Abstract
With the further development of the electric vehicle (EV) industry, the reliability of prediction and health management (PHM) systems has received great attention. The original Li-ion battery life prediction technology developed by offline training data can no longer meet the needs of use under complex working conditions. The existing methods pay insufficient attention to the dispersive information of health indicators (HIs) under EV driving conditions, and can only calculate through standard configuration files. To solve the problem that it is difficult to directly measure the capacity loss in real time, this paper proposes a battery HI called excitation response level (ERL) to describe the voltage variation at different lifetimes, which could be easily calculated according to the current and voltage under the actual load curve. In addition, in order to further optimize the proposed HI, Box–Cox transformation was used to enhance the linear correlation between the initially extracted HI and the capacity. Several Li-ion batteries were discharged to the 50% state of health (SOH) through profiles with different depths of discharge (DODs) and mean states of charge (SOCs) to verify the accuracy and robustness of the proposed method. The average estimation error of the tested batteries was less than 3%, which shows a good performance for accuracy and robustness.
Highlights
With the further development of the electric vehicle (EV) industry, and in order to avoid or warn of battery performance failures as much as possible, the reliability of the prediction and health management (PHM) system of EV batteries has received greater attention from enterprises [1,2].The PHM system uses the state of charge (SOC) and the state of health (SOH) to comprehensively evaluate the state of the EV battery
This is because the operating environment temperature, load conditions, and charging status of Li-ion batteries used in EVs undergo unpredictable and drastic changes under actual complex working conditions [5]
The excitation response level (ERL) can be calculated based on current and voltage under actual load profile, which is effective for the online lifetime estimation of the EV power Li-ion battery of the battery management system (BMS)
Summary
With the further development of the electric vehicle (EV) industry, and in order to avoid or warn of battery performance failures as much as possible, the reliability of the prediction and health management (PHM) system of EV batteries has received greater attention from enterprises [1,2]. This method takes a long time and requires additional capacity parameters when calculating sample entropy [17,18] These methods focus mostly on the batteries used in EV applications and pay attention insufficiently to the dispersion information of the HIs. In addition, the online lifetime estimation cannot be achieved because the HIs can only be calculated through standard profiles. The ERL can be calculated based on current and voltage under actual load profile, which is effective for the online lifetime estimation of the EV power Li-ion battery of the BMS. Compared with other HIs based on voltage variation mentioned above, which mostly require a certain excitation, the ERL can be calculated directly through the actual load profile. The ERL differs from methods such as information entropy because it requires no matrix inversion and is more applicable to the embedded system
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