subsystem for a hybrid locomotive»

Transcription

1 EMR 13 Lille Sept Summer School EMR 13 Energetic Macroscopic Representation «Different models of an energy storage subsystem for a hybrid locomotive» C. Mayet 1,2,4, A. Bouscayrol 1,2,4, J. Pouget 3,4, W. Lhomme 1,2,4, T. Letrouvé 1,2,4 1 L2EP, 2 Université Lille1, 3 SNCF, 4 MEGEVH network

2 EMR 13 Lille Sept Summer School EMR 13 Energetic Macroscopic Representation «Introduction»

3 - Context and Objectives - Problematic : even if electric railway transportation is often used, dieselelectric locomotives are still used for non-electrified segment and specific tasks (switching, shunting, assistance operation ). 3 1 SNCF has developed a demonstrator of a new hybrid locomotive 2 First energy managements have been developed in heuristic way Scaps fuel-cell ICE power electronics batteries Energy management Global objective: development of a systematize control of hybrid locomotive using the Energetic Macroscopic Representation Investigate and test of new energy management strategies Validate these strategies using HIL simulations 3

4 Locomotive BB Locomotive s Architecture - 4 Generation system DC Bus Traction system Moteur Diesel Diesel Engine 610 kw SM DCM 4*100 kw Wheels Aux. Drawbacks : - No energetic storage for traction, - Diesel engine is uninterrupted (Auxilairies, etc.) - Diesel engine is not always in its maximal efficiency point. 4

5 [Baert 11] Locomotive PLATHEE Dynamic model of generation and storage system Generation system Diesel Engine Diesel Engine kw Complete SM dynamic and - Locomotive s Architecture - Storage system DC Bus [Mayet 12] dynamic and simplified models of the traction system Traction system Objective: Define what is the convenient model of the generation and storage subsystems in order to make an energetic study? 1.Generation subsystem quasi-static models DCM 4*100 kw 5 Wheels Moteur Diesel Bat. Moteur Diesel Super Cap. 2.Energy NiCd storage batteries subsystem (194 Complete kwh) dynamic, simplified dynamic and static models Scaps (6,94 kwh) 3.Simulation results Comparisons between the different models Energy managment Aux. 5

6 EMR 13 Lille Sept Summer School EMR 13 Energetic Macroscopic Representation «Generation Subsystem» Model, description and control

7 // chopper & inverter - Complete dynamic model (EMR) - v rd WRSM v rsd i rsd EMR 13, Lille, Sept. e 2013 rd = 0 esd = p Lsqisq Ωsh esq = p Ωsh L sd isd + M k Tsm = p X dηsm isq 7 ( i ) sr rd DC bus i r sm m ch i rd i rsd e rsd T sm Ω sh ICE u ism c common = ir sm + is sm DC bus i sm chopper & Inverter i s sm m rec u s i s1,2 synchronous machine v sdq i sdq θ sm ICE v sq i sq i sq e sq f sh Ω Ω sh sh + J sh T ICE d dt Ω sh T ICE_ref = T sm T ICE i r sm Fuel consumption map i rd v rd i sm i s sm u s12 u s13 i s1 i s2 Ω sh T sm Ω sh T ICE All equations are included in the paper 7

8 - Complete dynamic model (IBC) - 8 // chopper & inverter WRSM v rd v rsd i rsd DC bus i r sm m ch i rd i rsd e rsd T sm Ω sh ICE i sm u s v sdq v sq i sq Ω sh T ICE T ICE_ref i s sm m rec i s1,2 θ sm i sdq i sq e sq Ω sh_ref u s_ref v sdq_ref v sq_ref i sq_ref i sm_ref v rd_ref K D1,2 v rsd_ref i rsd_ref T sm_ref strategy 8

9 - Quasi-static model - 9 i sm T Ω = η u sm k sm sh c 1 when uc ism 0 with k = and Tsm = T 1 when uc ism < 0 sm _ ref T sm Ω sh DC bus ICE i sm_ref i sm Ω sh T ICE T sm_ref strategy Ω sh_ref T ICE_ref Assumptions: Fast dynamics (electric) are neglected Torque control of the WRSM is perfectly achieved Closed-control of the electric machine is well achieved 9

10 EMR 13 Lille Sept Summer School EMR 13 Energetic Macroscopic Representation «Energy Storage Subsystem» Model, description and control

11 - Complete dynamic model (EMR) - bats smoothing Inductors choppers 11 DC bus i ch1,j i s1,j d dt R ind is, mj + Lind is, mj = ubttot, j vch, mj h1,j i ch2,j u bt tot i bt, j, j common 3 = q= 1 i s, mj series // smoothing inductor choppers // i u c modbt, j common 3 = m= 1 i ch, mj i bt,j i s2,j h2,j i s3,j i ch3,j i mod bt,j Bat. u bt tot,j i s1,j i ch1,j h3,j u bt tot,j Bat. Bat. u bt tot,j i s1,j u bt tot,j v ch1,j i s2,j m ch1j,. i ch2,j i mod bt,j i bt DC DC bus Bat. i bt,j i s2,j u bt tot,j v ch2,j i s3,j m ch2,j. i ch3,j 11 Bat. i s3 v ch3,j m ch3,j. All equations are included in the paper

12 - Simplified dynamic model - 12 series // smoothing inductor choppers // // u bt u bt tot u bt tot i s i ch i mod bt i bt DC Bat. i bt 290 i bt 3 i s v ch m ch. 3 4 DC bus v ch_ref 3 4 i s_ref i ch_ref i mod bt_ref i bt DC_ref Assumptions: All the battery cells, all the inductors and all the modules have the same behaviour coupling elements are replaced by adaptation elements. 12

13 - Static model - 13 i bt i = η u mod bt c k eqchubt tot 1 when uc imod bt 0 with k = and imod bt = i 1 when uc imod bt < 0 mod bt _ ref series chopper // Bat. u bt i bt 290 u bt tot i bt i mod bt i bt DC 4 DC bus 4 i mod bt_ref i bt DC_ref Assumptions: All the battery cells, all the inductors and all the modules have the same behaviour coupling elements are replaced by adaptation elements. Current module control is well achieved. 13

14 EMR 13 Lille Sept Summer School EMR 13 Energetic Macroscopic Representation «Simulation results»

15 - Comparisons of different models - ICE 215 kw SM Generation system Storage system DC Bus DCM 4*100 kw Traction system Wheels 15 Moteur Diesel Bat NiCd batteries (194 kwh) Aux. Velocity (km/h) Moteur Diesel Super cond Scaps (6,94 kwh) Energy managment Supercapacitors power (kw) Time (s) Time (s) Batteries power (kw) ICE power (kw) 15 Time (s) Time (s) Other simulation results are given in the paper

16 - Comparisons of different models - 16 Less element: storage system element have the same behavior No fast time constant Constant efficiency Can be improve using losses table Simulation time is divided by 22 for an accuracy of 99,5% More suitable for an energetic study 16

17 EMR 13 Lille Sept Summer School EMR 13 Energetic Macroscopic Representation «Conclusion»

18 - Conclusion - 18 Conclusion Determination of complete dynamic models using EMR, Simplification of these models in an EMR philosophy, Comparisons between the different models, Obtaining of a suitable model for the energetic study of the locomotive with quasistatic model (Simulation time is divided by 22 for an accuracy of 99,5%). Perspectives Use of this model for Hardware-In-the-Loop simulation, Tests new energy management strategy. 18