Kinematic model predictive control (MPC) is well known for its simplicity and computational efficiency for path tracking of autonomous vehicles, however, it merely works well at low speed. In addition, earlier studies have demonstrated that tracking accuracy is improved by the feedback of yaw rate, as it improves the system transients. With this in mind, it is expected that the performance of path tracking can be improved by a cascaded controller that utilizes kinematic MPC to determine desired yaw rate rather than steering angle, and uses proportional-integral-derivative (PID) control to follow the reference yaw rate. However, directly combining MPC with PID feedback control of yaw rate results in a controller with poor tracking accuracy. The simulation results show that the cascaded MPC-PID controller has relatively stable but larger error compared to classic kinematic and dynamic MPC. Based on the analysis of vehicle sideslip angle, a novel path tracking control method is proposed, which is designed using a kinematic MPC to handle the disturbances on road curvature, a PID feedback control of yaw rate to reject uncertainties and modeling errors, and a vehicle sideslip angle compensator to correct the kinematic model prediction. The proposed controller performances involving steady-state and transient response, robustness, and computing efficiency were evaluated on Carsim/Matlab joint simulation environment. Furthermore, field experiments were conducted to validate the robustness against sensor disturbances and time lag. The results demonstrate that the developed vehicle sideslip compensator is sufficient to capture steer dynamics, and the developed controller significantly improves the performance of path tracking and follows the desired path very well, ranging from low speed to high speed even at the limits of handling.

This paper reviews the serviceability of hydrous ethanol as a clean, cheap and green renewable substitute fuel for spark ignition engines and discusses the comparative chemical and physical properties of hydrous ethanol and gasoline fuels. The significant differences in the properties of hydrous ethanol and gasoline fuels are sufficient to create a significant change during the combustion phase of engine operation and consequently affect the performance of spark-ignition (SI) engines. The stability of ethanol-gasoline-water blends is also discussed. Furthermore, the effects of hydrous ethanol, and its blends with gasoline fuel on SI engine combustion characteristics, cycle-to-cycle variations, engine performance parameters, and emission characteristics have been highlighted. Higher water solubility in ethanol‑gasoline blends may be obviously useful and suitable; nevertheless, the continuous ability of water to remain soluble in the blend is significantly affected by temperature. Nearly all published engine experimental results showed a significant improvement in combustion characteristics and enhanced engine performance for the use of hydrous ethanol as fuel. Moreover, carbon monoxide and oxides of nitrogen emissions were also significantly decreased. It is also worth pointing out that unburned hydrocarbon and carbon dioxide emissions were also reduced for the use of hydrous ethanol. However, unregulated emissions such as acetaldehyde and formaldehyde were significantly increased.

The energy management system (EMS) of hybridization and electrification plays a pivotal role in improving the stability and cost-effectiveness of future vehicles. Existing efforts mainly concentrate on specific optimization targets, like fuel consumption, without sufficiently taking into account the degradation of on-board power sources. In this context, a novel multi-objective energy management strategy based on deep reinforcement learning is proposed for a hybrid electric vehicle (HEV), explicitly conscious of lithium-ion battery (LIB) wear. To be specific, this paper mainly contributes to three points. Firstly, a non-parametric reward function is introduced, for the first time, into the twin-delayed deep deterministic policy gradient (TD3) strategy, to facilitate the optimality and adaptability of the proposed energy management strategy and to mitigate the effort of parameter tuning. Then, to cope with the problem of state redundancy, state space refinement techniques are included in the proposed strategy. Finally, battery health is incorporated into this multi-objective energy management strategy. The efficacy of this framework is validated, in terms of training efficiency, optimality and adaptability, under various standard driving tests.

Ammonia (NH₃) is regarded as a promising medium of hydrogen storage, due to its large hydrogen storage density, decent performance on safety and moderate storage conditions. On the user side, NH₃ is generally required to decompose into hydrogen for utilization in fuel cells, and therefore it is vital for the NH₃-based hydrogen storage technology development to study NH₃ decomposition processes and improve the decomposition efficiency. Numerical simulation has become a powerful tool for analyzing the NH₃ decomposition processes since it can provide a revealing insight into the heat and mass transfer phenomena and substantial guidance on further improving the decomposition efficiency. This paper reviews the numerical simulations of NH₃ decomposition in various application scenarios, including NH₃ decomposition in microreactors, coupled combustion chemical reactors, solid oxide fuel cells, and membrane reactors. The models of NH₃ decomposition reactions in various scenarios and the heat and mass transport in the reactor are elaborated. The effects of reactor structure and operating conditions on the performance of NH₃ decomposition reactor are analyzed. It can be found that NH₃ decomposition in microchannel reactors is not limited by heat and mass transfer, and NH₃ conversion can be improved by using membrane reactors under the same conditions. Finally, research prospects and opportunities are proposed in terms of model development and reactor performance improvement for NH₃ decomposition.