In recent years, there has been a significant increase in braking energy regeneration for hybrid electric vehicles. To improve performance and reduce fuel consumption, a better control strategy composed of braking regeneration and gear shifting is needed. This work presents a braking energy regeneration control strategy for a hybrid electric vehicle (HEV). The mathematical model for the vehicle dynamic system is established, and the objective function of braking energy regeneration is presented based on system analysis. Taking the increased electric energy of a battery as the objective function of the economic downshift law, the multi-island genetic algorithm (MIGA) is used to solve the shifting condition factors corresponding to different deceleration speeds and motor torques and the optimal downshifting speed. The presented control strategy of braking energy regeneration is validated in a typical city cycle form in China, and the results show better energy efficiency.

Intelligent transportation systems (ITSs) present new opportunities for enhanced traffic management by leveraging advanced driving behavior sensors and real-time information exchange via vehicle-based and cloud–vehicle communication technologies. Specifically, onboard sensors can effectively detect whether human-driven vehicles are adhering to traffic management directives. However, the formulation and validation of effective strategies for vehicle implementation rely on accurate driving behavior models and reliable model-based testing; in this paper, we focus on large roundabouts as the research scenario. To address this, we proposed the Three-Stage Cellular Automata (TSCA) model based on empirical observations, dividing the vehicle journey over roundabouts into three stages: entrance, following, and exit. Furthermore, four optimization strategies were developed based on empirical observations and simulation results, using the traffic efficiency, delay time, and dangerous interaction frequency as key evaluation indicators. Numerical tests reveal that dangerous interactions and delays primarily occurred when the roundabout Road Occupancy Rate ($\rho$) ranged from 0.12 to 0.24, during which times the vehicle speed also decreased rapidly. Among the strategies, the Path Selection Based on Road Occupancy Rate Recognition Strategy (Simulation 4) demonstrated the best overall performance, increasing the traffic efficiency by 15.65% while reducing the delay time, dangerous interactions, and frequency by 6.50%, 28.32%, and 38.03%, respectively. Additionally, the Entrance Facility Optimization Strategy (Simulation 1) reduced the delay time by 6.90%. While space-based optimization strategies had a more moderate overall impact, they significantly improved the local traffic efficiency at the roundabout by approximately 25.04%. Our findings hold significant practical value, particularly with the support of onboard sensors, which can effectively detect non-compliance and provide real-time warnings to guide drivers in adhering to the prescribed traffic management strategies.

Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling (T ²) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T ² statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

In complex operational environments, signal ambiguity and false alarms make task allocation for multiple unmanned aerial vehicles (UAVs) challenging. To address this problem, we propose the Bayesian Rolling Information-Theoretic Reconnaissance Action Assignment (RITRA) framework. RITRA integrates Bayesian belief tracking, expected information gain (EIG), and a cooperative criticality model based on Shapley values in a unified receding-horizon strategy. By assessing reconnaissance and intervention utilities for each UAV–hazard pair, RITRA casts mission planning as a dynamic optimization problem. We use an enhanced Hungarian algorithm with idle states, allowing UAVs to remain on standby when no deployment has positive expected value. Ablation results indicate that combining Bayesian tracking, active information collection, and risk-aware execution contributes to robust performance under the evaluated uncertainty conditions. In the 200-run paired Monte Carlo stress test, RITRA achieved a mean mission utility of 9.4362, a normalized mission score of 0.7890, and 0.245 false deployments per run. Against the best-performing baseline, a CBBA-based allocation method, RITRA achieved a mean paired mission-utility gain of 1.4925. On publicly released dynamic MRTA instances augmented with false-alarm uncertainty, RITRA achieved the highest aggregate mission utility, normalized mission score, and mission accuracy among the compared controllers, providing additional evidence of its effectiveness. These results indicate that RITRA supports risk-aware multi-UAV allocation under the evaluated uncertain conditions.