Developing a qualified eco-driving strategy for plug-in hybrid electric vehicles (PHEVs) remains challenging in urban traffic scenarios, due to the comprehensive influence of random traffic flow and signal lights. To solve it, this study develops a hierarchical eco-driving framework integrating lane-changing decisions through a virtual force-based trigger mechanism and an adaptive energy management strategy that dynamically adjusts the equivalence factor. Firstly, considering the interference of neighbor vehicles, the velocity planning layer generates the economic velocity trajectories using dynamic programming in short discrete intervals, enabling the ego-vehicle to navigate through signal intersections smoothly. In addition, the lane changing is properly conducted according to the state of the ego-vehicle, traffic flow, and signal light. In the energy management layer, an adaptive equivalent fuel consumption minimization strategy accounting for trip distance, initial state of charge, and remaining electric mileage is developed to ensure a reasonable power split based on the reference velocity. Simulation and hardware-in-the-loop experimental results indicate that the developed strategy improves the traffic efficiency by 1.68%, while reducing energy consumption by 9.81% and 31.71%, compared with Pontryagin's minimum principle and nonlinear model predictive control based methods.

Engineering vehicles operate under high torque, high load, and complex environmental conditions, facing numerous technical challenges. Particularly during the starting phase, the significant slippage of clutch discs significantly impacts the precision of clutch torque control. Therefore, to achieve adaptive start-up control for AMT engineering vehicles, an adaptive control method combining linear quadratic regulator (LQR) and deep neural network was proposed for the AMT start-up process. At the upper level of the control strategy, a constant engine speed strategy was formulated based on different starting intentions, and the LQR was used to obtain the reference speed corresponding to the reference torque of the clutch under different environments. With considering the complexity of the operating environment, a certain range of perturbations was introduced into the vehicle dynamics model to generate a series of reference rotational speed trajectories as the training data set for the deep neural network, and a robust data model offline was obtained. At the lower level of the control strategy, a clutch friction factor adaptive controller was designed to estimate the clutch friction factor in real time. Finally, the effectiveness of the adaptive start control method for engineering vehicles equipped with AMT was verified by simulation tests. The results show that the proposed method has good starting performance under the condition of unknown friction coefficients and can adapt to different starting intentions and driving environments. Compared with the PID controller which does not depend on the mechanism model, it has higher adaptive ability and robustness.

With the wide application of the LFP lithium-ion batteries, more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging monitoring. In recent years, long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging conditions. Thus, it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging process. To address it, a novel transfer learning strategy combined with the cycle life prediction technology is presented in this paper. Specifically, a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life prediction. The transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging process. Then, taking the predicted cycle life as its prior information, the Bayesian model migration technology is employed to predict the aging trajectory accurately, and the uncertainty of the aging trajectory is quantified. Two batches of the battery datasets are used for performance evaluation and comparison with two benchmarks. It is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available (first 30%).