Joint Estimation of Ternary Lithium-ion Battery State of Charge and State of Power Based on Dual Polarization Model

Abstract

The battery state of charge (SOC) and state of power capability (SOP) are the core elements of a battery management system (BMS) for ensuring efficient and safe driving in electric vehicles. In this paper, the SOC and the SOP are jointly estimated under multiple constraints by using the dual polarization (DP) model for a ternary lithium-ion battery, and the extended Kalman filter (EKF) algorithm has been used to improve the accuracy of state estimation and the calculational simplicity. The calculation equations for the multiple-constraint parameters are deduced. The power capability can be calculated rapidly based on the constraints on the current, voltage and SOC. The simulation and experimental results demonstrate that for both the SOC and the SOP, highly satisfactory prediction accuracy is realized under various operating conditions. The maximum SOP estimation error in the DP model is less than 2.1% for a battery with various SOC states, which corresponds to higher estimation accuracy than that of the Thevenin model. In addition, the SOP estimation model has strong robustness, which renders the joint estimation of the SOC and the SOP more reliable in the practical application of electric vehicles.

A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

“…Moreover, a trade-off has to be made for the proposed model between the memory length and number of its coefficients, where extending memory length could attain a better tracking capability of battery nonlinearity but at the cost of identifying more coefficients. The dual polarisation (DP) model, as shown in Figure 14, is another widely used ECM in SOP estimation [34][35][36][37][38][39]. Compared with the 1-RC model, the DP model takes advantage of two RC networks with different time constants to describe the charge transfer process and double-layer effect at the mid frequency region, as well as the diffusion phenomenon at the low frequency region, thereby showing the improved model accuracy over basic 1-RC model, especially at extreme SOC regions [40].…”

“…The thermal effect inside a battery on model parameters can be reflected in real-time, aiming to enhance the robustness of SOP estimation at varying temperatures. In [35], a multi-constraint SOP estimation is implemented, based on a DP model. The authors evaluate the performance of the DP model with adaptive model parameters in SOC estimation, which demonstrates a SOC error of less than 2%, compared to 3% of a basic 1-RC model over a whole battery operating range.…”

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

“…In Ref. [30], through real-time measurement and update of the battery voltage and current, the battery parameters were accurately identified by HPPC test to guarantee a reliable battery state estimate. However, the offline method based on certain conditions relies on a large number of experiments, which requires long development cycles and high costs.…”

A real time state of charge estimation using Harris Hawks optimization‐based filtering approach for electric vehicle power batteries

“…Second-order ECM is an appropriate model for online applications due to its good features like high accuracy, simplicity, and excellent modeling requirements. 12,13 As the model parameters are strongly dependent on SOC, time-variant parameters are updated by SOC at every step. For online SOC estimation, the model parameters have been identified either online or offline.…”

“…16 Even though the Unscented Kalman Filter (UKF) and Particle Filter (PF) have been developed and give good results for SOC estimation, the EKF is preferred due to its trade-off between accuracy & complexity. 13 Because other advanced filters not only improve precision but also increase computational complexity. 17 For the harsh operating conditions of an EV, it is important to have a method that is accurate but not unnecessarily complicated.…”