Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

By Ephrem Chemali, P. Kollmeyer, Matthias Preindl, Ryan M. Ahmed, Ali Emadi. IEEE transactions on industrial electronics (1982. Print), 2018

687 citations recorded by the source corpus.

State of charge (SOC) estimation is critical to the safe and reliable operation of Li-ion battery packs, which nowadays are becoming increasingly used in electric vehicles (EVs), Hybrid EVs, unmanned aerial vehicles, and smart grid systems. We introduce a new method to perform accurate SOC estimation for Li-ion batteries using a recurrent neural network (RNN) with long short-term memory (LSTM). We showcase the LSTM-RNN's ability to encode dependencies in time and accurately estimate SOC without using any battery models, filters, or inference systems like Kalman filters. In addition, this machine-learning technique, like all others, is capable of generalizing the abstractions it learns during training to other datasets taken under different conditions. Therefore, we exploit this feature by training an LSTM-RNN model over datasets recorded at various ambient temperatures, leading to a single network that can properly estimate SOC at different ambient temperature conditions. The LSTM-RNN achieves a low mean absolute error (MAE) of 0.573% at a fixed ambient temperature and an MAE of 1.606% on a dataset with ambient temperature increasing from 10 to 25 $^{\circ }$ C.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/116ddfa9a79a53f0f4470eca862541c22fc7ec6a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Carlos Vidal
2. Pawel Malysz
3. P. Kollmeyer
4. Ali Emadi
5. Guangcai Zhao
6. Yandan Tan

In Electrical and Electronic Engineering

1. Carlos Vidal
2. Pawel Malysz
3. P. Kollmeyer
4. Ali Emadi
5. Guangcai Zhao
6. Yandan Tan

Citations in the corpus

• Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries (2020, methodology): “Currently, the LSTM was also applied to predict the remaining useful lifetime (RUL) [17] and SOC [18] of LIBs.”
• Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “A more recent work presented in [41], introduced a stacked BiLSTM model and compared its results with three previous publications [13], [38], [42] as the same dataset was used [40] in all four cases.”
• Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction (2021, methodology): “Chemali et al. [20] developed an RNN with LSTM for performing accurate state of charge estimation of lithium-ion batteries.”