On-line adaptive asynchronous parameter identification of lumped electrical characteristic model for vehicle lithium-ion battery considering multi-time scale effects

Shi, Haotian; Wang, Shunli; Wang, Liping; Xu, Wenhua; Fernandez, Carlos; Bobobee, Etse Dablu

doi:10.1016/j.jpowsour.2021.230725

Cited by 30 publications

(19 citation statements)

References 41 publications

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“…I represents the charge and discharge current of the battery. The OCV based on the hysteresis-compensated norder RC circuit comes from the optimized Nernst electrochemical empirical equation, and its feasibility and effectiveness are demonstrated in References [6,33]. Its function value reflects the voltage optimally at both ends of the battery after no loading or shelving, as shown in Equation (1).…”

Section: Hysteresis-compensated Rc Circuit Networkmentioning

confidence: 99%

“…15,16 To assess the uncertainty of the hysteretic effect, our group tested the OCV characteristics of LFP cells in early research experiments on lumped parameter electrical characteristics modeling. 6,17 The experimental results show that the SOC value of the same OCV fluctuates between 0% and 20% during the charge and discharge state transition. Given the fact that the OCV of the LFP cell has a strong uncertainty, it is imperative to carry out targeted modeling for the OCV of LFP batteries.…”

Section: Introductionmentioning

confidence: 99%

“…At present, researchers have made considerable efforts to reveal the complex battery modeling mechanism and state-of-charge (SOC) prediction. In the current commercial BMS, many models have been developed to provide applications for battery-integrated systems, such as lumped electrical characteristic models, 6 one-order resistor-capacitor (RC) networks, 7 fractional-order models, 8 and spliceelectrochemical circuit polarization models. 9 Their model accuracy is higher in the battery type where the hysteretic effect is not obvious and in an environment where the external temperature does not change much.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, a joint SOC estimator with multi-domain coupling and multi-algorithm fusion is usually developed. For example, an adaptive asynchronous parameter identification strategy is proposed to replace the traditional online identification method under single time scale in Reference [6] Meanwhile, the deep transfer neural network with multiscale distribution adaptation is used in Reference [37] In addition, some novel algorithms have excellent performance in battery SOC prediction ability. The more typical ones include Hu et al 38 by establishing a fractional-order equivalent circuit model and using a hybrid genetic algorithm/particle swarm optimization method for parameterization, which improves the modeling accuracy of the battery.…”

Section: Introductionmentioning

confidence: 99%

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Battery hysteresis compensation modeling and state‐of‐charge estimation adaptive to time‐varying ambient temperature conditions

Shi

Wang

Fernandez

et al. 2022

Intl J of Energy Research

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Temperature and cell hysteretic effects are two major factors that influence the reliability and safety in long-term management of battery-integrated systems.In this paper, a hysteresis-compensated electrical characteristic model is established to track the terminal voltage of batteries with the uncertain hysteretic effect of the open-circuit voltage. Then, an autoregressive exogenous model with multi-feature coupling is employed for the identification of the parameters to make them adaptive to the uncertainties of the temperature and hysteretic effects. After that, a novel method for state-of-charge (SOC) estimation based on an adaptive moving window-square root unscented Kalman filter is constructed to avoid the filtering divergence problem caused by the negative error covariance matrix. Multiple constraints, such as Coulombic efficiency, varying ambient temperatures, and hysteresis voltage, are considered for the SOC estimation. Experimental results show that the root-mean-square error for SOC calculation can be limited to 0.0211 when the temperature varied up to 40 C and the root-mean-square error of the voltage measurement noise up to 61.9 mV. The proposed method provides an effective way for batteryintegrated management of electric vehicles. K E Y W O R D Sadaptive moving window-square root unscented Kalman filter, adaptive noise matching, hysteresis-compensated modeling, lithium-ion battery, state-of-charge

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Section: Hysteresis-compensated Rc Circuit Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Battery hysteresis compensation modeling and state‐of‐charge estimation adaptive to time‐varying ambient temperature conditions

Shi

Wang

Fernandez

et al. 2022

Intl J of Energy Research

Self Cite

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“…For the lithium battery, different internal processes will occur during charging and discharging, resulting in sophisticated dynamic changes on different time scales, which is reflected in the distinct time constants of different R‐C couples in the established ECMs 27,28 …”

Section: Introductionmentioning

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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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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On-line adaptive asynchronous parameter identification of lumped electrical characteristic model for vehicle lithium-ion battery considering multi-time scale effects

Cited by 30 publications

References 41 publications

Battery hysteresis compensation modeling and state‐of‐charge estimation adaptive to time‐varying ambient temperature conditions

Battery hysteresis compensation modeling and state‐of‐charge estimation adaptive to time‐varying ambient temperature conditions

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

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

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