Optimal Sensor Selection for an Electrochemical Li-Ion Battery Model

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.