Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online

Ning, Bo; Cao, Binggang; Wang, Bin; Zou, Zhongyue

doi:10.1016/j.energy.2018.04.026

Cited by 103 publications

(46 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EM, as a first principle model, describes the spatiotemporal dynamics of electrochemical reaction inside the battery using a series of partial differential equations. To solve time-consuming and complex implementation problem, Ning et al 15 proposed a parameter adaptive sliding mode observer (SMO) based on model parameter identification for SOC and SOH estimation. To reduce the heavy computational burden of electrochemical full-order models, Bartlett et al 8 presented a reduced-order EM then realized the SOC and SOH estimation using the dual-nonlinear observer.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the battery electrochemical mechanism, Hu et al 14 devised a fractional-order model with two constant-phase elements then proposed a dual fractional-order EKF to estimate SOC and SOH. To solve time-consuming and complex implementation problem, Ning et al 15 proposed a parameter adaptive sliding mode observer (SMO) based on model parameter identification for SOC and SOH estimation. In the process of charging or discharging, the adaptive approaches estimate battery states under certain conditions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lyu

Gao

2019

Int J Energy Res

View full text Add to dashboard Cite

Summary Lithium‐ion battery state‐of‐health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model‐based and data‐driven estimator is developed to achieve accurate and reliable state‐of‐health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state‐space representation is constructed based on the data‐driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Lyu

Gao

2019

Int J Energy Res

View full text Add to dashboard Cite

show abstract

“…An ECM-based method is a method of estimating the capacity or impedance using an estimation algorithm based on an ECM, such as the extended Kalman filter (EKF) and observer algorithm [8][9][10][11][12]. An ECM is a physical representation of the electrochemical properties of a battery [13].…”

Section: Introductionmentioning

confidence: 99%

Incremental Capacity Curve Peak Points-Based Regression Analysis for the State-of-Health Prediction of a Retired LiNiCoAlO2 Series/Parallel Configured Battery Pack

Lee

Park

2019

Electronics

View full text Add to dashboard Cite

To recycle retired series/parallel battery packs, it is necessary to know their state-of-health (SOH) correctly. Unfortunately, voltage imbalances between the cells occur repeatedly during discharging/charging. The voltage ranges for the discharge/charge of a retired series/parallel battery pack are reduced owing to the voltage imbalances between the cells. To determine the accurate SOH of a retired series/parallel battery pack, it is necessary to calculate the total discharge capacity using fully discharging/charging tests. However, a fully discharging/charging test is impossible owing to the reduced voltage range. The SOH of a retired series/parallel battery pack with a voltage imbalance should be estimated within the reduced discharging/charging voltage range. This paper presents a regression analysis of the peak point in the incremental capacity (IC) curve from the fresh state to a 100-cycle aging state. Moreover, the SOH of the considered retired series/parallel battery pack was estimated using a regression analysis model. The error in the SOHs of the retired series/parallel battery pack and linear regression analysis model was within 1%, and hence a good accuracy is achieved.

show abstract

“…37 As the complex series-parallel combination is used in the LIB packs to break for the limitations of cell voltage and capacity, there are differences in cell-to-cell consistency and interaction. 41,42 The parameters such as ohmic internal resistance, polarization resistance, and capacitance in the ECM also need to be measured indirectly by the experimental means. 39 Therefore, the breakthrough of the ECM modeling is especially important to the energy management, which is also an effective means to avoid the safety accidents.…”

mentioning

confidence: 99%

“…40 Under the influence of the complex cell combination structure, the ECM is the key to improve its energy utilization efficiency and safety. 41,42 The parameters such as ohmic internal resistance, polarization resistance, and capacitance in the ECM also need to be measured indirectly by the experimental means. 43 Therefore, the mathematical description can only be achieved by using the external measurable parameters such as voltage, current, and temperature.…”

mentioning

confidence: 99%

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

Cao

Wang

Fernandez

et al. 2019

Energy Science & Engineering

View full text Add to dashboard Cite

The state of charge (SoC) estimation is the safety management basis of the packing lithium‐ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state‐space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.

show abstract

Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online

Cited by 103 publications

References 16 publications

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

A model‐based and data‐driven joint method for state‐of‐health estimation of lithium‐ion battery in electric vehicles

Incremental Capacity Curve Peak Points-Based Regression Analysis for the State-of-Health Prediction of a Retired LiNiCoAlO2 Series/Parallel Configured Battery Pack

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

Contact Info

Product

Resources

About