Innovative Thermal Management Systems for Autonomous Vehicles — Design, Model, and Test

Emphasis on reducing fossil fuel consumption and greenhouse gas emissions, besides the demand for autonomy in vehicles, made governments and automotive industries move towards electrification. The integration of an electric motor with battery packs and on-board electronics has created new thermal challenges due to the heat loads\u27 operating conditions, design configurations, and heat generation rates. This paradigm shift necessitates an innovative thermal management system that can accommodate low, moderate, and high heat dissipations with minimal electrical or mechanical power requirements. This dissertation proposes an advanced hybrid cooling system featuring passive and active cooling solutions in a thermal bus configuration. The main purpose is to maintain the heat loads’ operating temperatures with zero to minimum power requirements and improved packaging, durability, and reliability. In many operating instances, a passive approach may be adequate to remove heat from the thermal source (e.g., electric motor) while a heavy load would demand both the passive and active cooling systems operate together for reduced electric power consumption. Further, in the event of a failure (e.g., coolant hose leak, radiator tube leak) in the conventional system, the passive system offers a redundant operating mode for continued operation at reduced loads. Besides, the minimization of required convective heat transfer (e.g., ram air effect) about the components for supplemental cooling enables creative vehicle component placement options and optimizations. Throughout this research, several cooling system architectures are introduced for electric vehicle thermal management. Each design is followed by a mathematical model that evaluates the steady-state and transient thermal responses of the integrated heat load(s) and the developed cooling system. The designs and the mathematical models are then validated through a series of thermal tests for a variety of driving cycles. Then, the cooling system design configuration is optimized using the validated mathematical model for a particular application. The nonlinear optimization study demonstrates that a 50\% mass reduction could be achieved for a continuous 12kW heat-dissipating demand while the electric motor operating temperature has remained below 65 centigrade degrees. Next, several real-time controllers are designed to engage the active cooling system for precise, stable, and predictable temperature regulation of the electric motor and reduced power consumption. A complete experimental setup compares the controllers in the laboratory’s environment. The experimental results indicate that the nonlinear model predictive control reduces the fan power consumption by 73% for a 5% increase in the pump power usage compared to classical control for a specific 60-minute driving cycle. In conclusion, the conducted experimental and numerical studies demonstrate that the proposed hybrid cooling strategy is an effective solution for the next generation of electrified civilian and combat ground vehicles. It significantly reduces the reliance on fossil fuels and increases vehicle range and safety while offering a silent mode of operation. Future work is to implement the developed hybrid cooling system on an actual electric vehicle, validate the design, and identify challenges on the road

Optimal speed trajectory and energy management control for connected and automated vehicles

Connected and automated vehicles (CAVs) emerge as a promising solution to improve urban mobility, safety, energy efficiency, and passenger comfort with the development of communication technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). This thesis proposes several control approaches for CAVs with electric powertrains, including hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs), with the main objective to improve energy efficiency by optimising vehicle speed trajectory and energy management system. By types of vehicle control, these methods can be categorised into three main scenarios, optimal energy management for a single CAV (single-vehicle), energy-optimal strategy for the vehicle following scenario (two-vehicle), and optimal autonomous intersection management for CAVs (multiple-vehicle). The first part of this thesis is devoted to the optimal energy management for a single automated series HEV with consideration of engine start-stop system (SSS) under battery charge sustaining operation. A heuristic hysteresis power threshold strategy (HPTS) is proposed to optimise the fuel economy of an HEV with SSS and extra penalty fuel for engine restarts. By a systematic tuning process, the overall control performance of HPTS can be fully optimised for different vehicle parameters and driving cycles. In the second part, two energy-optimal control strategies via a model predictive control (MPC) framework are proposed for the vehicle following problem. To forecast the behaviour of the preceding vehicle, a neural network predictor is utilised and incorporated into a nonlinear MPC method, of which the fuel and computational efficiencies are verified to be effective through comparisons of numerical examples between a practical adaptive cruise control strategy and an impractical optimal control method. A robust MPC (RMPC) via linear matrix inequality (LMI) is also utilised to deal with the uncertainties existing in V2V communication and modelling errors. By conservative relaxation and approximation, the RMPC problem is formulated as a convex semi-definite program, and the simulation results prove the robustness of the RMPC and the rapid computational efficiency resorting to the convex optimisation. The final part focuses on the centralised and decentralised control frameworks at signal-free intersections, where the energy consumption and the crossing time of a group of CAVs are minimised. Their crossing order and velocity trajectories are optimised by convex second-order cone programs in a hierarchical scheme subject to safety constraints. It is shown that the centralised strategy with consideration of turning manoeuvres is effective and outperforms a benchmark solution invoking the widely used first-in-first-out policy. On the other hand, the decentralised method is proposed to further improve computational efficiency and enhance the system robustness via a tube-based RMPC. The numerical examples of both frameworks highlight the importance of examining the trade-off between energy consumption and travel time, as small compromises in travel time could produce significant energy savings.Open Acces

A Combined High-Efficiency Region Controller to Improve Fuel Consumption of Power-Split HEVs

An improved controller for the energy management system of a power-split hybrid electric vehicle (HEV) is developed with the objectives of minimizing fuel consumption and improving drivability. Considering the specific application of vehicles plying on scheduled trips such as public transport, this paper assumes that the controller is privileged with a priori knowledge of the estimated total tractive energy requirement and the duration of the journey. In comparison to a recently introduced constant high-efficiency region (CHER)-based controller, this paper demonstrates that further reductions in fuel consumption can be achieved under certain driving cycles by limiting the internal-combustion-engine (ICE) operation to a dynamically varying high-efficiency region and adopting state-of-charge (SOC) swing control for battery energy storage. The frequency of engine on/off is therefore directly decided by the size of the energy storage, allowable swing of the SOC, and the tractive energy required. Performances of the CHER and dynamic high-efficiency region (DHER) controllers are compared through simulations against the existing controller of a commercial vehicle. The results reveal that the DHER controller outperforms the other two controllers in terms of fuel consumption in highway-style-driving scenarios. Therefore, to minimize fuel consumption while improving drivability under all driving scenarios, this paper proposes to combine the CHER controller with the DHER controller such that the best features of both controllers can be utilized

Design and control of the energy management system of a smart vehicle

This thesis demonstrates the design of two high efficiency controllers, one non-predictive and the other predictive, that can be used in both parallel and power-split connected plug-in hybrid electric vehicles. Simulation models of three different commercially available vehicles are developed from measured data for necessary testing and comparisons of developed controllers. Results prove that developed controllers perform better than the existing controllers in terms of efficiency, fuel consumption, and emissions

Performance and Safety Enhancement Strategies in Vehicle Dynamics and Ground Contact

Recent trends in vehicle engineering are testament to the great efforts that scientists and industries have made to seek solutions to enhance both the performance and safety of vehicular systems. This Special Issue aims to contribute to the study of modern vehicle dynamics, attracting recent experimental and in-simulation advances that are the basis for current technological growth and future mobility. The area involves research, studies, and projects derived from vehicle dynamics that aim to enhance vehicle performance in terms of handling, comfort, and adherence, and to examine safety optimization in the emerging contexts of smart, connected, and autonomous driving.This Special Issue focuses on new findings in the following topics:(1) Experimental and modelling activities that aim to investigate interaction phenomena from the macroscale, analyzing vehicle data, to the microscale, accounting for local contact mechanics; (2) Control strategies focused on vehicle performance enhancement, in terms of handling/grip, comfort and safety for passengers, motorsports, and future mobility scenarios; (3) Innovative technologies to improve the safety and performance of the vehicle and its subsystems; (4) Identification of vehicle and tire/wheel model parameters and status with innovative methodologies and algorithms; (5) Implementation of real-time software, logics, and models in onboard architectures and driving simulators; (6) Studies and analyses oriented toward the correlation among the factors affecting vehicle performance and safety; (7) Application use cases in road and off-road vehicles, e-bikes, motorcycles, buses, trucks, etc

A real-time robust ecological-adaptive cruise control strategy for battery electric vehicles

This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is solved by a novel robust model predictive control (RMPC) method handling various disturbances due to modelling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semi-definite programming relaxation and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed real-time robust ecological-adaptive cruise control (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fitted convex powertrain model can be integrated into the space-domain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm

Kinetic energy recovery and power management for hybrid electric vehicles

The major contribution of the work presented in this thesis is a thorough investigation of the constraints on regenerative braking and kinetic energy recovery enhancement for electric/hybrid electric vehicles during braking. Regenerative braking systems provide an opportunity to recycle the braking energy, which is otherwise dissipated as heat in the brake pads. However, braking energy harnessing is a relatively new concept in the automotive sector which still requires further research and development. Due to the operating constraints of the drivetrain architecture and the varying nature of the braking conditions, it is unlikely that all the stored kinetic energy of the vehicle can be recovered during braking.The research work in this thesis addresses the effect of braking conditions on kinetic energy recovery enhancement of the vehicle. The challenge in kinetic energy recovery enhancement lies in braking conditions, power/torque handling ability of the electric propulsion system, managing the dual braking systems, employed energy conversion techniques, and energy storage capacity. In this work a novel braking strategy is introduced to increase the involvement of the regenerative braking system, so as to increase the kinetic energy recovery while achieving the braking performance requirements. Initially mathematical modelling and simulation based analysis are presented to demonstrate the effects of braking power variation with respect to braking requirements. A novel braking strategy is proposed to increase the kinetic energy recovery during heavy braking events. The effectiveness of this braking strategy is analyzed using a simulation model developed in matlab- simulink environment. Anexperimental rig is developed to test various braking scenarios and their effects on kinetic energy recovery. A variety of braking scenarios are tested and results are presented with the analysis. At the end, suggestions are made to further continue this research in the future

Trajectory optimization based on recursive B-spline approximation for automated longitudinal control of a battery electric vehicle

Diese Arbeit beschreibt ein neuartiges Verfahren zur linearen und nichtlinearen gewichteten Kleinste-Quadrate-Approximation einer unbeschränkten Anzahl von Datenpunkten mit einer B-Spline-Funktion. Das entwickelte Verfahren basiert auf iterativen Algorithmen zur Zustandsschätzung und sein Rechenaufwand nimmt linear mit der Anzahl der Datenpunkte zu. Das Verfahren ermöglicht eine Verschiebung des beschränkten Definitionsbereichs einer B-Spline-Funktion zur Laufzeit, sodass der aktuell betrachtete Datenpunkt ungeachtet des anfangs gewählten Definitionsbereichs bei der Approximation berücksichtigt werden kann. Zudem ermöglicht die Verschiebeoperation die Reduktion der Größen der Matrizen in den Zustandsschätzern zur Senkung des Rechenaufwands sowohl in Offline-Anwendungen, in denen alle Datenpunkte gleichzeitig zur Verarbeitung vorliegen, als auch in Online-Anwendungen, in denen in jedem Zeitschritt weitere Datenpunkte beobachtet werden. Das Trajektorienoptimierungsproblem wird so formuliert, dass das Approximationsverfahren mit Datenpunkten aus Kartendaten eine B-Spline-Funktion berechnet, die die gewünschte Geschwindigkeitstrajektorie bezüglich der Zeit repräsentiert. Der Rechenaufwand des resultierenden direkten Trajektorienoptimierungsverfahrens steigt lediglich linear mit der unbeschränkten zeitlichen Trajektorienlänge an. Die Kombination mit einem adaptiven Modell des Antriebsstrangs eines batterie-elektrischen Fahrzeugs mit festem Getriebeübersetzungsverhältnis ermöglicht die Optimierung von Geschwindigkeitstrajektorien hinsichtlich Fahrzeit, Komfort und Energieverbrauch. Das Trajektorienoptimierungsverfahren wird zu einem Fahrerassistenzsystem für die automatisierte Fahrzeuglängsführung erweitert, das simulativ und in realen Erprobungsfahrten getestet wird. Simulierte Fahrten auf der gewählten Referenzstrecke benötigten bis zu 3,4 % weniger Energie mit der automatisierten Längsführung als mit einem menschlichen Fahrer bei derselben Durchschnittsgeschwindigkeit. Für denselben Energieverbrauch erzielt die automatisierte Längsführung eine 2,6 % höhere Durchschnittsgeschwindigkeit als ein menschlicher Fahrer