State of Charge Estimation of Lithium-Ion Batteries Using  an Adaptive Cubature Kalman Filter

Xia, Bizhong; Wang, Haiqing; Tian, Yong; Wang, Mingwang; Sun, Wei; Xu, Zhijie

doi:10.3390/en8065916

Cited by 98 publications

(57 citation statements)

References 51 publications

(47 reference statements)

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“…At time k, the mean value and variance of the importance density function are calculated by the CKF algorithm based on the observation data [16,26]. A new posterior probability density distribution is generated, and a new posterior probability distribution can re-generate the new particles, calculate the weights of the particles, perform normalization, and then perform resampling to complete the state estimation.…”

Section: Cubature Particle Filtering Algorithmmentioning

confidence: 99%

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A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

et al. 2017

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Abstract:The state of charge (SOC) is the residual capacity of a battery. The SOC value indicates the mileage endurance, and an accurate SOC value is required to ensure the safe use of the battery to prevent over-and over-discharging. However, unlike size and weight, battery power is not easily determined. As a consequence, we can only estimate the SOC value based on the external characteristics of the battery. In this paper, a cubature particle filter (CPF) based on the cubature Kalman filter (CKF) and the particle filter (PF) is presented for accurate and reliable SOC estimation. The CPF algorithm combines the CKF and PF algorithms to generate a suggested density function for the PF algorithm based on the CKF. The second-order resistor-capacitor (RC) equivalent circuit model was used to approximate the dynamic performance of the battery, and the model parameters were identified by fitting. A dynamic stress test (DST) was used to separately estimate the accuracy and robustness of the CKF and the CPF algorithms. The experimental results show that the CPF algorithm exhibited better accuracy and robustness than the CKF algorithm.

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Section: Cubature Particle Filtering Algorithmmentioning

confidence: 99%

“…The cubature Kalman filter (CKF) algorithm is a kind of filtering algorithm that is free of differentiation and high in filtering precision [16,[26][27][28]. In this paper, we present the cubature particle filter (CPF) method for SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

et al. 2017

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“…The recursive process of the proposed adaptive cubature Kalman filter for battery SOC estimation can be summarized as follows [44]:…”

Section: An Improved Ackf For Soc Estimationmentioning

confidence: 99%

An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter

et al. 2018

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An accurate state of charge (SOC) estimation of the on-board lithium-ion battery is of paramount importance for the efficient and reliable operation of electric vehicles (EVs). Aiming to improve the accuracy and reliability of battery SOC estimation, an improved adaptive Cubature Kalman filter (ACKF) is proposed in this paper. The battery model parameters are online identified with the forgetting factor recursive least squares (FRLS) algorithm so that the accuracy of SOC estimation can be further improved. The proposed method is evaluated by two driving cycles, i.e., the New European Driving Cycle (NEDC) and the Federal Urban Driving Schedule (FUDS), and compared with the existing unscented Kalman filter (UKF) and standard CKF algorithms to verify its superiority. The experimental results reveal that comparing with the UKF and standard CKF, the improved ACKF algorithm has a faster convergence rate to different initial SOC errors with higher estimation accuracy. The root mean square error of SOC estimation without initial SOC error is less than 0.5% under both the NEDC and FUDS cycles.

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“…For example, long runtime is a key feature in the user's perception for purchasing the electronic products that use Li-ion batteries as power sources. To achieve this goal, a useful battery simulator is an essential tool for tracking battery state-of-charge (SOC), simulating dynamic behavior and predicting runtime [1][2][3][4][5]. The battery simulator should possess not only good accuracy, but also flexibility and Section 4 illustrates the model validation plan, the experimental setup and processes.…”

Section: Introductionmentioning

confidence: 99%

A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions

et al. 2016

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Abstract:A new battery simulator based on a hybrid model is proposed in this paper for dynamic discharging behavior and runtime predictions in existing electronic simulation environments, e.g., PSIM, so it can help power circuit designers to develop and optimize their battery-powered electronic systems. The hybrid battery model combines a diffusion model and a switching overpotential model, which automatically switches overpotential resistance mode or overpotential voltage mode to accurately describe the voltage difference between battery electro-motive force (EMF) and terminal voltage. Therefore, this simulator can simply run in an electronic simulation software with less computational efforts and estimate battery performances by further considering nonlinear capacity effects. A linear extrapolation technique is adopted for extracting model parameters from constant current discharging tests, so the EMF hysteresis problem is avoided. For model validation, experiments and simulations in MATLAB and PSIM environments are conducted with six different profiles, including constant loads, an interrupted load, increasing and decreasing loads and a varying load. The results confirm the usefulness and accuracy of the proposed simulator. The behavior and runtime prediction errors can be as low as 3.1% and 1.2%, respectively.

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State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

Cited by 98 publications

References 51 publications

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter

A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions

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