Abstract
Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.
Highlights
Electric vehicles (EV) are the focus of attention for today transportation, and their primary energy source is mainly rechargeable lithium ion batteries (LiB) due to their higher energy efficiency and longer lifetime as compared to their counterparts
Model to estimate SoH and its remaining useful life after a LiB is operated for different charge-discharge cycles
electrochemistry-based electrical model (ECBE) model is only used for initial SoH calculation twice to determine the parameters in the semi-empirical capacity fading (SECF) model, which can be used for the SoH and remaining useful life (RUL) estimation of cells for their subsequent charge-discharge cycles
Summary
Electric vehicles (EV) are the focus of attention for today transportation, and their primary energy source is mainly rechargeable lithium ion batteries (LiB) due to their higher energy efficiency and longer lifetime as compared to their counterparts. The estimation of their health and the prediction of their remaining useful life (RUL) for EV are the major issues. State-of-health (SoH), state of charge (SoC), state of energy (SoE), and state of safety (SoS) were discussed in detail for. Inaccurate SoH estimation can lead to unintentional over-discharge as the actual Qm (the remaining charge in LiB) can be a lot lower and 20% SoC that are ready for re-charge could be much lower. Such inaccuracy or uncertainty in SoH estimation can lead to being over conservative on
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