Aerodynamic disturbance on vehicle’s dynamic parameters

This research paper analysed the influence of aerodynamic disturbance on vehicle’s dynamic parameters. The vehicle dynamics were formulated from the Newton’s Second Law for modelling the vehicle. The vehicle was built using rigid body frames, mass and multi-body signal blocks of MapleSim2015 platform. Several vehicle masses were used to produce different vehicle dynamics with respect to the same aerodynamic drag and input force. Our analyses have shown that the mass of each vehicle is inversely proportional to the aerodynamic drag applied to it. At a given set-point of 25 ms-1, the vehicle tracked the given speed exactly in the absence of the drag. However, for the lag in displacement, speed and acceleration were found as 25 m, 17 ms-1 and 0.3 ms-2, respectively in the presence of drag with an average jerk of 45 ms-3. This has provided an interesting insight on the effects of drag on the moving vehicle. The proposed vehicle was subjected to the same control strategy to form a two-vehicle, look-ahead convoy as in conventional type. Improvements in the inter-vehicular spacing of 1.7 m, proper speed track, low acceleration(1.05 ms-2) and a suitable jerk of 0.04 ms-3 were achieved over the entire period (160 s) as compared to conventional vehicle. The proposed vehicle model scores higher accuracy than conventional vehicle on two-vehicle, look-ahead model and it has shown that the proposed model is more comfortable than the conventional one

AERODYNAMIC DISTURBANCE ON VEHICLE’S DYNAMIC PARAMETERS

This research paper analysed the influence of aerodynamic disturbance on vehicle’s dynamic parameters. The vehicle dynamics were formulated from the Newton’s Second Law for modelling the vehicle. The vehicle was built using rigid body frames, mass and multi-body signal blocks of MapleSim2015 platform. Several vehicle masses were used to produce different vehicle dynamics with respect to the same aerodynamic drag and input force. Our analyses have shown that the mass of each vehicle is inversely proportional to the aerodynamic drag applied to it. At a given set-point of 25 ms-1 , the vehicle tracked the given speed exactly in the absence of the drag. However, for the lag in displacement, speed and acceleration were found as 25 m, 17 ms-1 and 0.3 ms-2 , respectively in the presence of drag with an average jerk of 45 ms-3 . This has provided an interesting insight on the effects of drag on the moving vehicle. The proposed vehicle was subjected to the same control strategy to form a twovehicle, look-ahead convoy as in conventional type. Improvements in the intervehicular spacing of 1.7 m, proper speed track, low acceleration(1.05 ms-2) and a suitable jerk of 0.04 ms-3 were achieved over the entire period (160 s) as compared to conventional vehicle. The proposed vehicle model scores higher accuracy than conventional vehicle on two-vehicle, look-ahead model and it has shown that the proposed model is more comfortable than the conventional one

Improved information flow topology for vehicle convoy control

A vehicle convoy is a string of inter-connected vehicles moving together for mutual support, minimizing traffic congestion, facilitating people safety, ensuring string stability and maximizing ride comfort. There exists a trade-off among the convoy's performance indices, which is inherent in any existing vehicle convoy. The use of unrealistic information flow topology (IFT) in vehicle convoy control, generally affects the overall performance of the convoy, due to the undesired changes in dynamic parameters (relative position, speed, acceleration and jerk) experienced by the following vehicle. This thesis proposes an improved information flow topology for vehicle convoy control. The improved topology is of the two-vehicle look-ahead and rear-vehicle control that aimed to cut-off the trade-off with a more robust control structure, which can handle constraints, wider range of control regions and provide acceptable performance simultaneously. The proposed improved topology has been designed in three sections. The first section explores the single vehicle's dynamic equations describing the derived internal and external disturbances modeled together as a unit. In the second section, the vehicle model is then integrated into the control strategy of the improved topology in order to improve the performance of the convoy to two look-ahead and rear. The changes in parameters of the improved convoy topology are compared through simulation with the most widely used conventional convoy topologies of one-vehicle look-ahead and that of the most human-driver like (the two-vehicle look-ahead) convoy topology. The results showed that the proposed convoy control topology has an improved performance with an increase in the intervehicular spacing by 19.45% and 18.20% reduction in acceleration by 20.28% and 15.17% reduction in jerk by 25.09% and 6.25% as against the one-look-ahead and twolook- ahead respectively. Finally, a model predictive control (MPC) system was designed and combined with the improved convoy topology to strictly control the following vehicle. The MPC serves the purpose of handling constraints, providing smoother and satisfactory responses and providing ride comfort with no trade-off in terms of performance or stability. The performance of the proposed MPC based improved convoy topology was then investigated via simulation and the results were compared with the previously improved convoy topology without MPC. The improved convoy topology with MPC provides safer inter-vehicular spacing by 13.86% refined the steady speed to maneuvering speed, provided reduction in acceleration by 32.11% and a huge achievement was recorded in reduction in jerk by 55.12% as against that without MPC. This shows that the MPC based improved convoy control topology gave enough spacing for any uncertain application of brake by the two look-ahead or further acceleration from the rear-vehicle. Similarly, manoeuvering speed was seen to ensure safety ahead and rear, ride comfort was achieved due to the low acceleration and jerk of the following vehicle. The controlling vehicle responded to changes, hence good handling was achieved

Development of predictive energy management strategies for hybrid electric vehicles

2017 Fall.Includes bibliographical references.Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into the impact of real-world prediction error on FE improvements, and whether near-term technologies can be utilized to improve FE. This study seeks to research the effect of prediction error on FE. First, a speed prediction method is developed, and trained with real-world driving data gathered only from the subject vehicle (a local data collection method). This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a high-fidelity model of the FE of a Toyota Prius. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement. Results demonstrate that 60-90 second predictions resulted in the highest FE improvement over the baseline, achieving up to a 4.8% FE increase. A second speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication was developed to understand if incorporating near-term technologies could be utilized to further improve prediction fidelity. This prediction method produced lower variation in speed prediction error, and was able to realize a larger FE improvement over the local prediction method for longer prediction durations, achieving up to 6% FE improvement. This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability, as up to 85% of the FE benefit of perfect speed prediction was achieved with the proposed prediction methods

Battery Aging-Aware Online Optimal Control: An Energy Management System for Hybrid Electric Vehicles Supported by a Bio-Inspired Velocity Prediction

In this manuscript, we address the problem of online optimal control for torque splitting in hybrid electric vehicles that minimises fuel consumption and preserves battery life. We divide the problem into the prediction of the future velocity profile (i.e. driver intention estimation) and the online optimal control of the hybrid powertrain following a Model Predictive Control (MPC) scheme. The velocity prediction is based on a bio-inspired driver model, which is compared on various datasets with two alternative prediction algorithms adopted in the literature. The online optimal control problem addresses both the fuel consumption and the preservation of the battery life using an equivalent cost given the estimated speed profile (i.e. guaranteeing the desired performance). The battery degradation is evaluated by means of a state-of-the-art electrochemical model. Both the predictor and the Energy Management System (EMS) are evaluated in simulation using real driving data divided into 30 driving cycles from 10 drivers characterised by different driving styles. A comparison of the EMS performances is carried out on two different benchmarks based on an offline optimization, in one case on the entire dataset length and in the second on an ideal prediction using two different receding horizon lengths. The proposed online system, composed of the velocity prediction algorithm and the optimal control MPC scheme, shows comparable performances with the previous ideal benchmarks in terms of fuel consumption and battery life preservation. The simulations show that the online approach is able to significantly reduce the capacity loss of the battery, while preserving the fuel saving performances

Intelligent Transportation Systems, Hybrid Electric Vehicles, Powertrain Control, Cooperative Adaptive Cruise Control, Model Predictive Control

Information obtainable from Intelligent Transportation Systems (ITS) provides the possibility of improving the safety and efficiency of vehicles at different levels. In particular, such information has the potential to be utilized for prediction of driving conditions and traffic flow, which allows us to improve the performance of the control systems in different vehicular applications, such as Hybrid Electric Vehicles (HEVs) powertrain control and Cooperative Adaptive Cruise Control (CACC). In the first part of this work, we study the design of an MPC controller for a Cooperative Adaptive Cruise Control (CACC) system, which is an automated application that provides the drivers with extra benefits, such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as interfering vehicles cutting-into the CACC platoons or hard braking by leading cars. To address this problem, we first propose a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme. Then, the predicted trajectory of each vehicle in the adjacent lanes is used to estimate the probability of that vehicle cutting-into the CACC platoon. To consider the calculated probability in control system decisions, a Stochastic Model Predictive Controller (SMPC) needs to be designed which incorporates this cut-in probability, and enhances the reaction against the detected dangerous cut-in maneuver. However, in this work, we propose an alternative way of solving this problem. We convert the SMPC problem into modeling the CACC as a Stochastic Hybrid System (SHS) while we still use a deterministic MPC controller running in the only state of the SHS model. Finally, we find the conditions under which the designed deterministic controller is stable and feasible for the proposed SHS model of the CACC platoon. In the second part of this work, we propose to improve the performance of one of the most promising realtime powertrain control strategies, called Adaptive Equivalent Consumption Minimization Strategy (AECMS), using predicted driving conditions. In this part, two different real-time powertrain control strategies are proposed for HEVs. The first proposed method, including three different variations, introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. The factor is proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy by 4\%. Finally, we integrate the recent development in reinforcement learning to design a novel multi-level power distribution control. The proposed controller reacts in two levels, namely high-level and low-level control. The high-level control decision estimates the most probable driving profile matched to the current (and near future) state of the vehicle. Then, the corresponding low-level controller of the selected profile is utilized to distribute the requested power between Electric Motor (EM) and Internal Combustion Engine (ICE). This is important because there is no other prior work addressing this problem using a controller which can adjust its decision to the driving pattern. We proposed to use two reinforcement learning agents in two levels of abstraction. The first agent, selects the most optimal low-level controller (second agent) based on the overall pattern of the drive cycle in the near past and future, i.e., urban, highway and harsh. Then, the selected agent by the high-level controller (first agent) decides how to distribute the demanded power between the EM and ICE. We found that by carefully designing a training scheme, it is possible to effectively improve the performance of this data-driven controller. Simulation results show up to 6\% improvement in fuel economy compared to the AECMS