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Electrification of the transportation industry is increasing rapidly with batteries currently the technology of choice. At the end of life, the battery chemistry used to electrify the vehicle may not be easily identifiable. A simple, common identifier is required to allow consumers, service and waste management personnel to direct unknown battery types to appropriate recyclers or secondary use markets. Recyclers also benefit from this identifier as it allows them to sort, screen for potential contamination to existing process streams, and identify the manufacturer so they may contact them to find detailed information about the battery to ensure proper and safe recycling. The SAE Battery Recycling Committee has recommended that batteries be identified by battery system, miscellaneous hazards and date of manufacture be identified as part of chemistry identification code. For the lithium-ion chemistry it is further recommended that cathode and anode be specified.

This paper proposes a model-based “Cascaded Dual Extended Kalman Filter” (CDEKF) for combined vehicle state estimation, namely, tire vertical forces and parameter identification. A sensitivity analysis is first carried out to recognize the vehicle inertial parameters that have significant effects on tire normal forces. Next, the combined estimation process is separated in two components. The first component is designed to identify the vehicle mass and estimate the longitudinal forces while the second component identifies the location of center of gravity and estimates the tire normal forces. A Dual extended Kalman filter is designed for each component for combined state estimation and parameter identification. Simulation results verify that the proposed method can precisely estimate the tire normal forces and accurately identify the inertial parameters.