Real-Time State-of-Charge Estimation Using an Embedded Board for Li-Ion Batteries

Hong, Seonri; Kang, Moses; Park, Hwa-Pyeong; Kim, Jong-Hoon; Baek, Jongbok

doi:10.3390/electronics11132010

Cited by 11 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its operation is as follows: state-vector prediction, Jacobian linearization, error-covariance prediction, Kalman gain calculation, FIGURE 1. First-order ECM of battery calculation of the final estimate using the measured and predicted values, and error-covariance calculation [26]. Therefore, to estimate the SOC of a battery with nonlinear characteristics using the EKF, the following discrete state equations must be obtained:…”

Section: ) Battery State-space Equationsmentioning

confidence: 99%

State-of-Charge Estimation Method for Lithium-Ion Batteries Using Extended Kalman Filter With Adaptive Battery Parameters

Yun,

Choi,

Lee

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Accurate battery state-of-charge (SOC) estimation is important for the efficient and reliable operation of battery application systems. The extended Kalman filter (EKF), which is based on the battery model, is widely used as a real-time SOC estimation algorithm; furthermore, its accuracy depends on the model accuracy. However, the conventional EKF uses one value for each battery parameter (Ri, Rd and Cd) regardless of the SOC, even though their values change according to the SOC. To address this problem, this study proposes an improved EKF that applies battery parameters that change according to the SOC of a battery model. In the proposed method, the entire SOC was divided into several sections considering the deviation of the parameter values according to the SOC. Subsequently, the average values for each SOC section were calculated, and the values of the battery parameters were updated with the average values according to the SOC. To verify the performance of the proposed EKF, the parameters of commercial Li-ion batteries were extracted with dis-charge currents of 1C-and 2C-rates at ambient temperatures of 0 °C, 25 °C, and 45 °C, and MATLAB simulations were performed. Compared to the conventional EKF, the proposed EKF estimated the SOC more accurately under all the simulation conditions. Compared to the conventional EKF, the maximum reduced root-mean-square error and maximum error of the proposed method were 49.37% and 56.41%, respectively.INDEX TERMS State of charge, lithium-ion battery, extended Kalman filter, battery parameter, battery model, battery management system

show abstract

Section: ) Battery State-space Equationsmentioning

confidence: 99%

State-of-Charge Estimation Method for Lithium-Ion Batteries Using Extended Kalman Filter With Adaptive Battery Parameters

Yun,

Choi,

Lee

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The second-order RC equivalent circuit model can accurately describe the dynamic [ 25 ] and static characteristics [ 26 ] of the battery, with low complexity [ 27 , 28 ] and easy engineering implementation [ 29 , 30 ]. Considering the influence of different ambient temperatures on the SOC of batteries, an improved model of parameters changing with temperature, the DP equivalent circuit model with temperature changing parameters, is established.…”

Section: Establishment Of An Equivalent Circuit Model For Li-ion Batt...mentioning

confidence: 99%

State of Charge Estimation of Li-Ion Battery Based on Adaptive Sliding Mode Observer

Wang

Jiang

Gao

et al. 2022

Sensors

View full text Add to dashboard Cite

As the main power source of new energy electric vehicles, the accurate estimation of State of Charge (SOC) of Li-ion batteries is of great significance for accurately estimating the vehicle’s driving range, prolonging the battery life, and ensuring the maximum efficiency of the whole battery pack. In this paper, the ternary Li-ion battery is taken as the research object, and the Dual Polarization (DP) equivalent circuit model with temperature-varying parameters is established. The parameters of the Li-ion battery model at ambient temperature are identified by the forgetting factor least square method. Based on the state space equation of power battery SOC, an adaptive Sliding Mode Observer is used to study the estimation of the State of Charge of the power battery. The SOC estimation results are fully verified at low temperature (0 °C), normal temperature (25 °C), and high temperature (50 °C). The simulation results of the Urban Dynamometer Driving Schedule (UDDS) show that the SOC error estimated at low temperature and high temperature is within 2%, and the SOC error estimated at normal temperature is less than 1%, The algorithm has the advantages of accurate estimation, fast convergence, and strong robustness.

show abstract

“…Due to the shortage of oil resources and increasingly serious environmental problems, new energy vehicles have ushered in rapid development [1]. The testing of the energy consumption and driving range of electric vehicles [2], the estimation of the charging state [3] and the optimization of the energy management system [4] are all based on vehicle cycle driving conditions. They include UDDS of the American urban road cycle, FTP75 of the federal driving cycle, JC08 of the Japan cycle, NEDC of the new European driving cycle (ECE of the urban driving cycle is its low-speed segment), WLTP of the global unified light load test cycle and CLTC of the Chinese light vehicle driving cycle.…”

Section: Introductionmentioning

confidence: 99%

Influence of Different Data Interpolation Methods for Sparse Data on the Construction Accuracy of Electric Bus Driving Cycle

Wang

Deng

et al. 2023

Electronics

View full text Add to dashboard Cite

Battery electric vehicles (BEVs) are one of the most promising new energy models for industrialization and marketization at this stage, which is an important way to solve the current urban haze air pollution, high fuel cost and sustainable development of the automobile industry. This paper takes pure electric buses as the research object and relies on the operation information management platform of new energy buses in Nantong city to propose an electric bus cycle construction method based on the mixed interpolation method to process sparse data. Three different interpolation methods, linear interpolation, step interpolation and mixed interpolation, were used to preprocess the collected data. The principal component analysis method and K-means clustering algorithm were used to reduce and classify the eigen parameter matrix. According to the clustering results, different categories of moving section and idle section libraries were established. According to the length of time and the correlation among various types, several moving sections and idle sections were selected to form a representative driving cycle of Nantong city buses. The results show that the mixed interpolation method, based on linear interpolation and cubic spline interpolation, has a good processing effect. The average relative error between the synthesized working conditions and the measured data are 15.71%, and the relative error of the seven characteristic parameters is less than 10%, which meets the development requirements. In addition, the comparison and analysis with the characteristic parameters of the world typical cycle conditions (NEDC, WLTC) show that the constructed cycle conditions of Nantong city are reasonable and reliable to represent the driving conditions of pure electric buses in Nantong city, which can provide a reference for the optimization of the bus energy control strategy.

show abstract

Real-Time State-of-Charge Estimation Using an Embedded Board for Li-Ion Batteries

Cited by 11 publications

References 27 publications

State-of-Charge Estimation Method for Lithium-Ion Batteries Using Extended Kalman Filter With Adaptive Battery Parameters

State-of-Charge Estimation Method for Lithium-Ion Batteries Using Extended Kalman Filter With Adaptive Battery Parameters

State of Charge Estimation of Li-Ion Battery Based on Adaptive Sliding Mode Observer

Influence of Different Data Interpolation Methods for Sparse Data on the Construction Accuracy of Electric Bus Driving Cycle

Contact Info

Product

Resources

About