Toward Fast and Accurate SOH Prediction for Lithium-Ion Batteries

Shen, Shuaiqi; Лиу, Баочанг; Ci, Song

doi:10.1109/tec.2021.3052504

Cited by 35 publications

(9 citation statements)

References 29 publications

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“…(b) The initial data of one cell cycle are used as a training set, and the remaining cell cycle data are used as a test set [23]- [25]. (c) The data from a certain number of cycles before the cycles to be predicted are used as a training set for predicting the capacity [38]. Methods (b) and (c) have the disadvantage that the initial cycling data must be obtained first and are then used to train the model again for application to a new cell.…”

Section: Methodsmentioning

confidence: 99%

Impact of Data Partitioning to Improve Prediction Accuracy for Remaining Useful Life of Li-Ion Batteries

Kim

Park

et al. 2023

International Journal of Energy Research

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Predicting the remaining useful life (RUL) of a battery is critical to ensure the safe management of its manufacture and operation. In this study, a comprehensive investigation of the effect of data partitioning methods on RUL prediction was performed. To confirm the generality and transferability, the charge–discharge information of cathode materials with different chemical elements was adopted from previous research, including lithium iron phosphate, lithium nickel cobalt aluminum oxide, and lithium nickel cobalt manganese oxide cells. Among the partitioning procedures, the method of adding predicted data from the surrogate model to the training set exhibited the best accuracy, with an average mean absolute error (MAE) of 47 cycles. In contrast, the slide BOX method, which only used certain cycles before the test set as the training set, exhibited the worst MAE value of 60 cycles. In conclusion, the proposed data partitioning method could be implemented to predict the RUL of batteries to develop next-generation cathode materials with improved performance and stability, shorten the quality assessment time, and achieve stable predictive maintenance.

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Section: Methodsmentioning

confidence: 99%

Impact of Data Partitioning to Improve Prediction Accuracy for Remaining Useful Life of Li-Ion Batteries

Kim

Park

et al. 2023

International Journal of Energy Research

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“…As shown in Fig. 5, previous N−1 cycles are used to form the observation matrix, with the SOH of the subsequent cycles as the target value [65]. In our case, the size of the sliding window is ten, and the neuron in the single hidden layer is four.…”

Section: ) Long Short-term Memorymentioning

confidence: 99%

Data-Driven Methods for Battery SOH Estimation: Survey and a Critical Analysis

Oji

Zhou

et al. 2021

IEEE Access

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State-of-health (SOH) estimation is a critical factor in ensuring the efficiency, reliability, and safety of lithium-ion batteries (LIBs) in electric vehicles (EVs). However, due to the complexity of electrochemical processes in batteries and the dynamics of working conditions, it is challenging to estimate SOH accurately, especially in real-world EV application scenarios. Thus, various data-driven methods with robust and adaptive features for SOH estimation have been widely proposed in the current literature. However, there is a lack of a comprehensive investigation and performance comparison of those methods, which makes them hard to be adopted in practice. Hence, in this paper, we have studied current major data-driven methods with real-world EV battery data to evaluate the performance. Besides, we summarize each method's advantages and limitations with the consideration of the critical features required to achieve accurate SOH estimation in real-world applications. Hopefully, this paper provides a practical insight into the related fields.INDEX TERMS Lithium-ion battery, state of health, data-driven methods, state estimation.

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“…Li et al (2020) directly used the voltage data during charging as the input of the GRU model to evaluate SOH. Shen et al (2021) and Orchard et al (2015) took voltage, current, and temperature in each cycle as inputs and used the CNN to establish the SOH prediction model. However, a large number of input attributes or input data greatly increase the complexity and computational cost of battery management (Olivares et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the State of Health of Lithium-Ion Battery Based on the Temporal Convolution Network

Zhang

Zhao²,

Wang

et al. 2022

Front. Energy Res.

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The state of health (SOH) of lithium-ion batteries is an important part of the battery management system (BMS). Accurately grasping the SOH of the lithium-ion battery will help replace the battery in time, to avoid accidents. Aiming at the problems of complex BMS management and high calculation cost caused by too many inputs/attributes, this study used feature engineering to mine the higher temperature variety rate associated with degraded capacity as the input of temporal convolutional networks (TCNs) and SOH as the output to establish the TCN model. On this basis, three lithium-ion batteries, namely, as B0005, B0007, and B0018 are verified, and the mean absolute error (MAE) and root mean square error (RMSE) of predicted SOH are not more than 1.455% and 1.800%, respectively. To further obtain the uncertain expression of predicted SOH, this study adopts the sampling method to obtain the confidence interval of lithium-ion battery SOH prediction results.

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Toward Fast and Accurate SOH Prediction for Lithium-Ion Batteries

Cited by 35 publications

References 29 publications

Impact of Data Partitioning to Improve Prediction Accuracy for Remaining Useful Life of Li-Ion Batteries

Impact of Data Partitioning to Improve Prediction Accuracy for Remaining Useful Life of Li-Ion Batteries

Data-Driven Methods for Battery SOH Estimation: Survey and a Critical Analysis

Evaluation of the State of Health of Lithium-Ion Battery Based on the Temporal Convolution Network

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