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Energy management strategies greatly influence the power performance and fuel economy of series hybrid electric tracked bulldozers. In this paper, we present a procedure for the design of a power management strategy by defining a cost function, in this case, the minimization of the vehicle’s fuel consumption over a driving cycle. To explore the fuel-saving potential of a series hybrid electric tracked bulldozer, a dynamic programming (DP) algorithm is utilized to determine the optimal control actions for a series hybrid powertrain, and this can be the benchmark for the assessment of other control strategies. The results from comparing the DP strategy and the rule-based control strategy indicate that this procedure results in approximately a 7% improvement in fuel economy.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

Global aviation is growing exponentially and there is a great emphasis on trajectory optimization to reduce the overall environmental impact caused by aircraft. Many optimization techniques exist and are being studied for this purpose. The CLEAN SKY Joint Technology Initiative for aeronautics and Air transport, a European research activity run under the Seventh Framework program, is a collaborative initiative involving industry, research organizations and academia to introduce novel technologies to improve the environmental impact of aviation. As part of the overall research activities, “green” aircraft trajectories are addressed in the Systems for Green Operations (SGO) Integrated Technology Demonstrator. This paper studies the impact of large commercial aircraft trajectories optimized for different objectives applied to the on board systems.

The aviation sector has played a significant role in shaping the world into what it is today. The rapid growth of global economies and the corresponding sharp rise in the number of people now wanting to travel on business and for pleasure, has largely been responsible for the development of this industry. With a predicted rise in Revenue Passenger Kilometers (RPK) by over 150% in the next 20 years, the industry will correspondingly be a significant contributor to environmental emissions. Under such circumstances optimizing aircraft trajectories for lowered emissions will play a critical role amongst various other measures, in mitigating the probable environmental effects of increased air traffic. Aircraft trajectory optimization using evolutionary algorithms is a novel field and preliminary studies have indicated that a reduction in emissions is possible when set as objectives.

Testing real-time vehicular systems challenges the tester to design test cases for concurrent and sequential input events, emulating unexpected user and usage profiles. The vehicle response should be robust to unexpected user actions. Sequence Covering Arrays (SCA) offer an approach which can emulate such unexpected user actions by generating an optimized set of test vectors which cover all possible t-way sequences of events. The objective of this research was to find an efficient nonfunctional sequence testing (NFST) strategy for testing the robustness of real-time automotive embedded systems measured by their ability to recover (prove-out test) after applying sequences of user and usage patterns generated by combinatorial test algorithms, considered as “noisy” inputs. The method was validated with a case study of an automotive embedded system tested at Hardware-In-the-Loop (HIL) level. The random sequences were able to alter the system functionality observed at the prove-out test.

The installation of essential systems into aircraft wings involves numerous labour-intensive processes. Many human operators are required to perform complex manual tasks over long periods of time in very challenging physical positions due to the limited access and confined space. This level of human activity in poor ergonomic conditions directly impacts on speed and quality of production but also, in the longer term, can cause costly human resource problems from operators' cumulative development of musculoskeletal injuries. These problems are exacerbated in areas of the wing which house multiple systems components because the volume of manual work and number of operators is higher but the available space is reduced. To improve the efficiency of manual work processes which cannot yet be automated we therefore need to consider how we might redesign systems installations in the enclosed wing environment to better enable operator access and reduce production time.

This paper describes and demonstrates the use of an assembly centric design algorithm as an aid to achieving minimal hard tooling assembly concepts. The algorithm consists of a number of logically ordered design methodologies and also aids the identification of other enabling technologies. Included in the methodologies is an innovative systems analysis tool that enables the comparison of alternative assembly concepts, and the prediction and control of the total assembly error, at the outline stage of the design.

The flow field and body aerodynamic loads on the DrivAer reference model have been extensively investigated since its introduction in 2012. However, there is a relative lack of information relating to the models wake development resulting from the different rear-body configurations, particularly in the far-field. Given current interest in the aerodynamic interaction between two or more vehicles, the results from a preliminary CFD study are presented to address the development of the wake from the Fastback, Notchback, and Estateback DrivAer configurations. The primary focus is on the differences in the far-field wake and simulations are assessed in the range up to three vehicle lengths downstream, at Reynolds and Mach numbers of 5.2×106 and 0.13, respectively. Wake development is modelled using the results from a Reynolds-Averaged Navier-Stokes (RANS) simulation within a computational mesh having nominally 1.0×107 cells.

The vehicle dynamics control systems are traditionally based upon utilizing wheel brakes as actuators. However, there has been recently strong interest in the automotive industry for introduction of other vehicle dynamics actuators, in order to improve the overall vehicle stability, responsiveness, and agility features. This paper considers various actuators such as active rear and central differentials and active front and rear steering, and proposes design of related yaw rate control systems. Different control subsystems such as reference model, feedback and feedforward control, allocation algorithm, and time-varying controller limit are discussed. The designed control systems are verified and compared by computer simulation for double lane change and slalom maneuvers.

Significant effort has been applied to the introduction of automation for the structural assembly of aircraft. However, the equipping of the aircraft with internal services such as hydraulics, fuel, bleed-air and electrics and the attachment of movables such as ailerons and flaps remains almost exclusively manual and little research has been directed towards it. The problem is that the process requires lengthy assembly methods and there are many complex tasks which require high levels of dexterity and judgement from human operators. The parts used are prone to tolerance stack-ups, the tolerance for mating parts is extremely tight (sub-millimetre) and access is very poor. All of these make the application of conventional automation almost impossible. A possible solution is flexible metrology assisted collaborative assembly. This aims to optimise the assembly processes by using a robot to position the parts whilst an operator performs the fixing process.

In this paper, gear ratios of a two-speed transmission system are optimized for an electric passenger car. Quasi static system models, including the vehicle model, the motor, the battery, the transmission system, and drive cycles are established in MATLAB/Simulink at first. Specifically, since the regenerative braking capability of the motor is affected by the SoC of battery and motors torque limitation in real time, the dynamical variation of the regenerative brake efficiency is considered in this study. To obtain the optimal gear ratios, iterations are carried out through Nelder-Mead algorithm under constraints in MATLAB/Simulink. During the optimization process, the motor efficiency is observed along with the drive cycle, and the gear shift strategy is determined based on the vehicle velocity and acceleration demand. Simulation results show that the electric motor works in a relative high efficiency range during the whole drive cycle.

Co-production of aircraft is resulting in demands for higher standards of manufacturing quality to ensure that parts and sub-assemblies from different companies and countries are compatible and interchangeable. As a result the existing method of building aerostructure using large numbers of dedicated manufacturing jigs and assembly tools, is now seen as being commercially undesirable, and technologically flawed. This paper considers an alternative, potentially more cost-effective, approach that embraces digital design, manufacturing, and inspection techniques, and in which reference and tooling features are incorporated into the geometry of the component parts. Within the aerospace industry this technology is known as ‘Flyaway Tooling’.

The motion of a vehicle along a desired path is possible due to steering action of the driver. Hence, vehicle dynamics and control simulations should take into consideration the action of the driver. This work presents a preview based vehicle steering controller using Neural Networks which can be used in the vehicle lateral dynamics simulations. The training data for the Neural Network is being obtained using a steering controller from the existing literature and its gains are determined using Optimization. Three different architectures are being designed and conclusions are presented. These Neural Network models are validated by testing against real track data.

In the current literature, the research studies on the trajectory tracking control and stability control strategy for autonomous vehicles in limited condition mostly focus on the yaw plane control, but few of the studies have considered the combined control performance of trajectory tracking, yaw and roll stability, and the roll stability is critical under the extreme cornering condition for autonomous vehicles. Aiming at the above shortages, this study designs the model predictive control (MPC) strategy for the autonomous vehicles under the limited handling condition, which integrates the front and rear wheel active steering control, four-wheel independent drive and braking control and active suspension control to comprehensively improve the trajectory tracking accuracy, yaw plane stability and roll plane stability of the vehicle under the extreme condition.

This paper considers rhe use of structurally integrated location and reference features to simplify and to reduce the lead time and costs of assembling large aerospace structures. The location features are selected to fulfil a specific function based on restraint requirements and the necessary degree of precision to ensure that the Product Key Characteristics are achieved. Analysis of how to use structurally integrated location and reference features, indicates that their introduction will not be successful unless there is an integrated design team with a thorough understanding of the manufacturing processes and capabilities, the assembly processes, and the enabling technologies. The assembly of a single nose rib to a section of front spar is used as a typical assembly problem. Three alternative assembly processes are briefly described and used to illustrate the need for the industry to adopt an holistic approach to the design of aerostructure.

Digital Twin (DT) is a dynamic digital representation of a real-world asset, process or system. Industry 4.0 has recognised DT as the game changer for manufacturing industries in their digital transformation journey. DT will play a significant role in improving consistency, seamless process development and the possibility of reuse in subsequent stages across the complete lifecycle of the product. As the concept of DT is novel, there are several challenges that exist related to its phase of development and implementation, especially in high value manufacturing sector. The paper presents a thematic analysis of current academic literature and industrial knowledge. Based on this, eleven key challenges of DT were identified and further discussed. This work is intended to provide an understanding of the current state of knowledge around DT and formulate the future research directions.

With the rapid growth in passenger transportation through aviation projected to continue into the future, it is incumbent on aerospace engineers to seek ways to reduce the negative impact of airliner operation on the environment. Key metrics to address include noise, fuel consumption, Carbon Dioxide and Nitrous Oxide emissions, and contrail formation. The research presented in this paper generates new aircraft trajectories to reduce these metrics, and compares them with typical scheduled airline operated flights. Results and analysis of test cases on trajectory optimization are presented using an in-house aircraft trajectory optimization framework created under the European Clean Sky Joint Technology Initiative, Systems for Green Operation Integrated Technology Demonstrator. The software tool comprises an optimizer core and relatively high fidelity models of the aircraft's flight path performance, air traffic control constraints, propulsion and other systems.

Jigless assembly is an approach towards reducing the cost and increasing the flexibility of tooling systems for aircraft manufacture through the minimisation of productspecific jigs, fixtures and tooling. A new, integrated methodology has been developed, which uses a number of building blocks and tools, to enable design for jigless assembly as a result of a logical, step-by-step process. This methodology, AIM-FOR-JAM, is currently being applied to redesign the Airbus A320 Fixed Leading Edge for jigless assembly, as part of the ‘Jigless Aerospace Manufacture’ (JAM) project.

In the civil aircraft industry there is a continuous drive to increase the aircraft production rate, particularly for single aisle aircraft where there is a large backlog of orders. One of the bottlenecks is the wing assembly process which is largely manual due to the complexity of the task and the limited accessibility. The presented work describes a general wing build approach for both structure and systems equipping operations. A modified build philosophy is then proposed, concerned with large component pre-equipping, such as skins, spars or ribs. The approach benefits from an offloading of the systems equipping phase and allowing for higher flexibility to organize the pre-equipping stations as separate entities from the overall production line. Its application is presented in the context of an industrial project focused on selecting feasible system candidates for a fixed wing design, based on assembly consideration risks for tooling, interference and access.