11,324 research outputs found
Observer techniques for estimating the state-of-charge and state-of-health of VRLABs for hybrid electric vehicles
The paper describes the application of observer-based state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, an approach based on the well-known Kalman filter, is employed, to estimate SoC, and the subsequent use of the EKF to accommodate model non-linearities to predict battery SoH. The underlying dynamic behaviour of each cell is based on a generic Randles' equivalent circuit comprising of two-capacitors (bulk and surface) and three resistors, (terminal, transfer and self-discharging). The presented techniques are shown to correct for offset, drift and long-term state divergence-an unfortunate feature of employing stand-alone models and more traditional coulomb-counting techniques. Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC, with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior with SoC being estimated to be within 1% of measured. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack
An investigation on the effect of driver style and driving events on energy demand of a PHEV
Environmental concerns, security of fuel supply and CO2 regulations are driving innovation in the automotive industry towards electric and hybrid electric vehicles. The fuel economy and emission performance of hybrid electric vehicles (HEVs) strongly depends on the energy management system (EMS). Prior knowledge of driving information could be used to enhance the performance of a HEV. However, how the necessary information can be obtained to use in EMS optimisation still remains a challenge. In this paper the effect of driver style and driving events like city and highway driving on plug in hybrid electric vehicle (PHEV) energy demand is studied.
Using real world driving data from three drivers of very different driver style, a simulation has been exercised for a given route having city and highway driving. Driver style and driving events both affect vehicle energy demand. In both driving events considered, vehicle energy demand is different due to driver styles. The major part of city driving is reactive driving influenced by external factors and driver leading to variation in vehicle speed and hence energy demand. In free highway driving, the driver choice of cruise speed is the only factor affecting vehicle energy demand
Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance
Due to the current developments towards autonomous driving and vehicle active
safety, there is an increasing necessity for algorithms that are able to
perform complex criticality predictions in real-time. Being able to process
multi-object traffic scenarios aids the implementation of a variety of
automotive applications such as driver assistance systems for collision
prevention and mitigation as well as fall-back systems for autonomous vehicles.
We present a fully model-based algorithm with a parallelizable architecture.
The proposed algorithm can evaluate the criticality of complex, multi-modal
(vehicles and pedestrians) traffic scenarios by simulating millions of
trajectory combinations and detecting collisions between objects. The algorithm
is able to estimate upcoming criticality at very early stages, demonstrating
its potential for vehicle safety-systems and autonomous driving applications.
An implementation on an embedded system in a test vehicle proves in a
prototypical manner the compatibility of the algorithm with the hardware
possibilities of modern cars. For a complex traffic scenario with 11 dynamic
objects, more than 86 million pose combinations are evaluated in 21 ms on the
GPU of a Drive PX~2
Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation
The application of traction control systems (TCS) for electric vehicles (EV)
has great potential due to easy implementation of torque control with
direct-drive motors. However, the control system usually requires road-tire
friction and slip-ratio values, which must be estimated. While it is not
possible to obtain the first one directly, the estimation of latter value
requires accurate measurements of chassis and wheel velocity. In addition,
existing TCS structures are often designed without considering the robustness
and energy efficiency of torque control. In this work, both problems are
addressed with a smart TCS design having an integrated acoustic road-type
estimation (ARTE) unit. This unit enables the road-type recognition and this
information is used to retrieve the correct look-up table between friction
coefficient and slip-ratio. The estimation of the friction coefficient helps
the system to update the necessary input torque. The ARTE unit utilizes machine
learning, mapping the acoustic feature inputs to road-type as output. In this
study, three existing TCS for EVs are examined with and without the integrated
ARTE unit. The results show significant performance improvement with ARTE,
reducing the slip ratio by 75% while saving energy via reduction of applied
torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22
Jan 201
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