's open circuit potential dependence on the state of charge to continuous piecewise regions. The parameterization experiments were chosen to provide a trade-off between extensive experimental characterization techniques and purely identifying all parameters using optimization techniques. The model is then parameterized in each continuous, piecewise-linear, region. Applying the proposed technique cuts down the CPU run-time by around 20%, compared to the reduced-order, electrode-average model. Finally, the model validation against real-time driving profiles (FTP-72, WLTP) demonstrates the ability of the model to predict the cell voltage accurately with less than 2% error.
Pub.: 24 Dec '16, Pinned: 31 Aug '17
Abstract: Online state of charge (SOC) estimation of lithium-ion batteries (LIBs) relies not only on accurate battery model but also on effective state estimation method. In this study, a nonlinear battery state-space model based moving horizon estimation (MHE) approach is proposed to estimate SOC within the full range. The relationship between SOC and circuit parameters in battery model is captured by polynomial functions. The essential arrival cost in the MHE problem formulation is approximated by the filtering scheme and its covariance matrix is updated by extended Kalman filter (EKF) method. Hybrid pulse power characterization test is first used to guide battery model construction and tuning parameters determination in MHE. The constant current discharge test and dynamic stress test are then used to validate the applicability of the MHE and investigate the performance comparisons between MHE and EKF. The results demonstrate that compared to the EKF, the MHE is less sensitive to the poor initial SOC guesses and has faster convergence to the true SOC. The results thus validate that the MHE provides a potential promising approach to perform accurate, reliable and robust SOC estimation of LIBs.
Pub.: 28 Jun '16, Pinned: 31 Aug '17
Abstract: A battery’s state-of-charge (SOC) can be used to estimate the mileage an electric vehicle (EV) can travel. It is desirable to make such an estimation not only accurate, but also economical in computation, so that the battery management system (BMS) can be cost-effective in its implementation. Existing computationally-efficient SOC estimation algorithms, such as the Luenberger observer, suffer from low accuracy and require tuning of the feedback gain by trial-and-error. In this study, an algorithm named lazy-extended Kalman filter (LEKF) is proposed, to allow the Luenberger observer to learn periodically from the extended Kalman filter (EKF) and solve the problems, while maintaining computational efficiency. We demonstrated the effectiveness and high performance of LEKF by both numerical simulation and experiments under different load conditions. The results show that LEKF can have 50% less computational complexity than the conventional EKF and a near-optimal estimation error of less than 2%.
Pub.: 24 Aug '16, Pinned: 31 Aug '17
Abstract: This paper focuses on state of charge (SOC) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.
Pub.: 05 Sep '16, Pinned: 31 Aug '17
Abstract: Lithium-ion batteries are widely used in conventional hybrid vehicles and in some electrical devices. A lumped parameter model of lithium-ion battery is constructed and system parameters are identified by using the autoregressive moving average (ARMA) and a genetic algorithm (GA). The precise information of state-of-charge (SOC) and terminal voltage are required to prolong the battery life and to increase the battery performance, reliability, and economics. By assuming a priori knowledge of the process and measurement noise covariance values, Kalman filter or extended Kalman filter has been used to estimate the SOC and terminal voltage. However, the main drawbacks of the Kalman filter is to use correct a priori covariance values, otherwise, the estimation errors can be lager or even divergent. These estimation errors can be relaxed by using the H∞ filter, which does not make any assumptions about the noise, and it minimizes the worst case estimation error. In this paper, H∞ filter is used to estimate the SOC and terminal voltage. The H∞ filter can reduce SOC estimation error, making it more reliable than using a priori process and measurement noise covariance values.
Pub.: 19 Jan '13, Pinned: 31 Aug '17
Abstract: An accurate battery State-of-Charge (SoC) estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. This paper tries to make two contributions to the existing literatures through a robust extended Kalman filter (REKF) algorithm. (1) An improved lumped parameter battery model has been proposed based on the Thevenin battery model and the global optimization-oriented genetic algorithm is used to get the optimal polarization time constant of the battery model. (2) A REKF algorithm is employed to build an accurate data-driven based robust SoC estimator for a LiFePO4 lithium-ion battery. The result with the Federal Urban Driving Schedules (FUDS) test shows that the improved lumped parameter battery model can simulate the dynamic performance of the battery accurately. More importantly, the REKF based SoC estimation approach makes the SoC estimation with high accuracy and reliability, it can efficiently eliminate the problem of accumulated calculation error and erroneous initial estimator state of the SoC.
Pub.: 14 Feb '14, Pinned: 31 Aug '17
Abstract: This paper presents a systematic evaluation for five typical equivalent circuit models (ECMs) of ultracapacitors (UCs) under different ambient temperatures. A comprehensive experimental profile is designed to obtain the test datasets. The genetic algorithm (GA) is employed to identify the model parameters for five UC models under different temperatures and state of charge (SOCs). Three results can be obtained from the systematic analysis. (1) Due to the better model accuracy and robustness, the Thevenin model is preferred for UC cell modeling with the maximum errors less than 8 mV. (2) Compared with the other four UC models, the Thevenin model with one-state hysteresis (Thevenin-hys model) is preferred for UC pack modeling because of its better performance. (3) From the point of view of model accuracy and robustness against different ambient temperatures, if the SOC is less than 0.5, the UCs are not suitable for further application.
Pub.: 23 Mar '17, Pinned: 31 Aug '17
Abstract: The air supply system, which provides the oxygen for the fuel cell stack, is one of the most important subsystems of the proton exchange membrane fuel cell (PEMFC). In order to improve the performance of the air supply, a small rechargeable lithium-ion battery is utilized to start up the PEMFC system and provide buffer power supply for the load demand. With energy consumption of the compressor considered, a power coordinating algorithm utilizing a virtual potential field approach is presented to manage the power demand for the PEMFC and the battery while maintaining the battery's state of charge. A nonlinear observer is designed to estimate the unmeasurable states of the air supply system and its convergence is proven. A nonlinear MPC method is proposed to control the air flow and ensure the adequate oxygen supply. Simulation results are provided to validate the performance of the power management algorithm and the air supply control method. Compared with the results of the MPC algorithm and the nonlinear MPC method for the PEMFC system without an auxiliary battery, the method designed here has better performance.
Pub.: 30 Nov '16, Pinned: 31 Aug '17
Abstract: The modeling and state-of-charge estimation of the batteries and ultracapacitors are crucial to the battery/ultracapacitor hybrid energy storage system. In recent years, the model based state estimators are welcomed widely, since they can adjust the gain according to the error between the model predictions and measurements timely. In most of the existing algorithms, the model parameters are either configured by theoretical values or identified off-line without adaption. But in fact, the model parameters always change continuously with loading wave or self-aging, and the lack of adaption will reduce the estimation accuracy significantly. To overcome this drawback, a novel co-estimator is proposed to estimate the model parameters and state-of-charge simultaneously. The extended Kalman filter is employed for parameter updating. To reduce the convergence time, the recursive least square algorithm and the off-line identification method are used to provide initial values with small deviation. The unscented Kalman filter is employed for the state-of-charge estimation. Because the unscented Kalman filter takes not only the measurement uncertainties but also the process uncertainties into account, it is robust to the noise. Experiments are executed to explore the robustness, stability and precision of the proposed method.
Pub.: 11 Jan '17, Pinned: 31 Aug '17
Abstract: The estimation of state-of-charge (SOC) is crucial to determine the remaining capacity of the Lithium-Ion battery, and thus plays an important role in many electric vehicle control and energy storage management problems. The accuracy of the estimated SOC depends mostly on the accuracy of the battery model, which is mainly affected by factors like temperature, State of Health (SOH), and chemical reactions. Also many characteristic parameters of the battery cell, such as the output voltage, the internal resistance and so on, have close relations with SOC. Battery models are often identified by a large amount of experiments under different SOCs and temperatures. To resolve this difficulty and also improve modeling accuracy, a multiple input parameter fitting model of the Lithium-Ion battery and the factors that would affect the accuracy of the battery model are derived from the Nernst equation in this paper. Statistics theory is applied to obtain a more accurate battery model while using less measurement data. The relevant parameters can be calculated by data fitting through measurement on factors like continuously changing temperatures. From the obtained battery model, Extended Kalman Filter algorithm is applied to estimate the SOC. Finally, simulation and experimental results are given to illustrate the advantage of the proposed SOC estimation method. It is found that the proposed SOC estimation method always satisfies the precision requirement in the relevant Standards under different environmental temperatures. Particularly, the SOC estimation accuracy can be improved by 14% under low temperatures below 0 °C compared with existing methods. Copyright © 2017 John Wiley & Sons, Ltd.
Pub.: 28 Jan '17, Pinned: 31 Aug '17
Abstract: Battery energy storage management for electric vehicles (EV) and hybrid EV is the most critical and enabling technology since the dawn of electric vehicle commercialization. A battery system is a complex electrochemical phenomenon whose performance degrades with age and the existence of varying material design. Moreover, it is very tedious and computationally very complex to monitor and control the internal state of a battery’s electrochemical systems. For Thevenin battery model we established a state-space model which had the advantage of simplicity and could be easily implemented and then applied the least square method to identify the battery model parameters. However, accurate state of charge (SoC) estimation of a battery, which depends not only on the battery model but also on highly accurate and efficient algorithms, is considered one of the most vital and critical issue for the energy management and power distribution control of EV. In this paper three different estimation methods, i.e., extended Kalman filter (EKF), particle filter (PF) and unscented Kalman Filter (UKF), are presented to estimate the SoC of LiFePO4 batteries for an electric vehicle. Battery’s experimental data, current and voltage, are analyzed to identify the Thevenin equivalent model parameters. Using different open circuit voltages the SoC is estimated and compared with respect to the estimation accuracy and initialization error recovery. The experimental results showed that these online SoC estimation methods in combination with different open circuit voltage-state of charge (OCV-SoC) curves can effectively limit the error, thus guaranteeing the accuracy and robustness.
Pub.: 08 Sep '16, Pinned: 31 Aug '17
Abstract: As over-;charging and over-;discharging can cause damage to batteries, to guarantee the stability and duration of batteries, it is necessary to avoid being over-;charged and over-;discharged. Therefore, knowing the batteries' state of charge (SOC) is essential. In this paper, the author describes in detail the procedure of building a SOC estimation algorithm. The SOC estimation algorithm is based on unscented Kalman filter. Moreover, changes of battery capacity and internal resistance are considered. The impact of these changes on the estimation accuracy is explored. Experiments on an Iron Phosphate Li-;ion battery cell with real load were conducted to justify this method. This method was verified to be not only precise but also stable.
Pub.: 09 Jun '16, Pinned: 31 Aug '17
Abstract: With the research object of LiFePO4 battery, this paper aims to correctly estimate the battery state of charge (SOC) by constructing a comprehensive SOC estimation strategy. Firstly, recursive least square (RLS) algorithm is adopted to realize online parameter identification of the equivalent battery model; and then an elaborate combination of RLS and Unscented Kalman Filter (UKF) is established, thus the battery model parameters used in UKF are actually obtained recursively by RLS; finally, SOC can be estimated by UKF. This strategy has an obvious adaptability due to the adoption of online parameter identification, so it is also called adaptive SOC estimation technique. Experimental results show that sometimes battery model parameters of different cells can be much different even though terminal voltages of these cells are very close or same when they are under resting state, and this inconsistency among LiFePO4 batteries is captured by the RLS-UKF strategy presented in this paper; and of course battery SOC can also be correctly estimated by using the continuously updated model parameters.
Pub.: 03 Feb '16, Pinned: 31 Aug '17
Abstract: In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets of Q and R values (KF's parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tune Q and R parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area.
Pub.: 28 Aug '14, Pinned: 31 Aug '17