Deep learning-based battery state of charge estimation: Enhancing estimation performance with unlabelled training samples

The estimation of state of charge (SOC) using deep neural networks (DNN) generally requires a considerable number of labelled samples for training, which refer to the current and voltage pieces with knowing their corresponding SOCs. However, the collection of labelled samples is costly and time-consuming. In contrast, the unlabelled training samples, which consist of the current and voltage data with unknown SOCs, are easy to obtain. In view of this, this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples. Besides the traditional supervised network, the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current. In this way, the developed network can extract useful information from the unlabelled samples. The proposed method is validated under different drive cycles and temperature conditions. The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples. In addition, when the dataset with reduced number of labelled samples to some extent is used to test the developed network, it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training. Furthermore, the proposed method is evaluated with different recurrent neural networks (RNNs) applied to the input reconstruction module. The results indicate that the proposed method is feasible for various RNN algorithms, and it could be flexibly applied to other conditions as required.</p

Health indicators correlated to battery state of health estimation: a review

Accurate state of health (SOH) estimation plays a fundamental role in battery reliable operation. Recent research has achieved outstanding results on SOH estimation by extracting various health indicators (HIs) and developing advanced algorithms. Several studies have summarized the SOH estimation methods from the modelling perspective. This paper reviews the recent studies focusing on the utilization of different HIs. Including the commonly used charging/discharging curves, impedance, and advanced sensing technologies. Their advantages and limitations are analyzed from implementation and practical application. We also outlook the future research directions of deep learning applications and the requirement of sensing methods

Techno-economic Risk Analysis for Onboard Hydrogen Storage by Quantitative Risk Assessment

There are still safety concerns to use hydrogen as energy storage for commercial applications in a safe, efficient and cost-effective way. This paper is dedicated to discussion on safety improvement of hydrogen storage equipment for onboard applications. It provides a brief review, and then a quantitative risk assessment framework is presented. The procedure to execute the framework is elaborated in accordance with the associated mathematical equations. The thermal effect and high pressure leading to potential tank rupture are discussed. The probability of risk for human fatality is calculated. The calculated values using the case study data by following this framework are compared with the risk acceptance criteria. In the final stage of the analysis, the monetary values associated with human fatality are estimated as the result of risk evaluation. Finally, the future research directions are indicated, which include optimizing the risk in hydrogen storage using techniques such as central composite design, response surface method, machine learning and the numerical simulation; and sensitivity analysis of the solution using some available optimization software

SFINet: Shuffle–and–Fusion Interaction Networks for Wind Power Forecasting

Wind energy is one of the most important renewable energy sources in the world. Accurate wind power prediction is of great significance for achieving reliable and economical power system operation and control. For this purpose, this paper is focused on wind power prediction based on a newly proposed shuffle–and–fusion interaction network (SFINet). First, a channel shuffle is employed to promote the interaction between timing features. Second, an attention block is proposed to fuse the original features and shuffled features to further increase the model’s sequential modeling capability. Finally, the developed shuffle–and–fusion interaction network model is tested using real‐world wind power production data. Based on the results verified, it was proven that the proposed SFINet model can achieve better performance than other baseline methods, and it can be easily implemented in the field without requiring additional hardware and software

Deep learning method for fault detection of wind turbine converter

The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 back-bone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life (RUL). However, this task is challenging due to the diverse ageing mechanisms, various operating conditions, and limited measured signals. Although data-driven methods are perceived as a promising solution, they ignore intrinsic battery physics, leading to compromised accuracy, low efficiency, and low interpretability. In response, this study integrates domain knowledge into deep learning to enhance the RUL prediction performance. We demonstrate accurate RUL prediction using only a single charging curve. First, a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data. The parameters inform a deep neural network (DNN) to predict RUL with high accuracy and efficiency. The trained model is validated under 3 types of batteries working under 7 conditions, considering fully charged and partially charged cases. Using data from one cycle only, the proposed method achieves a root mean squared error (RMSE) of 11.42 cycles and a mean absolute relative error (MARE) of 3.19% on average, which are over 45% and 44% lower compared to the two state-of-the-art data-driven methods, respectively. Besides its accuracy, the proposed method also outperforms existing methods in terms of efficiency, input burden, and robustness. The inherent relationship between the model parameters and the battery degradation mechanism is further revealed, substantiating the intrinsic superiority of the proposed method.</p

Exploiting domain knowledge to reduce data requirements for battery health monitoring

Rechargeable batteries are becoming increasingly significant in decarbonising the world. For their widespread usage, to monitor and predict the battery health status has been essential. Although machine learning has the potential to tackle this issue, considerable degradation tests are required for model training, leading to prohibitive costs and labour. Here, we introduce a novel approach to constructing health monitoring models by fusing battery degradation knowledge with deep learning, using a substantially reduced amount of degradation data. We employ a lightweight and interpretable model to produce synthetic charging curves from highly limited realistic data. Subsequently, a transfer learning technique is implemented to train a convolutional neural network using both types of data and alleviate their gap. By employing only 8 realistic charging curves to develop the model, the method can precisely estimate the maximum and remaining capacities from 300 mV charging segments. The root mean square errors for these estimations are below 12.42 mAh. Additional 50 validation cases confirm that the proposed method can not only reduce the required degradation data but also shorten the input window length. Furthermore, it can be generalised and applied to different battery types under different operating conditions. This work highlights the promise of employing domain expertise to significantly decrease the amount of battery testing required for monitoring battery health