Advanced Electric Drive Vehicles Ali Emadi Tai ngay!!! Ban co the xoa dong chu nay!!! Advanced Electric Drive Vehicles Energy, Power Electronics, and Machines Series Editor Ali Emadi Advanced Electric Drive Vehicles Ali Emadi Switched Reluctance Motor Drives: Fundamentals of Magnetic Design and Control Babak Fahimi Power Converters and AC Electrical Drives with Linear Neural Networks Maurizio Cirrincione, Marcello Pucci, and Gianpaolo Vitale Energy Harvesting: Solar, Wind, and Ocean Energy Conversion Systems Alireza Khaligh and Omer C Onar Advanced Electric Drive Vehicles A l i E m a di M c M a s t e r U n i v e r s i t y, H a m i l t o n , O n t a r i o , C a n a d a Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an informa business CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2015 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S Government works Version Date: 20140825 International Standard Book Number-13: 978-1-4665-9770-9 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint Except as permitted under U.S Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers For permission to photocopy or use material electronically from this work, please access www.copyright.com (http:// www.copyright.com/) or contact the Copyright Clearance Center, Inc (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400 CCC is a not-for-profit organization that provides licenses and registration for a variety of users For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com To my family Ali Emadi Contents Preface .ix Editor .xi Contributors xiii Chapter Automotive Industry and Electrification Ali Emadi and Josipa G Petrunic´ Chapter Fundamentals of Conventional Vehicles and Powertrains 15 William Long and Berker Bilgin Chapter Internal Combustion Engines 27 Fengjun Yan Chapter Fundamentals of Power Electronics 43 Pierre Magne, Xiaodong Shi, and Mahesh Krishnamurthy Chapter Fundamentals of Electric Machines 107 Berker Bilgin and Anand Sathyan Chapter Fundamentals of Electric Motor Control 187 Nicholas J Nagel Chapter Fundamentals of Electric Energy Storage Systems 237 Pawel P Malysz, Lucia Gauchia, and Hong H Yang Chapter Hybrid Energy Storage Systems 283 Omer C Onar and Alireza Khaligh Chapter Low-Voltage Electrical Systems for Nonpropulsion Loads 317 Ruoyu Hou, Pierre Magne, and Berker Bilgin Chapter 10 48-V Electrification: Belt-Driven Starter Generator Systems 331 Sanjaka G Wirasingha, Mariam Khan, and Oliver Gross Chapter 11 Fundamentals of Hybrid Electric Powertrains 369 Mengyang Zhang, Piranavan Suntharalingam, Yinye Yang, and Weisheng Jiang vii viii Contents Chapter 12 Hybrid Electric Vehicles 411 Piranavan Suntharalingam, Yinye Yang, and Weisheng Jiang Chapter 13 Fundamentals of Chargers 439 Fariborz Musavi Chapter 14 Plug-In Hybrid Electric Vehicles .465 Yinye Yang, Weisheng Jiang, and Piranavan Suntharalingam Chapter 15 All-Electric Vehicles and Range-Extended Electric Vehicles 491 Weisheng Jiang, Yinye Yang, and Piranavan Suntharalingam Chapter 16 Vehicle-to-Grid Interface and Electrical Infrastructure 517 Giampaolo Carli, Arash Shafiei, Florence Berthold, and Sheldon S Williamson Chapter 17 Energy Management and Optimization 557 Ilse Cervantes 574 Advanced Electric Drive Vehicles Let us define α= Pbat (t )ηeff ,bat Pload (17.44) that is, α is the proportion of the power demand that is provided by the battery bank Note that 0 ≤ α ≤ 1; therefore, restrictions (17.10) through (17.13) can be written as Pload (t )(1 − α ) = PFC ηeff , FC PFCmin − − PFCmax + Pload (1 − α ) ≤0 ηeff , FC (17.45) Pload (1 − α ) ≤0 ηeff , FC (t ) −C ηeff ,bat Vbat SOC =α Pload (17.46) (17.47) Using the expressions above, it is now possible to visually identify the feasible region in terms of the current load (shaded region in Figure 17.7) Restrictions (17.45) and (17.46) constitute curves that approach α = 1 as Pload increases and they are denoted with PFCmin and PFCmax, respectively Note that as the restriction of the battery is dynamic, the curves A and B correspond to curves with initial conditions SOC = SOCmin and SOC = SOCmax respectively, but in an actual application, battery SOC will be varying according to the power demand displaying a curve between curves A and B (dashed line in Figure 17.7) Observe that reductions of fuel consumption are obtained as α increases Therefore, since the solution must belong to the feasible region, the solution necessary resides at the boundary of the feasible set and for low-power loads, this solution is the curve of minimum FC power This fact agrees with intuition, since to minimize the fuel consumption, the use of the FC must be minimized This condition rapidly changes as the current demand increases In this case, if the battery is sufficiently charged, the solution will move from the curve of minimum FC power to the maximum FC power along the line α = 1; otherwise, the solution will lay on the actual SOC trajectory (dashed line in Figure 17.7) and the value of α will depend on the current demand Figure 17.7 is also valuable as an auxiliary tool to design the power sources of an FCHEV To clarify this point, first observe that given a driving cycle, the maximum power demand (Pload,max) is fixed for a given vehicle and driving cycle The maximum FC power (the maximum capacity of the FC) can be fixed at the intersection of Pload,max = Pload and α = 1, to maximize the feasible region, and therefore the optimization events Let us denote the power demand at such intersection as Pload,C (see point C in Figure 17.7); then a final worthy comment is in order: power demands greater than Pload,C are not reachable for any value of α That is, the implicit controllability assumption from which we have departed to solve the optimization problem is not satisfied for Pload > Pload,C The time evolution of the system under the instantaneous optimal problem is displayed in Figure 17.8 for the initial condition SOC(t0) = 1 and for the driving cycle City II Observe that the fuel minimization problem can be solved due to the battery utilization; therefore, in such optimal strategy, the battery will be depleted to its minimum (allowed) level Moreover, observe that the case when the battery is charged by the FC is never an optimal solution since more fuel 575 Energy Management and Optimization α A SOC(t0) = SOCmax SOCmin restriction C PFCmin α=1 PFCmax B SOC(t0) = SOCmax Pload Feasible region SOCmax restriction FIGURE 17.7 Feasible region of the instantaneous optimization problem as a function of the current load is consumed by this charging process if the FCs were used to provide all the power demand.* To penalize the battery utilization, the equivalent fuel consumption term as in OF (17.8) can be used (i.e., J = Km Fuel (σ ) + K (SOC (σ ) − SOC * )2) Such strategy does not avoid battery depletion (to its minimum allowable level) but the rate of discharging is smaller than in the optimization strategy above, as a penalization of the battery utilization is performed However, the use of the equivalent fuel consumption has the disadvantage of having increased fuel consumption, as can be seen in Figure 17.9, where the FE as a function of the gain K2 is displayed 17.4.2 Integral OF Let us consider the DOP given in Section 17.2.4.2, given by Equation 17.14, under restrictions (17.10) through (17.13) The optimization problem has an integral OF defined from time t0 to T Here, VC methods introduced in Section 17.3.1 will be used to solve the optimization problem To this end, let us consider, as in Section 17.4.1, α(t) = u(t) as the proportion of the total power demand provided by the battery Since the FC is operating in the Ohmic region IFC (t) ∞ PFC (t), hence, the objective function (17.14) becomes T ∫ T α a K P (σ )d σ = α 2 FC ∫ T 2 (1 − α )2 Pload (σ ) 2 (1 − u) Pload (σ ) a K d σ = a K dσ u η2eff , FC η2eff , FC 2 ∫ t0 t0 t0 (17.48) * This is due to energy drop given by the converters’ efficiency 576 Advanced Electric Drive Vehicles 20 Hydrogen consumption (kg/s) 0.8 α 0.6 0.4 0.2 0 100 200 300 400 500 600 700 19.995 19.99 19.985 19.98 19.975 19.97 EMS FC only 19.965 19.96 800 100 200 300 Time (s) 140 120 0.9 100 600 700 800 500 600 700 800 0.7 60 SOC Ibat (A) 500 0.8 80 40 0.6 20 0.5 0.4 –20 0.3 –40 400 Time (s) 100 200 300 400 500 600 700 0.2 800 100 Time (s) 200 300 400 Time (s) FIGURE 17.8 Time evolution of an optimal EMS for a vehicle under the City II driving cycle 80 Fuel economy (%) 70 60 50 40 30 20 10 0 10 15 K2 20 25 FIGURE 17.9 FE as a function of the gain of the equivalent fuel consumption term 30 577 Energy Management and Optimization where K = {− NM H2 / F} and the dynamic of the battery SOC is given by Equation 17.47 Therefore, the functions in Theorem 17.2 are f(t,x,u) = B(t)u (17.49) M (t , x(t ), u(t )) = (1 − u)2 a K Pload η2eff , FC (17.50) G(x(T)) = 0 (17.51) ϕ(T,x(T)) = 0 (17.52) (17.53) Pload (1 − u) PFCmin − ηeff , FC Pload (1 − u) − PFCmax + η eff , FC R (t , x, u ) = SOCmin − x x − SOCmax −u u −1 Note that u(t) = α(t) and x(t) = SOC(t) have been used, and B(t ) (− Pload (t ) /CVbat ηeff ,bat) Since the OF and the dynamical system not depend on the state but only on the input u, hence ∇ x f (t , x, u ) = (17.54) ∇u f (t , x, u) = B(t ) (17.55) ∇ x M (t , x, u ) = (17.56) ∇ u M (t , x, u ) = − 2 a K Pload (t )(1 − u) ηeff , FC (17.57) ∇ ′x R(t , x, u) = (0 −1 0) (17.58) ∇u′ R(t , x, u) = (ρ(t , u) −ρ(t , u) 0 −1 1) (17.59) with ρ(t , u) = Pload (t ) According to Theorem 17.2, necessary conditions for the optimum are ηeff , FC λ (t ) = ν3 − ν4 (17.60) λ(T) = 0 (17.61) 578 Advanced Electric Drive Vehicles and from Equation 17.28 λ B(t ) + Ξ(t , u) + ν1 ρ(t , u) − ν2 ρ(t , u) − ν5 + ν6 = with Ξ(t , u) = − (17.62) 2 a K Pload (1 − u) and ηeff , FC > 0, R j (t , x, u) = νj = = 0, R j (t , x, u) < Observe from Equation 17.53 that ν1 and ν2 cannot be positive at the same time.* The same is true for ν3 and ν4† as well as for ν5 and ν6.‡ That means that Equation 17.60 can be rewritten as > 0, if SOC = SOCmin λ = = 0, if SOCmin < SOC < SOCmax < 0, if SOC = SOC max that is, λ = ν3 when SOC = SOCmin and λ = −ν4 when SOC = SOCmax Let θ(t,u) = λ B(t) + Ξ(t,u) + ν1ρ − ν2ρ; then Equation 17.62 can be rewritten as θ(t,u) > 0 if u = 0 (17.63) θ(t,u) = 0 if 0