Model-based State Of Charge Estimation Research Articles

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Four model-based State of Charge (SOC) estimation methods for lithium-ion (Li-ion) batteries are studied and evaluated in this paper. Different from existing literatures, this work evaluates different aspects of the SOC estimation, such as the estimation error distribution, the estimation rise time, the estimation time consumption, etc. The equivalent model of the battery is introduced and the state function of the model is deduced. The four model-based SOC estimation methods are analyzed first. Simulations and experiments are then established to evaluate the four methods. The urban dynamometer driving schedule (UDDS) current profiles are applied to simulate the drive situations of an electrified vehicle, and a genetic algorithm is utilized to identify the model parameters to find the optimal parameters of the model of the Li-ion battery. The simulations with and without disturbance are carried out and the results are analyzed. A battery test workbench is established and a Li-ion battery is applied to test the hardware in a loop experiment. Experimental results are plotted and analyzed according to the four aspects to evaluate the four model-based SOC estimation methods.

The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer

With the development of electric drive vehicles (EDVs), the state-of-charge (SOC) estimation for lithium-ion (Li-ion) batteries has become increasingly more important. Based on the analysis of some of the most popular model-based SOC estimation methods, the proportional-integral (PI) observer is proposed to estimate the SOC of lithium-ion batteries in EDVs. The structure of the proposed PI observer is analyzed, and the convergence of the estimation method with model errors is verified. To demonstrate the superiority and compensation properties of the proposed PI observer, the simple-structure RC battery model is utilized to model the Li-ion battery. To validate the results of the proposed PI-based SOC estimation method, the experimental battery test bench is established. In the validation, the urban dynamometer driving schedule (UDDS) drive cycle is utilized, and the PI-based SOC estimation results are found to agree with the reference SOC, generally within the 2% error band for both the known and unknown initial SOC cases.

Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation

This paper investigates a practical universal modeling of multi-cell battery strings in series and parallel connections to show high an accuracy SOC (state-of-charge) estimation based on the EKF (extended Kalman filter) if cell-to-cell variations are taken into account and settled by the screening process. Through the screening process for the selection of the cells that have similar electrochemical characteristics, this study describes an effort to provide each equivalent circuit model for multi-cell battery strings in series, parallel, and series/parallel connections. Three circuit-based multi-cell battery models are validated against the experimental data of the discharging/charging behavior in terms of the discharging/charging voltage, discharge capacity, OCV (open-circuit voltage), and internal resistances when compared with the experimental data of a single cell. According to the relation between a multi-cell battery string and a single cell, these models can be easily developed from a single cell model, the validity of which was demonstrated regarding its high accuracy in predicting cell performance. The proposed multi-cell battery model has been extensively validated by the model-based SOC estimation using the EKF for a Li-Ion cell. If the model parameters of a single cell are correctly measured and used in the multi-cell battery model, the accuracy in the SOC estimation of a multi-cell battery string could be significantly improved.

Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

An accurate State-of-Charge (SoC) estimation plays a significant role in battery systems used in electric vehicles due to the arduous operation environments and the requirement of ensuring safe and reliable operations of batteries. Among the conventional methods to estimate SoC, the Coulomb counting method is widely used, but its accuracy is limited due to the accumulated error. Another commonly used method is model-based online iterative estimation with the Kalman filters, which improves the estimation accuracy in some extent. To improve the performance of Kalman filters in SoC estimation, the adaptive extended Kalman filter (AEKF), which employs the covariance matching approach, is applied in this paper. First, we built an implementation flowchart of the AEKF for a general system. Second, we built an online open-circuit voltage (OCV) estimation approach with the AEKF algorithm so that we can then get the SoC estimate by looking up the OCV-SoC table. Third, we proposed a robust online model-based SoC estimation approach with the AEKF algorithm. Finally, an evaluation on the SoC estimation approaches is performed by the experiment approach from the aspects of SoC estimation accuracy and robustness. The results indicate that the proposed online SoC estimation with the AEKF algorithm performs optimally, and for different error initial values, the maximum SoC estimation error is less than 2% with close-loop state estimation characteristics.