A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles

Chen, Xiaokai; Hao, Liping; Xiong, Rui; Shen, Weixiang; Yang, Ruixin

doi:10.1016/j.apenergy.2019.113758

Cited by 122 publications

(47 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimates of R ohm and R p , plotted in Figure 12b,d with respect to SoC and temperature, respectively, are similar to those from Figure 6b,d, but with slightly higher correlation with temperature for both resistances. Finally, it should be noted that there are no distinguishable sets of estimated U oc curves in Figure 12c (unlike in Figure 6c), because estimated U oc (SoC) now describes the central curve while the hysteresis is accounted for in the model (see Figure 7 and Equations (10) and (11)). Estimated U oc (SoC) is slightly larger than the recorded one (see Figure 3c for details about U oc (SoC) identification) because the latter is discharge U oc (SoC) curve while we estimate the average U oc (SoC) since hysteresis is explicitly modelled in this case.…”

Section: Capacity Estimation Resultsmentioning

confidence: 97%

“…Equations (10) and (11)). Estimated ( ) is slightly larger than the recorded one (see Figure 3c for details about ( ) identification) because the latter is discharge ( ) curve while we estimate the average ( ) since hysteresis is explicitly modelled in this case.…”

Section: Capacity Estimation Resultsmentioning

confidence: 99%

“…There are several studies that account for U oc (SoC) variation with SoH by implementing the offline identified response surface model of U oc with respect to SoC and remaining capacity [10][11][12]. Authors in [13] use the model migration method to adapt an offline trained model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack

2020

View full text Add to dashboard Cite

Accurate, real-time estimation of battery state-of-charge (SoC) and state-of-health represents a crucial task of modern battery management systems. Due to nonlinear and battery degradation-dependent behavior of output voltage, the design of these estimation algorithms should be based on nonlinear parameter-varying models. The paper first describes the experimental setup that consists of commercially available electric scooter equipped with telemetry measurement equipment. Next, dual extended Kalman filter-based (DEKF) estimator of battery SoC, internal resistances, and parameters of open-circuit voltage (OCV) vs. SoC characteristic is presented under the assumption of fixed polarization time constant vs. SoC characteristic. The DEKF is upgraded with an adaptation mechanism to capture the battery OCV hysteresis without explicitly modelling it. Parameterization of an explicit hysteresis model and its inclusion in the DEKF is also considered. Finally, a slow time scale, sigma-point Kalman filter-based capacity estimator is designed and inter-coupled with the DEKF. A convergence detection algorithm is proposed to ensure that the two estimators are coupled automatically only after the capacity estimate has converged. The overall estimator performance is experimentally validated for real electric scooter driving cycles.

show abstract

Section: Capacity Estimation Resultsmentioning

confidence: 97%

Section: Capacity Estimation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack

2020

View full text Add to dashboard Cite

show abstract

“…The OCV-SOC curve is a particular influence on the accuracy of the ECM and the model-based SOC estimation [34]. The accuracy of the OCV-SOC curve is of considerable importance because the model-based methods obtain an OCV value that minimizes the model's terminal voltage error and then estimates the SOC by considering this OCV value and the OCV-SOC curve.…”

Section: Introductionmentioning

confidence: 99%

Online State-of-Charge Estimation for Lithium-Ion Batteries Considering Model Inaccuracies Under Time-Varying Current Conditions

et al. 2020

View full text Add to dashboard Cite

Load currents of batteries undergo real-time change depending on the operations of battery-powered devices, such as acceleration, deceleration, and sudden braking of electric vehicles. With the variation in current magnitude, parameters in an equivalent circuit model of the battery change depending on its chemical characteristics. Therefore, accurately identifying model parameters offline for various sizes and patterns of load currents with few experiments is difficult. Besides, there is a model uncertainty due to the difference between the linearity of the model structure and the nonlinearity of the real battery; hence, an experimentally obtained open-circuit voltage (OCV)-state-of-charge (SOC) curve has difficulty in providing an accurate OCV value for the model. Consequently, these difficulties lead to inaccuracies in the model and SOC estimation. In this paper, novel methods are proposed to reduce these inaccuracies under timevarying current conditions, where the magnitude of the load current changes in real time. These are (1) an OCV-SOC curve estimation for reducing the model uncertainty and (2) a current-adaptive extended Kalman filter algorithm for decreasing the effect of inaccurately identified model parameters. The experimental results show that the maximum-absolute-errors of the proposed SOC estimation were reduced to <1.5%. Consequently, battery-powered applications can obtain highly accurate SOC values even the magnitude of the load current changes in real time. INDEX TERMS Battery management system, electric vehicles, open-circuit voltage-state-of-charge curve estimation, adaptive extended Kalman filter, state-of-charge estimation.

show abstract

“…As energy storage systems, lithium-ion batteries have significant advantages in terms of power density [1], self-discharge rate [2], energy density [3,4], and cycle life compared to other types of batteries [5,6]. For these reasons, they are widely used in EVs [7,8].…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery Parameters and State of Charge Joint Estimation Using Bias Compensation Least Squares and the Alternate Algorithm

Wei

Yi-min

Yan

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

For safe and efficient operation of electric vehicles (EVs), battery management system is essential. Nevertheless, a challenge lying in battery management systems is how to obtain an algorithm for state of charge (SOC) estimation that has both high accuracy and low computational cost. For this purpose, the battery parameters and SOC joint estimation algorithm based on bias compensation least squares and alternate (BCLS-ALT) algorithm are proposed in this paper. The battery model parameters are identified online using the bias compensation least squares (BCLS), while the SOC is estimated applying the alternate (ALT) algorithm, which can switch the computational logic between H-infinity filter (HIF) and ampere-hour integral (AHI) to improve the computational efficiency and accuracy. The experimental results show that the accuracy of the SOC estimated by the BCLS-ALT algorithm is the highest, and the computational efficiency is also high, with the switching threshold SOCALT being set to 25%. Despite the 20% initial error and the 10% current drift, the proposed BCLS-ALT algorithm can obtain high accuracy and robustness of SOC estimation under different ambient temperatures and dynamic load profiles.

show abstract

A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles

Cited by 122 publications

References 31 publications

Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack

Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack

Online State-of-Charge Estimation for Lithium-Ion Batteries Considering Model Inaccuracies Under Time-Varying Current Conditions

Lithium-Ion Battery Parameters and State of Charge Joint Estimation Using Bias Compensation Least Squares and the Alternate Algorithm

Contact Info

Product

Resources

About