Abstract
The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li1(NiCoAl)1O2-Li1(NiCoMn)1O2/graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the α-based, β-based, and parameter–fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. The largest error drops from 3% under the α-based method to 1.8% under the parameter–fusion method.
Highlights
Battery electric vehicles (BEV) with lithium-ion batteries as the main energy source have been promoted and popularized worldwide
Considering that the main purpose of this research was to establish a state of health (SOH) estimation method based on the voltage relaxation model under different state of charge (SOC), the factors considered in this research are the SOC and aging state
A voltage relaxation model based on the linear relationship between the time coefficient and open circuit time was established for a Li1 (NiCoAl)1 O2 -Li1 (NiCoMn)1 O2 battery
Summary
Battery electric vehicles (BEV) with lithium-ion batteries as the main energy source have been promoted and popularized worldwide. Compared with traditional internal-combustion vehicles, a critical drawback of the BEVs is that the commercial lithium-ion batteries will have a significant aging phenomenon with the increase of the operational time, resulting in an attenuation in energy and power performance [1]. Research on the SOH estimation for lithium-ion batteries has been a hotspot for several years, and lots of effective methods have been proposed in the literature [4,5,6]. In order to estimate the SOH, the first step is to determine the evaluation parameter of the battery SOH [4]. The key step of a SOH estimation method is to accurately obtain the SOH evaluation parameter of Energies 2019, 12, 1349; doi:10.3390/en12071349 www.mdpi.com/journal/energies
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