Optimal Sensor Selection for an Electrochemical Li-Ion Battery Model

Abstract

This work rigorously evaluates which sensor combinations are best for estimating lithium-ion battery internal states related to state-of-charge, state-of-power, and state-of-health. Lithium-ion batteries have emerged as an enabling technology for many industries that seek the benefits of electrification such as automotive, power systems, and consumer electronics. Central to the operation of lithium-ion battery technology is the battery management system (BMS), whose role is to monitor the status of and facilitate the charging and discharging behavior of the system. Fundamentally, the core challenges of a BMS are modeling, estimation, and control problems. To date, the vast majority of BMS technology and research operates based on the assumption of what is currently possible to measure in a battery system: current, voltage, and temperature. However, valuable advanced BMS capabilities have proven to be challenging to develop based on this sensor limitation.To systematically evaluate the value of different sensing technologies in BMS's, we formulate the sensor selection problem. In other research domains, the sensor selection problem is motivated by the goal of maximizing the usefulness of available sensor data for a given estimation or controls goal [1-3]. The research here examines the sensor selection problem from a mathematical point-of-view without limiting ourselves to the practicality of the sensing technology. That is, instead of asking if some existing sensing technology improves state observability, we ask what sensors are theoretically best for estimating internal states. We seek to motivate the pursuit of new sensing technologies or more rigorously justify the use of existing sensing technologies.We first present a methodology for evaluating the impact of different potential novel sensing capabilities based on an observability analysis for a reduced-order electrochemical lithium-ion battery model, specifically, the Single Particle Model with Thermal Dynamics (SPM+T) [4-5]. We demonstrate the impact of incorporating different sensors on observability of the reduced-order electrochemical model. We find that the inclusion of certain sensors has a greater impact on model observability than others. The result in Figure 1 for the SPM+T model proves that voltage and surface temperature sensing is, in fact, best for state observability. However, this result may change for more complex models, such as the SPMeT [6-7] or others that incorporate additional dynamics such as multiple materials, stress mechanics, etc.