A battery health monitoring framework for planetary rovers

Abstract

Batteries have seen an increased use in electric ground and air vehicles for commercial, military, and space applications as the primary energy source. An important aspect of using batteries in such contexts is battery health monitoring. Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. For planetary rovers, battery health estimation and prediction is critical to mission planning and decision-making. We develop a model-based approach utilizing computationally efficient and accurate electrochemistry models of batteries. An unscented Kalman filter yields state estimates, which are then used to predict the future behavior of the batteries and, specifically, end of discharge. The prediction algorithm accounts for possible future power demands on the rover batteries in order to provide meaningful results and an accurate representation of prediction uncertainty. The framework is demonstrated on a set of lithium-ion batteries powering a rover at NASA Ames Research Center using real experimental field test data.

Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

“…Bole et al [25][26][27] stated that the unscented Kalman filter (UKF) generally has better accuracy than the EKF [28] and derived a physics-based model for Li-ion batteries, in which an electrochemical model was constructed and a state age-dependent parameter was identified over randomized discharge profiles data via a UKF algorithm. Researches regarding battery prognosis and remaining useful life (RUL) prediction are also available.…”

Towards Characterizing the Variability in the Loading Demands of an Unmanned Aerial Vehicle

“…3,4 In particular, the use of lithium-ion batteries and health monitoring of such lithium-ion batteries have been studied in detail. 5 Battery modeling techniques 6 have been combined with system-level models 7 for the use of prognostic tools and methods based on filtering approximations, 8 analytical approximations, 9 and sampling techniques. 10 A typical approach to prognostics and health management of batteries consists of two important steps: (1) an estimation step through filtering, where the state-of-charge of the battery is calculated; and (2) a prediction step, where the future discharge of the battery is computed, and the time at which end-of-discharge (EOD) occurs is calculated.…”

Design and implementation of an FPGA-based piecewise affine Kalman Filter for Cyber-Physical Systems

“…For the second part, the core idea is that adding architecture support for hardware-based region identification leads to a fully hardware-based Piecewise-Affine Kalman Filter which achieves timing-determinism and full partitioning from software tasks. Our contribution is (2) an approach that integrates hardware architecture and a hyperrectangular model partitioning scheme to eliminate the non-accelerated, application-specific software code stub that exists in all mixed hardware-software Kalman Filter accelerators to date. This leads to a slight speedup over the mixed hardware-software Piecewise Affine Kalman Filter in part 1, but more importantly unlocks the capability for fully time-deterministic control loops.…”

“…There are a number of ways to model a cell. Some approaches such as [2,86] rely on a deep analysis of the physical and chemical properties of the cell in order to derive a model of its high-level behavior. Despite the high degree of accuracy, typically reduced-order versions of a chemical models must be identified in an order to reduce the computational complexity to a level which can be handled in online applications [87].…”

“…The Kalman Filter is a robust algorithm often employed as a means to estimate the state of a physical process in a wide array of sensing and control tasks in Cyber-Physical Systems, ranging from radar [1] to battery monitoring for planetary rovers [2] to virtual reality [3]. Cyber-Physical System (CPS) have been described as the integration of computing with physical processes,…”