43 research outputs found
A semiempirical dynamic model of reversible open circuit voltage drop in a PEM fuel cell
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149313/1/er4127_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149313/2/er4127.pd
ABatRe-Sim: A Comprehensive Framework for Automated Battery Recycling Simulation
With the rapid surge in the number of on-road Electric Vehicles (EVs), the
amount of spent lithium-ion (Li-ion) batteries is also expected to explosively
grow. The spent battery packs contain valuable metal and materials that should
be recovered, recycled, and reused. However, only less than 5% of the Li-ion
batteries are currently recycled, due to a multitude of challenges in
technology, logistics and regulation. Existing battery recycling is performed
manually, which can pose a series of risks to the human operator as a
consequence of remaining high voltage and chemical hazards. Therefore, there is
a critical need to develop an automated battery recycling system. In this
paper, we present ABatRe-sim, an open-source robotic battery recycling
simulator, to facilitate the research and development in efficient and
effective battery recycling au-omation. Specifically, we develop a detailed CAD
model of the battery pack (with screws, wires, and battery modules), which is
imported into Gazebo to enable robot-object interaction in the robot operating
system (ROS) environment. It also allows the simulation of battery packs of
various aging conditions. Furthermore, perception, planning, and control
algorithms are developed to establish the benchmark to demonstrate the
interface and realize the basic functionalities for further user customization.
Discussions on the utilization and future extensions of the simulator are also
presented
Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors
Data-driven cooperative control of connected and automated vehicles (CAVs)
has gained extensive research interest as it can utilize collected data to
generate control actions without relying on parametric system models that are
generally challenging to obtain. Existing methods mainly focused on improving
traffic safety and stability, while less emphasis has been placed on energy
efficiency in the presence of uncertainties and diversities of human-driven
vehicles (HDVs). In this paper, we employ a data-enabled predictive control
(DeePC) scheme to address the eco-driving of mixed traffic flows with diverse
behaviors of human drivers. Specifically, by incorporating the physical
relationship of the studied system and the Hankel matrix update from the
generalized behavior representation to a particular one, we develop a new
Physics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handle
human driver diversities. In particular, a power consumption term is added to
the DeePC cost function to reduce the holistic energy consumption of both CAVs
and HDVs. Simulation results demonstrate the effectiveness of our approach in
accurately capturing random human driver behaviors and addressing the complex
dynamics of mixed traffic flows, while ensuring driving safety and traffic
efficiency. Furthermore, the proposed optimization framework achieves
substantial reductions in energy consumption, i.e., average reductions of 4.83%
and 9.16% when compared to the benchmark algorithms
Simultaneous Suspension Control and Energy Harvesting through Novel Design and Control of a New Nonlinear Energy Harvesting Shock Absorber
Simultaneous vibration control and energy harvesting of vehicle suspensions
have attracted significant research attention over the past decades. However,
existing energy harvesting shock absorbers (EHSAs) are mainly designed based on
the principle of linear resonance, thereby compromising suspension performance
for high-efficiency energy harvesting and being only responsive to narrow
bandwidth vibrations. In this paper, we propose a new EHSA design -- inerter
pendulum vibration absorber (IPVA) -- that integrates an electromagnetic rotary
EHSA with a nonlinear pendulum vibration absorber. We show that this design
simultaneously improves ride comfort and energy harvesting efficiency by
exploiting the nonlinear effects of pendulum inertia. To further improve the
performance, we develop a novel stochastic linearization model predictive
control (SL-MPC) approach in which we employ stochastic linearization to
approximate the nonlinear dynamics of EHSA that has superior accuracy compared
to standard linearization. In particular, we develop a new stochastic
linearization method with guaranteed stabilizability, which is a prerequisite
for control designs. This leads to an MPC problem that is much more
computationally efficient than the nonlinear MPC counterpart with no major
performance degradation. Extensive simulations are performed to show the
superiority of the proposed new nonlinear EHSA and to demonstrate the efficacy
of the proposed SL-MPC