Abstract
Battery state of health (SOH) is related to the reduction of total capacity due to complicated aging mechanisms known as calendar aging and cycle aging. In this study, a combined multiple factor degradation model was established to predict total capacity fade considering both calendar aging and cycle aging. Multiple factors including temperature, state of charge (SOC), and depth of discharge (DOD) were introduced into the general empirical model to predict capacity fade for electric vehicle batteries. Experiments were carried out under different aging conditions. By fitting the data between multiple factors and model parameters, battery degradation equations related to temperature, SOC, and DOD could be formulated. The combined multiple factor model could be formed based on the battery degradation equations. An online state of health estimation based on the multiple factor model was proposed to verify the correctness of the model. Predictions were in good agreement with experimental data for over 270 days, as the margin of error between the prediction data and the experimental data never exceeded 1%.
Highlights
As the global population grows and the Earth’s natural resources diminish, electric vehicles have been rapidly developed in recent years
Accurate capacity degradation modeling plays a significant role in battery prognostics and health management (PHM), as state of charge (SOC) estimation is related to battery capacity
This paper focuses on the impact of stress factors on battery calendar and cycle life
Summary
As the global population grows and the Earth’s natural resources diminish, electric vehicles have been rapidly developed in recent years. When used in electric vehicles, lithium-ion batteries possess advantages, such as high energy density, long lifespans, no memory effect, environmental benignity, good stability, and affordability, when compared to other battery types, such as lead-acid batteries. As such, they are quickly becoming the first choice of battery type for electric vehicles. The state of health (SOH) of batteries can be estimated, which provides useful information for battery management systems (BMS). Accurate capacity degradation modeling plays a significant role in battery prognostics and health management (PHM), as state of charge (SOC) estimation is related to battery capacity. Capacity prediction is very important for reliable operation and reduced maintenance costs of the battery management system [1]
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