Battery SOC Estimation Research Articles

Lithium-ion Battery SoC Estimation with UKF and RTOS μCOS-II Platform

To develop a battery management system for lithium-Ion batteries used in electric vehicles, the state of charge (SoC) estimation is improved with an unscented Kalman filter (UKF) and realized with the RTOS μCOS-II platform. The Kalman filters approach is broadly used for the battery SOC estimation recently. Here, we select the UKF algorithm, for which utilizes the unsented transform to solve filtering problems and is able to accurately capture the posterior mean and covariance to 3rd order of Taylor series expansion, as a result, the SoC estimation accuracy is improved with a faster convergence ability. To further evaluate the real-time performance of the SoC estimation, a battery-in-loop platform is built and the SoC estimation is calculated with a RTOS μCOS-II platform. The analog acquisition, communication system and SoC estimation algorithms were programmed, the performance of the proposed SoC estimation with UKF algorithm was finally investigated. The battery management system with UKF algorithm and RTOS μCOS-II platform has good performance and can apply for electric vehicles.

The Research of Power Battery SOC Estimation Based on Adaptive Kalman Filter Algorithm

Taking the lithium iron phosphate power battery as the research object, through analysis on characteristics of the battery, this paper chooses the improved second-order RC model as the model of battery whose complexity is moderate and it can better reflect the battery dynamic and static characteristics. Then by pulse discharge experiments and with improved recursive least squares algorithm to identify model parameters online, and puts forward up the adaptive kalman filtering algorithm to estimate battery SOC. The results show that the adaptive kalman filter algorithm can effectively improve battery SOC estimation precision.

State of Charge Estimation of Lithium-ion Batteries Considering Model and Parameter Uncertainties


 
 
 Up to date, model and parameter uncertainties are generally overlooked by majority of researchers in the field of battery study. As a consequence, accuracy of the SOC estimation is dominated by the model fidelity and may vary from cell-to- cell. This paper proposes a systematic framework with associated methodologies to quantify the battery model and parameter uncertainties for more effective battery SOC estimation. Such a framework is also generally applicable for estimating other battery performances of interest (e.g. capacity and power capability). There are two major benefits using the proposed framework: i) consideration of the cell-to-cell variability, and ii) accuracy improvement of the low fidelity model comparable to the high fidelity without scarifying computational efficiency. One case study is used to demonstrate the effectiveness of the proposed framework.
 
 

State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures

Ambient temperature is a significant factor that influences the accuracy of battery SOC estimation, which is critical for remaining driving range prediction of electric vehicles (EVs) and optimal charge/discharge control of batteries. A widely used method to estimate SOC is based on an online inference of open-circuit voltage (OCV). However, the fact that the OCV–SOC is dependent on ambient temperature can result in errors in battery SOC estimation. To address this problem, this paper presents an SOC estimation approach based on a temperature-based model incorporated with an OCV–SOC–temperature table. The unscented Kalman filtering (UKF) was applied to tune the model parameters at each sampling step to cope with various uncertainties arising from the operation environment, cell-to-cell variation, and modeling inaccuracy. Two dynamic tests, the dynamic stress test (DST) and the federal urban driving schedule (FUDS), were used to test batteries at different temperatures. Then, DST was used to identify the model parameters while FUDS was used to validate the performance of the SOC estimation. The estimation was made covering the major working range from 25% to 85% SOC. The results indicated that our method can provide accurate SOC estimation with smaller root mean squared errors than the method that does not take into account ambient temperature. Thus, our approach is effective and accurate when battery operates at different ambient temperatures. Since the developed method takes into account the temperature factor as well as the complexity of the model, it could be effectively applied in battery management systems for EVs.

SOC Estimation of Lithium-Ion Battery Packs Based on Thevenin Model

Due to the immeasurability of SOC in battery and inevitability of error in current collection, SOC estimation of Lithium-ion battery has become a focus of EV research. With Thevenin equivalent circuit model, this paper employs EKF algorithm to estimate SOC, which takes into consideration both precision requirement of the estimation and amount of computation involved in online estimation. Based on above-mentioned objectives and principles, a test platform composed of Digatron battery test system and thermostat was built. Experimental result has confirmed that the combination of EKF algorithm with Thevenin model can improve precision and reduce amount of computation.

ZEBRA battery SOC estimation using PSO-optimized hybrid neural model considering aging effect

ZEBRA battery SOC estimation using PSO-optimized hybrid neural model considering aging effect

A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation

not Available.

Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering

The extended Kalman filter (EKF) method for SOC estimation has some problems such as the lack of an accurate model, and model errors due to the variation in the parameters of the model due to the nonlinear behavior of a battery. To solve the aforementioned issues, this paper proposes a reduced order EKF including the measurement noise model and data rejection. In order to do so, the model of a battery in the EKF is simplified into the type of reduced order to decrease the calculation time. Additionally, to compensate the model errors caused by the reduced order model and variation in parameters, a measurement noise model and data rejection are implemented because the model accuracy is critical in the EKF algorithm in order to obtain a good estimation. Finally, the proposed algorithm is verified by short and long term experiments.