Estimation of Battery State of Charge With $H_{\infty}$ Observer: Applied to a Robot for Inspecting Power Transmission Lines

Battery state-of-charge (SOC) estimation is essential for a mobile robot, such as inspection of power transmission lines. It is often estimated using a Kalman filter (KF) under the assumption that the statistical properties of the system and measurement errors are known. Otherwise, the SOC estimation error may be large or even divergent. In this paper, without the requirement of the known statistical properties, a SOC estimation method is proposed using an H_∞ observer, which can still guarantee the SOC estimation accuracy in the worst statistical error case. Under the conditions of different currents and temperatures, the effectiveness of the proposed method is verified in the laboratory and field environments. With the comparison of the proposed method and the KF-based one, the experimental results show that the proposed method can still provide accurate SOC estimation when there exist inexact or unknown statistical properties of the errors. The proposed method has been applied successfully to the robot for inspecting the running 500-kV extra high voltage power transmission lines.

I. Introduction

In recent years, autonomous mobile robots (AMRs) have found many applications in high-risk and unstructured environments, such as inspection of high or extra high voltage power transmission lines. As the performance of such robots depends heavily on the power capability of the battery onboard, it is important that the battery is reliable and capable of delivering enough energy or power when it is required. The state of charge (SOC) is an essential indicator for a battery, which is the percentage of stored charge available relative to that after a full charge of the battery [1]. It directly indicates the residual energy of a battery and indirectly shows the operation scope of an AMR. Reliable and accurate SOC estimation, one of the main tasks of battery management systems [1], can improve battery performance and reliability and prolong battery lifetime [2]. Accurate SOC estimation is also crucial for monitoring the state of health of the battery onboard and improving the performance of the AMR [3].