Energy management is an enabling technology for increasing the economy of fuel cell/battery hybrid electric vehicles. Existing efforts mostly focus on optimization of a certain control objective (e.g., hydrogen consumption), without sufficiently considering the implications for on-board power sources degradation. To address this deficiency, this article proposes a cost-optimal, predictive energy management strategy, with an explicit consciousness of degradation of both fuel cell and battery systems. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, a model predictive control framework, for the first time, is established to minimize the total running cost of a fuel cell/battery hybrid electric bus, inclusive of hydrogen cost and costs caused by fuel cell and battery degradation. The efficacy of this framework is evaluated, accounting for various sizes of prediction horizon and prediction uncertainties. Second, the effects of driving and pricing scenarios on the optimized vehicular economy are explored.

Significant progress has been made in transportation electrification in recent years. As the main energy storage device, lithium-ion batteries are one of the key components that need to be properly managed. The remaining useful life, which represents battery health, has attracted increasing attention. Because accurate and robust predictions provide important information for predictive maintenance and cascade utilization. This paper proposes a novel method to predict remaining useful life based on the optimized health indicators and online model correction with transfer learning. Gaussian process regression is used to optimize the threshold for health indicators to determine the end of life, and a usefulness evaluation strategy is proposed to assess the health indicators. Then, a combination of transfer learning and gated recurrent neural network is designed to predict the remaining useful life based on the optimized health indicators directly, which can promote online applications. The prediction model initially trained based on a relevant battery is further fine-tuned according to the early degradation cycling data of the test battery to provide accurate predictions. Moreover, a self-correction strategy is proposed to retrain the regression models so that the models can gradually reach the optimal prediction performance during the operating cycles, which could not be achieved by traditional methods. The recommended input sequence lengths for potential applications are discussed. The method is verified by experiments of a batch of batteries under fast charging conditions, and the results show that, after fine-tuning, the proposed method predicts remaining useful life with an error of fewer than 5 cycles.

In a mixed traffic environment of human and autonomous driving, it is crucial for an autonomous vehicle to predict the lane change intentions and trajectories of vehicles that pose a risk to it. However, due to the uncertainty of human intentions, accurately predicting lane change intentions and trajectories is a great challenge. Therefore, this paper aims to establish the connection between intentions and trajectories and propose a dual Transformer model for the target vehicle. The dual Transformer model contains a lane change intention prediction model and a trajectory prediction model. The lane change intention prediction model is able to extract social correlations in terms of vehicle states and outputs an intention probability vector. The trajectory prediction model fuses the intention probability vector, which enables it to obtain prior knowledge. For the intention prediction model, the accuracy can be improved by designing the multi-head attention. For the trajectory prediction model, the performance can be optimized by incorporating intention probability vectors and adding the LSTM. Verified on NGSIM and highD datasets, the experimental results show that this model has encouraging accuracy. Compared with the model without intention probability vectors, the impact of the model on NGSIM dataset and highD dataset in RMSE is improved by 57.27% and 58.70% respectively. Compared with two existed models, evaluation metrics of the intention prediction can be improved by 7.40-10.09% on NGSIM dataset and 2.17-2.69% on highD dataset within advanced prediction time 1s. This method provides the insights for designing advanced perceptual systems for autonomous vehicles.

State of health (SOH) is essential for battery management, timely maintenance, and safety incident avoidance. For specific applications, a variety of SOH estimation methods have been proposed. However, it is often difficult to apply these methods to other applications. In this paper, a novel feature extraction method is proposed to extract health indicators (HIs) from general discharging conditions. A voltage partition strategy is used to obtain the discharge capacity differences of two cycles (ΔQ(V)) from non-monotonic or pulse discharge voltage curve, and a filtering strategy is employed to obtain smooth voltage curves under dynamic discharging conditions. The standard deviations of the discharge capacity curve and ΔQ(V) are selected as HIs and are verified to have strong correlations to battery capacity under different data sets for three types of batteries. By using these HIs as input features, typical data-driven methods including linear regression, support vector machine, relevance vector machine, and Gaussian process regression (GPR) are constructed to predict battery SOH. The estimation results of these methods are compared under different operating conditions for the three types of batteries. Good estimation accuracy is achieved for all these methods. Among them, the GPR has the best performance, and its MAE and RMSE are lower than 1% and 1.3%, respectively.