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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.


Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method

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

Energies, Vol. 9, Pages 472: Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries

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

Estimation method of state-of-charge for lithium-ion battery used in hybrid electric vehicles based on variable structure extended kalman filter

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

State of charge estimation of lithium-ion batteries using an optimal adaptive gain nonlinear observer

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

A new state-of-charge estimation method for electric vehicle lithium-ion batteries based on multiple input parameter fitting model

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

Energies, Vol. 9, Pages 720: A Comparative Study Based on the Least Square Parameter Identification Method for State of Charge Estimation of a LiFePO4 Battery Pack Using Three Model-Based Algorithms for Electric Vehicles

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