A proper battery management system (BMS) plays a vital role in ensuring the safety and reliability of electric vehicles (EVs) and other electronic products. Accurate State-of-Health (SOH) estimation of Lithium-ion (Li-ion) batteries is a key factor in a BMS. It is difficult to determine SOH because of the complexity of the electrochemical reactions within the battery. To improve the accuracy of SOH estimation, a dynamic spatial-temporal attention-based gated recurrent unit (DSTA-GRU) model is proposed in this paper. First, we extract six features from the battery's charging and discharging processes that can reflect the aging degree of the battery to some extent. Second, this paper proposes a model to combine spatial attention and temporal attention that can not only consider the effects of states at different time step on the results, but also consider the effects of different features in the space domain. Third, the proposed model is trained and tested on NASA battery datasets and compared with other conventional models. Experiments carried on these data sets demonstrate that our model achieves higher accuracy than other conventional models.

An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author’s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long–Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others.

A low power wide area network (LPWAN) is becoming a popular technology since more and more industrial Internet of things (IoT) applications rely on it. It is able to provide long distance wireless communication with great power saving. Given the fact that an LPWAN covers a wide area where all end nodes communicate directly to a few gateways, a large number of devices have to share the gateway. In this situation, chances are many collisions could occur, leading to waste of limited wireless resources. However, many factors affecting the number of collisions that cannot be solved by traditional time series analysis algorithms. Therefore, deep learning methods can be applied here to predict collisions by analyzing these factors in an LPWAN system. In this paper, we propose long short-term memory extended Kalman filter (LSTMEKF) model for collision prediction in the LPWAN in terms of the temporal correlation which can improve the LSTM performance. The efficacies of our models are demonstrated on the data set simulated by LoRaSim.

For most machine learning and deep learning models, the selection of hyperparameters has a significant impact on the performance of the model. Therefore, deep learning and data analysis experts have to spend a lot of time on hyperparameter tuning when building a model for accomplishing a task. Although there are many algorithms used to solve hyperparameter optimization (HPO), these methods require the results of the actual trials at each epoch to help perform the search. To reduce the number of trials, model-based reinforcement learning adopts multilayer perceptron (MLP) to capture the relationship between hyperparameter settings and model performance. However, MLP needs to be carefully designed because there is a risk of overfitting. Thus, we propose a random forest-enhanced proximal policy optimization (RFEPPO) reinforcement learning algorithm to solve the HPO problem. In addition, reinforcement learning as a solution to HPO will encounter the sparse reward problem, eventually leading to slow convergence. To address this problem, we employ the intrinsic reward, which introduces the prediction error as the reward signal. Experiments carried on nine tabular datasets and two image classification datasets demonstrate the effectiveness of our model.

Hybrid energy storage systems for hybrid electric vehicles (HEVs) consisting of multiple complementary energy sources are becoming increasingly popular as they reduce the risk of running out of electricity and increase the overall lifetime of the battery. However, designing an efficient power split optimization algorithm for HEVs is a challenging task due to their complex structure. Thus, in this paper, we propose a model that jointly learns the optimal power split for a battery/ultracapacitor/fuel cell HEV. Concerning the mechanical system of the HEV, two propulsion machines with complementary operation characteristics are employed to achieve higher efficiency. Additionally, to train and evaluate the model, standard driving cycles and real driving cycles are employed as input to the mechanical system. Then, given the inputs, a temporal attention long short-term memory model predicts the next time step velocity, and through that velocity, the predicted load power and its corresponding optimal power split is computed by a soft actor–critic deep reinforcement learning model whose training phase is aided by shaped reward functions. In contrast to global optimization techniques, the local velocity and load power prediction without future knowledge of the driving cycle is a step toward real-time optimal energy management. The experimental results show that the proposed method is robust to different initial states of charge values, better allocates the power to the energy sources and thus better manages the state of charge of the battery and the ultracapacitor. Additionally, the use of two motors significantly increases the efficiency of the system, and the prediction step is shown to be a reliable way to plan the HESS power split in advance.

Named entity recognition (NER) is one of the widely studied natural language processing tasks in recent years. Conventional solutions treat the NER as a sequence labeling problem, but these approaches cannot handle nested NER. This is due to the fact that nested NER refers to the case where one entity contains another entity and it is not feasible to tag each token with a single tag. The pyramid model stacksLflat NER layers for prediction, which subtly enumerates all spans with length less than or equal toL. However, the original model introduces a block consisting of a convolutional layer and a bidirectional long short-term memory (Bi-LSTM) layer as the decoder, which does not consider the dependency between adjacent inputs and the Bi-LSTM cannot perform parallel computation on sequential inputs. For the purpose of improving performance and reducing the forward computation, we propose a Multi-Head Adjacent Attention-based Pyramid Layered model. In addition, when constructing a pyramid structure for span representation, the information of the intermediate words has more proportion than words on the two sides. To address this imbalance in the span representation, we fuse the output of the attention layer with the features of head and tail words when doing classification. We conducted experiments on nested NER datasets such as GENIA, SciERC, and ADE to validate the effectiveness of our proposed model.