Simulink电池仿真模型

【实例简介】
Simulink电池仿真模型. mathlab应用于动力电池 SOC 仿真模型
validated, the power requirement of the auxiliary load for drive train have constant times of the order of 100 ms each of the buses was accurately ascertained much faster phenomena, such as combustion dynamics or lIve switching inside electronic converters, were IV. MODELLING THE SERIES HYBRID BUSES considered to be algebraic. Figure 5 depicts the simulation The ice of the conventional vehicle is sized for the scheme in AMESim. The main subsystems were maximurn power and torque requirenents since it is the only source to meet the power and torque requirements of Internal combustion engine (ICE), the main source for the wheels. Apart from disadvantages like higher size the vehicle energy propulsion. The model for the ICe weight and cost, the ICe needs to operate at inefficient used the BsFC maps. The model could emulate the parts of the Brake Specific Fuel Consumption(BSFC) map, engine torque, mechanical power, engine efficiency, thereby consuming higher fuel. It would be more energy fuel consumption, emissions and engine speed efficient, if the ICe operated only at the most efficient parts Electric generator (EGS)coupled to the ICe that of the bsfc map secondly, conventional vehicles do not generated electricity available for propulsion have the ability to recuperate braking energies. Instead, Rechargeable energy storage system (RESS) that could hybrid propulsion systems employ a Rechargeable Energy be composed of devices such as electrochemical Storage System (RESS), which stores energy(either from batteries, supercapacitors, fly-wheels etc. The authors braking or from the primary converter) that is later used for propulsion: and could employ an ON/OFF strategy [10]for modelled the ress with a lithium ion battery, the primary converter, which works to switch off the ICE considering its good performance in terms of power when the vehicle is stationary(bus stops, traffic signals or density and energy density jams etc), to enhance the comfort of the passengers by Auxiliary electrical load (AUX) that could be lights eliminating noise and pollutant emissions. In this paper, the the control system, the air-conditioner etc authors employed the on/off strategy Electric Drive (ED), to provide power and torque to the wheels and also to produce regenerative power, A. The series hybrid propulsion architecture consisting of an electric motor and a power controller The series-hybrid propulsion architecture adopted for and this paper is shown in Figure 4, with the arrows along the The vehicle, which also contained sub-systems for power fluxes showing the actual directions when the mechanical transmission and vehicle dynamics. The numerical values are positive final drive consisted of a fixed gear ratio driven by the primary conver electrc drve mechanial traction motor. Mechanical equations describing the vehicle longitudinal behaviour was used EGS- EPC EPC EM Each sub-model was carefully constructed to be able to accurately emulate experimental results. e.g. the model for RESS AUX the electric motor/generator needed parameters in a file defining the lost power vs. the torque and the rotary Othe! velocity. The model of the battery needed data files listing signals the internal resistance vs. the depth of discharge. Similarly the complete bsfC maps were red to the ice sub-mode rinciple scheme of a series-hybrid vehicle drive-train and the model could determine the most efficient operating The iCe is coupled to an electrical generator(EGs) line, for the each level of power demanded. Efforts were The presence of a storage system (RESS) provides the made to model realistic sub-systems, building in parameters vehicle flexibility in sharing the power required for for efficiencies propulsion. This management is carried out by the on-board The pmm was the most critical elernent in the nodel Power Management Module(PMm), which continuously monitors the load requirement and decides how to share the It was programmed to determine the most optimal way to meet the driver's requirements for power while utilizing the power amongst the two sources according to predefined two energy sources of the vehicle. The authors employed goals(typical goal is vchicle efficiency maximization) the forward approach for this model, which was B. Sizing of components of the series hybrid propulsion a driving duty cycle (called mission profile in the system rogram) fed to the driver block An efficient series-hybrid electric propulsion system the driver block converted the reference speed into requires optimal sizing of all its components Models of the commands for the pmm series-hybrid bus were built on both AMESim and matlab the pmm determined the most optimal strategy based on The results of simulation were comparable, which gave the the instantaneous power demand levels, and feedback authors confidence in the quality of the modelling from key parameters of the ICE, EGS, RESS and ED All subsystems were modelled weighting the accuracy and complexity for the purpose considered. In particular, since the fastest transients useful to globally size the hybrid Series hybrid bus model DRIVER ELECTRIC GENERATOR POWER MANACEMENT MODULE (PM ENGINE ICE) 快 ACHNE Primary converter RECHARGEABLE ENE RGY STORAGE SYSTEM(RESS) 3 凸 UXILLIARY Fig. 5 Series-llybrid bus model in AME Sim C. Possible energy management strategies It is possible to control the system such that the quantity r(t) is completely delivered by Press(t), and does not form In a series hybrid solution, all power for traction is part of the primary converter electric. The sum of energies of the two(or more)power sources in the series-hybrid vehicle is usually depicted as a PREss(()=r(t),:. PEcs(l)=PeDa(t) dc bus. Power needed for the traction and auxiliaries is taken from this dc bus Hence, the ice delivers only the average power The fundamental role of the PMM is to interpret requested by propulsion, leaving the ress to deliver the drivers commands, and accordingly determine which par rest of the requested propulsive power would be delivered by The strategy presented above requires a consideration the fgs and which by the ress. In other words. to even approximate), of the future system load determine how to decompose the quantity PED(D) into PEGS future behaviour of the power demand PED(0, which is a (t)and PRess(t) function of the drivers request for torque and the vehicle duty cycle. The approximate level of power needed by the PeD(t= Peas(t)+ PREss(t) vehicle in future could be obtained by multiplying the past history of PED(C with a simple filter, e. g This degree of freedom could be used to minimize an PeGs(D is the output of a filter having as input Pen(D objective function that could be fuel consumption. The relationship(1)is guaranteed by the physics of drive train and as a transfer function 1/(1+St) A possible control strategy could be PEGs(1)= Pep(t)dt PED(e is determined to answer the driver's commands as closely as possible. It could be considered a direct In both cases a suitable value for t needs to be chosen consequence of trip characteristics, vehicle mass and DIESEL ENGINE ower losses in the ed PEGS(t is determined by PMM according to some 700 optimization rule(that will be discussed later) PREsS()is automatically determined by difference The user load Ped draws the main focus in this control strategy, while the power generation from fuel; PEGS,is given a supporting role. This control strategy (described in detail in [11],[12]) is very often used in a hybrid vehicle, and is also adopted for this paper. The useful power that goes into the load PED(t)could be imagined to be constituted by an average value and a ripple. Eg. (1)is thus 3球(雪 Iso fuel rate IgkWh modified as follows: 1000 00140016001B002000220 Pep(t)=PEna (t)+r(t) Engine Speed [rpm] Fig. 6 Output of the internal optimisation algorithm: the optimal valucs of thc ICe angular vclocity arc rcportcd directly on thc engine map After determining PEGS(O), an internal algorithm was used to choose the optimal values of the ICe rotary velocity Pcft) (Fig. 6), corresponding to the minimum fuel consumption E1 More details on possible hybrid vchicle energy management strategies can be found in [13]-[17] min V. RESULtS The objective of the study was the sizing of a complete Ng line of series-hybrid buses for BMB to augment their conventional buses. The propulsion system sizing was sized in accordance with the boundary conditions regardin Fig. 7 Meaning of the quantities used for describing ICE ON-OFF performance of the vehicle at lull load strategy correction 1. Level road The main characteristics of the sizing are listed in Table a)max speed: 80 km/h II b)pure electric mode on urban cycles(22% of the TABLE II SIZING OF THE CONSIDERED HYBRID BUSES total time). as a zero emission vehicle(ZEv) Bus models 2. Road with 16% gradient V'ivacirym AvanciryL AvanciryS a)max speed: 10 km/h(range 0, 5 km) b) start-up acceleration: 0, 3 m/s2 Wcight(full load) 13, 95 tonnes,96 tonnes 26, 67 tonnes Weight(partial load) 11. 3 tonnes 14. 3 tonnes 21,1 tunnes Auxiliaries 6kW 9 k 12 kW The reSs was sized considering condition 1b)(pure ICE max powcr 48kW 61 kW 84 kW electric mode on level gradient), according to the specified ICE efficient power 36kW 48kW 69kw range and maximum current limits for the Li-ion battery RESS Energy 31.1 kWh 38.9kWh51, 8kWh Conditions 2a) and 2b), respectively, identified the max tractive power and the max(starting) tractive effort for the from the main results it was interesting to note that the propulsion system: therefore they completely defined size of the ICE needed reduced significantly from the one characteristics for the ed in the conventional bus The authors did not have access to The sizing of the ICE could be evaluated with reference specific fuel consumption maps for smaller engines, and hence, to evaluate the fuel consumptions, programmed the to the maximum power, i.e., the max power needed by the model to downscale the existing BSFC maps. The engine ICe, or the efficient power, i.e., the power at which the was downsized with reference to the torque, keeping the engine efficiency is highest same BSFC values as the bigger engine for the smaller Due to the power management algorithms selected, the thus avoiding, the poor efficiency zones of the original efficient power would also be the average useful power engine. Fuel consumptions are summarised in Table Ill evaluated by considering the ON/OFF strategy for the comparing performance of the hybrid buses to the primary converter. In fact, the two different sizing criteria conventional ones. This, however, was only an ad-hoc could be summarised as solution but the authors are confident that the results constant speed drive at 50 km/h, ICE always ON, fully obtained through this approach would also be similar. loaded vehicle: the constant ICE power obtained is the maximum power required; TABLE IIL. FUEL CONSUMPTION Vchicle running the SORTl cycle on level gradient Bus models with the on/off strategy when the vehicle is Vivacity M Vancity S stationary or in ZEV mode the ice is switched off. g mctcrs 12 mctcrs i8 mctcrs The ice power was hence evaluated through the HEV(full load) 4, 57 83,68 following energy balance equation SORTI litres/100km litres/100kmlitres/100km Conv(full loac 7589 SORTI cyclc litres/100km litres/100km litres/100km Figure 8 shows the results of the simulation for the power fluxes (definitions of quantities in Figure 4)in the where, Pef is the efficient power generated from ICE during model for the Vancity L bus on the SORTI cycle. It the ON-state, Pavg is the average power requested from the clearly shows the distribution of the power from the two propulsion. Emax and Emin represent the switch ON-OFF sources. The RESS, ED, EGs, AUX power profiles are limits (also indicated in Figure 7). The calculated power, related by the following equation Peff, could be termed as efficient power PEn(t)+ Paux (t)=Pegs(t)+ Press (t) 200 technology is well-suited to the variable load requirements of urban buses ACKNOWLEDgMENT The authors would like to thank the anonymous reviewers for their insightful comments and suggestions 100 This study was supported in part by the Industries 2015 Project of the Italian ministry of Universities RESS ED AUX 200 100 REFERENCES 150 time [s 1]M. Ehsani,Y. Gao, S. E Gay, and A. Emadi, Modern Electra Fig.8 Simulalion of the Series-Hybrid avancily l bus nudel on Hybrid Electric, und Fuel Cell Vehicles: Fundamentals, Theory, SORTI cycle: power fluxes of the drive train and Design. New York: Marcel Dekker, 2004 [2] A Emadi, M. Ehsani, and J M. Miller, Vehicular Electric Power Similar such simulations were carried out for all the Systems Land Sea, Air, and space vehicles. New York: Marcel different bus models and on all the possible city driving Dekker. Dec 2003 cycles where the buses are finally targeted Figure 9 and 3 G. Brusaglino, G. Pede, E. vitale, Sistemi di Propulsion elettrica Figure 10 are related to the simulation on a real profile of ed tbrida. eNEA. Roma 2009 urban cycle, measured by BMB during normal operation of P. V den Bossche, " Power sources for hybrid buses: comparative Bologna city evaluation of the state of the art. journal of power sources. vol 80 pp213-216, July I999 http://www.bredamenarinibus.it/ [6http://www.amesim.com nhttp:/www.mathworks.com/ 8http://www.tuv.it/home/default.asp [9]http://www.uitp.org/knowledge/projects-details.cfmid=439 200 400 600 800 10001200 [10]M. Ceraolo, A. di donato, G. Franceschi, "A general approach to time [s] energy optimization of hybrid electric vehicles", IEEE Tran Fig. 9 Rolognal cycle: speed profile Vehicular Technology, May 2008, Vol. 57,N3, pp. 1433-1441 [Il S. Barsali, C. Miulli, A. Posscnti, " A control strategy to minimize fuel consumption of series hybrid electric vehicles, " nergy Conversion, IEEE TransactionS on, vol 19, no 1, pp. 187-195, March 2004 10 [12 D. Poli, A di Donato, G. Lutremberger: "Experiences in modeling and simulation uf hydrogen luel-cell based propulsion systens International Conference on Engines and Vehicles (ICE2009) Capri, 13-18 September 2009 100 [13 S. Barsali, M. Ceraolo A. Possenti, Techniques to control the electricity generation in a series hybrid electrical vehicle", IEEE RESS ED EGS AUX Trans. Energy Conversion, June 2002, vol 17, pp. 260-26 [14]K. T. Chau, Y.S. Wong, Overview of power management in 200 400 800 1000 1200 hybrid electric vehicles", Energy Conversion and Management, time [s October 2002, Volumc 43, Issuc 15, Pagcs 1953-1968 Fig. 10 Simulation of the Series-Hybrid Avancity I bus model on 115M. Ccraolo, A di donato, G. Franceschi, Encrgy optimisation of Bolognal cycle: power fluxes of the drive train hybrid-clectric vehicles The Pisa Experience", IEEE Vehicular Power and Propulsion Conference VPPC05, 7-9 September 200.5 VⅠ. CONCLUSION Chicago(USA) The paper presents the effort by the authors to develop a [16]M. Salman, M. Chang, I Chen,"Predictive energy management complete line of series-hybrid buses for the Italian strategies for hybrid vehicles", IEEE Vehicular Power and manufacturer bmb to enhance their line of commercial Propulsion Conference VPPC05, 7-9 September 2005, Chicago buses. First, the existing buses were accurately modelled [17 A.I. Antoniou, A. Emadi, " Adaptive control strategy for hybrid The results of the simulation matched the results provided electric vehicles, "Vehicle Power and Propulsion Conference, 2009 by the manufacturer of actual tests conducted on their PPPC 09. IEEE, vol, no, pp. 242-246, 7-10 Sept 2009 buses. This gave the authors confidence in their modelling and they proceeded lo model the series-hybrid models of the buses. These were then simulated for the different cit cycles, and the different components of their hybrid propulsion systems were specifically sized and optimised The simulation results showed a significant reduction of over 20% in fuel consunption of the series-hybrid driveline compared to the conventional one, besides a reduction in the primary converter size. This confirms that series-hy brid 【实例截图】
【核心代码】