Robust State of Charge Estimation for Hybrid Electric Vehicles: Framework and Algorithms

Abstract

State of Charge (SoC) estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs). Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1) an adaptive-κ nonlinear diffusion filter to reduce the noise in current measurement, (2) a self-learning strategy to estimate and remove the zero-drift, (3) a coulomb counting algorithm to realize open-loop SoC estimation, (4) an H ∞ filter to implement closed-loop robust estimation, and (5) a data fusion unite to achieve the final estimation by integrating the advantages of the two SoC estimators. The availability and efficacy of each component have been demonstrated based on comparative studies in simulation with the conventional approaches respectively, under the testing conditions of noises with various signal-noise-ratios, varying zero-drifts, and different model errors. The overall framework has also been verified to rationally and efficiently combine these components and achieve robust estimation results in the presence of kinds of noises and uncertainties.

State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model

“…Because of all kinds of noise, such as the sensor calibration bias, temperature effect, and environment electro-magnetic interference and so on, the noise of process and measurement is variable. A better solution to this problem may refer to the adaptive techniques [4,6,8,41] and markov chain ideas.…”

State of Charge Prediction for Lithium-ion Batteries

“…The prediction of the lithium-ion battery's state of charge (SOC) plays more importance role in lithium-ion battery management [1][2][3][4][5]. However, the method of SOC prediction is very difficult because of such influencing factors as the battery's complex contracture, charge-discharge current, internal temperature, self-discharge, and aging.…”

State of Charge Estimation for Li-ion Batteries based on double Extended Kalman Filtering Method

“…The open circuit voltage method estimates the SOC based on the voltage of battery after a long time of still standing, and achieves the SOC close to its real value, however, which is hard to meet the requirement of dynamic estimation [5]. And Kalman filtering method regards battery as a power system, and makes the optimal estimation for this power system in minimum range, which can filter the fixed noise and get the optimal estimation of SOC [6][7][8].…”