Energy Consumption Modeling and Optimization of Traction Control for High-speed Railway Trains

Transcription

1 Internationa Journa of Contro and Automation Vo8, No0 (05), pp Energy Consumption Modeing and Optimization of Traction Contro for High-speed Raiway Trains Jiang Liu, *, Bai-gen Cai, Gang Xu and Gao-feng Sun Schoo of Eectronic and Information Engineering, Beiing Jiaotong University, Beiing 00044, China State Key Laboratory of Rai Traffic Contro and Safety, Beiing Jiaotong University, Beiing 00044, China CSR Sifang Locomotive and Roing Stock Co Ltd, Qingdao 66, China *E-mai: Abstract The energy consumption modeing and the optimization soution for the traction contro of high-speed trains are addressed in this paper Based on the anaysis of the operation process, an improved train traction anaysis with the muti-mass modeing approach is presented, and the corresponding energy consumption mode is investigated By considering the issues of the passenger comfort and traveing time, an optimization agorithm for traction contro of the high-speed trains is presented by using a mutiobective-fitness partice swarm optimization technique Simuations are performed with practica parameters of the CRH EMU (Eectric Mutipe Unit) and raiway ine, and the resuts demonstrate the performance of the proposed soution over the conventiona traction cacuation method Keywords: High-Speed Raiway; Traction Contro; Muti-Mass EMU Mode; Energy- Efficient Contro; Partice Swarm Optimization; Muti-Obective Optimization Introduction In recent years, the high-speed raiway in China is experiencing a rapid deveopment based on an ambitious pan for the future raiway transportation architecture, and operation ength of the high-speed raiway in China by the end of 0 has reached 50 of the tota ength in the word [,] Besides raiway infrastructures, the high-speed Eectronic Mutipe Units (EMUs) are the decisive eements that undertake the suppement of transport capabiities In the past decade, Chinese Train Contro System (CTCS) was estabished and became a comprehensive system for the operation contro, dispatching and safety assurance of EMUs [] Considering the optimization of transport services in a system eve, there have been severa key aspects addressed, incuding the traffic safety, efficiency, environmenta friendiness, and mutimoda interoperabiity, etc Among these issues, the energy efficiency is another significant factor in the design and optimization of the raiway operations, especiay the energy consumption of each individua train [4] The energy consumption of traction contro for the high-speed trains depends on the train s configuration, propusion system, traction current, and the operation conditions Since the trains have to operate according to pans from the raiway dispatching system, the panned traectories, which are used as the inputs to the driver guidance system or an automatic contro system, describe the permitted motion of the trains, and hence the energy consumption coud be identified and optimized from the panning of traectories [5] From the view of train traectory optimization when considering the energy saving probem of traction contro, there have been many resuts by the researchers and engineers ISSN: IJCA Copyright c 05 SERSC

2 Internationa Journa of Contro and Automation Vo8, No0 (05) Moritani [6] anayzed the trade off reation between the tota consumed energy, the running time, the oss of traction motors and inverters Feng [7] summarized the traction energy cost and transport operation time of high-speed trains with a range of target speeds through simuations Li [8] proposed a green train scheduing mode and fuzzy mutiobective optimization agorithm to minimize the energy consumption, where the simiar approaches can be found in [9,0] For pursuing an identified effect for the traectory or the speed-curve, some inteigent methods, incuding genetic agorithm [], swarm inteigence [] and neura network [], have been introduced and improved in the energy efficient soutions However, besides the assurance of energy efficiency, it is expected to achieve a comprehensive effect concerning severa aspects based on a considerabe eve of energy capabiity, which means the adaptabiity, precision of the traction mode and computation efficiency of the soution shoud aso be concerned In this paper, we focus on the traction energy consumption modeing of high-speed trains A muti-mass approach is adopted to generate an optimized traction contro soution by using the Partice Swarm Optimization (PSO) technique with an improved fitness Simuations are performed to demonstrate the capabiity and potentia of the proposed soution The rest of this paper is organized as foows Section detais the train traction mode using PSO, and the resuts and discussions are given in Section Finay, Section 4 concudes this paper Design of Traction Contro Method Traction Mode and Energy Consumption Anaysis The practica operation of a high-speed train corresponds to severa issues, incuding the traction contro, the dynamic characteristics of the train, the rai signaing contro conditions and the experience and status of the drivers, etc In order to evauate and optimize the train operation process, the nematic mode of the high-speed trains is essentia to provide an effective approach Athough there are some differences between CRH EMU and the existing raiway ocomotives [4], the stress condition and operation characteristics of the trains can be anayzed according to the Newton s second aw Considering the operation modes of a CRH train and its force anaysis, the resutant force is derived from the integration of the traction force, the brang force and the resistance force The practica force situation of a train is determined by the operating condition concerning the nematica imits, rai track situations and the operation conditions A typica CRH train operation process can be divided into three operation conditions, where Figure shows the speed-distance curve between two adacent raiway stations () Traction condition The resutant force of the train is cacuated through an integration of the traction force and the resistance, where the train s speed increases with the running distance (eg b 0 -b, b 4 -b 5 ) () Brang condition The brake force and resistance contribute to a decreased train speed under the pu-into or the brang scenarios (eg b -b, b 6 -b 7 ) () Intermediate condition The intermediate condition is used for keeping the train speed within a range from a ower bound to an upper bound, where the coasting or cruising coud be invoved (eg b - b, b -b 4, b 5 -b 6 ) This is an indispensabe procedure between the traction and brang conditions 0 Copyright c 05 SERSC

3 Internationa Journa of Contro and Automation Vo8, No0 (05) Figure Speed-Distance Curve of a Whoe Trip between Two Stations Based on the above mentioned principe, a the operation conditions and corresponding force situations shoud be concentrated in the traction modeing process Hence, the nematic mode of a high-speed train can be written as dv v u f ( v) u b( v) r( v) g( x, v) t b dx () where v represents the train speed, x is traveing distance from a certain reference track ocation, ut(0 ut ) and ub( ub 0) denote the traction coefficient and brang coefficient, and f ( v), b( v), r( v), g( x, v ) are traction force, brang force, basic resistance and the additiona resistance, respectivey In most traditiona resuts of force anaysis and nematic modeing, ony a singe partice mode is concerned for pursuing the simpicity and cacuation efficiency However, with a high resoution for anayzing the nematics of the high-speed raiway trains, the muti-mass EMU mode is definitey required to iustrate the inherent mechanism When using a CRH- EMU as an exampe, which usuay consists of 8 compartments incuding 4 motor units and 4 traier units, the muti-mass mode can be achieved as shown in Figure Figure Force Anaysis of an EMU using the Muti-Mass Mode At instant k, the resutant force of compartment i can be cacuated as F u f ( v ) u b( v ) W Q () t b qk ( i i) i Q qk ( i i) i 8 qk ( i i) qk ( i i) i,,7 () Copyright c 05 SERSC

4 Internationa Journa of Contro and Automation Vo8, No0 (05) k( i i) i, i k ( i) q x x (4) where W denotes the resistance force for the i th train compartment which is an integration of the basic and additiona resistance, qk ( i ) represents the interaction force between two adacent compartments from i to ( i, i ), i, i is the easticity coefficient between two compartments, and x denotes the D ocation of compartment i at instant k Thus, the acceeration of the train compartment i is a F (5) ( ) m where is the rotary mass coefficient which is aways set 006, and weight of the i th compartment at instant k m denotes the When considering the train as a rigid body and the damping is ignored, each compartment is supposed to have the same acceeration as the whoe EMU, which means that a a a a (6) k k k8 k Therefore, the interaction forces fufi the foowing constraints when we ust consider the ongitudina force, where q q (7) k( i i) k( ii) 0 Under this circumstance, the force anaysis of the whoe EMU is reaized by integrating the resutant force of a the compartments, which means that 8 8 F F ( ) a m u f ( v ) u b( v ) W q k k t k b k k k () i i 7 u f ( v ) u b( v ) W q q u f ( v ) t k b k k k ( ) k ( ) t k8 u b( v ) W q b k8 k8 k (78) 8 ut f ( vk ) ubb( v ) W,,6,7 i (8) The Eq (8) shows a simpified muti-mass mode of the CRH- EMU where the reative motion of two adacent compartments, which can be represented by the reative dispacement xi, i( i,,7) according to mi xi, i, is repaced by the rigid body as Eq (6) With the resutant force anaysis, the train traction mode coud be derived according to the different conditions, where the traction, brang, coasting and cruising f conditions are concerned Here we use the traction acceeration a k to describe the traction contro strategies Copyright c 05 SERSC

5 Internationa Journa of Contro and Automation Vo8, No0 (05) a f k 8 W i ak traction 8 ( ) m i 0 brang, coasting 8 ubb( v ) i W cruising 8 ( ) m i For the energy-saving purpose in determining the traction strategy, an energy consumption mode is basicay required to evauate the energy situations of a certain traction process The energy consumption index J for a high-speed train can be cacuated as bas aux (9) J J J (0) where J bas is the basic traction energy derived by the traction actions, which correspond to the traction and cruising conditions, and J aux represents the auxiiary energy component These two components can be computed as J f ( v) vdt b( v) vdt () bas Jaux T () where is the coefficient referring the conversion from the eectric energy to the mechanica energy, refers the conversion from the mechanica energy to the eectric energy, is the auxiiary power of the train, and T denotes the time consumption of the whoe trip To determine the optima train traction soution, the whoe trip is divided into severa sub-sections based on the speed imit, and the energy consumption of a sub-section ( N) can be evauated as the corresponding conditions, where 8 mi ( v (tra) end, vinit, ) i J () g g J s W m s r v g v m where 8 8 (cru) cru, i cru, ( ) ( ) i 600 i 600 i (4) J tra, and J cru, are energy consumption for traction and cruising conditions in the th sub-section, i means the number of train compartment, v init, and v end, denote the initia and target end speed of a sub-section, v is the average speed, and rv ( ) and gv ( ) are the basic resistance and the additiona resistance with an average speed v For simpification purposes, the energy consumption for the brang and coasting (brk) (coa) conditions are set as J J 0 Copyright c 05 SERSC

6 Internationa Journa of Contro and Automation Vo8, No0 (05) According to the above anaysis, when the condition of each sub-section is determined, the tota energy consumption during a whoe trave can be derived by using a summation, which is a soution to Eq (0) as where N ( ) aux (5) J J J represents the condition mode of section, which beongs to tra,brk,coa,cru Optimization Traction Contro with PSO Different from the traditiona traction contro method using the singe-partice EMU mode, in this paper, the muti-mass EMU mode is empoyed to ensure an effective description for traction operations Under a target of energy-efficiency optimization, the energy consumption mode can be used to estimate and determine a sequence of operation conditions and the corresponding condition conversion points A condition sequence chain traction-cruising-coasting-brang is taken as the minimum unit to compete a whoe trip Besides that, speed imits determined by the rai track features (eg gradient, curvature, and signaing conditions) Uncertainty exists in the combination of operation condition sequences and the energy-saving target Therefore, inteigent agorithms are introduced in this paper to determine an optimized soution of train traction contro Due to the constraint from the speed imit condition, there are mainy two steps to cacuate the optimized traction contro soution: ) determination of sub-sections; and ) cacuation of the condition sequence First, the determination of sub-sections is carried out according to the features of the track, incuding track ength, speed imits and gradients For the sub-section (,,, N) with a ength s x x and the gradient grad, where denotes ongitudina ocation of a train, the situation of speed imits ( v, v) between adacent sub-sections may resut in different strategies for the traction and operations Figure indicates the four situations x Figure Operation Contro Strategies under Different Speed Limit Situations When the speed imits of a the sub-sections are determined, the foowing probem is to determine the sub-sequence of a traction strategy for each sub-section, which is an integration of the above mentioned four operation conditions According to the principes as shown in the Figure, the soution of a th sub-sequence is described by a set of speed indices, incuding the initia speed v init,, target speed v tar,, initia brang speed 4 Copyright c 05 SERSC

7 Internationa Journa of Contro and Automation Vo8, No0 (05) v brk, and the end speed v end, For the seven situations as shown in Figure, the precondition for cacuating an optimized sequence is isted as foows, vinit, 0, vtag, (0, v ], vend, (0, vtag, ), v v, v (0, v ], v ( v, v ) init, end, tag, end, tag,, vinit, vend,, vtag, (0, v ], vbrk, (0, v ), vend, (0, v ) 4, vinit, vend, vtag,, vend, (0, vtag, ] 5, vinit, vend,, vtag, (0, v ], vbrk, (0, v ), vend, ( v, v ) 6, vinit, vend,, vtag, (0, v ], vbrk, (0, v ), vend, (0, v ) 7, vinit, vend,, vtag, 0, vbrk, (0, v ), vend, 0 (6) Second, by matching the situations as (6), the state vector T x ˆ ( v, v, v, v ) can be initiaized and used for generating the optimized init, tag, brk, end, resuts With the energy consumption mode that is shown in Eq (5), the energy efficiency of an operation sequence is a function of speed indices from x ˆ However, it is difficut to determine a minimum-energy soution since we cannot directy buid a theoretica description for x ˆ and the obect function J min Therefore, the partice swarm agorithm is considered for soving the energy-saving probem, where the widey used PSO (Partice Swarm Optimization) technique is invoved The PSO technique, which promises to provide an effective optimization capabiity with a simpe impementation, is appied here to sove the optimization probem by using a singe obective for minimizing the energy consumption With the definition of the fitness function, the partice popuation is evauated for determining the oca and goba optimization resuts For the sub-section, we use x and x to indicate oca and goba best ocations After initiaization, the position x () i and veocity μ () i of the partice at the i th evoution step are defined for the iteration, where the popuation is with a constant vaue Np( Np) and the iteration is infinite under certain termination conditions In each iteration step, the partices are updated as p p g μ ( i) μ ( i ) c r x ( i) x ( i ) c r x x ( i ) (7) x ( i) x ( i ) μ ( i) (8) where is the inertia weight, c and c denote the acceeration coefficients, and r and r are the parameters with random vaues By using the above principes to update the ocation and veocity of the partices, the goba best ocation wi graduay converge to the optimized vaue, on the basis of a suitabe fitness function and the corresponding decision mang ogic A constant target threshod for the fitness is empoyed for terminating the iteration whie the foowing criterion is fufied g Copyright c 05 SERSC 5

8 Internationa Journa of Contro and Automation Vo8, No0 (05) ( i),,,, N g x () i x (9) N,( ) aux, ( i) J ( i) J ( i) J ( i) (0) which means the goba best fitness is within the bound of a given threshod eve In order to constrain the cacuation to avoid infinite iterations without fufiing criterion (0), an assistant principe for breang the PSO cacuation is invoved by using a maximum iteration step L, which indicates that the PSO iteration wi be terminated when the current step exceeds the threshod (as i L) and the criterion (9) is not fufied for a the past steps ( i,,, L ) As an important branch of pubic transportation services, the modern high-speed raiway is concerning more on the service quaity With a basis of safety, the passenger comfort is aso a decisive factor for ensuring the service acceptance, which shoud aso be considered in the design of the optimized train traction operation strategies According to the dynamics of high-speed trains, the degree of discomfort coud be evauated by the integra d a () i N ( i) dt a ( i) () dt where a () i denotes the acceeration of a train for the partice at step i, and a () i is the equivaent difference of a () i for each sub-section with different operation modes considered The vaue of () i is inversey proportiona to the comfort of passengers under the current traction contro strategy, and hence, the optimization process shoud concern this restraint Furthermore, the pre-condition for the train traction optimization shoud be considered due to the priori constraint from the operation panning of high-speed trains Since the timetabes for the trains have been designed before the operation activities, the optimization oriented to the energy efficiency or the passenger comfort shoud aso take into account the constraint of the traveing time The traveing time of a sub-section can be cacuated as N vend, ( ) v init, F ( i) () T () i dv dv F i where () F i denotes the resutant force of the train when the traction strategy from the th partice is adopted Based on the above anaysis, it can be seen that the severa factors shoud be considered in the design of an optimized traction soution, which requires an effective optimization method that coud provide certain coverage to these issues Besides the muti-obective optimization techniques, the singe-obective optimization is with its advantage in simpicity for pursuing the unique soution by PSO, where the design of the fitness function is a key factor Here we can integrate the three obectives using a inear weighing strategy, and thus a fitness function that is appied for evauating the partices can be re-written as the foowing form 6 Copyright c 05 SERSC

9 Internationa Journa of Contro and Automation Vo8, No0 (05) J ( i) ( i) T ( i) () i () where () i represents the improved fitness for the partice at step i,, and are the weight coefficients for tuning the fitness,, and are normaization coefficients The weight coefficients are decisive factors with the given modes of these indices, since they can directy adust the importance degrees of the three aspects under different scenarios There can be the inear or noninear weight coefficient strategies for an enhanced fitness with the constraint, such as and Vaue spaces of the two exampes are shown in Figure 4 Figure 4 Vaue Spaces of Weight Coefficients (Left: Linear Strategy; Right: Noninear Strategy) By using the terminating principe for PSO with a fitness imit or a maximum step L, the speed indices for each sub-section are extracted from the partice with a goba best fitness, and thus the whoe traectory of the high-speed train in the trip is derived Based on the force mode and the parameters of the EMU, the optimized traction contro soution is obtained Agorithm PSO-based train traction contro soution Begin Input the rai track parameters s, v, grad Determine the sub-sections and the corresponding pre-conditions according to Eq (6) oop Initiaize the popuation of partices x, μ for a sub-section Initiaize the PSO parameters, c, c, r, r,, L,,,,,, Set the oca best p x and goba best g x with the current position 7 oop 8 Update the partices x ( i), μ ( i) within one iteration step as Eq (7) and (8) Copyright c 05 SERSC 7

10 Internationa Journa of Contro and Automation Vo8, No0 (05) End Evauate the fitness () i of each partice as Eq () Update the oca best fitness Update the oca best fitness If the condition end oop g p () if g if p p ( i) ( ) () g or i L is fufied, exit oop Determine the optimized state vector x ˆ for sun-section If a the sub-sections are cacuated, exit oop end oop Cacuate the optimized traectory and operation condition sequences using x ˆ Cacuate the traction contro sequences a f k as Eq (9) According to the above mentioned PSO principes for the energy-efficient traction contro issue for high-speed trains, the cacuation procedure to generate an optimized soution can be summarized as Agorithm From the indicated procedure and predefined conditions, the optimized traction contro soution can be derived by the off-ine operations The strategy for the weight coefficients in a partice fitness function promotes a compromise to comprehensive effectiveness under an origina intention for the energyefficient contro Simuation and Anaysis In this section, in order to vaidate the performance of the proposed agorithm, simuations are carried out with practica track conditions and parameters of Beiing- Shanghai high-speed raiway The performances from different optimization methods are compared using specific EMU parameters Three strategies for traction contro optimization are invoved: () Conventiona traction contro without optimization () Optimized traction contro using PSO with a singe-obective-based fitness function () Optimized traction contro using PSO with a muti-obective-based fitness function The ength of the test track between two successive train stations is 58km, and the speed imits are pre-defined for the sub-sections as 00km/h, 00km/h and 00km/h respectivey The speed imits and gradient condition of the test track are shown in Figure 5 8 Copyright c 05 SERSC

11 Internationa Journa of Contro and Automation Vo8, No0 (05) Figure 5 Speed Limits and Gradient Condition of the Track Section for Test First, without any optimization operation, we anayze the train operation process according to the conventiona CRH train traction cacuation mode and agorithms [5] The parameters appied for the traction cacuation are isted in Tabe Based on the obtained indices of the running process, the running curve of the whoe trip is shown in Figure 6, which aso depicts the switch of operation conditions and the acceeration of the train To distinguish the different operation conditions, we use the vaues -, 0 and to represent the brang, intermediate and the traction conditions respectivey From the figures, it can be found that the derived traction contro soution is capabe of tracng the constraints of track conditions Under a boundary operation scheme and the requirement of time panning, the conversion of operation conditions guarantees the fufiment of the traffic principes and the EMU equipment characters However, there are frequent switches of the contro strategies during the intermediate conditions, and the vaue of acceeration is reguary fuctuated due to the ideaization of track conditions and the identified operation strategies Tabe Parameter Settings in Conventiona Train Traction Cacuation Parameter Vaue EMU component mass m (constant vaue) / t 55 Gravitationa acceeration / m/s 98 Rotary mass coefficient 006 Basic resistance rv () / N/kN v+00005v Maximum brang force bv () / N/kN 855 Length of sub-section s / km 505 Length of sub-section s / km 48 Length of sub-section s / km 657 Figure 6 Resuts of Train Traction Cacuation using the Conventiona Method Copyright c 05 SERSC 9

12 Internationa Journa of Contro and Automation Vo8, No0 (05) Second, in order to vaidate the presented PSO-based optimization agorithm for the EMU traction contro, the PSO-based optimization methods are performed with the singe-obective fitness strategy, where three indices are tested separatey, incuding the energy consumption, passenger comfort and the traveing time The parameters used in the PSO-based cacuations are isted in Tabe Tabe Parameter Settings in the PSO-Based Cacuation Parameter Vaue Inertia weight 08 Acceeration coefficient c 494 Acceeration coefficient c 494 Partice popuation N p 50 Maximum iteration step L 50 Vaue space of partice veocity μ () i [-05, 05] Resuts from the singe-obective-based PSO method are compared with the conventiona traction strategy Tabe summarizes the resuts of the four strategies, where some additiona indices are invoved to evauate the improvements We use J [( J0 J) / J0] 00 and [( 0 ) / 0] 00 to represent the improvement of energy consumption and the comfort, where the subscript 0 denotes resuts from the conventiona strategy T indicates the deviation of the traveing time from the reference vaue Tabe Resuts of the Conventiona and Singe-Obective-Fitness-Based PSO Soutions Mode Conventiona traction PSO with min J () i PSO with min () i PSO with min T () i Energy consumption J J Discomfort Time consumption T T From the comparisons, it can be found that the appication of PSO technique provides great improvement to these indices over the conventiona method Within the same traction contro scheme, the energy consumption is reduced to a certain extent by PSO optimization as we as the passenger discomfort However, an increase of traveing distance becomes the expense for pursuing the energy and comfort targets As the theoretica anaysis, a baanced effectiveness of the optimization, under the premise of energy efficiency, is of great necessity Hence, we further investigate the performance of a PSO-based cacuation using a muti-obective fitness definition A inear weight strategy with [,, ] [05,05,05] as shown in Figure 4 is adopted for test and anaysis Figure 7 shows the resuts of fitness from each partice during the iterations, and the evoution of the goba best fitness is iustrated The resuts of traction contro with 0 Copyright c 05 SERSC

13 Internationa Journa of Contro and Automation Vo8, No0 (05) the optimized soution are shown in Figure 8, where the speed-distance curve, operation condition and the traveing acceeration are a provided Figure 7 Partice Fitness and the Goba Best Vaue during the Iterations Figure 8 Resuts of Train Traction Cacuation using the Muti-Obective- Fitness PSO Figure 9 The Derived Traction Acceeration and the Corresponding Energy Consumption Compared with the resuts in Figure 6, there is not great difference in condition conversion when the optimization strategy is adopted However, the fuctuation of the train s acceeration is reativey eased, especiay in the intermediate condition periods Since coasting operations are invoved and gradient constraints are concerned in the dynamic anaysis of the resutant force, the derived speed profie is not as smooth as the idea resuts Consequenty, the energy consumption is obviousy reduced as the theoretica anaysis Figure 9 shows the required traction acceeration and the corresponding energy consumption with the traveing distance Copyright c 05 SERSC

14 Internationa Journa of Contro and Automation Vo8, No0 (05) Tabe 4 Resuts of the Muti-Obective-Fitness-Based PSO Soutions Energy consumption J J Discomfort Time consumption T T To iustrate the effectiveness of the weighted fitness strategies, two strategies, which ony consider the comfort or the running time, are invoved for comparison and vaidation Tabe 4 depicts the weights and the optimization resuts A the three strategies take fu advantages of the proposed muti-fitness definition, which provides notabe improvements of the energy consumption (with a maximum of 564) and the discomfort eve (with a maximum of 4868) Particuary, the resuts converge as the distribution of Overa, the traction energy consumption of a whoe trip definitey decreases with the weight in an integrated fitness function, and a onger traveing time wi be derived in genera 4 Concusions In this paper, we have proposed a PSO-based agorithm for generating the traction contro soution for high-speed trains In this approach, the muti-mass EMU mode enhances the anaysis of the resutant force during the different operation conditions Based on the energy consumption mode, partice swarm optimization is enabed with an improved fitness strategy, where the additiona performance indices, incuding the passenger comfort and traveing time, coud be covered By this method, the train traction contro soution can be computed though a finite iteration process, and some additiona vaues are achieved as we as energy efficiency The simuation resuts show that the presented approach is capabe of optimizing the traction contro within an identified operation scheme In the future work, an optimization concerning the train tracing scenarios and raiway signaing constraints wi be investigated Furthermore, the operation contro using extended equipment modes is expected for further evauations Acknowedgements This research was supported by Nationa Key Technoogy R&D Program of China (0BAG4B0), Internationa Science & Technoogy Cooperation Program of China (04DFA8060), Nationa Natura Science Foundation of China (6400, U4), and the Fundamenta Research Funds for the Centra Universities (04JBM00) References [] M Yin, L Bertoini and J Duan, The Effects of the High-speed Raiway on Urban Deveopment: Internationa Experience and Potentia Impications for China, Progress in Panning In press (04) [] C Sheng, Y Liu, F Lin, X You and T Q Zheng, Power Quaity Anaysis of Traction Suppy Systems with High Speed Train, Proceedings of the 4th IEEE Conference on Industria Eectronics and Appications, (009) May 5-7; Xi'an, China [] H Yang, Y Fu, K Zhang and Z Li, Speed Tracng Contro using an ANFIS Mode for High-speed Eectric Mutipe Unit, Contro Engineering Practice, (04) i Copyright c 05 SERSC

15 Internationa Journa of Contro and Automation Vo8, No0 (05) [4] YV Bocharnikov, AM Tobias and C Roberts, Reduction of Train and Net Energy Consumption using Genetic Agorithms for Traectory Optimisation, Proceedings of IET Conference on Raiway Traction Systems, (00) Apri -5; Stevenage, UK [5] S Lu, S Himansen, TK Ho and C Roberts, Singe-Train Traectory Optimization, IEEE Transactions on Inteigent Transportation Systems, 4 (0) [6] T Moritani and K Kondo, Basic Study on a Designing Method of the Traction Equipments to Save the Running Energy with an Optimization Method, Proceedings of Internationa Conference on Eectrica Systems for Aircraft, Raiway and Ship Propusion, (00) October 9-; Boogna, Itay [7] X Feng, Optimization of Target Speeds of High-speed Raiway Trains for Traction Energy Saving and Transport Efficiency Improvement, Energy Poicy, 9 (0) [8] X Li, D Wang, K Li and Z Gao, A Green Train Scheduing Mode and Fuzzy Muti-obective Optimization Agorithm, Appied Mathematica Modeing 4, 7 (0) [9] Y Sun, C Cao and C Wu, Muti-obective Optimization of Train Routing Probem Combined with Train Scheduing on a High-speed Raiway Network, Transportation Research Part C: Emerging Technoogies 44 (04) [0] S Dundar and I Sahin, Train Re-scheduing with Genetic Agorithms and Artificia Neura Networks for Singe-track Raiways, Transportation Research Part C: Emerging Technoogies 7 (0) [] R Chen, L Liu and J Guo, Optimization of High-Speed Train Contro Strategy for Traction Energy Saving Using an Improved Genetic Agorithm, Journa of Traffic and Transportation Engineering, (0) [] S Sun, Y Li and H Xu, Energy Consumption Optimization for High-speed Raiway based on Partice Swarm Agorithm, Proceedings of 4th Internationa Conference on Computationa Inteigence and Communication Networks, (0) November -5; Mathura, India [] J Yu, Q Qian and Z He, Study on Train Traction Simuation of High Speed Motor Train Set, Journa of the China Raiway Society 5, 0 (008) [4] X Kang, Study on Train Traction Simuation of High Speed Motor Train Set, Engineering Science, (0) [5] J Peng, Editor, Traction and Brake of Eectronic Mutipe Unit, China Raiway Pubishing House, Beiing (009) Copyright c 05 SERSC

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