Abstract
Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system.
Highlights
Lithium ion battery is deemed as one of the most promising sources of alternative energy storage devices for numerous applications, like hybrid electric, plug-in hybrid electric and electric vehicles, as well as major portable electronic devices [1]
Multiple model adaptive estimation (MMAE) algorithm has been used in the detection of the stated battery operating conditions, i.e., fault diagnosis of the battery for an input load current from the hybrid pulse power characterization (HPPC) cycle simulated on a hybrid vehicle
Experimentally identified electrochemical battery model parameters for different abusive operating conditions of a Li-Ion battery in prior studies were adopted for the fault diagnosis purpose
Summary
Lithium ion battery is deemed as one of the most promising sources of alternative energy storage devices for numerous applications, like hybrid electric, plug-in hybrid electric and electric vehicles, as well as major portable electronic devices [1]. Electrochemical model based multiple model adaptive estimation has been introduced to detect the ongoing fault(s) in a lithium ion battery in [15]. This adaptive estimation method requires representation of different fault scenarios, generation of the residual signals, and the isolation of the different types of faults using the proposed algorithm. Experimentally identified electrochemical models have been used to detect fault conditions in a lithium ion battery using multiple model adaptive estimation method. Multiple model adaptive estimation (MMAE) algorithm has been used in the detection of the stated battery operating conditions, i.e., fault diagnosis of the battery for an input load current from the hybrid pulse power characterization (HPPC) cycle simulated on a hybrid vehicle.
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