Estimation Of Lithium-ion Batteries Research Articles

State-of-charge estimation for lithium-ion batteries under various operating conditions using an equivalent circuit model

This paper describes a state-of-charge estimation methodology for lithium-ion batteries in hybrid electric vehicles. The proposed methodology is intended for SOC estimation under various operating conditions including changes in temperature, driving mode or power duty. The suggested methodology consists of a recursive estimator and employs an equivalent circuit as the electrochemical cell model. Model parameters are estimated by parameter map on experimental cell data with various temperatures and current conditions. The parameter map is developed by a least sum square error estimation method based on nonlinear programming. An adaptive estimator is employed and is based on the combination of current integration and battery model based estimation. The proposed SOC estimation methodology is demonstrated with experimental LiB pack data under various driving schedules with low and ambient temperature and sensor failure cases. Our results show that the proposed methodology is appropriate for estimating SOC under various conditions.

State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF

This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF). The NN is trained offline using the data collected from the battery-charging process. This network finds the model needed in the state-space equations of the EKF, where the state variables are the battery terminal voltage at the previous sample and the SOC at the present sample. Furthermore, the covariance matrix for the process noise in the EKF is estimated adaptively. The proposed method is implemented on a Li-Ion battery to estimate online the actual SOC of the battery. Experimental results show a good estimation of the SOC and fast convergence of the EKF state variables.

Composite Titanate–Graphite Negative Electrode for Improved State-of-Charge Estimation of Lithium-Ion Batteries

We have constructed a negative-electrode material system that provides an abrupt voltage change near the end of discharge, providing a clear marker for the voltage vs state of charge (SOC) relationship. Distinctive voltage features during the operation of a battery enable an accurate state estimation of the cell, and thus efficient energy and power management as well as health assessment. The approach we have developed should be useful for lithium-ion batteries with cell voltages remaining nearly constant over a large SOC range such as cells constructed with graphite negative electrodes and iron phosphate positive electrodes. Specifically, we describe the incorporation of a lithium titanate (LTO) material into a substantially graphitic negative electrode so as to provide a distinctive voltage change at a preselected SOC. A simple analytic procedure based on juxtaposing open-circuit potential curves is shown to represent well the combined system open-circuit potential, thereby confirming the efficacy of the approach. Fabricated full cells comprising lithium iron phosphate positive and carbon–LTO negative electrodes are used to demonstrate the effectiveness of the concept in a battery.

Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries

The coulomb counting method is expedient for state-of-charge (SOC) estimation of lithium-ion batteries with high charging and discharging efficiencies. The charging and discharging characteristics are investigated and reveal that the coulomb counting method is convenient and accurate for estimating the SOC of lithium-ion batteries. A smart estimation method based on coulomb counting is proposed to improve the estimation accuracy. The corrections are made by considering the charging and operating efficiencies. Furthermore, the state-of-health (SOH) is evaluated by the maximum releasable capacity. Through the experiments that emulate practical operations, the SOC estimation method is verified to demonstrate the effectiveness and accuracy.