State of charge estimation of lithium-ion phosphate battery based on weighted multi-innovation cubature Kalman filter

Fu, Shiyi; Li, Wen; Luo, Weilin; Zhang, Zhifang; Zhang, Maohui; Wu, Lei; Luo, Chengdong; Lv, Taolin; Xie, Jun

doi:10.1016/j.est.2022.104175

Cited by 26 publications

(13 citation statements)

References 44 publications

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“…Meanwhile, the FOAUKF estimation strategy is proposed to better adapt to noisy environments. The discrete state space equation for fractional-order systems is shown in Equation (14).…”

Section: Fractional-order Adaptive Unscented Kalman Filtermentioning

confidence: 99%

Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

Zeng,

Wang,

Cao

et al. 2023

Circuit Theory & Apps

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SummaryAccurate state of charge (SOC) estimation is based on a precise battery model and is the focus of the battery management system (BMS). First, based on the second‐order RC equivalent circuit model and Grunwald–Letnikov (G‐L) definition, the high‐precision fractional‐order hysteresis‐equivalent circuit model (FH‐ECM) is established considering the open‐circuit voltage hysteresis effect. Then, the global parameters of the battery model are identified using a particle swarm algorithm optimized by the genetic algorithm (GA‐PSO). Third, a fractional‐order adaptive unscented Kalman filter (FOAUKF) algorithm is derived to estimate the SOC of lithium‐ion batteries. Finally, the feasibility of the model and algorithm is verified under complex working conditions. Under the dynamic stress test (DST) condition, the accuracy of model terminal voltage has been improved by 37.83%, and the error of SOC estimation has been reduced by 11.28%. Under Beijing bus dynamic stress test (BBDST) condition, the model terminal voltage accuracy has been improved by 51.44%, and the SOC estimation error has been reduced by 35.71%. The experimental results fully confirm the accuracy of the fractional‐order hysteresis‐equivalent circuit modeling method.

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“…Meanwhile, the FOAUKF estimation strategy is proposed to better adapt to noisy environments. The discrete state space equation for fractional-order systems is shown in Equation (14).…”

Section: Fractional-order Adaptive Unscented Kalman Filtermentioning

confidence: 99%

Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

Zeng,

Wang,

Cao

et al. 2023

Circuit Theory & Apps

View full text Add to dashboard Cite

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“…According to the improved equivalent circuit model, the expressions for the voltage can be obtained, as shown in Equation (3).…”

Section: Improved Equivalent Circuit Model Of Li-ion Batterymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In addition, one of the critical technologies of the battery management system is to estimate SOC accurately and quickly. [3] The accurate SOC estimation is essential to prevent the Li-ion battery from being overdischarged and overcharged effectively, guaranteeing the batteries' safe operation. [4] The SOC of Li-ion batteries is estimated by analyzing and calculating parameters such as the battery's internal resistance instead of being measured by sensors.…”

Section: Introductionmentioning

confidence: 99%

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State‐of‐Charge Estimation of Lithium‐Ion Battery Based on an Improved Dual‐Polarization Model

et al. 2023

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This article proposes a state‐of‐charge (SOC) estimation method to eliminate the influence of the hysteresis effect and the ambient temperature. First, an improved dual‐polarization (DP) model considering the hysteresis effect and the ambient temperature is established. A hysteresis voltage source is connected in series with a couple of resistance–capacitance pairs in the improved DP model, all the parameters of which are related to the ambient temperature to depict the temperature characteristics of the battery. Second, the forgetting factor recursive least squares method is utilized to identify the parameters under the battery dynamic test data at different temperatures. The proposed model and parameterization scheme integrate the effects of hysteresis and temperature, greatly enhancing the performance of the proposed method at different temperatures. Finally, an extended Kalman filter algorithm for SOC estimation is adopted to verify the improved DP model and the simulation indicates that the error of SOC estimation is within 1.5% at different ambient temperatures. The proposed method can improve the precision of the SOC estimation even if the temperature is below −10 or above 50 °C.

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“…In recent years, researchers have begun to apply the multiple innovation theory to estimate battery SOC. 32,33 Ding first presented the multi-innovation theory to identify a regression model in 2007. 34 Li et al studied a multiinnovation EKF method to estimate battery SOC.…”

Section: The Related Multiple Innovation Theorymentioning

confidence: 99%

A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation

Wang

2022

Intl J of Energy Research

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Summary For a lithium battery state space model, to solve the unsuitable problems caused by the single‐rate (same rate) sampled state space model facing resistor‐capacity (R‐C) couples with different time scales, a dual‐rate sampled exponential weighted multiple innovation adaptive extended Kalman filter (AEKF) method is investigated. The details include: (a) By using dual‐rate sampled method, select the suitable sampled rate for their own time scale for different couples (R1‐C1 and R2‐C2) to establish dual‐rate sampled state space equations; (b) Considering noise variation, a recursive noise estimation based AEKF algorithm is adopted to realize adaptive correction of noise; (c) Considering the influences of present and past data, an AEKF algorithm based exponential weighted multiple innovation is studied. Experiment results show that the investigated method is clearly better than the classical single‐rate sampled method with high precision.

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State of charge estimation of lithium-ion phosphate battery based on weighted multi-innovation cubature Kalman filter

Cited by 26 publications

References 44 publications

Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

State‐of‐Charge Estimation of Lithium‐Ion Battery Based on an Improved Dual‐Polarization Model

A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation

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