Zero Torque Control for EV Coasting Considering Cross-Coupling Inductance

Lee, Heekwang; Woldesemayat, Muluneh Lemma; Nam, Kwanghee

doi:10.1109/tie.2017.2681973

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…This compensation results in the motor drawing some power, but torque remains at zero through the field-weakening region. As a result, the vehicle cruises longer in both simulated and HIL experiments [20]. The ability to control a BEV powertrain in this manner, and the work completed by [19] and [17], demonstrate that there are other methods for decelerating a BEV besides regen braking.…”

Section: Chapter 2: Literature Reviewmentioning

confidence: 92%

“…The initial, uncontrolled deceleration of the BEV is less drastic than that of the reference vehicle since there are less losses in the BEV drivetrain than the ICEV one. Another strategy for coasting in BEVs is zero-torque coasting as proposed by [20]. Rather than setting motor current to zero, which results in negative torque output due to motor electromagnetic properties, motor current is controlled in such a way that motor output torque is set to zero.…”

Section: Chapter 2: Literature Reviewmentioning

confidence: 99%

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Energy Modeling of Deceleration Strategies for Electric Vehicles

Hom¹,

Nelson²

2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Rapid adoption of battery electric vehicles means improving the energy consumption and energy efficiency of these new vehicles is a top priority. One method of accomplishing this is regenerative braking, which converts kinetic energy to electrical energy stored in the battery pack while the vehicle is decelerating. Coasting is an alternative strategy that minimizes energy consumption by decelerating the vehicle using only road load. A battery electric vehicle model is refined to assess regenerative braking, coasting, and other deceleration strategies. A road load model based on public test data calculates tractive effort requirements based on speed and acceleration. Bidirectional Willans lines are the basis of a powertrain model simulating battery energy consumption. Vehicle tractive and powertrain power are modeled backward from prescribed linear velocity curves, and the coasting trajectory is forward modeled given zero tractive power. Decel modes based on zero battery and motor power are also forward modeled. Multi-mode decel (using a low power decel mode with regenerative braking) is presented as a set of intermediate strategies. An example vehicle using these strategies is modeled in fixed-route simulations, and scoring is based on travel time, energy consumption, and bias towards minimizing one of those metrics. Regenerative braking has the lowest travel time, and coasting the lowest energy consumption, but such bias increases overall cost. Multi-mode strategies lower overall cost by balancing reductions in travel time and energy consumption. Model sensitivity to grade and accessory load fluctuation makes it adaptable to different vehicles and environments. Simulation results demonstrate how regen braking alternatives could be modeled to enhance connected and automated vehicle systems in battery electric vehicles.</div></div>

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Section: Chapter 2: Literature Reviewmentioning

confidence: 92%

Section: Chapter 2: Literature Reviewmentioning

confidence: 99%

Energy Modeling of Deceleration Strategies for Electric Vehicles

Hom¹,

Nelson²

2023

SAE Technical Paper Series

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“…The magnetic model of IPMSM, interpreted by inverse flux functions (Equation ( 2)), requires quantification in the form of a certain algebraic expression. The magnetic model representation in the form of LUTs, which directly represents the correlation between flux linkages and currents in the dq reference frame, is the most common representation of conventional flux maps [19,24]. The primary issue in obtaining inverse flux maps is that the excitation of the dq currents is referenced and controlled during simulations or experiments, and corresponding flux linkages are acquired for that specific operating point, and not vice versa.…”

Section: Algebraic Magnetic Model Representationmentioning

confidence: 99%

“…maximize T e i ds , i qs subject to G i ds , i qs = 0, i ds ≤ 0 (23) where T e i ds , i qs is the objective function, in general form, of (3) and G i ds , i qs , and i ds ≤ 0 are the equality and inequality constraints. Newton's method requires that (23) should be reduced to a discrete number (i) of approximate maximization problems (24), with the addition of an logarithmic term, known as a barrier function:…”

Section: Mtpa Nonlinear Strategy Based On the Proposed Magnetic Modelmentioning

confidence: 99%

“…where µ > 0 is the barrier parameter, and s is a vector of the slack variables. The trust region (conjugate gradient) is used in each iteration to solve Equation (24). The approximate problem (24) converges to the original problem (23) as the sequence of barrier parameters µ converges to zero with each iteration.…”

Section: Mtpa Nonlinear Strategy Based On the Proposed Magnetic Modelmentioning

confidence: 99%

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Nonlinear Magnetic Model of IPMSM Based on the Frozen Permeability Technique Utilized in Improved MTPA Control

Vučković,

Popović,

Jerkan

et al. 2024

Electronics

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In this paper, the enhanced nonlinear magnetic model of the low voltage interior permanent magnet synchronous machine (IPMSM) is developed using the frozen permeability (FP) technique in finite element analysis (FEA) FEMM 4.2 software. The magnetic model is derived by obtaining flux saturation maps for a wide range of dq stator currents. Furthermore, the FEA FP technique accounts for the corresponding offset in the flux maps due to the excitation of the permanent magnets, and well as for fitting the coefficients for the curve-fitting procedure. In order to demonstrate the usefulness of the proposed magnetic model, a nonlinear control strategy based on the maximum torque per ampere (MTPA) optimal algorithm for IPMSM is employed. The magnetic model and the MTPA control strategy are validated through a variety of computer simulations based on FEMM 4.2 and MATLAB R2023a software, as well as on a real IPMSM electric vehicle (EV) traction drive experimental setup.

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