A Novel Sliding Mode Observer for State of Charge Estimation of EV Lithium Batteries

Chen, Qiaoyan; Jiang, Jiuchun; Liu, Sijia; Zhang, Caiping

doi:10.6113/jpe.2016.16.3.1131

Cited by 13 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With Equations (6)- (12), the algorithm of standard Kalman lter can be implemented. In Equation 8, the time constants of the 2 RC circuits are xed as Equation (9). e two constants consist of smaller and greater values in order to ensure that the short-term and long-term polarization e ects can be simulated as closely as possible.…”

Section: System Control Inputmentioning

confidence: 99%

Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

2020

View full text Add to dashboard Cite

Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation.

show abstract

Section: System Control Inputmentioning

confidence: 99%

Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

2020

View full text Add to dashboard Cite

show abstract

“…The estimation result may diverge if the parameters isn't properly selected. Observer-based methods include the Luenberger observer [21,22], the sliding mode observer [23][24][25][26][27][28][29][30][31][32]. The advantage of observer-based methods is that the parameters of these methods are designed to let the observer be stable.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer

Zheng

Xia

Wang³

et al. 2019

Energies

View full text Add to dashboard Cite

State of charge (SOC) estimation is of vital importance for the battery management system in electric vehicles. This paper proposes a new fuzzy logic sliding mode observer for SOC estimation. The second-order resistor-capacitor equivalent circuit model is used to describe the discharging/charging behavior of the battery. The exponential fitting method is applied to determine the parameters of the model. The fuzzy logic controller is introduced to improve the performance of sliding mode observer forming the fuzzy logic sliding mode observer (FLSMO). The Federal Urban Driving Schedule (FUDS), the West Virginia Suburban Driving Schedule (WUBSUB), and the New European Driving Cycle (NEDC) schedule test results show that the average SOC estimation error of FLSMO algorithm is less than 1%. When the initial SOC estimation error is 20%, the FLSMO algorithm can converge to 3% error boundary within 2400 s. Comparison test results show that the FLSMO algorithm has better performance than the sliding mode observer and the extended Kalman filter in terms of robustness against measurement noise and parameter disturbances.

show abstract

“…Besides, the artificial neural network is mainly applied to estimate SOC [17] or predict capacity [18, 19] rather than predict SOC in the future. Apart from this, the sliding mode observation method [20] is also used to estimate current SOC instead of future SOC. If commuters know the future SOC, which can provide divers with an early warning of supercapacitors’ residual capacity in advance, they can judge whether they are able to arrive at the destination or whether they should find an EV charger.…”

Section: Introductionmentioning

confidence: 99%

State of charge prediction of supercapacitors via combination of Kalman filtering and backpropagation neural network

Wang

Zhou

2018

IET Electric Power Applications

View full text Add to dashboard Cite

Supercapacitors are increasingly applied to the field of electric vehicles. Although the state of charge (SOC) directly shows the remaining capacity of supercapacitors in an energy management system, vehicle drivers also require the changes of supercapacitors in the future for driving reference. How to predict SOC of supercapacitors has become a pressing problem. In order to solve the above problem, a method combining backpropagation (BP) neural network with the Kalman filtering algorithm is proposed to predict SOC in the future. The BP neural network inputs SOC estimated by the Kalman filtering algorithm as the training data to train network, and thereby being able to forecast the SOC in the future period. The algorithm is verified in simulations and experiments under the two conditions: NewYorkBus and NYCC, with the consideration of the influence of the length of training data and temperature. The results show the max absolute error during prediction at different lengths is under 6% in simulations and experiments. In addition, temperature has almost no effect on the prediction accuracy. The current research implies that this method can be applied to predict supercapacitors' SOC in the future.

show abstract

A Novel Sliding Mode Observer for State of Charge Estimation of EV Lithium Batteries

Cited by 13 publications

References 19 publications

Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

State of Charge Estimation for Power Lithium-Ion Battery Using a Fuzzy Logic Sliding Mode Observer

State of charge prediction of supercapacitors via combination of Kalman filtering and backpropagation neural network

Contact Info

Product

Resources

About