Aging mechanisms under different state-of-charge ranges and the multi-indicators system of state-of-health for lithium-ion battery with Li(NiMnCo)O2 cathode
By Yang Gao, Jiuchun Jiang, Caiping Zhang, Weige Zhang, Yan Jiang. Journal of Power Sources, 2018
151 citations recorded by the source corpus.
Abstract Understanding the degradation of battery cycled under different state-of-charge ranges is important for the optimal control of operation state-of-charge range. Cycle life tests on 8 A h pouch batteries with Li(NiMnCo)O2 cathode are conducted under 0–20%, 20%–40%, 40%–60%, 60%–80%, 80%–100% and 0–100% state-of-charge ranges with current and temperature at 6C and 25 °C. Results show that among the five ranges with 20% depth-of-discharge, cycling under 0–20% causes more impedance increase and less capacity loss, cycling under 80%–100% cause more capacity loss. The degradation behaviors of batteries under the remaining three ranges are similar. Battery degradation under 20% depth-of-discharge is significantly slower than 100% depth-of-discharge. Battery aging mechanisms are investigated using incremental capacity analysis method. It is indicated that loss of active material in positive electrode and loss of lithium inventory are comparable during battery aging process under 100% depth-of-discharge. However, for the battery under 20% depth-of-discharge, the dominant factor causing aging is loss of lithium inventory. Moreover, six characteristic parameters corresponding to loss of lithium inventory and loss of electrode material respectively are extracted from incremental capacity curve to establish the multi-indicators system describing battery state-of-health, which enhances the convenience of incremental capacity analysis to identify battery aging mechanisms on-board.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/a5bdcd018ce72ea9067969379346f25df8d18fd6
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Citations in the corpus
- Temperature, Ageing and Thermal Management of Lithium-Ion Batteries (2021, methodology): “[81] show that the resistance increase in their study with an NMC cathode and graphite anode is most pronounced at low SOC windows.”
- Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “Based on the DV curve, the peak (DVP) [58]and valley (DVV) [35], the peak location (DVPL) [32], [59], and the valley location (DVVL) [34], [60] are extracted as HIs.”