Online State Of Charge Estimation Research Articles

State of charge estimation of Li-ion batteries in an electric vehicle based on a radial-basis-function neural network

The on-line estimation of the state of charge (SOC) of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network (RBF NN) has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhances the real-time performance of estimation. Finally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.

Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications

For the vehicular operation, due to the voltage and power/energy requirements, the battery systems are usually composed of up to hundreds of cells connected in series or parallel. To accommodate the operation conditions, the battery management system (BMS) should estimate State of Charge (SOC) to facilitate safe and efficient utilization of the battery. The performance difference among the cells makes a pure pack SOC estimation hardly provide sufficient information, which at last affects the computation of available energy and power and the safety of the battery system. So for a reliable and accurate management, the BMS should “know” the SOC of each individual cell. Several possible solutions on this issue have been reported in the recent years. This paper studies a method to determine online all individual cell SOCs of a series-connected battery pack. This method, with an equivalent circuit based “averaged cell” model, estimates the battery pack’s average SOC first, and then incorporates the performance divergences between the “averaged cell” and each individual cell to generate the SOC estimations for all cells. This method is developed based on extended Kalman filter (EKF), and to reduce the computation cost, a dual time-scale implementation is designed. The method is validated using results obtained from the measurements of a Li-ion battery pack under three different tests, and analysis indicates the good performance of the algorithm.

Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles

The accurate estimation of internal parameters and state-of-charge (SoC) of battery, which greatly depends on proper models and corresponding high-efficiency, high-accuracy algorithms, is one of the critical issues for the battery management system. A model-based online estimation method of a LiFePO4 battery is presented for application in electric vehicles (EVs) by using an adaptive extended Kalman filter (AEKF) algorithm. The Thevenin equivalent circuit model is selected to model the LiFePO4 battery and its mathematics equations are deduced to some extent. Additionally, an implementation of the AEKF algorithm is elaborated and employed for the online parameters’ estimation of the LiFePO4 battery model. To illustrate advantages of the online parameters’ estimation, a comparison analysis is performed on the terminal voltages between the online estimation and the offline calculation under the Hybrid pulse power characteristic (HPPC) test and the Urban Dynamometer Driving Schedule (UDDS) test. Furthermore, an efficient online SoC estimation approach based on the online estimation result of open-circuit voltage (OCV) is proposed. The experimental results show that the online SoC estimation based on OCV–SoC can efficiently limit the error below 0.041.