Transportation Mission-Based Optimization of Heavy Combination Road Vehicles and Distributed Propulsion, Including Predictive Energy and Motion Control

This thesis proposes methodologies to improve heavy vehicle design by reducing the total cost of ownership and by increasing energy efficiency and safety.Environmental issues, consumers expectations and the growing demand for freight transport have created a competitive environment in providing better transportation solutions. In this thesis, it is proposed that freight vehicles can be designed in a more cost- and energy-efficient manner if they are customized for narrow ranges of operational domains and transportation use-cases. For this purpose, optimization-based methods were applied to minimize the total cost of ownership and to deliver customized vehicles with tailored propulsion components that best fit the given transportation missions and operational environment. Optimization-based design of the vehicle components was found to be effective due to the simultaneous consideration of the optimization of the transportation mission infrastructure, including charging stations, loading-unloading, routing and fleet composition and size, especially in case of electrified propulsion. Implementing integrated vehicle hardware-transportation optimization could reduce the total cost of ownership by up to 35% in the case of battery electric heavy vehicles. Furthermore, in this thesis, the impacts of two future technological advancements, i.e., heavy vehicle electrification and automation, on road freight transport were discussed. It was shown that automation helps the adoption of battery electric heavy vehicles in freight transport. Moreover, the optimizations and simulations produced a large quantity of data that can help users to select the best vehicle in terms of the size, propulsion system, and driving system for a given transportation assignment. The results of the optimizations revealed that battery electric and hybrid heavy combination vehicles exhibit the lowest total cost of ownership in certain transportation scenarios. In these vehicles, propulsion can be distributed over different axles of different units, thus the front units may be pushed by the rear units. Therefore, online optimal energy management strategies were proposed in this thesis to optimally control the vehicle motion and propulsion in terms of the minimum energy usage and lateral stability. These involved detailed multitrailer vehicle modeling and the design and solution of nonlinear optimal control problems

An Algorithm for Structural Topology Optimization of Multibody Systems

Topology Optimization (TO) of static structures with fixed loading is a very interesting topic in structural mechanics that has found many applications in industrial design tasks. The extension of the theory to dynamic loading for designing a Multibody System (MBS) with bodies which are lighter and stronger can be of high interest. The objective of this thesis work is to investigate one of the possible ways of extending the theory of the static structural Topology Optimization to Topology Optimization of dynamical bodies embedded in a Multibody System (TOMBS) with large rotational and transitional motion. The TOMBS is performed for all flexible bodies simultaneously based on the overall system dynamical response. Simulation of the MBS behavior is done using the finite element formalism and modal reduction. A modified formulation of Solid Isometric Material with Penalization (SIMP) method is suggested to avoid numerical instabilities and non-convergence of the optimization algorithm implemented for TOMBS. The nonlinear differential algebraic equation of motion is solved numerically using Backward Differential Formula (BDF) with variable step size in SundialsTB and Assimulo integrators implemented in Matlab and Python. The approach can find many applications in designing vehicle systems, high speed robotic manipulators, airplanes and space structures. Also, to show the current capability of the tools in the industry to design a body under dynamic loading using the multiple static load cases, the lower A-arm of double wishbone suspension system is designed in Abaqus/TOSCA, where, the loads are collected from rigid multibody simulation in Dymola.In everyday life, people deal with different kinds of mechanical machines and mechanisms. These mechanisms are a set of mechanical and electrical parts designed to perform a specific task. Among the others, the task of a mechanical part is to carry a load or transfer it. The key question a designer should ask is how to design the part in terms of the shape, material, weight, etc. in order for the part to be optimal. This is a question that can be answered using structural optimization. Particularly in this thesis work it is tried to suggest an algorithm for optimizing the shape or material distribution of the parts within a multibody system. The method is called topology optimization of multibody system. The behavior of the system as a whole is considered to design each individual mechanical part

Transportation Mission Based Optimization of Heavy Vehicle Fleets including Propulsion Tailoring

Over decades freight vehicles were produced for a wide range of operational domains so that vehicle-manufacturers were not concerned much about the actual use-cases of the vehicles. Environmental issues, costumer expectations along with growing demand on freight transport created a competitive environment in providing better transportation solutions. In this thesis, it was proposed that freight vehicles can be designed more cost- and energy-efficiently targeting rather narrow ranges of operational domains and transportation use-cases. For this purpose, optimization-based methods were applied to deliver customized vehicles with tailored propulsion components that fit best given transportation missions and operational environment. Optimization-based design of vehicle components showed to be more effective considering optimization of transportation mission infrastructure simultaneously, including charging stations, routing and fleet composition and size, especially in case of electrified propulsion. It was observed that by implementing integrated vehicle hardware-transportation optimization, total cost of ownership can be reduced up to 35\%, in case of battery electric heavy vehicles.Furthermore, throughout thesis, the effect of propulsion system components size on optimal energy management strategy in hybrid heavy vehicles was studied; a methodology for solving fleet-size and mix-vehicle routing problem including enormous number of vehicle types were introduced; and the impact of Automated Driving Systems on electrified propulsion was presented

A Vehicle Longitudinal Dynamical Model for Propulsion System Tailoring

Integrated vehicle-transportation design, based on specific transportation assignments, has resulted in cost- and energy-efficient transport solutions especially in case of battery electric heavy vehicles. This report presents a longitudinal dynamical vehicle model for fast evaluation of the cost function and constraints within a vehicle-transportation optimization. The model includes conventional, fully electric and hybrid vehicles. The presented model evaluates energy consumption and battery degradation on driving cycles with varying speed limit and topography. The energy consumption accuracy of the presented model compared to a high fidelity vehicle model has been seen to be about 3% for the tested driving cycles, which can be further improved by tuning parameters

Impact of automated driving systems on road freight transport and electrified propulsion of heavy vehicles

The technological barriers to automated driving systems (ADS) are being quickly overcome to deploy on–road vehicles that do not require a human driver on–board. ADS have opened up possibilities to improve mobility, productivity, logistics planning, and energy consumption. However, further enhancements in productivity and energy consumption are required to reach CO2–reduction goals, owing to increased demands on transportation. In particular, in the freight sector, incorporation of automation with electrification can meet necessities of sustainable transport. However, the profitability of battery electric heavy vehicles (BEHVs) remains a concern. This study found that ADS led to profitability of BEHVs, which remained profitable for increased travel ranges by a factor of four compared to that of BEHVs driven by humans. Up to 20% reduction in the total cost of ownership of BEHVs equipped with ADS could be achieved by optimizing the electric propulsion system along with the infrastructure for a given transportation task. In that case, the optimized propulsion system might not be similar to that of a BEHV with a human driver. To obtain the results, the total cost of ownership was minimized numerically for 3072 different transportation scenarios that showed the effects of travel distance, road hilliness, average reference speed, and vehicle size on the incorporated electrification and automation, and compared to that of conventional combustion–powered heavy vehicles

Trajectory-Following and Off-Tracking Minimization of Long Combination Vehicles: A Comparison Between Nonlinear and Linear Model Predictive Control

In this paper, we compared the linear and nonlinear motion prediction models of a long combination vehicle (LCV). We designed a nonlinear model predictive control (NMPC) for trajectory-following and off-tracking minimisation of the LCV. The used prediction model allowed coupled longitudinal and lateral dynamics together with the possibility of a combined steering, propulsion and braking control of those vehicles in long prediction horizons and in all ranges of forward velocity. For LCVs where the vehicle model is highly nonlinear, we showed that the control actions calculated by a linear time-varying model predictive control (LTV-MPC) are relatively close to those obtained by the NMPC if the guess linearisation trajectory is sufficiently close to the nonlinear solution, in contrast to linearising for specific operating conditions that limit the generality of the designed function. We discussed how those guess trajectories can be obtained allowing off-line fixed time-varying model linearisation that is beneficial for real-time implementation of MPC in LCVs with long prediction horizons. The long prediction horizons are necessary for motion planning and trajectory-following of LCVs to maintain stability and tracking quality, e.g. by optimally reducing the speed prior to reaching a curve, and by generating control actions within the actuators limits

Real-time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

With the objective of reducing fuel consumption, this paper presents real-time predictive energy management of hybrid electric heavy vehicles. We propose an optimal control strategy that determines the power split between different vehicle power sources and brakes. Based on the model predictive control (MPC) and sequential programming, the optimal trajectories of the vehicle velocity and battery state of charge are found for upcoming horizons with a length of 5-20 km. Then, acceleration and brake pedal positions together with the battery usage are regulated to follow the requested speed and state of charge that is verified using a vehicle plant model. The main contribution of this paper is the development of a sequential linear program for predictive energy management that is faster and simpler than sequential quadratic programming in tested solvers and gives trajectories that are very close to the best trajectories found by nonlinear programming. The performance of the method is also compared to two different sequential quadratic programs

Computationally Efficient Nonlinear One-and Two-Track Models for Multitrailer Road Vehicles

This paper presents nonlinear mathematical models of one-and two-track multitrailer vehicles. We derive nonlinear equations of motion in the form of a system of implicit ordinary differential equations (ODEs) by using Lagrangian mechanics. The system of ODEs has the minimum number of states and equations that enables efficient computations yet maintains the most important nonlinear vehicle dynamic behavior and allows actuator coordination and energy consumption evaluation. As examples, we build different models of a 4-unit long combination vehicle, i.e., two-track 11-axle and single-track 6-axle nonlinear models as well as a linear single-track 6-axle model. We compare the performance of these models to experimental data of different driving maneuvers. The nonlinear single-track model demonstrates close dynamic behavior to the experiment, which makes it an efficient alternative to the two-track model. The vehicle equations can be generated automatically by using the code provided in this paper and subsequently used for conducting frequency analysis, evaluating energy consumption, deriving performance measures from simulations, and facilitating optimal control applications that involve combined steering, braking and propulsion control

Transportation-mission-based Optimization of Heterogeneous Heavy-vehicle Fleet Including Electrified Propulsion

Commercial-vehicle manufacturers design vehicles to operate over a wide range of transportation tasks and driving cycles. However, certain possibilities of reducing emissions, manufacturing and operational costs from end vehicles are neglected if the target range of transportation tasks is narrow and known in advance, especially in case of electrified propulsion. Apart from real-time energy optimization, vehicle hardware can be meticulously tailored to best fit a known transportation task. As proposed in this study, a heterogeneous fleet of heavy-vehicles can be designed in a more cost- and energy-efficient manner, if the coupling between vehicle hardware, transportation mission, and infrastructure is considered during initial conceptual-design stages. To this end, a rather large optimization problem was defined and solved to minimize the total cost of fleet ownership in an integrated manner for a real-world case study. In the said case-study, design variables of optimization problem included mission, recharging infrastructure, loading--unloading scheme, number of vehicles of each type, number of trips, vehicle-loading capacity, selection between conventional, fully electric, and hybrid powertrains, size of internal-combustion engines and electric motors, number of axles being powered, and type and size of battery packs. This study demonstrated that by means of integrated fleet customization, battery-electric heavy-vehicles could strongly compete against their conventional combustion-powered counterparts. Primary focus has been put on optimizing vehicle propulsion, transport mission, infrastructure and fleet size rather than routing

Multitrailer Vehicle Simulation: Generation and Integration of Differential Equations

This code generates differential equations describing the dynamic motion of a multitrailer vehicle, for simulation, optimization, and optimal control purposes.A user has options to create a model including:- any number of vehicle units;- any number of axles in each vehicle unit;- a single-track vehicle or a two-track vehicle;- linear or nonlinear tyre models;- inclusion of lateral load transfer;- inclusion of combined slip tyre model;- inclusion of the force caused by the road grade, and air and rolling resistance forces.The code was generated using MATLAB R2019b, and works in this or higher versions of MATLAB.The code is exprimentally validated (the related paper doi: 10.1109/ACCESS.2020.3037035)