Key technologies for electric vehicles

Xiong, Rui; Kim, Jong-Hoon; Shen, Weixiang; Lv, Chen; Li, Hailong; Zhu, Xiaoyong; Zhao, Wanzhong; Gao, Bingzhao; Guo, Hongyan; Zhang, Chenming; Sun, Fuchun

doi:10.1016/j.geits.2022.100041

“…Lithium-ion batteries are commonly utilized in various industries, including consumer electronics, electric vehicles, communications, and airplanes [1], due to their advantageous characteristics such as extended lifespan [2], high energy density, low self-discharge rate, and environmental friendliness [3]. Nevertheless, as the charging and discharging cycles increase, he materials within these batteries are subject to irreversible depletion, resulting in battery aging manifested by capacity loss, power loss, and thermal instability [4].…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of lithium battery based on ACNN-Mogrifier LSTM-MMD

Li,

Bai

et al. 2023

Meas. Sci. Technol.

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Predicting the remaining useful life (RUL) of lithium batteries is crucial for predicting battery failure and health management. Accurately estimating the RUL allows for timely maintenance and replacement of batteries that pose safety risks. To enhance the safety and reliability of lithium battery operations, this paper proposes a lithium battery life prediction model, attention mechanism-convolutional neural network (ACNN)-Mogrifier long and short-term memory network (LSTM)-maximum mean discrepancy (MMD), based on ACNN, Mogrifier LSTM, and MMD Feature Transfer Learning. Firstly, the capacity degradation data from historical life experiments of lithium batteries in both source and target domains are extracted. The whale optimization algorithm (WOA) is employed to optimize the parameters of variational modal decomposition, enabling the decomposition of the historical capacity degradation data into multiple intrinsic mode functions (IMFs) components. Secondly, highly correlated IMF components are identified using the Pearson correlation coefficient (Pearson) to reconstruct the capacity sequence, which characterizes the capacity degradation information of the lithium batteries. These reconstructed sequences are inputs to the ACNN model to extract features from the capacity degradation data. The extracted features are then utilized to compute MMD values, quantifying the distribution differences between the two domains. The Mogrifier LSTM neural network estimates the capacity values of the source and target domains and calculates the loss functions by comparing them to the actual capacity values. These loss functions, along with the computed MMD values, are combined to obtain the combined loss function of the model. Finally, the ACNN-Mogrifier LSTM-MMD is applied to the target domain data to formulate the lithium battery RUL prediction model. The effectiveness of the proposed method is validated using CACLE and NASA lithium battery datasets, The experimental results demonstrate that the predicted error of the RUL for the B5 battery is less than 6% for mean absolute percentage error (MAPE) and less than 1 for RU L Error . Similarly, the RUL prediction error for the B6 battery is below 10% for MAPE and less than 1 for RU L Error . This indicates higher accuracy compared to other prediction methods, along with improved robustness and practicality.

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“…Compared with gasoline vehicles, EVs have many advantages, including low noise emissions, low pollution, and high power efficiency [3]. EVs are the key technology used to solve the problem of carbon dioxide emissions, and the core component that EVs rely on is the battery [4][5][6]. Among them, lithium-ion (Li-ion) batteries are widely employed on account of their electrochemical energy storage properties [7,8].…”

Section: Introductionmentioning

confidence: 99%

Co-Estimation of State-of-Charge and State-of-Health for High-Capacity Lithium-Ion Batteries

Xiong,

Wang,

Feng

et al. 2023

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To address the challenges of efficient state monitoring of lithium-ion batteries in electric vehicles, a co-estimation algorithm of state-of-charge (SOC) and state-of-health (SOH) is developed. The algorithm integrates techniques of adaptive recursive least squares and dual adaptive extended Kalman filtering to enhance robustness, mitigate data saturation, and reduce the impact of colored noise. At 25 °C, the algorithm is tested and verified under dynamic stress test (DST) and Beijing bus DST conditions. Under the Beijing bus DST condition, the algorithm achieves a mean absolute error (MAE) of 0.17% and a root mean square error (RMSE) of 0.19% for SOC estimation, with a convergence time of 4 s. Under the DST condition, the corresponding values are 0.05% for MAE, 0.07% for RMSE, and 5 s for convergence time. Moreover, in this research, the SOH is described as having internal resistance. Under the Beijing bus DST condition, the MAE and the RMSE of the estimated internal resistance of the proposed approach are 0.018% and 0.075%, with the corresponding values of 0.014% and 0.043% under the DST condition. The results of the experiments provide empirical evidence for the challenges associated with the efficacious estimation of SOC and SOH.

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“…New electric energy vehicles are playing an increasingly important role in decarbonization in the transportation industry. They constitute a promising solution to a set of global challenges such as climate change and air pollution . Developing new energy vehicles has been a global consensus, and developing new energy vehicles with pure electric characteristics has become China’s national strategy .…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Method for Lithium-Ion Battery Cells Based on Time Series Decomposition and Improved Manhattan Distance Algorithm

Wu,

Zhang,

Zhang

et al. 2023

ACS Omega

0

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Abnormalities in individual lithium-ion batteries can cause the entire battery pack to fail, thereby the operation of electric vehicles is affected and safety accidents even occur in severe cases. Therefore, timely and accurate detection of abnormal monomers can prevent safety accidents and reduce property losses. In this paper, a battery cell anomaly detection method is proposed based on time series decomposition and an improved Manhattan distance algorithm for actual operating data of electric vehicles. First, time series decomposition is performed on the voltage data of all battery cells in the battery pack to obtain the voltage trend component of each cell. Then, the improved Manhattan distance algorithm is utilized to calculate and compare the Manhattan distance values between adjacent cell trend components, to determine the abnormal cells inside the battery pack. Furthermore, the Manhattan distance values at the same sampling moment are calculated within the data sequence to detect the specific time when the abnormal cells malfunction. The data analysis and experimental verification results based on actual vehicle operating conditions indicate that this method can accurately identify an abnormal cell within the battery pack and diagnose the specific moment of abnormality in the battery cell at an early stage of failure, with good robustness.

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Key technologies for electric vehicles

Cited by 71 publications

References 21 publications

Remaining useful life prediction of lithium battery based on ACNN-Mogrifier LSTM-MMD

Remaining useful life prediction of lithium battery based on ACNN-Mogrifier LSTM-MMD

Co-Estimation of State-of-Charge and State-of-Health for High-Capacity Lithium-Ion Batteries

Anomaly Detection Method for Lithium-Ion Battery Cells Based on Time Series Decomposition and Improved Manhattan Distance Algorithm

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