17 research outputs found
Deceleration Planning Algorithm Based on Classified Multi-Layer Perceptron Models for Smart Regenerative Braking of EV in Diverse Deceleration Conditions
The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algorithm is necessary to generate the deceleration used in automatic regenerative control. To reduce the discomfort from the automatic regeneration, the deceleration should be similar to human driving. In this paper, a deceleration planning algorithm based on multi-layer perceptron (MLP) is proposed. The MLP models can mimic the human driving behavior by learning the driving data. In addition, the proposed deceleration planning algorithm has a classified structure to improve the planning performance in each deceleration condition. Therefore, the individual MLP models were designed according to three different deceleration conditions: car-following, speed bump, and intersection. The proposed algorithm was validated through driving simulations. Then, time to collision and similarity to human driving were analyzed. The results show that the minimum time to collision was 1.443 s and the velocity root-mean-square error (RMSE) with human driving was 0.302 m/s. Through the driving simulation, it was validated that the vehicle moves safely with desirable velocity when SRS is in operation, based on the proposed algorithm. Furthermore, the classified structure has more advantages than the integrated structure in terms of planning performance.
Document type: Articl
Automatic Longitudinal Regenerative Control of EVs Based on a Driver Characteristics-Oriented Deceleration Model
To preserve the fun of driving and enhance driving convenience, a smart regenerative braking system (SRS) is developed. The SRS provides automatic regeneration that is appropriate for the driving conditions, but the existing technology has a low level of acceptability and comfort. To solve this problem, this paper presents an automatic regenerative control system based on a deceleration model that reflects the driver&rsquo
s characteristics. The deceleration model is designed as a parametric model that mimics the driver&rsquo
s behavior. In addition, it consists of parameters that represent the driver&rsquo
s characteristics. These parameters are updated online by a learning algorithm. The validation results of the vehicle testing show that the vehicle maintained a safe distance from the leading car while simulating a driver&rsquo
s behavior. Of all the deceleration that occurred during the testing, 92% was conducted by the automatic regeneration system. In addition, the results of the online learning algorithm are different based on the driver&rsquo
s deceleration pattern. The presented automatic regenerative control system can be safely used in diverse car-following situations. Moreover, the system&rsquo
s acceptability is improved by updating the driver characteristics. In the future, the algorithm will be extended for use in more diverse deceleration situations by using intelligent transportation system information.
Document type: Articl
Set-Point Adaptation Strategy of Air Systems of Light-Duty Diesel Engines for NOx Emission Reduction Under Acceleration Conditions
Fault Management System of LP-EGR Using In-Cylinder Pressure Information in Light-Duty Diesel Engines
Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles
The connected powertrain control, which uses intelligent transportation system information, has been widely researched to improve driver convenience and energy efficiency. The vehicle state prediction on decelerating driving conditions can be applied to automatic regenerative braking in electric vehicles. However, drivers can feel a sense of heterogeneity when regenerative control is performed based on prediction results from a general prediction model. As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with an automatic regenerative braking control. Thus, in this paper, we proposed a deceleration prediction model based on the parametric mathematical equation and explicit model parameters. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics. Furthermore, the explicit model parameters are updated according to individual driver characteristics using the driver’s braking data during real driving situations. The proposed algorithm was integrated and validated on a real-time embedded system, and then, it was applied to the model-based regenerative control algorithm as a case study
Estimation of Intake Oxygen Concentration Using a Dynamic Correction State With Extended Kalman Filter for Light-Duty Diesel Engines
Deceleration Planning Algorithm Based on Classified Multi-Layer Perceptron Models for Smart Regenerative Braking of EV in Diverse Deceleration Conditions
Nonlinear Compensators of Exhaust Gas Recirculation and Variable Geometry Turbocharger Systems using Air Path Models for a CRDI Diesel Engine
Influence of Thermally Activated Solid-State Crystal-to-Crystal Structural Transformation on the Thermoelectric Properties of the Ca<sub>5–<i>x</i></sub>Yb<sub><i>x</i></sub>Al<sub>2</sub>Sb<sub>6</sub> (1.0 ≤ <i>x</i> ≤ 5.0) System
The solid-solution Zintl compounds
with the mixed cations of Ca<sup>2+</sup>and Yb<sup>2+</sup> in the
Ca<sub>5–<i>x</i></sub>Yb<sub><i>x</i></sub>Al<sub>2</sub>Sb<sub>6</sub> (1.0 ≤ <i>x</i> ≤
5.0) system have been
synthesized by high-temperature solid-state reactions. Two slightly
different crystal structures of the Ba<sub>5</sub>Al<sub>2</sub>Bi<sub>6</sub>-type and Ca<sub>5</sub>Ga<sub>2</sub>Sb<sub>6</sub>-type
phases have been characterized for seven compounds with 2.5 ≤ <i>x</i> ≤ 5.0 and three compounds with 1.0 ≤ <i>x</i> ≤ 2.0, respectively, by both powder and single-crystal
X-ray diffraction analyses. The two title phases adopt the orthorhombic
space group <i>Pbam</i> (<i>Z</i> = 2, <i>oP</i>26) with seven independent asymmetric atomic sites and
share certain structural similarities, including infinite one-dimensional
[Al<sub>2</sub>Sb<sub>8</sub>] double chains and isolated space-filling
Ca<sup>2+</sup>/Yb<sup>2+</sup> cations. Interestingly, we reveal
the crystal-to-crystal solid-state structural transformation of the
Yb-rich compound Ca<sub>1.5</sub>Yb<sub>3.5</sub>Al<sub>2</sub>Sb<sub>6</sub> from the Ba<sub>5</sub>Al<sub>2</sub>Bi<sub>6</sub>-type
to the Ca<sub>5</sub>Ga<sub>2</sub>Sb<sub>6</sub>-type phase through
the postannealing process, which can be rationalized as the phase
transition from the kinetically more stable structure to the thermodynamically
more stable crystal structure on the basis of theoretical calculations.
Discrepancies of the local coordination geometries of the anionic
[Al<sub>2</sub>Sb<sub>8</sub>] units and the geometrical arrangements
of structural building moieties in the two distinct phases provoke
the different electrical properties of metallic and semiconducting
conduction, respectively, for the Ba<sub>5</sub>Al<sub>2</sub>Bi<sub>6</sub>-type and Ca<sub>5</sub>Ga<sub>2</sub>Sb<sub>6</sub>-type
phases. Density of states and crystal orbital Hamilton population
analyses based on tight-binding linear muffin-tin orbital calculations
prove that the band-gap opening in the Ca<sub>5</sub>Ga<sub>2</sub>Sb<sub>6</sub>-type phase should mainly be attributed to an extended
bond distance of the bridging Sb–Sb in the [Al<sub>2</sub>Sb<sub>8</sub>] unit. A series of thermoelectric (TE) property measurements
indicates that the phase transition via the postannealing process
eventually results in an enhancement of the TE performance of Yb-rich
Ca<sub>1.5</sub>Yb<sub>3.5</sub>Al<sub>2</sub>Sb<sub>6</sub>
Multiscale structural control of thiostannate chalcogels with two-dimensional crystalline constituents
Chalcogenide aerogels (chalcogels) are amorphous structures widely known for their lack of localized structural control. This study, however, demonstrates a precise multiscale structural control through a thiostannate motif ([Sn2S6]4−)-transformation-induced self-assembly, yielding Na-Mn-Sn-S, Na-Mg-Sn-S, and Na-Sn(II)-Sn(IV)-S aerogels. The aerogels exhibited [Sn2S6]4−:Mn2+ stoichiometric-variation-induced-control of average specific surface areas (95–226 m2 g−1), thiostannate coordination networks (octahedral to tetrahedral), phase crystallinity (crystalline to amorphous), and hierarchical porous structures (micropore-intensive to mixed-pore state). In addition, these chalcogels successfully adopted the structural motifs and ion-exchange principles of two-dimensional layered metal sulfides (K2xMnxSn3-xS6, KMS-1), featuring a layer-by-layer stacking structure and effective radionuclide (Cs+, Sr2+)-control functionality. The thiostannate cluster-based gelation principle can be extended to afford Na-Mg-Sn-S and Na-Sn(II)-Sn(IV)-S chalcogels with the same structural features as the Na-Mn-Sn-S chalcogels (NMSCs). The study of NMSCs and their chalcogel family proves that the self-assembly principle of two-dimensional chalcogenide clusters can be used to design unique chalcogels with unprecedented structural hierarchy. © 2022, The Author(s).11Nsciescopu