Kalman Filter SoC estimation for Li-Ion batteries

Spagnol, Pierfrancesco; Rossi, Stefano; Savaresi, S. M.

doi:10.1109/cca.2011.6044480

Cited by 58 publications

(30 citation statements)

References 6 publications

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“…Compared with the Ah integral method or open circuit voltage method, a closed-loop SOC estimation method based filter is attracting more attentions; Neural network method is required to be used within the scope of training data, when the application scope is beyond training experimental data, the accuracy will be decreased [9,10]; Kalman filtering algorithm can be used for various state estimation of the battery [11,12]. And the algorithm is only applicable to linear system, but the Li-ion battery model is nonlinear [13]; Unscented Kalman filter (Unscented Kalman Filtering, UKF) as a kind of special estimation method is proposed for nonlinear systems, and it has obtained the rapid development in recent years [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…A number of methods are used to estimate the battery SOC, such as ampere-hour integral method [5], open circuit voltage method [6,7], the internal impedance method [8], neural network [9,10], Kalman filter algorithm [11,12], unscented Kalman filter [14][15][16][17], particle filter [2,3,18,19] and other nonlinear observers [1,20]. Compared with the Ah integral method or open circuit voltage method, a closed-loop SOC estimation method based filter is attracting more attentions; Neural network method is required to be used within the scope of training data, when the application scope is beyond training experimental data, the accuracy will be decreased [9,10]; Kalman filtering algorithm can be used for various state estimation of the battery [11,12].…”

Section: Introductionmentioning

confidence: 99%

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State of charge estimation of power Li-ion batteries using a hybrid estimation algorithm based on UKF

Dong

Pan

et al. 2016

Electrochimica Acta

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State of charge estimation of power Li-ion batteries using a hybrid estimation algorithm based on UKF

Dong

Pan

et al. 2016

Electrochimica Acta

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“…To overcome the drawback of Ampere hour counting and OCV based methods, model based estimation such as Kalman filter and observers are used [12][13][14][15]. However, these methods have certain drawbacks.…”

Section: Introductionmentioning

confidence: 99%

State-of-charge estimation using particle swarm optimization with inverse barrier constraint in a nanosatellite

Aung

Low

Soon

2015

2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA)

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Lithium ion batteries have become the preferred choice for many applications due to their high energy density and low self-discharge rate. State-of-charge (SOC) information is required in many applications to optimize and safeguard the performance of the batteries. Different methods of SOC estimation such as Ampere counting and model-based estimation have been used in SOC estimation. Among the model based estimation, Kalman filter based method is one of the most commonly used method. However, it requires linearization, an accurate battery model and information on measurement and process noise. In this paper, a SOC estimation based on particle swarm optimization (PSO) with inverse barrier constraint is proposed. This method overcomes the need to linearize the model and does not require the information on measurement and process noise. The proposed method has been verified experimentally. From the experimental results, the root mean square error (RMSE) is 1.03% and absolute maximum error is 3.35%.

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“…The Randles' model as a standard battery model is very popular in the contexts of lead-acid and lithium-ion batteries because of its cost-effectiveness and the similarities of both types. By similarity it is meant that the same model can be reasonably used for the parameter estimation of both battery types [2][3].…”

Section: Introductionmentioning

confidence: 99%

Initial Electrical Parameter Validation in Lead-Acid Battery Model Used for State Estimation

Csomós¹,

Fodor²,

Kohlrusz³

2017

Hungarian Journal of Industry and Chemistry

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The paper presents a current impulse-based excitation method for lead-acid batteries in order to define the initial electrical parameters for model-based online estimators. The presented technique has the capability to track the SoC (State of Charge) of a battery, however, it is not intended to be used for online SoC estimations. The method is based on the battery’s electrical equivalent Randles’ model [1]. Load current impulse excitation was applied to the battery clamps during discharge while the voltage and current was logged. Based on the Randles’ model, a model function and a fit function were implemented and used by exponential regression based on the measured data. The diffusion-related non-linear characteristic of the battery was approximated by a capacitorlike linear voltage function for speed and simplicity. The initial capacitance of this bulk capacitor was estimated by linear regression on measurements recorded in the laboratory. Then, the RC parameters of the equivalent battery model were derived from exponential regression on transients during each current impulse cycle. The battery model with initial RC parameters is suitable for model-based online observers.

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Kalman Filter SoC estimation for Li-Ion batteries

Cited by 58 publications

References 6 publications

State of charge estimation of power Li-ion batteries using a hybrid estimation algorithm based on UKF

State of charge estimation of power Li-ion batteries using a hybrid estimation algorithm based on UKF

State-of-charge estimation using particle swarm optimization with inverse barrier constraint in a nanosatellite

Initial Electrical Parameter Validation in Lead-Acid Battery Model Used for State Estimation

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