Abstract
Accurate estimation of the state of charge (SOC) is critical for the normal use of lithium-ion battery equipment like electric vehicles. However, the SOC of lithium-ion battery is not available by direct measure, but can only indirectly be estimated by measurable variables. According to the nonlinear characteristics between the measured values and SOC during the working period of lithium-ion batteries, we propose a method to estimate the SOC of lithium-ion batteries with Temporal Convolutional Network (TCN). The measured values of voltage, current, and temperature during the use of lithium-ion batteries can be directly mapped to accurate SOC in this method without using a battery model or adaptive filter. The network can self-learning and update parameters by being fed datasets collected under various working conditions and then obtain a model that can correctly estimate SOC under different estimation conditions. In addition, it can also be applied to different types of lithium-ion batteries through transfer learning with only a small amount of battery data. At various ambient temperature conditions, the average MAE estimated by the proposed method is 0.67% for all the tests, which proves that the TCN network is an effective tool to estimate the SOC of lithium-ion batteries.
Highlights
The world is undergoing an energy transition driven both by improving climate change and promoting sustainable growth
Based on the Long Short-Term Memory (LSTM) neural network, three kinds of models were implemented for state of charge (SOC) estimation for four different lithium-ion batteries, including models trained traditionally with no transfer learning, models trained with transfer learning using full target dataset, and models trained with transfer learning using partial target dataset
This is the first time for Temporal Convolutional Network (TCN) to be applied in the SOC estimation of lithium-ion batteries and the first time for transfer learning to be used on the TCN network
Summary
The world is undergoing an energy transition driven both by improving climate change and promoting sustainable growth. Based on the LSTM neural network, three kinds of models were implemented for SOC estimation for four different lithium-ion batteries, including models trained traditionally with no transfer learning, models trained with transfer learning using full target dataset, and models trained with transfer learning using partial target dataset. Results showed the prospect of transfer learning in reducing training time, improving SOC estimation accuracy, and reducing the amount of training data required To our knowledge, this is the first time for TCN to be applied in the SOC estimation of lithium-ion batteries and the first time for transfer learning to be used on the TCN network.
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