APPRAISAL OF TAKAGI–SUGENO TYPE NEURO-FUZZY NETWORK SYSTEM WITH A MODIFIED DIFFERENTIAL EVOLUTION METHOD TO PREDICT NONLINEAR WHEEL DYNAMICS CAUSED BY ROAD IRREGULARITIES

Wheel dynamics play a substantial role in traversing and controlling the vehicle, braking, ride comfort, steering, and maneuvering. The transient wheel dynamics are difficult to be ascertained in tire–obstacle contact condition. To this end, a single-wheel testing rig was utilized in a soil bin facility for provision of a controlled experimental medium. Differently manufactured obstacles (triangular and Gaussian shaped geometries) were employed at different obstacle heights, wheel loads, tire slippages and forward speeds to measure the forces induced at vertical and horizontal directions at tire–obstacle contact interface. A new Takagi–Sugeno type neuro-fuzzy network system with a modified Differential Evolution (DE) method was used to model wheel dynamics caused by road irregularities. DE is a robust optimization technique for complex and stochastic algorithms with ever expanding applications in real-world problems. It was revealed that the new proposed model can be served as a functional alternative to classical modeling tools for the prediction of nonlinear wheel dynamics

The influence of different fuels and injection methods of RCCI and DCI in hybrid ICE-Battery vehicle performance

The incorporation of two recent technologies of using the dual-fuel reactivity controlled compression ignition (RCCI) combustion engine within the hybrid electric vehicle (HEV) is practiced to show how this combination can reduce the emission and enhance the thermal efficiency of the system. In particular, the heat transfers from the engine wall and the exhaust heat flow from the engine under different injection modes and fuels are of interest. The study in terms of thermal performance, fuel consumption, and battery state of charge (SOC) focuses mainly on the comparison between three cases of D100 (pure diesel) as the reference (baseline conventional direct pure diesel injection) case, D80H20 (80% diesel, 20% hydrogen) direct co-injection (DCI), and D80H20 RCCI (port + direct dual fuel injection). The NOx emission and engine power in the simulated drive cycle are investigated where the battery capacity and D50M50 (direct co-injection of 50% diesel with 50% methanol) are the additional cases. The findings indicate that the Battery SOC is preserved in better condition when the RCCI mode engine is coupled in the hybrid vehicle. The piston wall heat flux for D80H20 in DCI increases by 45.2% and for the RCCI increases by 60.5% compared to baseline diesel injection mode. It is also proved that the HEV releases considerably lower NOx compared to DCI and more NOx compared to D100 and D50M50

New insight into air/spray boundary interaction for diesel and biodiesel fuels under different fuel temperatures

The liquid fuel breakup mechanism in spray injection with ambient air for diesel and biodiesel at different fuel temperatures is studied numerically. We find that biodiesel fuel type injection with low fuel temperature induces more air entrainment volume to the boundary of the spray than diesel fuel injection and higher fuel temperature. Meanwhile, the normalized parcel density for biodiesel is 12% larger than that of diesel and peaks at a shorter distance along the spray line from the injection point (42 vs. 46 mm). Biodiesel fuel demonstrates a maximum 0.395 mg/s of air mass flow while diesel max mass flow is 0.279 mg/s. As a result, the air entrainment volume of biodiesel to the moving spray area at 1.4 ms reaches 3723.98 mm3 while for diesel the amount is 3151.27 mm3. However, the absorbed y-direction air velocity into the spray core for diesel fuel is dominant. The results give new insights into air exchange to spray boundary in the near nozzle and spray tip area: towards the tip of spray the air pushout is remarkable. Higher fuel temperature leads to slightly lower air exchange flow and entrainment (5.2%), cone angle reduction from 300 to 325 K fuel temperature, and increased surface area:volume ratio for diesel

RISE-Based Integrated Motion Control of Autonomous Ground Vehicles With Asymptotic Prescribed Performance

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim-Simulink simulation

Data-driven modeling of energy-exergy in marine engines by supervised ANNs based on fuel type and injection angle classification

The application of artificial neural networks with the involvement of a modified homogeneity factor to predict exergetic terms from combustive and/or mixing dynamics in a marine engine is considered in this study. This is a significant step since the mathematical formulation of exergy in combustion is complicated and even unconvincing due to the turbulent and highly nonlinear nature of the combustion process. The computational simulations are carried out on a marine CI (compression ignition) engine and the respective data per different fuel types that are used for thermodynamic exergetic computations as well as energetic simulations. A new parameter namely the modified homogeneity factor derived by an artificial neural network (ANN) is considered for the mixing dynamics, i.e. as an input parameter for the availability and irreversibility predictions. This parameter is based on the standard deviation from an ideal air-fuel mixture formed within the combustion chamber of the marine engine. Furthermore, spray and injection quantities along with the combustion process and its heat transfer parameters are served to predict the exergetic terms for two study cases: (a) fuel type and (b) injection orientation. It is shown that using data analytics that consists of neural networks can provide an adequate approach in diesel engines for improving energy efficiency and reducing emissions