System architecture for the Canadian interim mobile satellite system

The system architecture for the Canadian Interim Mobile Satellite Service (IMSS) which is planned for commencement of commercial service in late 1989 is reviewed. The results of an associated field trial program which was carried out to determine the limits of coverage and the preliminary performance characteristics of the system are discussed

An ECMS-based powertrain control of a parallel hybrid electric forklift

n this paper we focus on the supervisory control problem of a parallel hybrid electric vehicle (HEV): minimize fuel consumption while ensuring self-sustaining State-of-Charge (SoC). We reapply the state of the art methodology by comparing optimal results of Dynamic Programming (DP) against a real-time control candidate. After careful selection, we opted for an Equivalent Consumption Minimization Strategy (ECMS) based approach for the following reasons: (i) results are quite remarkable with less than 5% fuel usage increase when compared to DP; (ii) simple and intuitive tuning of control parameters; (iii) readily usable for code generation (prototyping). Topics that distinguish this article from others in the literature include: (i) the usage of trapezoidal rule of integration implementing DP and ECMS; consequently, the offline simulation results are intended to be more precise and representative when compared against the more common, often used rectangular rule; (ii) a particular post-processing procedure of the recorded driving cycle data based on physical interpretation; it allows consistent offline simulations with quite high sampling period (in the order of seconds); (iii) tuning of control parameters in such a way that control system is robust towards new, unknown, unpredictable but closely resembling driving cycles. In particular, we focus on the supervisory control of a forklift truck. The real-time control is able to compute: (i) the power split (i.e. a balanced usage between an internal combustion engine and a supercapacitor); (ii) the drivetrain control (i.e. automatic gear shifting and clutching). Numerous numerical implementation issues are discussed along our presentation

Optimisation under uncertainty applied to a bridge collision problem

We consider the problem of modelling the load on a bridge pillar when hit by a vehicle. This load depends on a number of uncertain variables, such as the mass of the vehicle and its speed on impact. The objective of our study is to analyse their effect on the load. More specifically, we are interested in finding the minimum distance of the pillar to the side of the road passing under the bridge such that a given constraint on the load is satisfied in 99% of impact cases, i.e., such that the probability of satisfying the constraint is 0.99. In addition, we look for solutions to the following optimisation problem: find the distance that minimises a given cost function while still satisfying a given constraint on the load. This optimisation problem under uncertain constraints is not a well-posed problem, so we turn it into a decision problem under uncertainty. For both problems, we consider two typical cases. In the first, so-called precise-probability case, all uncertain variables involved are modelled using probability distributions, and in the second, so-called imprecise-probability case, the uncertainty for at least some of the variables (in casu the mass) is modelled by an interval of possible values, which is a special imprecise-probabilistic model. In the first case, we compute the joint distribution using simple Monte Carlo simulation, and in the second case, we combine Monte Carlo simulation with newly developed techniques in the field of imprecise probabilities. For the optimisation problem with uncertain constraints, this leads to two distinct approaches with different optimality criteria, namely maximality and maximinity, which we discuss and compare.We consider the problem of modelling the load on a bridge pillar when hit by a vehicle. This load depends on a number of uncertain variables, such as the mass of the vehicle and its speed on impact. The objective of our study is to analyse their effect on the load. More specifically, we are interested in finding the minimum distance of the pillar to the side of the road passing under the bridge such that a given constraint on the load is satisfied in 99% of impact cases, i.e., such that the probability of satisfying the constraint is 0.99. In addition, we look for solutions to the following optimisation problem: find the distance that minimises a given cost function while still satisfying a given constraint on the load. This optimisation problem under uncertain constraints is not a well-posed problem, so we turn it into a decision problem under uncertainty. For both problems, we consider two typical cases. In the first, so-called precise-probability case, all uncertain variables involved are modelled using probability distributions, and in the second, so-called imprecise-probability case, the uncertainty for at least some of the variables (in casu the mass) is modelled by an interval of possible values, which is a special imprecise-probabilistic model. In the first case, we compute the joint distribution using simple Monte Carlo simulation, and in the second case, we combine Monte Carlo simulation with newly developed techniques in the field of imprecise probabilities. For the optimisation problem with uncertain constraints, this leads to two distinct approaches with different optimality criteria, namely maximality and maximinity, which we discuss and compare.status: publishe

An ECMS-based powertrain control of a parallel hybrid electric forklift

© 2017 IEEE. In this paper we focus on the supervisory control problem of a parallel hybrid electric vehicle (HEV): minimize fuel consumption while ensuring self-sustaining State-of-Charge (SoC). We reapply the state of the art methodology by comparing optimal results of Dynamic Programming (DP) against a real-time control candidate. After careful selection, we opted for an Equivalent Consumption Minimization Strategy (ECMS) based approach for the following reasons: (i) results are quite remarkable with less than 5% fuel usage increase when compared to DP; (ii) simple and intuitive tuning of control parameters; (iii) readily usable for code generation (prototyping). Topics that distinguish this article from others in the literature include: (i) the usage of trapezoidal rule of integration implementing DP and ECMS; consequently, the offline simulation results are intended to be more precise and representative when compared against the more common, often used rectangular rule; (ii) a particular post-processing procedure of the recorded driving cycle data based on physical interpretation; it allows consistent offline simulations with quite high sampling period (in the order of seconds); (iii) tuning of control parameters in such a way that control system is robust towards new, unknown, unpredictable but closely resembling driving cycles. In particular, we focus on the supervisory control of a forklift truck. The real-time control is able to compute: (i) the power split (i.e. a balanced usage between an internal combustion engine and a supercapacitor); (ii) the drivetrain control (i.e. automatic gear shifting and clutching). Numerous numerical implementation issues are discussed along our presentation.status: publishe