Cabin heating requirement and engine efficiency degradation in cold weather lead to considerable increase in fuel consumption of hybrid electric vehicles (HEVs). This article focuses on the development of a real-time control framework for integrated power and thermal management (i-PTM) of a connected HEV through incorporating vehicle speed preview informed by traffic connectivity. To evaluate the ceiling of the fuel economy improvement via i-PTM, forward dynamic programming (FDP) is adopted as a benchmark study to find the global optimization solution and then implemented into a centralized model predictive controller (CMPC). Regarding the slow response associated with the coupled electric-thermal dynamics in the HEV, a two-layer hierarchical MPC (HMPC) framework is developed, which exploits vehicle speed preview predictions over short and long prediction horizons to optimize fuel consumption while satisfying power and cabin heating demand. The simulation results show that the proposed HMPC provides near-optimal solutions in reference to the benchmark while reduces up to 79% computation burden compared with CMPC. Additionally, compared to a baseline power management (PM) controller, up to 5.46% improvement on fuel economy can be achieved by HMPC for congested driving scenarios. Finally, the real-time feasibility of the HMPC is assessed in a dSPACE rapid prototyping system.

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output (MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control (ILC) scheme based on the zeroing neural networks (ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer (IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise, an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

As environmental regulations become stricter, the advantages of pure electric vehicles over fuel vehicles are becoming more and more significant. Due to the uncertainty of the actual operating conditions of the vehicle, accurate estimation of the state-of-charge (SOC) of the power battery under multi-temperature scenarios plays an important role in guaranteeing the safety, economy, and reliability of electric vehicles. In this paper, a SOC estimation method based on the fusion of convolutional neural network-transformer (CNN-Transformer) and square root unscented Kalman filter (SRUKF) for lithium-ion batteries in low-temperature scenarios is proposed. First, the CNN-Transformer base model is established. Then, the SRUKF algorithm is used to update the state of the Coulomb counting method results based on the base model results. Finally, ensemble learning theory is applied to estimate SOC in multi-temperature scenarios. Data is obtained from laboratory conditions at −20 °C, −7 °C, and 0 °C. The experimental results show that the SOC estimation method proposed in this study is stable in terms of the root mean square error (RMSE) being between 2.69% and 4.22%. The proposed base model is also compared with the long short-term memory (LSTM) network and gated recurrent unit (GRU) network to demonstrate its relative advantages.

Intelligent transportation creates opportunities for optimizing fuel cell hybrid electric vehicles (FCHEVs) energy. However, accurately predicting speeds is challenging for energy management. To address this problem, a model predictive control strategy considering (Con-MPC) vehicle speed inaccuracy is proposed. First, a Gaussian process (GP) is used to predict the vehicle speed with uncertainty. Second, under the MPC framework, the inaccuracy prediction is processed using a hierarchical structure. In the upper layer, the forward dynamic programming (FDP) is used to incorporate long-term inaccurate predictive information for solving the state of charge (SoC). The SoC is served as a reference and then transmitted to the lower layer at a frequency. In the lower layer, the Pontryagin minimum principle (PMP) is used to solve the optimization problem based on SoC guidance. Finally, the real-time implementation is evaluated in a dSPACE rapid prototyping system. The simulation results demonstrate that the Con-MPC strategy can enhance fuel economy by 1.7%-5.7% when compared to the basic MPC (Bas-MPC). Meanwhile, the improvement margin between Con-MPC and the benchmark is only 0.4%-10.93%. Furthermore, compared to the strategy that does not consider inaccurate vehicle speed, this strategy improves fuel economy by 1.11%.

Trajectory prediction for traffic participants is a critical task for autonomous vehicles. The long-term trajectory prediction is challenging due to limited data and the dynamic characteristics of traffic participants. This paper presents an innovative interactive multiple model algorithm considering inter-vehicle interaction and driving behavior for the traffic participant's short-term and long-term trajectory prediction. The field experiment is conducted to acquire the human driver data, which is then preprocessed and analyzed with statistical methods. The clustering result of the critical gap is used to include the interactions between them, on which the gap satisfaction probability function is designed and aimed at describing the satisfaction probability of the current lane. The driving behavior is another promising candidate to improve the long-term prediction accuracy. The clustering results of the lane change duration are used to establish the lane changing models considering the driving behavior, the driving behavior probability function is designed based on the probability of each model. Then the two functions are incorporated into the adaptive transition probability matrix, where the quantitative probabilistic relations between the gap satisfaction probability and the driving behavior probability are established. The adaptive transition probability matrix is then used in the interactive multiple model algorithm. Based on the improved interactive multiple model, the personalized trajectory prediction for the traffic participant is obtained. The effectiveness of the framework is validated by simulation and field experiment.