dynamic programming DP 1 DP 1 13 Fig. 1 Passive parallel configuration DP 18 DP 19-0 plug-in hybrid e

Size: px

Start display at page:

Download "dynamic programming DP 1 DP 1 13 Fig. 1 Passive parallel configuration DP 18 DP 19-0 plug-in hybrid e"

贸果舒
5 years ago
Views:

1 Electri c Machines and Control Vol. 1 No. 11 Nov % % kw 0. 74% 0. 6% % DOI /j. emc U A X Power distribution optimization for electric vehicles with hybrid energy storage system WU Xiao-gang 1 HOU Wei-xiang 1 SHUAI Zhi-bin 3 GAO Ming-ming 1 1. College of Electrical and Electronics Engineering Harbin University of Science and Technology Harbin China. State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing China 3. Science and Technology on Vehicle Transmission Laboratory China North Vehicle Research Institute Beijing China AbstractConvex optimization was proposed in order to minimize energy consumption and battery power variation for a semi-active hybrid energy storage system of an electric city bus. Rule-based optimization method and proposed power allocation strategy were compared based on an electric city bus running the Harbin city driving cycle in respect of analysis of energy efficiency and battery power variation. The simulation results show with convex optimization power allocation strategy battery energy efficiency super capacitor comprehensive energy efficiency and the battery power mean square error were % % and kw. Compared with rule-based power allocation strategy comprehensive energy efficiency of batteries and super capacitors of convex optimization results were increased by 0. 74% and 0. 6% respectively and its battery power variance was decreased by %. The operating characteristics of electric vehicles was improved by the proposed power optimization method. Keywordshybrid energy storage systempower allocationconvex optimizationenergy efficiency analysis E01451 KF

2 dynamic programming DP 1 DP 1 13 Fig. 1 Passive parallel configuration DP 18 DP 19-0 plug-in hybrid electric vehicle PHEV fuel cell hybrid vehicle FCHV - Fig. Fully active configuration 3

3 11 1 DC /DC F i = mgsinα 3 F w = 1 C daρu a 4 F j = ma 5 f m g 9. 8 m /s α α = 0C d A ρ u a t P m + P mloss + P aux = P hess 6 3 P m P mloss Fig. 3 Battery / SC configuration P aux P hess 4. DC /DC 5 R bat DC /DC DC /DC DC /DC 5 Fig. 5 Equivalent model of power battery state of charge SOC 6 6 SOC u b u b = asoc b + u b0 7 u b soc b SOCu b0 SOC 0 a du b /dsoc b 4 u b0 a Fig. 4 SC / Battery configuration R bat P b P bt P bt = P b - P bloss 8. 1 P bt P b P bloss F t F f F w E b F i F j F t = F f + F w + F i + F j 1 F f = fmgcosα E b = n bc b u b - u b0 9 n b C b u b

4 u b0 SOC 0 u bsoc bmin - u b0 E b u bsoc bmax - u b ~ 15 7 R sc Fig. 6 6 SOC Relationship between SOC and battery open circuit voltage Fig. 7 Equivalent model of SC P nb = u b i b 10 SOC P nb u b u sc i b u sc = bsoc sc + u sc u sc soc sc i b = P nb u b P bloss P b 3 de b C b = - P b 13 ( b ) I bmax n b b ( + u b0nb ) 14 C b SOC SOC bmin SOC bmax SOC bmin SOC SOC bmax SOC E b 7 SOCu sc0 SOC 0 0b du sc /dsoc sc b 7 R P bloss = n b i b C b P b br b = 1 P sct = P sc - P scloss 17 b + u b0 P sct P sc R b i b C b P scloss E b = n scc sc u sc - u sc0 18 i b I bmin I bmax I bmin E sc n sc I bmax C sc u sc P b u sc0 SOC 0 0 I bmin n b b + u b0n P b P nsc = u sc i sc 19 E sc P nsc u sc i sc 19 i sc = P nsc 0 u sc 18 0

5 114 1 P sct E sc P scloss = R sc C sc P sc sc + u sc0c sc n sc 1 R sc P bt = P b - P bloss E sc P sc de sc = - P sc i sc I scmin I scmax I scmin I scmax I bmax n b b + u b0n b P sc I scmin n sc sc ( + u sc0nsc ) P sc C sc I scmax n sc sc ( + u sc0nsc ) 3 C sc SOC SOC scmin SOC scmax SOC scmin SOC SOC scmax SOC C sc n sc u scsoc scmin - u sc0 E sc C sc n sc u scsoc scmax - u sc0 4 C sc n sc u scsoc scmin - u sc0 E sc C sc n sc u scsoc scmax - u sc0 E SC t 0 = E SC t f 5 E SC t 0 = E SC t f t 0 t f P bloss 3~ 5 3 minimize subject to f 0 x f i x 0 i = 1 m a T i x = b i i = 1 p f 0 f m h i x = P m + P mloss + P aux = P hess a T i x - b i P scloss 17 P b P bt E b P sc P sct = P sc - P scloss - Δ de b = - P b I bmin n b b ( + u b0nb ) P b C b ( ) C b u bsoc bmin - u b0 E b u bsoc bmax - u b0 P sct = P sc - P scloss de sc = - P sc I scmin n sc sc ( + u sc0nsc ) P sc C sc I scmax n sc sc ( + u sc0nsc ) C sc 6 P bt = P b - P bloss P bt = P b - P bloss - Δ 1 Δ 1 P b P bt P bloss P b P bt P b - P bloss 7 7 P bt - +P bloss

6 Δ E cb C sc n sc P sct P sc - P scloss 8 s ele USD / P m + P mloss + P aux = P hess WhP nbmax 1 P bt P b - P bloss de b = - P b 3 I bmin n b 1 b ( + u C b0nb b ) P b Table 1 Parameters of electric vehicle with semi-active I bmax n b b ( + u C b0nb b ) hybrid energy storage system u bsoc bmin - u b0 E b m /kg A /m u bsoc bmax - u C b0 d f P sct P sc - P scloss de sc = - P sc I scmin n sc sc ( + u C sc0nsc sc ) P sc I scmax n sc sc ( + u C sc0nsc sc ) u scsoc scmin - u sc0 E sc C sc n sc u scsoc scmax - u sc0 E SC t 0 = E SC t f tf t 0 P b - P thre tf P b P thre t 0 P thre kw f = η t f t 0 P b - P thre η tf t 0 P b 30 η 5-6 η η 1 - η = tf vt t c b E cb m 0 b y p s 3. 6n b s ele P nbmax t f - t 0 31 c b USD /Whm b Wh /kgvt y p s ρ /kg /m r /m P aux /kw 5 i 0 6. y p / 1 s /km Table Parameters of power battery c b/ USD /Wh Q b /Ah 80 u b /V 3. I bmax /A 80 m b /kg. 5 P nbmax /kw E cb /Wh /kg Table 3 Parameters of super capacitor C s /F 000 u sc /V. 70 I scmax /A m /g 360

7 P d P t 0 P sc { 0 if P d P min t= P d - P min if P d > P min andsoc s > 0.5 t Δt t f 0 if P d > P min andsoc s SOC s SOCP d P min 9 Fig. 9 Harbin city driving cycle Fig. 8 8 Two power source power allocation process P b 0 sc 3 P b t= { P d if P d P min P min if P d > P min andsoc s > 0. 5 P d if P d > P min andsoc s 0. 5 Fig. 10 Rule-based optimization method 10 P d < 0 P b P sc P b t= P dif SOC S 0. 9 { 0if SOC S < 0. 9

8 P sc t= 0if SOC S 0. 9 { P d if SOC S < t f ( P b t- P ) avg t s = 0 34 SOC s SOCP d t f - t0 s P b tt P avg t 0 t f 11 1 s 14 Δt Fig. 1 Power distribution of energy management strategy based on convex optimization 11 4 Fig. 11 Rule-based optimization simulation result kw 34 s %

9 Table 4 Mean square error of the battery power of two different power allocation method s c /kw s r /kw Δs /kw Fig. 13 Power distribution of two strategies Fig. 15 Results of the analysis of two power allocation strategy energy efficiency % 9. 91% % 0. 97% % % 0. 74% 0. 6% Fig Mean square error calculation 5

10 cost J. Applied Energy FANG L XU G LI T kw % 11SONG Z HOFMANN H LI J et al. A comparison study of different semi-active hybrid energy storage system topologies for electric. Journal of Power Sources et al. Optimization for a hybrid energy storage system in electric vehicles using dynamic programing MURGOVSKI N et al. Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric busj. Applied En- vehicles J 1SONG Z HOFMANN H LI J % 9. 91% approach J. Applied Energy % 13HU X JOHANNESSON L 0. 97% % % ergy % 14ZHAO C YIN H YANG Z 0. 6% 1ZHANG P YAN F DU C. Acomprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics J. Renewable & Sustainable Energy Reviews J WU Xiaogang LU Languang. System matching and simulation of a plug-in series hybrid electric vehicle J. Automotive Engineering J LIU Ping LIU Heping GUO Qiang et al. Effects of battery direct power supply on driving performance of electric vehicles J. Electric Machines and Control WANG B XU J CAO B et al. A novel multimode hybrid energy storage system and its energy management strategy for electric vehicles J. Journal of Power Sources BLANS J M GUTIERREZ R GARRIGOS A et al.. Electricvehicle battery life extension using ultracapacitors and an FPGA controlled interleaved buck boost converterj. Power Electronics IEEE Transactions on SANTUCCI A SORNIOTTI A LEKALKOU C. Power split strategies for hybrid energy storage systems for vehicular applications J. Journal of Power Sources AHARON I KUPERMAN A. Topological overview of powertrains for battery-powered vehicles with range extenders J. IEEE Transactions on Power Electronics BURKE A. Ultracapacitor technologies and application in hybrid and electric vehicles J. International Journal of Energy Research XU L OUYANG M LI J et al. Optimal sizing of plug-in fuel cell electric vehicles using models of vehicle performance and system et al. Optimal control of parallel hybrid e- lectric vehicles C/ /Industrial Electronics and Applications ICIEA 01 7th IEEE Conference on. IEEE 01. et al. Equivalent Series Resistancebased energy loss analysis of a battery semiactive hybrid energy storage systemj. Energy Conversion IEEE Transactions on WANG B XU J CAO B et al. A novel multimode hybrid energy storage system and its energy management strategy for electric vehicles J. Journal of Power Sources SANTUCCI A SORNIOTTI A LEKAKOU C. Power split strate-. gies for hybrid energy storage systems for vehicular applications J. Journal of Power Sources GARCIA F S FERREIRA A A POMILIO J A. Control Strategy for Battery-Ultracapacitor Hybrid Energy Storage SystemC/ / Applied Power Electronics Conference and Exposition. APEC Twenty-Fourth Annual IEEE. IEEE BERTSEKAS D P. Dynamic programming and optimal control J. Athena Scientific MURGOVSKI N JOHANNESSON L M EGARDT B. Optimal battery dimensioning and control of a CVT PHEV powertrain J. IEEE Transactions on Vehicular Technology HU X MURGOVSKI N JOHANNESSON L M et al. Optimal dimensioning and power management of a fuel cell / battery hybrid bus via convex programmingj. IEEE / ASME Transactions on Mechatronics LUKIC S M CAO J BANSAL R C et al. Energy storage systems for automotive applicationsj. IEEE Transactions on Industrial Electronics MICHALCZUK M GRZESIAK L UFNALSKI B. A lithium batteryand ultracapacitor hybrid energy source for an urban electric vehicle J. PrzegladElektrotechniczny 014B HU X MURGOVSKI N JOHANNESSON L M et al. Optimal dimensioning and power management of a fuel cell / battery hybrid bus via convex programmingj. IEEE / ASME Transactions on Mechatronics

11 10 1 4BOYD VANDENBERGHE FAYBUSOVICH. Convexoptimization J. IEEE Transactions on Automatic Control CHOI M E KIM S W SEO S W. Energy Managementoptimization in a battery /supercapacitor hybrid energy storage system J. IEEE Transactions on Smart Grid MURGOVSKI N JOHANNESSON L SJOBERG J et al. Component sizing of a plug-in hybrid electric powertrain via convex optimization J. Mechatronics X Zhang C Mi. Vehicle Power ManagementModeling Control 7. J. and OptimizationM. Berlin GermanySpringer-Verlag 011 Technology HU Chen WU Xiaogang LI Xiaojun. Construction of harbin urban public transport J. Journal of Harbin University of Science and Technology SONG Z HOFMANN H LI J et al. Energy management strategies comparison for electric vehicles with hybrid energy storage system J. Applied Energy C 31. 9TROVAO J P PEREIRINHA P G JORGE H M et al. A multilevel energy management system for multi-source electric vehiclesan integrated rule-based meta-heuristic approach J. Applied Energy CHOI M LEE J SEO S. Real-time optimization for power management systems of a battery / supercapacitor hybrid energy storage system in electric vehiclesj. IEEE Transactions on Vehicular 3SCHALENBACH M CARMO M FRITZ D L et al. Energy efficiency and fuel economy analysis of a series hybrid electric bus in different Chinese city driving cyclesj. International Journal of Hydrogen Energy

Vol. 22 No. 4 JOURNAL OF HARBIN UNIVERSITY OF SCIENCE AND TECHNOLOGY Aug GPS,,, : km, 2. 51, , ; ; ; ; DOI: 10.

22 4 2017 8 Vol. 22 No. 4 JOURNAL OF HARBIN UNIVERSITY OF SCIENCE AND TECHNOLOGY Aug. 2017 150080 GPS,,, : 27. 36 km, 2. 51, 110. 43, ; ; ; ; DOI: 10. 15938 /j. jhust. 2017. 04. 015 U469. 13 A 1007-2683