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<div class="section abstract"><div class="htmlview paragraph">The core idea of advanced eco-driving is to optimize the vehicle’s speed and acceleration profile from the energy point of view using real-time data from the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I). However, the main assumption of most of the existing advanced eco-driving approaches is that vehicles are maintained on a single-lane road that considers only the longitudinal motion of the vehicle. In multi-lane roads, controlling the lateral movement of the vehicle or the dynamic lane-changing along with the longitudinal movement can have a positive effect on traffic flow, travel time, fuel economy, and exhaust emission of the vehicle. This paper presents a bi-level model predictive control strategy for connected and automated hybrid electric vehicles (CAHEVs) to optimize inter-vehicle safety, energy-saving, and emission reduction while considering both the lateral and longitudinal motions of the vehicle. The proposed control strategy consists of two control levels: 1) the calculation and optimization of the power distribution between the internal combustion engine and an electric motor, which is referred to as the low-level control, and 2) the optimization of the vehicle speed profile, which is the high-level control. Both levels are used to control the longitudinal motion of the vehicle. Lateral motion or lane changing decision is controlled in another control layer based on the energy consumption prediction on each lane. The proposed control strategy is evaluated under different driving conditions in a realistic urban traffic simulation environment in SUMO. Simulation results show a 6.18 % reduction in the fuel economy while the state of charge of the battery maintained in the standard range and 5.94%, 5.01%, and 5.09% decrease in hydrocarbon (HC), carbon monoxide (CO), nitrogen oxides (NOx) respectively compared to the bi-level MPC-based controller without lane-changing approach.</div></div>

Transition metals (TMs) are being investigated as electrodes for pseudocapacitors, where an oxide layer is necessary to allow for rapid redox reactions. In this work, we utilized an in situ, rapid, binder-free, and green method for the fast fabrication of pseudocapacitor electrodes called ultrashort laser pulses for in situ nanostructure generation (ULPING) to form oxide layers on a titanium sheet. By utilizing this fabrication technique on a titanium sheet, a specific areal capacitance of 0.3579 mF cm −2 was achieved at a current density of 0.25 mA cm −2 . However, the laser fabrication parameters were selected experimentally and resulted in low performance of pseudocapacitors. Therefore, one of the main objectives of this study was to find the optimal laser fabrication parameters to achieve the highest specific areal capacitance. A large dataset was generated to find the relationship between the laser fabrication parameters and the electrochemical behavior performance (impedance and specific areal capacitance) of the fabricated electrodes by using an artificial neural network (ANN). We used an optimization algorithm (simulated annealing-SA) to overlook the trained ANN model as a black box and try to maximize the objective function, which in our case is a specific capacitance value, to find the most optimal laser fabrication parameters. Using SA, optimal laser fabrication parameters were found, which increased the specific areal capacitance to 0.9999 mF cm −2 at a current density of 0.25 mA cm −2 . The results demonstrated that the conducted study has the potential to introduce effective techniques for utilizing ULPING to produce nanoscale structures on TMs. These structures have the potential to be employed as electrodes in pseudocapacitors. Additionally, the research underscores the significance of employing data-driven approaches in electrode design procedures.