S&AS: INT: Autonomous Battery Operating System (ABOS): An Adaptive and Comprehensive Approach to Efficient, Safe, and Secure Battery System Management

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Effective battery systems are a cornerstone of the next wave ofinnovation. Emerging technologies including electric-drive vehicles(EVs) and stationary energy storage systems require integratedbattery systems to provide sustained and reliable power. A uniqueopportunity exists for the software-controlled battery managementsystem (BMS) to play a crucial role in enabling continuedinnovation. However, there are several unaddressed challenges tocurrent BMS design. A battery's decreasing capacity over time dueto aging necessitates that BMS automatically adapt to batterychanges. Furthermore, future BMS systems will require autonomousreasoning capabilities to make economically-sound decisions onusers' behalf (e.g., scheduling battery charging times in apersonalized fashion). This project injects intelligencecapabilities into BMS design with the development of the AutonomousBattery Operating System (ABOS). ABOS enables moreenergy-efficient, long-lasting, and secure battery-driven systems.Furthermore, the PIs incorporate computational and cyber-securityaspects of ABOS design into their undergraduate- and graduate-levelcourses.ABOS advances the science of autonomous system design withsimultaneously introspective and extrospective learning. ABOS willlearn and adapt to user-initiated charging/discharging patterns,reason about how these patterns affect the battery'sstate-of-health, and respond to potential faults or attacks. ThePIs and their external collaborators will develop a hybrid EVsimulation environment to test the ability of ABOS to control aphysical battery system. The simulation environment will evaluatethe effectiveness of ABOS in predicting battery state, minimizingcost of operation, and handling failures and threats.