Li-Ion Batteries State-of-Charge Estimation Using Deep LSTM at Various Battery Specifications and Discharge Cycles

Wong, Kei Long; Bosello, Michael; Tse, Rita; Falcomer, Carlo; Rossi, Claudio; Pau, Giovanni

doi:10.1145/3462203.3475878

Cited by 21 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Mendeley data website was used to access the UNIBO Dataset also that is utilized. The values in this dataset [16,17] were obtained from 27 distinct battery cells from an Italian equipment manufacturer that were designed to power a variety of electrical products. The use of batteries from several manufacturers with differing nominal capacity and the cycle phases being carried out till the end of life of cell are the main highlights.…”

Section: B Unibo Powertools Battery Datasetmentioning

confidence: 99%

A Comprehensive Exploration on Different Machine Learning Techniques for State of Charge Estimation of EV Battery

A T,

Ratnam,

et al. 2023

2023 58th International Universities Power Engineering Conference (UPEC)

View full text Add to dashboard Cite

The State of Charge (SoC) is a measurement of the amount of energy available in a battery at a specific interval of time, mostly expressed as percentage. Proportional relationships between the electromotive force of a battery, current, terminal voltage and temperature determine the SoC. There can be a considerable error in the calculations due to a sharp drop of the terminal voltage at the end of discharge. This research has explored how important SoC is, as a factor in Battery Management Systems. The work focuses on using machine learning techniques to obtain an accurate and reliable status of battery charge, this includes Random Forest, Decision Tree, Gradient Boosting, Support Vector Regression, Polynomial Regression and Multilayer Perceptron. In this paper, these techniques are tested and compared with two real world captured datasets of Lithium-ion batteries which includes LG Battery and Unibo Powertools Battery. For supporting this study, statistical methods like K-fold cross validation and Grid Search cross validation techniques are used to estimate the skill of machine learning models. After implementing these techniques, it is found that Random Forest model returns the best Accuracy and Decision Tree returns the least Mean Absolute Error.

show abstract

Section: B Unibo Powertools Battery Datasetmentioning

confidence: 99%

A Comprehensive Exploration on Different Machine Learning Techniques for State of Charge Estimation of EV Battery

A T,

Ratnam,

et al. 2023

2023 58th International Universities Power Engineering Conference (UPEC)

View full text Add to dashboard Cite

show abstract

“…After evaluation, the model achieved the lowest MAE of 0.573% at 10 • C and an MAE of 1.606% with ambient temperature from 10 to 25 • C. Cui et al [68] used LSTM with an encoder-decoder [69] structure in the dataset [43]; the input was "I t , V t , I avg , V avg ", and the test result was an RMSE of 0.56% and MAE of 0.46% in US06, which was higher than that using only LSTM and GRU in that paper. Wong et al [70] used the undisclosed 'UNIBO Power-tools Dataset' as a training dataset and dataset [51] as a test dataset in the LSTM structure; the input variables were current, voltage, and temperature, and the MAE was 1.17% at 25 • C. Du et al [71] tested two LR1865SK Li-ion battery cells at room temperature and used the dataset in [45] as the comparative case to test the model trained by LSTM; the input variables were current, voltage, temperature, cycles, energy, power, and time; the MAE was 0.872% at an average level. YANG et al [72] used the LSTM to build a model for lithium battery SOC estimation; the data were obtained from the A123 18560 lithium battery under three drive cycles, i.e., DST, US06, and FUDS; the input vectors were current, voltage, and temperature.…”

Section: Recurrent Type-lstmmentioning

confidence: 99%

Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review

Zhang

Zhong

et al. 2022

Machines

View full text Add to dashboard Cite

As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application of deep learning in electric vehicles’ power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, as well as the structural characteristics and advantages and disadvantages of lithium battery SOC estimation in deep learning methods. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Second, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of three types of deep learning methods were obtained using the structure of the neural network used for training as the classification criterion; further, the selection of the deep learning structure for SOC estimation was discussed. Finally, the challenges and future development directions of lithium battery SOC estimation using the deep learning method were explained. Over all, this review provides insights into deep learning for EVs’ Li-ion battery SOC estimation in the future.

show abstract

“…Cui et al [63] used LSTM with encoder-decoder [64] structure in the dataset [41], the input is It, Vt, Iavg, Vavg, and the test result of it is the RMSE of 0.56% and MAE of 0.46% in US06, which is higher than that of only using LSTM and GRU in that paper. Wong et al [65] used the undisclosed dataset of 'UNIBO Powertools Dataset' as a training dataset and dataset [46] as a test dataset in LSTM structure, the input variable is current, voltage and temperature, the result of MAE is 1.17% in 25 ℃. Du et al [66] tested two LR1865SK Li-ion battery cells at room temperature, and used the dataset [40] as the comparative case to test the model trained by LSTM, the input variable is current, voltage, temperature, cycles, energy, power, and time; the result of MAE is 0.872% at an average level.…”

Section: Recurrent Type -Lstmmentioning

confidence: 99%

“…( ), ( ), ( ) V k I k T k RMSE: 0.7%, MAE: 0.6%, MAX (25 °C): 2.6% [63] [41] avg , , , avg V T I V RMSE: 0.45% ~ 1.89%, MAE: 0.37% ~ 1.48% [65] […”

Section: Evaluation and Future Developmentmentioning

confidence: 99%

Deep Learning in State of Charge Estimation for Li-ion Battery: A Review

Zhong¹,

Zhang²,

Xu³

et al. 2022

Preprint

View full text Add to dashboard Cite

As one of the critical state parameters of the battery management system, lithium battery state of charge (SOC) can provide an essential reference for battery safety management, charge/discharge control, and energy management of electric vehicles. To analyze the application of deep learning in electric vehicle power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, structural characteristics, advantages and disadvantages of lithium battery SOC estimation in deep learning method. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Secondly, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of four types of deep learning methods, were concluded using the structure of neural network used for training as the classification criterion. Finally, the challenges and future development directions of lithium battery SOC estimation in deep learning method were explained.

show abstract

Li-Ion Batteries State-of-Charge Estimation Using Deep LSTM at Various Battery Specifications and Discharge Cycles

Cited by 21 publications

References 17 publications

A Comprehensive Exploration on Different Machine Learning Techniques for State of Charge Estimation of EV Battery

A Comprehensive Exploration on Different Machine Learning Techniques for State of Charge Estimation of EV Battery

Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review

Deep Learning in State of Charge Estimation for Li-ion Battery: A Review

Contact Info

Product

Resources

About