8 research outputs found
Fast Optimal Energy Management with Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles
In this paper we demonstrate a novel alternating direction method of
multipliers (ADMM) algorithm for the solution of the hybrid vehicle energy
management problem considering both power split and engine on/off decisions.
The solution of a convex relaxation of the problem is used to initialize the
optimization, which is necessarily nonconvex, and whilst only local convergence
can be guaranteed, it is demonstrated that the algorithm will terminate with
the optimal power split for the given engine switching sequence. The algorithm
is compared in simulation against a charge-depleting/charge-sustaining (CDCS)
strategy and dynamic programming (DP) using real world driver behaviour data,
and it is demonstrated that the algorithm achieves 90\% of the fuel savings
obtained using DP with a 3000-fold reduction in computational time
Parallel ADMM for robust quadratic optimal resource allocation problems
An alternating direction method of multipliers (ADMM) solver is described for
optimal resource allocation problems with separable convex quadratic costs and
constraints and linear coupling constraints. We describe a parallel
implementation of the solver on a graphics processing unit (GPU) using a
bespoke quartic function minimizer. An application to robust optimal energy
management in hybrid electric vehicles is described, and the results of
numerical simulations comparing the computation times of the parallel GPU
implementation with those of an equivalent serial implementation are presented
An ADMM Algorithm for MPC-based Energy Management in Hybrid Electric Vehicles with Nonlinear Losses
In this paper we present a convex formulation of the Model Predictive Control
(MPC) optimisation for energy management in hybrid electric vehicles, and an
Alternating Direction Method of Multipliers (ADMM) algorithm for its solution.
We develop a new proof of convexity for the problem that allows the nonlinear
dynamics to be modelled as a linear system, then demonstrate the performance of
ADMM in comparison with Dynamic Programming (DP) through simulation. The
results demonstrate up to two orders of magnitude improvement in solution time
for comparable accuracy against DP
ADMM for MPC with state and input constraints, and input nonlinearity
In this paper we propose an Alternating Direction Method of Multipliers
(ADMM) algorithm for solving a Model Predictive Control (MPC) optimization
problem, in which the system has state and input constraints and a nonlinear
input map. The resulting optimization is nonconvex, and we provide a proof of
convergence to a point satisfying necessary conditions for optimality. This
general method is proposed as a solution for blended mode control of hybrid
electric vehicles, to allow optimization in real time. To demonstrate the
properties of the algorithm we conduct numerical experiments on randomly
generated problems, and show that the algorithm is effective for achieving an
approximate solution, but has limitations when an exact solution is required
Fast dual-loop nonlinear receding horizon control for energy management in hybrid electric vehicles
This paper proposes a receding horizon optimization strategy for the problem of energy management in plug-in hybrid electric vehicles. The approach employs a dual-loop model predictive control strategy. An inner feedback loop addresses the problem of optimally tracking a given reference trajectory for the battery state of energy over a short future horizon using knowledge of the predicted driving cycle. An outer feedback loop generates the battery state of energy reference trajectory by solving approximately the optimal energy management problem for the entire driving cycle. The receding horizon optimization problems associated with both inner and outer loops are solved using a specialized projected Newton method. The controller is compared with existing approaches based on Pontryagin's minimum principle and the effects of imprecise knowledge of the future driving cycle are discussed. This paper contains a detailed simulation study: first, this assesses the optimality of the associated uncertainty-free approach and its computational load. Second, the effects of imprecise knowledge of the future driving cycle are illustrated
Fast dual-loop nonlinear receding horizon control for energy management in hybrid electric vehicles
This paper proposes a receding horizon optimization strategy for the problem of energy management in plug-in hybrid electric vehicles. The approach employs a dual-loop model predictive control strategy. An inner feedback loop addresses the problem of optimally tracking a given reference trajectory for the battery state of energy over a short future horizon using knowledge of the predicted driving cycle. An outer feedback loop generates the battery state of energy reference trajectory by solving approximately the optimal energy management problem for the entire driving cycle. The receding horizon optimization problems associated with both inner and outer loops are solved using a specialized projected Newton method. The controller is compared with existing approaches based on Pontryagin's minimum principle and the effects of imprecise knowledge of the future driving cycle are discussed. This paper contains a detailed simulation study: first, this assesses the optimality of the associated uncertainty-free approach and its computational load. Second, the effects of imprecise knowledge of the future driving cycle are illustrated
Intelligent-based hybrid-electric propulsion system for aero vehicle
To address the sustainability challenges for air transport, electrified aviation
delivers promising benefits to the whole air transportation system. Focusing on
reducing environmental impact and raising competitiveness, this thesis presents
a research regarding the Distributed Series Hybrid-electric Propulsion System for
aero vehicles, which involves study fields of system configuration design,
component sizing and energy management strategies.
Based on the state-of-art of hybrid-electric aircraft and hybrid-electric propulsion
systems, the study firstly improved the conventional series hybrid configuration
by adopting distributed propulsion technology and more electric aircraft concept.
These improvements can compensate for the drawbacks caused by the
conventional series hybrid layout, so that the new designed propulsion system
has the potential to reduce system weight and increase fuel economy.
After that, a comprehensive sizing method was particularly designed for the
proposed system. The engine, as the primary power source, was firstly selected
via the battery parametrisation criteria. Then, other components were selected
according to a proposed sizing flowchart by using the genetic algorithm. System
performance can also be demonstrated during the sizing process.
Finally, three different control methods had been applied to manage energy flows.
The first supervisory controller is a deterministic rule-based controller, which was
designed based on human experiences and can reduce 12% fuel consumption.
The second is a battery-friendly fuzzy controller. It was particularly designed to
improve the battery operating environment and can simultaneously achieve a 5%
improvement on fuel economy compared to the rule-based. The third controller
applied model predictive control algorithm, which can further improve the fuel
efficiency by 4% and reveal the relationship between the fuel consumption and
emissions.Aerospac