Abstract
A proper battery management system (BMS) plays a vital role in ensuring the safety and reliability of electric vehicles (EVs) and other electronic products. Accurate State-of-Health (SOH) estimation of Lithium-ion (Li-ion) batteries is a key factor in a BMS. It is difficult to determine SOH because of the complexity of the electrochemical reactions within the battery. To improve the accuracy of SOH estimation, a dynamic spatial-temporal attention-based gated recurrent unit (DSTA-GRU) model is proposed in this paper. First, we extract six features from the battery’s charging and discharging processes that can reflect the aging degree of the battery to some extent. Second, this paper proposes a model to combine spatial attention and temporal attention that can not only consider the effects of states at different time step on the results, but also consider the effects of different features in the space domain. Third, the proposed model is trained and tested on NASA battery datasets and compared with other conventional models. Experiments carried on these data sets demonstrate that our model achieves higher accuracy than other conventional models.
Highlights
Due to the characteristics of high energy density and long lifetime, battery stacks based on Lithium-ion (Li-ion) batteries have been used in many fields, such as hybrid electric vehicles (HEVs), electric vehicles (EVs), ships and satellites [1]–[3]
EXPERIMENTAL RESULTS In order to verify the proposed DSTA-Gated recurrent unit (GRU) model, real world Li-ion batteries cycle data collected from NASA are applied for performing experiments
We study the robustness of DSTA-GRU model and compare the performance of our model with different healthy features (HFs) as input
Summary
Due to the characteristics of high energy density and long lifetime, battery stacks based on Lithium-ion (Li-ion) batteries have been used in many fields, such as hybrid electric vehicles (HEVs), electric vehicles (EVs), ships and satellites [1]–[3]. SOH is an indicator of battery aging, which is usually referred to as capacity or power degradation. It provides very useful information for when to remove or replace batteries. Relatively accurate SOH can be obtained by measuring the data of the discharge process under laboratory conditions using special high-precision equipment, this method has special limitations on the current and temperature of the discharge. These settings are not realistically available with commercial BMS in general. It is important to use proper methods for SOH estimation since SOH cannot be measured directly with
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