This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/The cost and reliability of fuel cells are major obstructions preventing fuel cell hybrid electric vehicle (FCHEV) from entering the mainstream market. However, many of the degradation methods are strongly affected by the operating conditions of the fuel cell and therefore can be mitigated by optimisation of the Energy Management Strategy (EMS). The major causes of fuel cell degradation are identified from the literature and a model is produced in order to estimate the effect of the EMS on the fuel cell degradation. This is used to produce an optimal strategy for a low speed campus vehicle using Stochastic Dynamic Programming (SDP). The SDP controller attempts to minimise the total running\ud cost of the fuel cell, inclusive of both fuel consumption and degradation, each weighted by their respective costs. The new strategy is shown to increase the lifetime of the fuel cell by 14%, with only a 3.5% increase in fuel consumption, largely by avoiding transient loading on the fuel cell stack

In this paper, a new method for planning coverage paths for fixed-wing Unmanned Aerial Vehicle (UAV) aerial surveys is proposed. Instead of the more generic coverage path planning techniques presented in previous literature, this method specifically concentrates on decreasing flight time of fixed-wing aircraft surveys. This is achieved threefold: by the addition of wind to the survey flight time model, accounting for the fact fixed-wing aircraft are not constrained to flight within the polygon of the region of interest, and an intelligent method for decomposing the region into convex polygons conducive to quick flight times. It is shown that wind can make a huge difference to survey time, and that flying perpendicular can confer a flight time advantage. Small UAVs, which have very slow airspeeds, can very easily be flying in wind, which is 50% of their airspeed. This is why the technique is shown to be so effective, due to the fact that ignoring wind for small, slow, fixed-wing aircraft is a considerable oversight. Comparing this method to previous techniques using a Monte Carlo simulation on randomised polygons shows a significant reduction in flight time.

The size of the fuel cell and battery of a Fuel Cell Hybrid Electric Vehicle (FCHEV) will heavily affect the overall performance of the vehicle, its fuel economy, driveability, and the rates of fuel cell degradation observed. An undersized fuel cell may experience accelerated ageing of the fuel cell membrane and catalyst due to excessive heat and transient loading. This work describes a multi-objective design exploration exercise of fuel cell size and battery capacity comparing hydrogen fuel consumption, fuel cell lifetime, vehicle mass and running cost. For each system design considered, an individually optimised Energy Management Strategy (EMS) has been generated using Stochastic Dynamic Programming (SDP) in order to prevent bias to the results due to the control strategy. It has been found that the objectives of fuel efficiency, lifetime and running cost are largely complimentary, but degradation and running costs are much more sensitive to design changes than fuel efficiency and therefore should be included in any optimisation. Additionally, due to the expense of the fuel cell, combined with the dominating effect of start/stop cycling degradation, the optimal design from an overall running cost perspective is slightly downsized from one which is optimised purely for high efficiency.

This paper presents a new method for planning fixed‐wing aerial survey paths that ensures efficient image coverage of a large complex agricultural field in the presence of wind. By decomposing any complex polygonal field into multiple convex polygons, the traditional back‐and‐forth boustrophedon paths can be used to ensure coverage of these decomposed regions. To decompose a complex field in an efficient and fast manner, a top‐down recursive greedy approach is used to traverse the search space to minimize the flight time of the survey. This optimization can be computed fast enough for use in the field. As wind can severely affect flight time, it is included in the flight time calculation in a systematic way using a verified cost function that offers greatly reduced survey times in the wind. Other improved cost functions have been developed to take into account real‐world problems, for example, No‐Fly Zones, in addition to flight time. A number of real surveys are performed to show the flight time in wind model is accurate, to make further comparisons to previous techniques and to show that the proposed method works in real‐world conditions providing total image coverage. A number of missions are generated and flown for real complex agricultural fields. In addition to this, the wind field around a survey area is measured from a multirotor carrying an ultrasonic wind speed sensor. This shows that the assumption of steady uniform wind holds true for the small areas and time scales of an unmanned aerial vehicle aerial survey.