A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health

Yang, Qi; Ma, Ke; Xu, Liyou; Song, Lintao; Li, Xiuqing; Li, Yefei

doi:10.3390/coatings12081047

Cited by 5 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Miao miao Zeng et al [20] introduced a novel fuzzy unscented Kalman filtering algorithm, combined with improved second-order RC ECMs, to enhance the accuracy of lithium battery SOH estimation but with higher computational complexity of the model. Qingxia Yang et al [21] proposed a joint estimation method based on Kalman filtering, with results indicating improved accuracy in estimating the SOC and SOH of power batteries. MadhuSudana Rao Ranga et al [22] proposed a method based on unscented Kalman filtering, utilizing sigma points to fit non-linearity, thereby improving the prediction accuracy of SOH.…”

Section: Introductionmentioning

confidence: 99%

State of Health (SOH) Estimation of Lithium-Ion Batteries Based on ABC-BiGRU

Li,

Chen,

Wei

et al. 2024

Electronics

View full text Add to dashboard Cite

As a core component of new energy vehicles, accurate estimation of the State of Health (SOH) of lithium-ion power batteries is essential. Correctly predicting battery SOH plays a crucial role in extending the lifespan of new energy vehicles, ensuring their safety, and promoting their sustainable development. Traditional physical or electrochemical models have low accuracy in measuring the SOH of lithium batteries and are not suitable for the complex driving conditions of real-world vehicles. This study utilized the black-box characteristics of deep learning models to explore the intrinsic correlations in the historical cycling data of lithium batteries, thereby eliminating the need to consider the internal chemical reactions of lithium batteries. Through Pearson correlation analysis, this study selects health indicators (HIs) from lithium battery cycling data that significantly impact SOH as input features. In the field of lithium batteries, this paper applies ABC-BiGRU for the first time to SOH prediction. Compared with other recursive neural network models, ABC-BiGRU demonstrates superior predictive performance, with maximum root mean square error and mean absolute error of only 0.016799317 and 0.012626847, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

State of Health (SOH) Estimation of Lithium-Ion Batteries Based on ABC-BiGRU

Li,

Chen,

Wei

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…In order to ensure the driving safety of the whole vehicle and monitor the power battery status, a battery management system (BMS) must be equipped [2]. The estimation of the state of charge (SOC) of the battery power is one of the most basic and core functions of the battery management system (BMS); this function is similar to the traditional vehicle fuel gauge [3]. Accurate estimation of the SOC can effectively prevent overcharge and over-discharge of the battery, which is closely related to the safety and reliability of the battery.…”

Section: Introductionmentioning

confidence: 99%

Study of SOC Estimation by the Ampere-Hour Integral Method with Capacity Correction Based on LSTM

et al. 2022

View full text Add to dashboard Cite

The estimation of the state of charge (SOC) of a battery’s power is one of the key technologies in a battery management system (BMS). As a common SOC estimation method, the traditional ampere-hour integral method regards the actual capacity of the battery, which is constantly changed by the usage conditions and environment, as a constant for calculation, which may cause errors in the results of SOC estimation. Considering the above problems, this paper proposes an improved ampere-hour integral method based on the Long Short-Term Memory (LSTM) network model. The LSTM network model is used to obtain the actual battery capacity variation, replacing the fixed value of battery capacity in the traditional ampere-hour integral method and optimizing the traditional ampere-hour integral method to improve the accuracy of the SOC estimation method. The experimental results show that the errors of the results obtained by the improved ampere-hour integral method for the SOC estimation are all less than 10%, which proves that the proposed design method is feasible and effective.

show abstract

State-of-Charge and State-of-Health Joint Estimation of Lithium-Ion Battery Based on Iterative Unscented Kalman Particle Filtering Algorithm With Fused Rauch–Tung–Striebel Smoothing Structure

Zhu

2023

Journal of Electrochemical Energy Conversion and Storage

View full text Add to dashboard Cite

Traditional particle filtering has a large estimation error in the state of charge and Lithium-ion battery health of electric Vehicle lithium batteries. For the above problems, the lithium battery second-order RC equivalent circuit model is established, and then the model parameters are identified using the multi-innovation least square algorithm (MILS). Finally, Iterative unscented Kalman particle filtering algorithm with fused Rauch-Tung-Striebel Smoothing Structure (RTS-IUPF) applied to Li-ion battery SOC and SOH joint estimation is proposed. The algorithm is based on the identification of battery parameters, the controller reads the sensor data and predicts the state results. RTS smoothing structure can do posterior estimation, and a significant probability density function is generated to select the optimal particle, and unscented Kalman algorithm regularized particles. The algorithm reduces the effect of the process noise covariance matrix and the measured noise covariance matrix on the filter accuracy and response time in traditional unselected Kalman filters. The algorithm proposed in the paper improves particle degradation and increases the estimation accuracy. Finally, the RTS-IUPF algorithm performs simulation analysis in Pulse current discharge condition and dynamic current condition (NEDC) respectively. The pulse current experimental results show that the mean absolute value error of UKF and PF (Number of particles N is 300) are 1.26% and 1.24%, respectively, while the error of the RTS-IUPF is 0.748%. The RMSE of the RTS-IUPF is reduced by 66.5% and 77.8% compared with UKF and PF. Furthermore, The error of joint estimation using this algorithm is smaller than that of single estimation. The RMSE of the RTS-IUPF Joint is reduced by 27.4% compared with RTS-IUPF. The feasibility and effectiveness of the algorithm for the joint estimation of SOC and SOH of lithium batteries were verified.

show abstract

A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health

Cited by 5 publications

References 21 publications

State of Health (SOH) Estimation of Lithium-Ion Batteries Based on ABC-BiGRU

State of Health (SOH) Estimation of Lithium-Ion Batteries Based on ABC-BiGRU

Study of SOC Estimation by the Ampere-Hour Integral Method with Capacity Correction Based on LSTM

State-of-Charge and State-of-Health Joint Estimation of Lithium-Ion Battery Based on Iterative Unscented Kalman Particle Filtering Algorithm With Fused Rauch–Tung–Striebel Smoothing Structure

Contact Info

Product

Resources

About