Design of and Research into a Multiple-Fuzzy PID Suspension Control System Based on Road Recognition

By analyzing the shortcomings of the traditional fuzzy PID(Abbreviation for Proportional, Integral and Differential) control system (FPID), a multiple fuzzy PID suspension control system based on road recognition (MFRR) is proposed. Compared with the traditional fuzzy PID control system, the multiple fuzzy control system can identify the road grade and take changes in road conditions into account. Based on changes in road conditions and the variable universe and secondary adjustment of the control parameters of the PID controller were carried out, which makes up for the disadvantage of having too many single input parameters in the traditional fuzzy PID control system. A two degree of freedom 1/4 vehicle model was established. Based on the suspension dynamic parameters, a road elevation algorithm was designed. Road grade recognition was carried out based on a BP neural network algorithm. The experimental results showed that the sprung mass acceleration (SMA) of the MFRR was much smaller than that of the passive suspension system (PS) and the FPID on single-bump and sinusoidal roads. The SMA, suspension dynamic deflection (SDD) and tire dynamic load (TDL) of the MFRR were significantly less than those of the other two systems on roads of each grade. Taking grade B road as an example, compared with the PS, the reductions in the SMA, SDD and TDL of the MFRR were 40.01%, 34.28% and 32.64%, respectively. The control system showed a good control performance.

Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics

This paper investigates the theoretical Cram´er-Rao bounds on estimation accuracy of longitudinal vehicle dynamics parameters. This analysis is motivated by the value of parameter estimation in various applications, including chassis model validation and active safety. Relevant literature addresses this demand through algorithms capable of estimating chassis parameters for diverse conditions. While the implementation of such algorithms has been studied, the question of fundamental limits on their accuracy remains largely unexplored. We address this question by presenting two contributions. First, this paper presents theoretical findings which reveal the prevailing effects underpinning vehicle chassis parameter identifiability. We then validate these findings with data from on-road experiments. Our results demonstrate, among a variety of effects, the strong relevance of road grade variability in determining parameter identifiability from a drive cycle. These findings can motivate improved experimental designs in the future.

Modeling and Integrated Optimization of Power Split and Exhaust Thermal Management on Diesel Hybrid Electric Vehicles

To simultaneously achieve high fuel efficiency and low emissions in a diesel hybrid electric vehicle (DHEV), it is necessary to optimize not only power split but also exhaust thermal management for emission aftertreatment systems. However, how to coordinate the power split and the exhaust thermal management to balance fuel economy improvement and emissions reduction remains a formidable challenge. In this paper, a hierarchical model predictive control (MPC) framework is proposed to coordinate the power split and the exhaust thermal management. The method consists of two parts: a fuel and thermal optimized controller (FTOC) combining the rule-based and the optimization-based methods for power split simultaneously considering fuel consumption and exhaust temperature, and a fuel post-injection thermal controller (FPTC) for exhaust thermal management with a separate fuel injection system added to the exhaust pipe. Additionally, preview information about the road grade is introduced to improve the power split by a fuel and thermal on slope forecast optimized controller (FTSFOC). Simulation results show that the hierarchical method (FTOC + FPTC) can reach the optimal exhaust temperature nearly 40 s earlier, and its total fuel consumption is also reduced by 8.9%, as compared to the sequential method under a world light test cycle (WLTC) driving cycle. Moreover, the total fuel consumption of the FTSFOC is reduced by 5.2%, as compared to the fuel and thermal on sensor-information optimized controller (FTSOC) working with real-time road grade information.

Influence of the Cyclist’s Characteristics on the Optimal Pacing Strategy for an Ascending Road

Pacing strategies are used in cycling to optimize the power delivered by the cyclist during a race. Gains in race time have been obtained when using these strategies compared to self-paced approaches. For this reason, this study is focused on revising the effect that the variation of the cyclist’s parameters has on the pacing strategy and its results. A numeric method was used to propose pacing strategies for a cyclist riding on an ascending 3.7 km route with a constant 6.26% road grade. The method was validated and then implemented to study the effect of aerobic and anaerobic power delivery capacity, mass, and drag area on the pacing strategies and their corresponding estimated race times. The results showed that modifying 1% of the aerobic capacity or cyclist mass value led to a change of 1% on the race time. Modifying 1% the anaerobic capacity and the drag area led to changes of 0.03% and 0.02% on the race time, respectively. These results are strongly dependent on the route characteristics. It was concluded that for the studied route (constantly ascending), the variation of the cyclist’s aerobic capacity influences the pacing strategy (i.e., the power delivery over the distance). The anaerobic capacity and mass of the cyclist also influence the pacing strategy to a lesser extent.

Characteristics of Crashes Caused by Distracted Driving on Rural and Suburban Roadways in Jordan

The objective of this study was to identify the most salient driver faults that cause crashes on some Jordanian rural and suburban roadway segments, to examine crashes with distracted driving as the driver’s fault, and to investigate the differences between crashes caused by distracted driving. Data for more than 10,200 crashes on nine roadway segments (five rural and four suburban) were accessed from the relevant government agency, but only n = 2472 were used for analysis after controlling for crashes specified as being caused by drivers’ distracted driving. IBM SPSS version 22 was used to perform descriptive analysis and independent samples’ t-tests. The results revealed that distracted driving was the second most common driver fault to cause crashes and the second main cause of fatalities and injuries on both rural and suburban roadways. Distracted driving on rural highways appears to be more fatal, whereas it caused more crashes with severe injuries on suburban roads. The variables at junction, road grade, number of lanes, weather condition, crash type, and number of vehicles involved were found to be statistically significant but with a small effect size. The following categories showed high percentages of distracted driving crashes on rural and suburban roadways: males, drivers 25–39 years old, non-holidays, weekdays, tangent sections, two-way divided roads, not at junction, level roads, two-lane roads, clear weather, dry surface, daylight, and automobile vehicles showed high percentages of distracted driving crashes on rural and suburban roadways. Differences between crashes on rural and suburban roadways caused by distracted driving were found to be small.