Abstract
Aging assessment is critical for lithium-ion batteries (LIBs) as the technology of choice for energy storage in electrified vehicles (EVs). Existing research is mainly focused on either increasing modeling precision or improving algorithm efficiency, while the significance of data applied for aging assessment has been largely overlooked. Moreover, reported studies are mostly confined to a specific condition without considering the impacts of diverse usage patterns on battery aging, which is practically challenging and can greatly affect battery degradation. This paper addresses these issues through incremental capacity (IC) analysis, which can both utilize data directly available from on-board sensors and interpret degradations from a physics-based perspective. Through IC analysis, the optimal health feature (HF) and the state of charge (SOC)-based optimal data profile for battery aging assessment have been identified. Four stress factors, i.e., depth-of-discharge (DOD), charging C-rate, operating mode, and temperature, have been selected to jointly characterize diverse usage patterns. Impact analysis of different stress factors through the optimal HF with the SOC-based optimal data profile from aging campaign experiments have generated practical guidance on usage patterns to improve battery health monitoring and lifetime control strategies.
Highlights
With the continuously increasing energy and power densities as well as the decreasing cost, the lithium-ion battery (LIB) has been widely acknowledged as the most promising technology for energy storage in electric vehicles (EVs) [1,2,3]
In aging tests (Figures 3 and 4), the cell is cycled under the mode of charge depleting (CD), charge sustaining (CS), or a combination of both, to simulate the practical plug-in hybrid electric vehicle (PHEV) operating profile charge sustaining (CS), or a combination of both, to simulate the practical PHEV operating profile defined by the United States Advanced Battery Consortium (USABC) [32]
The peak amplitude in the relatively lowest SOC level, namely, dQ/dV of peak A, is eventually selected as the optimal health feature (HF), given that it is effective in characterizing the long-term cell capacity fade and is straight forward to obtain
Summary
With the continuously increasing energy and power densities as well as the decreasing cost, the lithium-ion battery (LIB) has been widely acknowledged as the most promising technology for energy storage in electric vehicles (EVs) [1,2,3]. The reported research on IC analysis for LIB health assessment are mainly focused on either the degradation mechanisms [26,27] or the design of robust algorithms to obtain an improved IC curve [23,24,25,28,29,30,31], often confined to a specific battery cell under a given load profile. The optimal data profile for capacity fade assessment are identified through IC analysis and further validated with real experimental aging data; different stress factors, which jointly characterize diverse usage patterns, are studied for their impacts on battery capacity fade, providing useful information for BMS design to improve the prediction and control of RUL. Of the optimal HF and the optimal data profile through IC analysis, details the impacts of different stress factors on capacity fade; Section 4 summarizes the discoveries and clarifies the future work
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