Prediction of Li-ion battery state of health based on data-driven algorithm

Sun, Hanlei; Yang, Dongfang; Du, Jiaxuan; Li, Ping; Wang, Kai

doi:10.1016/j.egyr.2022.11.134

Cited by 17 publications

(9 citation statements)

References 16 publications

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“…The Li-ion SOC for the BMS is predicted by Khalid et al [53] with an RMSE of 1.527%. References [54][55][56] demonstrate the data-driven methodology for SOH prediction using data on the voltage and current from Li-ion batteries. In spite of this, references [57,58] depict the RUL prediction on the Li-ion battery.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

“…Multi-dimensional time-series data are challenging to apply to the majority of statistical models, including hidden Markov model (HMM) [9][10][11][12][13][14], Gaussian mixture model (GMM) [15][16][17][18][19], support vector machine (SVM) [20][21][22][23][24][25][26][27], Naive Bayes (NB) [28][29][30], fuzzy logic (FL) [31][32][33][34][35][36], and k-nearest neighbor (KNN) [20,[37][38][39][40]. The deep learning based models, including convolutional neural network (CNN) [41][42][43][44][45][46][47][48], recurrent neural network (RNN) [49][50][51][52][53][54][55]…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

et al. 2023

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In the foreseeable future, electric vehicles (EVs) will play a key role in the decarbonization of transport systems. Replacing vehicles powered by internal combustion engines (ICEs) with electric ones reduces the amount of CO2 being released into the atmosphere on a daily basis. The Achilles heel of electrical transportation lies in the car battery management system (BMS) that brings challenges to lithium-ion (Li-ion) battery optimization in finding the trade-off between driving and battery health in both the long- and short-term use. In order to optimize the state-of-health (SOH) of the EV battery, this study focuses on a review of the common Li-ion battery aging process and behavior detection methods. To implement the driving behavior approaches, a study of the public dataset produced by real-world EVs is also provided. This research clarifies the specific battery aging process and factors brought on by EVs. According to the battery aging factors, the unclear meaning of driving behavior is also clarified in an understandable manner. This work concludes by highlighting some challenges to be researched in the future to encourage the industry in this area.

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Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

et al. 2023

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“…GRU, as a special form of RNN, solves the problem of gradient explosion and has fewer parameters and faster training speed. Sun et al [7] proposed a battery SOH estimation method based on EMD-ICA-GRU, extracted battery health characteristics through incremental capacity analysis, and predicted battery SOH combined with GRU model. Literature [8] proposes an integrated learning method based on convolution and recursive autoencoder to estimate the SOH of Li-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

State of health estimation of lithium-ion battery based on DAGRU

Hong,

Wang,

Chai

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Accurate estimation of the state of health (SOH) of Li-ion batteries is an important guarantee to ensure safe and reliable operation of battery systems, and is also a key indicator for battery management systems. In this paper, a method for SOH estimation of lithium-ion batteries based on double-stage attention-based gated recurrent unit (DAGRU) is proposed. Firstly, depth wise separable convolution (DSC) was used to extract the health features (HFs) of battery charging voltage. Secondly, the model uses double-stage attention, which enables more information on input features to be obtained at both the temporal and spatial scales. Finally, NASA battery dataset was used to verify the proposed method. Experimental results show that the proposed method can accurately estimate the SOH of lithium-ion batteries.

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“…However, the accuracy of these methods depends on the complexity and precision of the electrochemical model, and models constructed under different operating conditions are not applicable to each other. With the advancement of artificial intelligence techniques, data-driven methods gain wide popularity in recent years, including machine learning methods such as SVM [16,17], GPR [18], RF [19], and deep learning methods such as CNN [20], IRBFNN [21], LSTM [22], and GRU [23]. The datadriven models can extract information from the data and do not depend on the electrochemical model, which make them suitable for different types of batteries and operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Measuring Hilbert-Schmidt Independence Criterion with Different Kernels

Zhang

Zhou

et al. 2021

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)

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State-of-health (SOH) estimation is a key step in ensuring the safe and reliable operation of batteries. Due to issues such as varying data distribution and sequence length in different cycles, most existing methods require health feature extraction technique, which can be time-consuming and labor-intensive. GRU can well solve this problem due to the simple structure and superior performance, receiving widespread attentions. However, redundant information still exists within the network and impacts the accuracy of SOH estimation. To address this issue, a new GRU network based on Hilbert-Schmidt Independence Criterion (GRU-HSIC) is proposed. First, a zero masking network is used to transform all battery data measured with varying lengths every cycle into sequences of the same length, while still retaining information about the original data size in each cycle. Second, the Hilbert-Schmidt Independence Criterion (HSIC) bottleneck, which evolved from Information Bottleneck (IB) theory, is extended to GRU to compress the information from hidden layers. To evaluate the proposed method, we conducted experiments on datasets from the Center for Advanced Life Cycle Engineering (CALCE) of the University of Maryland and NASA Ames Prognostics Center of Excellence. Experimental results demonstrate that our model achieves higher accuracy than other recurrent models.

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Prediction of Li-ion battery state of health based on data-driven algorithm

Cited by 17 publications

References 16 publications

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

State of health estimation of lithium-ion battery based on DAGRU

Measuring Hilbert-Schmidt Independence Criterion with Different Kernels

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