Sensor Placement with Optimal Precision for Temperature Estimation of Battery Systems

TL;DR

This paper proposes a greedy sensor placement method combined with an $$ observer design to accurately estimate temperature fields in large-scale battery systems while minimizing sensor costs.

Contribution

It introduces a novel greedy approach for sensor placement and an $$ observer framework to optimize temperature estimation and reduce costs in battery thermal management.

Findings

Effective sensor placement improves temperature estimation accuracy.

The $$ observer design enhances thermal field estimation.

Cost reduction achieved through minimized sensor precisions.

Abstract

The temperature distribution in the battery significantly impacts the short-term and long-term performance of battery systems. Therefore, efficient, safe, and reliable battery system operation requires an accurate estimation of the temperature field. The current industry standard for sensors to battery cell ratio is quite frugal. Thus, the problem of sensor placement for accurate temperature estimation becomes non-trivial, especially for large-scale systems. In this paper, we explore a greedy approach for sensor placement suitable for large-scale battery systems. An observer to estimate the thermal field is designed in an $\mathcal{H}_{\infty}$ framework while simultaneously minimizing the sensor precisions, thus lowering the overall thermal management system's economic cost.