Real-time State Of Charge Research Articles

Improved Realtime State-of-Charge Estimation of LiFePO $_{\boldsymbol 4}$ Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-of-charge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is first built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviors are captured, thus offering a comprehensive description of the battery thermal and electrical behavior. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a metaheuristic algorithm and the least-square approach. Further, time-varying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO $_4$ batteries confirm the efficacy of the proposed method.

Kalman filter for onboard state of charge estimation and peak power capability analysis of lithium-ion batteries

Abstract To evaluate the continuous and instantaneous load capability of a battery, this paper describes a joint estimator for state-of-charge (SOC) and state-of-function (SOF) of lithium-ion batteries (LIB) based on Kalman filter (KF). The SOC is a widely used index for remain useful capacity left in a battery. The SOF represents the peak power capability of the battery. It can be determined by real-time SOC estimation and terminal voltage prediction, which can be derived from impedance parameters. However, the open-circuit-voltage (OCV) of LiFePO 4 is highly nonlinear with SOC, which leads to the difficulties in SOC estimation. To solve these problems, this paper proposed an onboard SOC estimation method. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery, where the OCV is regarded as a linearized function of SOC. Then, the system states are estimated based on the KF. Besides, the factors that influence peak power capability are analyzed according to statistical data. Finally, the performance of the proposed methodology is demonstrated by experiments conducted on a LiFePO 4 LIBs under different operating currents and temperatures. Experimental results indicate that the proposed approach is suitable for battery onboard SOC and SOF estimation.

A Novel Titanium/Manganese Redox Flow Battery

Toward the realization of a low-carbon society, renewable energies such as wind and solar power have been promoted rapidly around the world. Since such renewable energies have a fluctuating power output due to the variability of weather, the increasing amounts of such energy sources will bring stability problems to conventional power system. Electrical energy storage systems are expected to solve these problems, and large-scale batteries attach more attention than ever. Redox flow (RF) batteries have several advantages comparing to solid batteries, such as easy to capacity scale up, long cycle life, real time state of charge (SOC) monitor, which make them the most suitable for power network stabilization. RF batteries have been enthusiastically developed worldwide, since its principle was publicized in the 1970s by NASA[1], all vanadium system as a representative of them has been put in practical applications[2]. To meet the growing demand for electrolytes, it is generally desired that active metals should be lower cost, higher electrode potential and stable supply than vanadium. Mn(II)/Mn(III) redox couple as a positive active material meets the requirements mentioned above. However, Mn(III) ion is chemically unstable and tends to disproportionate to Mn(II) ion and MnO2 oxide, which make it difficult to be used in flow batteries. We found that the precipitation of MnO2 can be effectively suppressed by containing Mn ions as well as Ti(IV) ions in positive electrolyte. We gained a very good cycle charge-discharge performance in a cell test, even at SOC in a range of more than 100%, in this case SOC is calculated on Mn(II)/Mn(III) one-electron reaction. It considered that Ti(IV) ions exiting in positive electrolyte suppress the Mn(III) disproportion reaction, as well as particle growth of MnO2. The theoretical energy density of positive Mn and negative Ti electrolytes RF battery is around 26kWh/m3, which is comparable to that in all vanadium RF battery.

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

State-of-charge and state-of-health estimation for lithium-ion batteries based on dynamic impedance technique

This study proposes a dynamic impedance method to estimate the state of charge (SOC) and a projection method to estimate the state of health (SOH) of lithium-ion batteries. We defined changes in voltage over changes in current during charging and discharging processes as the dynamic impedance, which was then used to calculate SOC. The proposed methods do not require initial values, and dynamic impedance provides a more accurate reflection of the electrical characteristics of batteries compared to conventional estimation methods. This approach also enables calculations in real-time. The SOH of batteries degenerates with age, which also alters the characteristics of dynamic impedance. The proposed projection method determines the SOH according to the rate of change in dynamic impedance with respect to the SOC. We also developed a real-time SOC and SOH estimation system, combining a microcontroller and estimation methods with a battery management system (BMS). The BMS retrieves the data required for SOC and SOH estimation and includes various protection mechanisms designed to extend the lifespan of the battery. The hardware structure and the software procedure of the developed system are described in detail. We also present the results of experiments verifying the accuracy and feasibility of the proposed estimation methods.

State of charge estimation for lithium-ion batteries: An adaptive approach

State of charge (SoC) estimation is of key importance in the design of battery management systems. An adaptive SoC estimator, which is named AdaptSoC, is developed in this paper. It is able to estimate the SoC in real time when the model parameters are unknown, via joint state (SoC) and parameter estimation. The AdaptSoC algorithm is designed on the basis of three procedures. First, a reduced-complexity battery model in state-space form is developed from the well-known single particle model (SPM). Then a joint local observability/identifiability analysis of the SoC and the unknown model parameters is performed. Finally, the SoC is estimated simultaneously with the parameters using the iterated extended Kalman filter (IEKF). Simulation and experimental results exhibit the effectiveness of the AdaptSoC.

Iterative capacity estimation of LiFePO4 cell over the lifecycle based on SoC estimation correction

Real time state of charge (SoC) and state of health (SoH) monitoring plays an essential role in electric vehicles, hybrid electric vehicles and generally in battery powered applications. Between these two state estimations, only the SoC has been studied rigorously until the current dates, while SoH or capacity estimation are much less referenced on the literature. Additionally, the SoC and the SoH estimation are strongly correlated by widely used coulomb counting equation and consequently wrong capacity estimation would lead to a SoC estimation error, which in turn, will lead to a further capacity estimation error. In this sense, the first job was to develop the equivalent electric model, design SoC estimator and to verify both of them experimentally. Then, different alternative techniques for estimating the capacity are analyzed, selecting the best choice considering the observability degree of the object of estimation: capacity. Then, in this paper we propose a new method for estimating the capacity, called iterative transferred charge, which adapts the current capacity estimation value based on the SoC correction made by the corresponding estimator. Finally, the developed algorithm is evaluated by comparing the capacity estimation with the reference over the life of the cell, by extensive experimental tests.

State of Charge Estimation Using EKF Method for VRB

Vanadium redox battery (VRB) system is a new type of energy storage system, which can be used to shave power grid peak, improve power quality, and smooth fluctuations of power and Voltage in photovoltaic and wind power systems. The state of charge (SOC) of VRB is an indication of how much energy is stored in the battery. Estimating the VRB SOC accurately in real time is very important when the battery is used to power system. In consideration of deficiencies of existing methods, a new approach is introduced to use Extended Kalman Filter (EKF) method. In this paper, state space model of VRB based on its equivalent circuit model is proposed, and SOC Estimation Using EKF Method for VRB is described in detail. By using Matlab software, it can be proved that the EKF method can estimate real-time SOC of VRB and is more accurate than existing methods.