352 research outputs found
Time-optimal Control Strategies for Electric Race Cars with Different Transmission Technologies
This paper presents models and optimization methods to rapidly compute the
achievable lap time of a race car equipped with a battery electric powertrain.
Specifically, we first derive a quasi-convex model of the electric powertrain,
including the battery, the electric machine, and two transmission technologies:
a single-speed fixed gear and a continuously variable transmission (CVT).
Second, assuming an expert driver, we formulate the time-optimal control
problem for a given driving path and solve it using an iterative convex
optimization algorithm. Finally, we showcase our framework by comparing the
performance achievable with a single-speed transmission and a CVT on the Le
Mans track. Our results show that a CVT can balance its lower efficiency and
higher weight with a higher-efficiency and more aggressive motor operation, and
significantly outperform a fixed single-gear transmission.Comment: 5 pages, 4 figures, submitted to the 2020 IEEE Vehicle Power and
Propulsion Conferenc
Transmission Ratio Design for Electric Vehicles via Analytical Modeling and Optimization
In this paper we present an effective analytical modeling approach for the
design of the transmission of electric vehicles. Specifically, we first devise
an analytical loss model for an electric machine and show that it can be
accurately fitted by only sampling three points from the original motor map.
Second, we leverage this model to derive the optimal transmission ratio as a
function of the wheels' speed and torque, and use it to optimize the
transmission ratio. Finally, we showcase our analytical approach with a
real-world case-study comparing two different transmission technologies on a
BMW i3: a fixed-gear transmission (FGT) and a continuously variable
transmission (CVT). Our results show that even for e-machines intentionally
designed for a FGT, the implementation of a CVT can significantly improve their
operational efficiency by more than 3%. The provided model will ultimately
bridge the gap in understanding how to efficiently specify the e-machine and
the transmission technology in an integrated fashion, and enable to effectively
compare single- and multi-speed-based electric powertrains.Comment: 5 pages, 4 figures, submitted to the 2020 IEEE Vehicle Power and
Propulsion Conferenc
Cost-optimal Fleet Management Strategies for Solar-electric Autonomous Mobility-on-Demand Systems
This paper studies mobility systems that incorporate a substantial solar
energy component, generated not only on the ground, but also through solar
roofs installed on vehicles, directly covering a portion of their energy
consumption. In particular, we focus on Solar-electric Autonomous
Mobility-on-Demand systems, whereby solar-electric autonomous vehicles provide
on-demand mobility, and optimize their operation in terms of serving passenger
requests, charging and vehicle-to-grid (V2G) operations. We model this fleet
management problem via directed acyclic graphs and parse it as a mixed-integer
linear program that can be solved using off-the-shelf solvers. We showcase our
framework in a case study of Gold Coast, Australia, analyzing the fleet's
optimal operation while accounting for electricity price fluctuations resulting
from a significant integration of solar power in the total energy mix. We
demonstrate that using a solar-electric fleet can reduce the total cost of
operation of the fleet by 10-15% compared to an electric-only counterpart.
Finally, we show that for V2G operations using vehicles with a larger battery
size can significantly lower the operational costs of the fleet,
overcompensating its higher energy consumption by trading larger volumes of
energy and even accruing profits
Cost-optimal Fleet Management Strategies for Solar-electric Autonomous Mobility-on-Demand Systems
This paper studies mobility systems that incorporate a substantial solar energy component, generated not only on the ground, but also through solar roofs installed on vehicles, directly covering a portion of their energy consumption. In particular, we focus on Solar-electric Autonomous Mobility-on-Demand systems, whereby solar-electric autonomous vehicles provide on-demand mobility, and optimize their operation in terms of serving passenger requests, charging and vehicle-to-grid (V2G) operations. We model this fleet management problem via directed acyclic graphs and parse it as a mixed-integer linear program that can be solved using off-the-shelf solvers. We showcase our framework in a case study of Gold Coast, Australia, analyzing the fleet's optimal operation while accounting for electricity price fluctuations resulting from a significant integration of solar power in the total energy mix. We demonstrate that using a solar-electric fleet can reduce the total cost of operation of the fleet by 10-15% compared to an electric-only counterpart. Finally, we show that for V2G operations using vehicles with a larger battery size can significantly lower the operational costs of the fleet, overcompensating its higher energy consumption by trading larger volumes of energy and even accruing profits
Electric Autonomous Mobility-on-Demand: Jointly Optimal Vehicle Design and Fleet Operation
The advent of autonomous driving and electrification is enabling the
deployment of Electric Autonomous Mobility-on-Demand (E-AMoD) systems, whereby
electric autonomous vehicles provide on-demand mobility. Crucially, the design
of the individual vehicles and the fleet, and the operation of the system are
strongly coupled. Hence, to maximize the system-level performance, they must be
optimized in a joint fashion. To this end, this paper presents a framework to
jointly optimize the fleet design in terms of battery capacity and number of
vehicles, and the operational strategies of the E-AMoD system, with the aim of
maximizing the operator's total profit. Specifically, we first formulate this
joint optimization problem using directed acyclic graphs as a mixed integer
linear program, which can be solved using commercial solvers with optimality
guarantees. Second, to solve large instances of the problem, we propose a
solution algorithm that solves for randomly sampled sub-problems, providing a
more conservative solution of the full problem, and devise a heuristic approach
to tackle larger individual sub-problem instances. Finally, we showcase our
framework on a real-world case study in Manhattan, where we demonstrate the
interdependence between the number of vehicles, their battery size, and
operational and fixed costs. Our results indicate that to maximize a mobility
operator's profit, a fleet of small and light vehicles with battery capacity of
20 kWh only can strike the best trade-off in terms of battery degradation,
fixed costs and operational efficiency
Electric Motor Design Optimization: A Convex Surrogate Modeling Approach
This paper instantiates a convex electric powertrain design optimization
framework, bridging the gap between high-level powertrain sizing and low-level
components design. We focus on the electric motor and transmission of electric
vehicles, using a scalable convex motor model based on surrogate modeling
techniques. Specifically, we first select relevant motor design variables and
evaluate high-fidelity samples according to a predefined sampling plan. Second,
using the sample data, we identify a convex model of the motor, which predicts
its losses as a function of the operating point and the design parameters. We
also identify models of the remaining components of the powertrain, namely a
battery and a fixed-gear transmission. Third, we frame the minimum-energy
consumption design problem over a drive cycle as a second-order conic program
that can be efficiently solved with optimality guarantees. Finally, we showcase
our framework in a case study for a compact family car and compute the optimal
motor design and transmission ratio. We validate the accuracy of our models
with a high-fidelity simulation tool and calculate the drift in battery energy
consumption. We show that our model can capture the optimal operating line and
the error in battery energy consumption is low. Overall, our framework can
provide electric motor design experts with useful starting points for further
design optimization.Comment: 6 pages, 5 figures, final submission for the 10th IFAC Symposium on
Advances in Automotive Control, 202
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