Simulation of second-order RC equivalent circuit model of lithium battery based on variable resistance and capacitance

Ji, Yanju; Qiu, Shilin; Li, Gang

doi:10.1007/s11771-020-4485-9

Cited by 32 publications

(22 citation statements)

References 24 publications

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“…Common lithium-ion battery equivalent models include Thevenin equivalent model, PNGV model, and second-order RC model. [33][34][35][36][37][38] Taking into account the requirements of battery simulation accuracy and the principle of minimizing the amount of calculation, a secondorder RC equivalent model is selected to perform equivalent simulations of lithium-ion batteries. For the internal resistance of the battery that is different during charging and discharging, the internal resistance of the battery for charging and discharging is calculated separately to form a double internal resistance second-order RC model.…”

Section: Mathematical Analysismentioning

confidence: 99%

“…A reasonable battery equivalent model is the basis for accurate SOC estimation. Common lithium‐ion battery equivalent models include Thevenin equivalent model, PNGV model, and second‐order RC model 33–38 . Taking into account the requirements of battery simulation accuracy and the principle of minimizing the amount of calculation, a second‐order RC equivalent model is selected to perform equivalent simulations of lithium‐ion batteries.…”

Section: Mathematical Analysismentioning

confidence: 99%

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Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Yang

Wang

et al. 2021

Circuit Theory & Apps

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To solve the problem of the slow convergence speed for the battery stateof-charge estimation of cubature Kalman filter algorithm, the ternary lithium-ion battery is taken as the research object, and an algorithm combining the fuzzy self-adaptation and singular value decomposition cubature Kalman filtering is proposed. The algorithm takes the system innovation and its change rate as the fuzzy input and the output as the adjustment factor, which is used to adjust the process noise covariance matrix R. The Kalman gain is adjusted through the fuzzy control of R. To ensure the stability of the algorithm in the calculation process, the singular value decomposition is applied to cubature Kalman algorithm. Then, a second-order RC equivalent circuit model with double internal resistance is built and tested under different conditions to verify the rationality of the improved algorithm. The verification results show that under the simple condition, the convergence speed of the proposed algorithm in the different initial state-of-charge values increased by 40.00% and 25.00%, the maximum estimation error of the state-of-charge is 2.52% and 2.51%, the Mean Absolute Error is 0.816% and 0.880%, and the Root Mean Square Error is 1.276% and 1.380%. When the initial state-of-charge value is 0.8, the convergence speed in the complex condition is increased by about 30.00%; the maximum estimation result error, Mean Absolute Error, and Root Mean Square Error are 2.21%, 0.222%, and 1.327%, respectively. When the initial state-of-charge value is 0.6, the convergence speed in the complex condition is increased by about 10.00%; the maximum estimation result error, Mean Absolute Error, and Root Mean Square Error are 2.72%, 0.941%, and 1.327%, respectively. Without reducing the estimation accuracy, the improved algorithm can significantly increase the convergence speed of predictive value tracking, which provides a theoretical basis for the wide application of lithium-ion batteries.

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Section: Mathematical Analysismentioning

confidence: 99%

Section: Mathematical Analysismentioning

confidence: 99%

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Yang

Wang

et al. 2021

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

“…Moreover, equivalent circuit models can also be employed to simulate the characteristics of lithium-ion batteries for power supply system in electric vehicles. Generally, the second-order RC equivalent circuit model is a commonly used equivalent circuit model for serving as a baseline to predict SOC of lithium-ion batteries [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Equivalent Circuit Model For a Lithium-ion Batterymentioning

confidence: 99%

“…To perform KF and/or EKF and their improved versions, a second-order RC equivalent model of the lithium-ion battery is commonly employed to serve as a baseline model with changeable parameters [ 30 , 31 ]. Xia et al systematically proposed a hybrid of the second-order RC equivalent circuit and parameters of the battery model were determined by the forgetting factor least-squares method, the state space equation observer method, and the adaptive extended Kalman particle filter (AEKPF) using a particle filter (PF) and adaptive Kalman filter (AKF) [ 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

Lin

Tu³

et al. 2023

Sensors

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The state of charge (SOC) for a lithium-ion battery is a key index closely related to battery performance and safety with respect to the power supply system of electric vehicles. The Kalman filter (KF) or extended KF (EKF) is normally employed to estimate SOC in association with the relatively simple and fast second-order resistor-capacitor (RC) equivalent circuit model for SOC estimations. To improve the stability of SOC estimation, a two-stage method is developed by combining the second-order RC equivalent circuit model and the eXogenous Kalman filter (XKF) to estimate the SOC of a lithium-ion battery. First, approximate SOC estimation values are observed with relatively poor accuracy by a stable observer without considering parameter uncertainty. Second, the poor accuracy SOC results are further fed into XKF to obtain relative stable and accurate SOC estimation values. Experiments demonstrate that the SOC estimation results of the present method are superior to those of the commonly used EKF method. It is expected that the present two-stage XKF method will be useful for the stable and accurate estimation of SOC in the power supply system of electric vehicles.

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“…This method can realize real‐time estimation of SOC 19 . Ji Yan‐Ju et al 20 combined the ampere‐hour integration and open‐circuit voltage methods to realize the estimation of SOC, the estimation accuracy was improved effectively. However, the accuracy of the method is highly dependent on the sensor, and the current and voltage under working conditions must be measured as accurately as possible, 21 hence the anti‐interference ability of this method is poor 22 …”

Section: Introductionmentioning

confidence: 99%

Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries

Ren

Wang

Chen

et al. 2022

Intl J of Energy Research

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Summary To accurately evaluate the state of charge (SOC) and state of health (SOH) of Li‐ion battery, the second‐order RC equivalent‐circuit model is used to characterize the battery performance, a novel dual adaptive Kalman filtering algorithm is presented by adding double cycles and noise adaptive steps to realize the joint estimation of the SOC and internal resistance. The state variables can be corrected with each other as go through the cycle under three operating conditions. The accuracy of the SOC estimation method proposed in this paper is significantly improved compared with the extended Kalman filtering and the unscented Kalman filtering algorithm. Under three operating conditions, the average error and the maximum error decreased obviously. An equation for calculating the SOH in terms of internal resistance increase was built. The estimation result of the SOH effectively simulated the actual situation, compared with the actual result, the maximum error under the three operating conditions are within a lower level than the improved unscented Kalman filtering algorithm. The convergence effect of the algorithm has obvious advantages over that of the algorithm used for comparison, which could effectively track the state change of the battery.

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Simulation of second-order RC equivalent circuit model of lithium battery based on variable resistance and capacitance

Cited by 32 publications

References 24 publications

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Fuzzy adaptive singular value decomposition cubature Kalman filtering algorithm for lithium‐ion battery state‐of‐charge estimation

Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

Fusion estimation strategy based on dual adaptive Kalman filtering algorithm for the state of charge and state of health of hybrid electric vehicle Li‐ion batteries

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