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

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

Vehicle Parameters Estimation and Driver Behavior Classification for Adaptive Shift Strategy of Heavy Duty Vehicles

Commercial vehicles fulfill the majority of inland freight transportation in the United States, and they are very large consumers of fuels. The increasingly stringent regulation on greenhouse-gas emission has driven manufacturers to adopt new fuel efficient technologies. Among others, advanced transmission control strategy can provide tangible improvement with low incremental cost. An adaptive shift strategy is proposed in this work to optimize the shift maps on-the-fly based on the road load and driver behavior while reducing the initial calibration efforts. In addition, the adaptive shift strategy provides the fleet owner a mean to select a tradeoff between fuel economy and drivability, since the drivers are often not the owner of the vehicle. In an attempt to develop the adaptive shift strategy, the vehicle parameters and driver behavior need to be evaluated first. Therefore, three research questions are addressed in this dissertation: (i) vehicle parameters estimation; (ii) driver behavior classification; (iii) online shift strategy adaption. In vehicle parameters estimation, a model-based vehicle rolling resistance and aerodynamic drag coefficient online estimator is proposed. A new Weighted Recursive Least Square algorithm was developed. It uses a supervisor to extracts data during the constant-speed event and saves the average road load at each speed segment. The algorithm was tested in the simulation with real-world driving data. The results have shown a more robust performance compared with the original Recursive Least Square algorithm, and high accuracy of aerodynamic drag estimation. To classify the driver behavior, a driver score algorithm was proposed. A new method is developed to represent the time-series driving data into events represented by symbolic data. The algorithm is tested with real-world driving data and shows a high classification accuracy across different vehicles and driving cycles. Finally, a new adaptive shift scheme was developed, which synthesizes the information about vehicle parameters and driver score developed in the previous steps. The driver score is used as a proxy to match the driving characteristics in real time. Drivability objective is included in the optimization through a torque reserve and it is subsequently evaluated via a newly developed metric. The impact of the shift maps on the objective drivability and fuel economy metrics is evaluated quantitatively in the vehicle simulation. The algorithms proposed in this dissertation are developed with practical implementation in mind. The methods can reduce the initial calibration effort and provide the fleet owner a mean to select an appropriate tradeoff between fuel economy and drivability depending on the vocation

INCORPORATING DRIVER’S BEHAVIOR INTO PREDICTIVE POWERTRAIN ENERGY MANAGEMENT FOR A POWER-SPLIT HYBRID ELECTRIC VEHICLE

The goal of this series of research is to advance hybrid electric vehicle (HEV) energy management by incorporating driver’s driving behavior and driving cycle information. To reduce HEV fuel consumption, the objectives of this research are divided into the following three parts. The first part of the research investigates the impact of driver’s behavior on the overall fuel efficiency of a hybrid electric vehicle and the energy efficiency of individual powertrain components under various driving cycles. Between the sticker number fuel economy and actual fuel economy, it is well known that a noticeable difference occur when a driver drives aggressively. To simulate aggressive driving, the input driving cycles are scaled up from the baseline driving cycles to higher levels of acceleration/deceleration. The simulation study is conducted using Autonomie®, a powertrain simulation and analysis software. The performance of the major powertrain components is analyzed when the HEV is operated at different level of aggressiveness. In the second part of the study, the vehicle driving cycles affect the performance of a hybrid vehicle control strategy and the corresponding overall performance of the vehicle. By identifying the driving cycles of a vehicle, the HEV supervisor controller system will be dynamically adapt the control strategy to the changes of vehicle driving patterns. With pattern recognition method, a driving cycle is represented by feature vectors that are formed by a set of parameters to which the driving cycle is sensitive. To establish reference driving cycle database, the representative feature vectors of four federal driving cycles are generated using feature extraction method. The performance of the presented adaptive control strategy based on driving pattern recognition is evaluated using Autonomie. In the last part of the study, a predictive control method is developed and investigated for hybrid electric vehicle energy management in effort to improve HEV fuel economy. Model Predictive Control (MPC), a predictive control method, is applied to improve the fuel economy of a power-split HEV. The study compares the performance of MPC method and conventional rule-base control method. A parametric study is conducted to understand the influence of 3 weighting factors in MPC formulation on the performance of the vehicles

Shortest path stochastic control for hybrid electric vehicles

When a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP-SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite-horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time-invariant causal state-feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite-horizon case, and for the same level of complexity as a discounted future-cost controller, the shortest-path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/60896/1/1288_ftp.pd