A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

Abstract

A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquired from data in a semiphysical arrangement. The dynamic behaviour of the battery is embodied in the parametric definition of a set of differential equations, and fuzzy knowledge bases are embedded as nonlinear blocks in these equations, providing a human understandable reading of the State of Health of the CPVS that can be easily integrated in the fleet through-life management.

Assessing the Health of LiFePO4 Traction Batteries through Monotonic Echo State Networks

“…These learning algorithms operate with sequences of voltages and currents streamed by on-vehicle sensors [ 3 ]. The combination of a learning battery model and a suitable virtual lab procedure, with the purpose of synthesizing health-related variables from operational data, will be referred to as a “soft sensor” in this contribution [ 4 ].…”

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

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Learning human-understandable models for the health assessment of Li-ion batteries via Multi-Objective Genetic Programming

“…The neural estimation of the OCV curve was obtained by simulating a C25 cycle with the learned battery model. The GFS is a soft sensor described in the paper (Sánchez et al, 2017). Table 3 provides a summary of the results.…”