1、集成选址与库存优化创新电动汽车服务网络设计Scaling Up Electric-Vehicle Battery Swapping Services in Cities:A Joint Location and Repairable-Inventory Model张玉利 博士北京理工大学管理与经济学院Joint work with Wei Qi and Ningwei ZhangYuli Zhang2BIT个人介绍张玉利 博士北京理工大学管理与经济学院副教授、特别研究员、博士生导师2014年于清华大学自动化系获工学博士学位2011-2012年在加州大学伯克利分校IEOR系分校访学北京运筹学会副

2、秘书长、理事；中国运筹学会不确定系统分会常务理事国际运筹与管理学会(INFORMS)、国际生产与运营管理学会(POMS)会员中国物流与采购联合会-采购与供应链专家委员会委员工信部工业互联网产业联盟-供应链特设组副主席Yuli Zhang3BIT主要研究方向复杂系统建模复杂系统建模&优化算法设计优化算法设计数学规划模型数学规划模型线性/非线性规划混合整数规划随机/鲁棒/动态规划机器统计模型机器统计模型监督学习模型无监督模型强化学习模型精确优化算法精确优化算法基于梯度的算法基于分支的算法基于分解的算法智能优化算法智能优化算法启发式算法随机搜索算法机器学习增强算法系统设计与优化问题系统设计与优化问题

3、需求预测网络布局库存控制供需平衡生产计划运输物流产线排程资源调度欢迎对运筹优化与供应链管理、大数据决策感兴趣的同学，联系攻读硕士、博士或者开展博士后工作。欢迎对运筹优化与供应链管理、大数据决策感兴趣的同学，联系攻读硕士、博士或者开展博士后工作。Yuli Zhang4BIT主要研究方向复杂系统建模复杂系统建模&优化算法设计优化算法设计混流复杂生产系统混流复杂生产系统调度排程调度排程钢铁生产炼钢-连铸-热轧联合调度问题主持：科技创新2030“新一代人工智能”项目子课题复杂制造环境下人机物三元协同决策优化方法合作企业：宝钢、新钢一体化供应链物流一体化供应链物流网络优化网络优化面向运营与中断风险的物流

4、网络布局与决策主持：国家自然科学基金面上项目-一体化供应链弹性物流网络设计与优化合作企业：京东物流电动汽车服务网络服电动汽车服务网络服务运营务运营电动汽车充换电设施网络规划与调度主持：国家自然科学基金面上项目-电动汽车光伏充换电站网络随机鲁棒运营优化合作企业：北汽、奥动新能源Yuli Zhang5BIT战略层研究：电动汽车充换电系统网络设计研究城市基础设施网络下换电站和充电站设施部署，以满足快速充换电需求，缓解电网压力，减少用户等待时间。1）提出了“本地本地换电，集中换电，集中充电”充电”的基础设施电池交换服务的配置2）提出一种数据驱动的联合选址-库存模型3）开发了一种新的算法框架，结合约束生

5、成约束生成(CG)(CG)和参数搜索参数搜索(PS)(PS)技术Yuli Zhang6BIT战略层研究：城市内公交车充电设施选址考虑交通网和电网整合的情况下，多阶段的电动公交车充电设施选址规划问题。MP目标：最小化总成本（充电站、充电桩、电网线路建设成本，公交车交通成本、充电耗电成本）约束：Special case:single-stage planning(SP)modelModel extension:Budgeted multistage planning(BMP)model；Multistage planning no waste(MPNW)model；Budgeted multist

6、age planning-no waste(BMPNW)model.SOC转化线性转化10,(,)11(1)min*2*11(1)aaaddaddaddjjji ji ji jjmjmjjjimjorimnmnmnmm nfxK Tb htlYlr1)交通网2)电网3)两网耦合S=+:S,=2 2,=2,0=1+:1S1 11=,=+,0 ,=1,=1,*,1,0,jjlmiijmmjiijm piijmjmijmijmijmsHd WsmWYi j mWi j mWYi j mWi j m,22,2mnmnmnmmnmPQlvlv非线性项Yuli Zhang7BIT运营层研究：用户行为数据分

7、析根据出行链时空矩阵投影模型，预测城市各个空间的车辆时空分布，基于车辆的充电需求画像，预测车辆充电需求预测发生的时空分布。结合充电桩的布局，建立优化车辆充电调度模型，得到充电车桩匹配推荐结果。7营运类车辆充电需求预测及充电场站匹配推荐Yuli Zhang8BIT运营层研究：基于价格激励的车辆共享再平衡Yuli Zhang9BIT调度层研究：基于区块链的智能有序充电引导针对充电站服务商单桩收益率低、用户充电难等问题，提出基于区块链技术的充电平台，开发有序充电智能合约算法，通过有序充电引导保障用户和运营商的双边利益。9运营商2运营商1政府运营商2运营商1政府区块链系统Yuli Zhang10BIT

8、调度层研究：Vehicle-to-Grid充放电调度Yuli Zhang11BITEV Service Network DesignScaling Up EV Battery Swapping Services in Cities Motivation Operations of Swapping and Charing Stations Model and Algorithms Case Studies&InsightsYuli Zhang12BITElectric Vehicles are BoomingScalingScaling Up EV Battery Swapping Servi

9、ces in CitiesUp EV Battery Swapping Services in CitiesSoure:BNEFEV share of new car sales worldwide:EV share of new car sales worldwide:500M EVs will be on the road by 2040!500M EVs will be on the road by 2040!Yuli Zhang13BITScalingScaling Up EV Battery Swapping Services in CitiesUp EV Battery Swapp

10、ing Services in CitiesElectric Vehicles are BoomingNational Electric Vehicle Supervision and Management Center 新能源汽车产业发展新能源汽车产业发展规划（规划（20235年）年）Yuli Zhang14BITCharging Mode vs Swapping Mode-UsersSwapping ModeSwiftness3-5 minutesCompactnessSame service level,less spaceSafetyProperly charge

11、,test and maintain batteriesScaling Up EV Battery Swapping Services in CitiesCharging ModeRange anxiety30 minutes 58 hoursResale anxietyUsed values of EVs may deteriorate quicklyOther issuesInconvenience,unsafety,battery degradationYuli Zhang15BITCharging Mode vs Swapping Mode-GovernmentScaling Up E

12、V Battery Swapping Services in Cities“碳达峰、碳中和”纯电动乘用车平均生命周期碳排放比传统汽油车降低26%，但存在6年左右减排滞后期（动力蓄电池制造碳排放占50%，10年/15万公里）换电模式具有更高的动力蓄电池利用率换电模式具有更高的动力蓄电池利用率 换电模式使纯电动汽车生命周期碳排放较传统汽油车降低36%。换电模式将A级纯电动汽车减排滞后期降低到2年4年。来源：中汽数据，换电模式加速汽车产业碳中和变革，https:/newenergy.in- Zhang16BITBattery Swapping is RevivingScalingScaling Up

13、 EV Battery Swapping Services in CitiesUp EV Battery Swapping Services in CitiesNIO(蔚来)offers its car owners battery swapping service free of charge,and is expected to deploy 1,100 swapping stations by 2020.BAIC(北汽)plans to invest$1.4 billion in building 3,000 swapping stations by 2022.Motivation:Pr

14、oject Optimus Prime,BAIC BJEV-Aulton（奥动新能源）-BTC,10000 EVsYuli Zhang17BITBattery Swapping is RevivingScalingScaling Up EV Battery Swapping Services in CitiesUp EV Battery Swapping Services in CitiesMotivation:Project Optimus Prime,BAIC BJEV-Aulton-BTC,10000 EVsYuli Zhang18BITChallenges of Scaling Up

15、Battery SwappingScaling Up EV Battery Swapping Services in CitiesMismatching between Transportation network and Power gridSwapping demandsEnergy supplyYuli Zhang19BITHow to Address Phase II ChallengesScaling Up EV Battery Swapping Services in Cities Swap locally,charge centrally Locally swapped at d

16、ecentralized swapping stations(DSS)Transported to and charged at centralized charging stations(CCS)Strategies of the State Grid Corporation of China Currently piloted in cities such as HangzhouYuli Zhang20BITSetup(Configuration-macro level)Scaling Up EV Battery Swapping Services in CitiesCharging st

17、ations are colocatedwith grid nodes with sufficient capacityEach swapping station independently follows an policy(,)r QSwapping demand:a general renewal process with given mean and varianceYuli Zhang21BITScaling Up EV Battery Swapping Services in CitiesBattery deficitProportion of unfulfilled orders

18、 is less than Battery stock of charging station satisfies()()()EIiiiD tntn tProviding batteries to before timeitFully charged batteries from till time Model the counting process of“battery deficit at a charging station with respect to one swapping station iitProb()hCiiD tRCRSetup(Configuration-micro

19、 level)Yuli Zhang22BITScalingScaling Up EV Battery Swapping Services in CitiesUp EV Battery Swapping Services in CitiesSetup(Configuration-micro level)Battery expenditure:()=Battery income:()=:the transportation time:the charging time:=+,the effective charging timek(t):the number of orders that the

20、swapping station has placed before tBattery deficit:=:the number of customers in the queueYuli Zhang23BITOur ObjectiveScaling Up EV Battery Swapping Services in CitiesWe address the phase-II challenges by studying the Swap locally,charge centrally system:Analysis and Models:Analytical results for a

21、special two echelon service network DSS:Battery swapping with(r,Q)policies CCS:Battery charging with R initial fully charged batteries.Non-convex mixed-integer programming model The optimal charging station location Joint optimization of(r,Q)and RSolution algorithm Submodularity-based constraint gen

22、eration algorithm Parametric search algorithm for the non-convex sub-problemInsights:Cost and environmental implications as swap demand scales upYuli Zhang24BITRelated LiteratureScaling Up EV Battery Swapping Services in CitiesMulti-Echelon Inventory for Repairable Items:Sherbrook(1968,OR)Grave(1985

23、,MS)Lee(1987,IIE)Gerard(2001,OR)Closed-Loop Supply Chain Management:Savaskan et al.(2004 MS)Abbey et al.(2014 POM)Calmon et al.(2020 MSOM)Metric methodExact computation method based on numerical integrationNo closed-form formula Moment matching methodClosed-form formula based onRestrictive assumptio

24、ns,e.g.,symmetric batch size and transportation timeProblem settings:Reverse flow channel;Consumer perceptions;Large time-scaleMethodology:Game theory;Empirical method;Dynamic programmingNo location-inventory analysis Yuli Zhang25BITRelated LiteratureScaling Up EV Battery Swapping Services in Cities

25、Battery swapping and phase-I challenges:Mak et al.(2013,MS)Avci et al.(2015,MS)Lim(2015,MSOM)Swap-locally&charge-centrally:Zhang and Wang(2016 IEEE PS)Liu et al.(2019 IEEE SG)Meta-heuristic algorithm to dispatch batteries between DSS and CSSDeterministic mixed-integer linear program to schedule batt

26、eries and inventoryOur models provide closed-form analysis,which preserves tractability while taking into account demand uncertainty and networked operations.Exact algorithm for the non-convex mixed-integer programming problem by exploiting sub-modularity and concavity.Yuli Zhang26BITOutlineScaling

27、Up EV Battery Swapping Services in Cities Motivation Operations of Swapping and Charing Stations Non-passion Swap Demands Operations of a Swapping Station Operations of a Charging Station Model and Algorithms Case Studies&InsightsYuli Zhang27BITNon-Poisson Swap DemandsScaling Up EV Battery Swapping

28、Services in CitiesData:National Electric Vehicle Supervision and Management Center of China,include more than 20.97 million real-time records.Identified 8,333 swaps in Beijing in December 2019.Aggregate Hourly Demand Profile:Yuli Zhang28BITNon-Poisson Swap DemandsScaling Up EV Battery Swapping Servi

29、ces in CitiesFinding:Non-Poisson swap demand arrivalsPeriodNum.of Non-Poisson DaysPeriodNum.of Non-Poisson Days00:00-00:59 212:00-12:59 301:00-01:59 313:00-13:59 202:00-01:59 114:00-14:59 003:00-03:59 215:00-15:59 104:00-04:59 016:00-16:59 205:00-05:59 017:00-17:59 006:00-06:59 118:00-18:59 007:00-0

30、7:59 119:00-19:59 008:00-08:59 020:00-10:59 009:00-09:59 321:00-21:59 110:00-10:59 222:00-22:59 211:00-11:59 223:00-23:59 0Yuli Zhang29BITOperations of a Swapping StationScaling Up EV Battery Swapping Services in CitiesRenewal process counts the EVs arrivals up to time ,Stockout probability is no gr

31、eater than Re-order level satisfiesProb()1STSNTr(),0SNtt tSr11(1)TSTrTT 212TTrTT Result 1.The reorder point of a swapping station is given byand are mean and variance of swap requirements;is the one-way transit time;is the inverse of the cumulative standard normal distribution function;Result 2.The

32、reorder point under the worst-case is given by1()2(1)2SS 2TTYuli Zhang30BITOperations of a Charging StationScaling Up EV Battery Swapping Services in CitiesBattery deficitProportion of unfulfilled orders is less than Battery stock of charging station satisfies()()()EIiiiD tntn tProviding batteries t

33、o before timeitFully charged batteries from till time Model the counting process of“battery deficit at a charging station with respect to one swapping station iitProb()hCiiD tRCRYuli Zhang31BITOperations of a Charging Station-DeterministicScaling Up EV Battery Swapping Services in CitiesMean of Batt

34、ery DeficitVariance of Battery DeficitThe inventory position of an inventory system following the(r,Q)policy is uniformly distributed over r+1,r+Q in the steady state when the discrete-valued demandforms a renewal process(Sivazlian 1974).Yuli Zhang32BITOperations of a Charging Station-DeterministicS

35、caling Up EV Battery Swapping Services in CitiesLemma 1.Ifis uniformly distributed over ,thenmod(,)Z Q0,1,1QLemma 2.If ,then()0ZQLemma 3.The variance of is bounded asLemma 4.X22Var()Var()2 D tXWVWW22WWVWVar()Var()Var()Var()Var()XWD tXW2221,if 0,()6(),if.QQQQQ 2233()61 Yuli Zhang33BITOperations of a

36、Charging Station-DeterministicScaling Up EV Battery Swapping Services in CitiesResult 3.The mean of the battery deficit at a charging station incurred by a swapping station is given byResult 4.The piecewise expression also approximatesVar()D t222(),if ,Var()1f.,i 06QQD tQQ ()QMain result:We analytic

37、ally characterize the mean and the variance of the battery deficit by Lemma 1-Lemma 4Yuli Zhang34BITOperations of a Charging Station-DeterministicScaling Up EV Battery Swapping Services in CitiesResult 5.The number of batteries to stock at a charging station needs to satisfyhResult 7.The stock level

38、 for a swapping station with on-site charging needs to satisfy111BCBCrTT Battery stock level at a charging station:Benchmark:on-site charging at decentralized swapping stationsYuli Zhang35BITOperations of a Charging Station-RandomScaling Up EV Battery Swapping Services in Cities :the number of custo

39、mers in the queueRandom discrete(DR)service timeYuli Zhang36BITOperations of a Charging Station-RandomScaling Up EV Battery Swapping Services in Cities :the number of customers in the queueRandom discrete(DR)service timeYuli Zhang37BITOperations of a Charging Station-RandomScaling Up EV Battery Swap

40、ping Services in CitiesFor each sub-queue lFor the considered queue Yuli Zhang38BITOperations of a Charging Station-RandomScaling Up EV Battery Swapping Services in CitiesFor the considered queue Yuli Zhang39BITOperations of a Charging Station-RandomScaling Up EV Battery Swapping Services in CitiesF

41、or the considered queue deterministic effective charging timerandom effective charging timeYuli Zhang40BITOutlineScaling Up EV Battery Swapping Services in Cities Motivation Operations of Swapping and Charing Stations Model and Algorithms Integrated Location Model Constraint Generation Algorithm Par

42、ameter Search Algorithm for Subproblem Case Studies&InsightsYuli Zhang41BITIntegrated Location Model Scaling Up EV Battery Swapping Services in Cities2(,)()()()TihtitihSCBtihhihihhihihhihihSCBihihihhihihihhihihihhihihiihcyC z y Qcc zcr yRQycc zyc QQQ Swapping station depreciation costCharging statio

43、n depreciation costBattery depreciation costcost of trucking batteriesNon-convex,piecewisewhether or not build a charging stationwhether or not charging station serves swapping station swapping station reorder quantity from charging station 0 1hz，hh0 1ihy，i0ihQ sih Decision variables:Yuli Zhang42BIT

44、Integrated Location Model Scaling Up EV Battery Swapping Services in Cities,)(P)min(,)s.t.,1,(1)(11)(1)(1,0,0,.z y QihhihhihhihihC z y QyzihyiyzihQQyihabcd min,(,)=+()s.t.(1)(1).()=min0,()+whereLocation and inventory decisionsMulti-variable non-convex concave optimizationYuli Zhang43BITAlgorithm Sum

45、maryScaling Up EV Battery Swapping Services in Cities,.(MP)min s.t.(1)(1),Ckhhihihhhz y whhihkhi hi hhic zywadwgyh To solve the original non-convex,piecewise problem:Constraint Generation Module:We iteratively solve a relaxed mixed 0-1 master problem(P)()min()+:0 ,=0,.Parameter Search Module:We solv

46、e the following subproblem by iteratively solving a parameterized problem.Yuli Zhang44BITAlgorithm SummaryScalingScaling Up EV Battery Swapping Services in CitiesUp EV Battery Swapping Services in CitiesB&B:Branch-and-bound;B&C:Branch-and-cut;BD:Benders decomposition;B&P:Branch-and-pricing;CG:Constr

47、aint generation;CQMIP:Conic quadratic mixed integer program;CQP:Conic quadratic program;EPI:Extended polymatroid inequality;LA:Lagrangian relaxation;MIP:Mixed integer program;PS:Parametric search;UFLP:Uncapacitated facility location problemYuli Zhang45BITScaling Up EV Battery Swapping Services in Ci

48、ties()min()+:0 ,=0,+()+()Constraint Generation AlgorithmYuli Zhang46BITConstraint Generation AlgorithmScaling Up EV Battery Swapping Services in CitiesFor any charging station ,is a submodular function over .h()hg|0,1Lemma 5.For any ,the submodular function satisfies(P)()min()+:0 ,=0,Let be set of p

49、ermutations of elements in ,be the j-th element in permutation ,and be the position of element in jS ii10()(),()0.jjjgg Sg Sg S|0,1yg()max.iiig yg y12|,.()iiig yg yyyy(i)We can construct g(y)by adding up the marginal cost of including swapping stations following a sequence(ii)If we knew y,we could e

50、asily find the optimal sequence by sortingYuli Zhang47BITConstraint Generation AlgorithmScaling Up EV Battery Swapping Services in Cities(P)can be further reformulated as a mixed 0-1 linear program(MIP)min,+s.t.(1)(1),CG:Iteratively solve the following relaxedmaster problem with a subset of constrai

51、nts:(MP)min,+s.t.(1)(1),.,CGSolve(,)Solve MP,obtain,and Inputstop gap CGGenerate by sorting NoOutput,and YesYuli Zhang48BITParameter Search Algorithm for SubproblemScaling Up EV Battery Swapping Services in CitiesConstraint generation algorithm requires solving the subproblem (P)()min()+:0 ,=0,.This

52、 is a multi-variable non-convex problem,not easy to solveYuli Zhang,Zuo-Jun Max Shen,Shiji Song,Exact Algorithms for Distributionally Robust Machine Scheduling with Uncertain Processing Times.INFORMS Journal on Computing,2018,30(4):662-676.Yuli Zhang,ZuoJun Max Shen,Shiji Song,Parametric Search for

53、the Bi-attribute Concave Shortest Path Problem,Transportation Research Part B:Methodological,2016,94:150168.Parametric Search Algorithm for discrete concave minimizationYuli Zhang49BITParameter Search Algorithm for SubproblemScaling Up EV Battery Swapping Services in CitiesThis parameterized problem

54、 is a single-variable convex problem,which is easy to solve(P)SFor given ,can be reformulated to()()()+S(P)Constraint generation algorithm requires solving the subproblem (P)()min()+:0 ,=0,.This is a multi-variable non-convex problem,not easy to solveYuli Zhang50BIT(ii)Given any ,let be the intersec

55、tion point of lines .Let then,any satisfiesParameter Search Algorithm for SubproblemScaling Up EV Battery Swapping Services in CitiesWe can efficiently search for the optimal using a branch and bound procedure based on the following proposition.(P)S*Q*1 2()u Q*Q*(P)(P)S120(,)u v:()(1,2),jjjjlvuuvj12

56、1122,min,guvuvuv 12,Result 6.(i)Let be the optimal solution to and ,then any optimal solution to is also optimal12,uvg(,)(,)f z yf zy(,)zy(,)zy(,)zyuvslope:slope:conv(H)slope:Yuli Zhang51BITParameter Search Algorithm for SubproblemScaling Up EV Battery Swapping Services in Cities,(P)min():0,.iiiiiii

57、bQaQQQiSQ 12,22,(P)min:0,6,(P)min():,iiiiiiiiiiiiiiiibQaQQQbaQQQQ Closed-form solution existsThe remaining task is to solve the easier problem(P)Finally,we also develop routines for algorithm initialization and speedupYuli Zhang52BITParameter Search Algorithm for SubproblemScaling Up EV Battery Swap

58、ping Services in CitiesLet ,and be the optimal solution to ,and .Closed-form solutions()iQ,1()iQ,2()iQ,(P)i1,(P)i2,(P)iSolving(P)Yuli Zhang53BITParameter Search Algorithm for SubproblemScaling Up EV Battery Swapping Services in CitiesLet ,and be the optimal solution to ,and ,thenLemma 6.,(i),where i

59、s unique nonnegative real root of the cubic equation ;(ii);(iii)if or ,where ,otherwise,;(iv),and are non-increasing in .()iQ,1()iQ,2()iQ,(P)i1,(P)i2,(P)i0iS,1()min,iiiQQiQ3230iQaQb,2()min max/(),iiiiQbaQ()iiQQ/iiba,1i,123iiiiba,2()()iiQQ()iQ,1()iQ,2()iQSolving(P)Yuli Zhang54BITParameter Search Algo

60、rithm for SubproblemScaling Up EV Battery Swapping Services in CitiesLemma 7.For any,let be the unique nonnegative real root of equation ,theniSInitializationip32(1/6(3)6iipp bap min,min/,12()12().SpeedupConsider ,let for ,and be the optimal solution and the optimal parameter for(1,2,|)1,2,kSkkkQkPk

61、SResult 7.,(i);and(ii).1,|1k 1kkiiQQ1kkYuli Zhang55BITOutlineScaling Up EV Battery Swapping Services in Cities Motivation Operations of Swapping and Charging Stations Model and Algorithms Case Studies&Insights Calibration Computational Efficiency Managerial Insights:Scalability,FlexibilityYuli Zhang

62、56BITCalibrationScaling Up EV Battery Swapping Services in CitiesParameters about Battery41kWh capacity battery,weighted 330kg7,062.15$with a eight-year lifespan20%remaining SOC of swapped-off batteriesStandard 42kW direct-current fast chargerDelivered by 10-ton deadweight truck 1.13$/kmWe calibrate

63、 the model with real data of EV battery swaps from NEVSMCempirical setting in Beijing consultation with practitioners from BAIC BJEV,Aulton and manufacturerSwapping RequirementsPeriodMeanStandard DeviationMinMax0:00-8:595.683.710229:00-23:5914.513.90328Data From National Electric Vehicle Supervision

64、 and Management Center of ChinaYuli Zhang57BITCalibrationScaling Up EV Battery Swapping Services in CitiesDifferent stations Cost ComponentsEquipmentUnit CostQuantityLifeCharging StationPlant33.90$/Year/m2500 m2-Worker9,604.52$/Year/Person5-Charger(42kW)1,129.94$20015Battery Conveyer14,124.29$110Tra

65、nsformer(2500kVA)35,310.73$230Power Grid Expansion Costs11,299.44$-10Construction Project21,186.44$-10Swapping StationPlant33.90$/Year/m2100 m2-Worker9,604.52$/Year/Person2-Swapping Operators112,994.35$215Construction Project14,124.29$-10Swapping Station with ChargersPlant33.90$/Year/m2300 m2-Worker

66、9,604.52$/Year/Person4-Charger(42kW)1,129.94$3015Transformer(630kVA)11,299.43$130Swapping Operators112,994.35$215Power Grid Expansion Costs11,299.44$-10Construction Project21,186.44$-10Yuli Zhang58BITComputational EfficiencyScaling Up EV Battery Swapping Services in CitiesEffectiveness of the parame

67、ter search algorithm forSince is non-convex,we force to ensure that Gurobi can solve it,the same operation is used in following parts.All solving methods stop gaps are .For each problem size(30,60,90)we solve 100 instances.22()16,0.QQQ()Q710Yuli Zhang59BITComputational EfficiencyScaling Up EV Batter

68、y Swapping Services in CitiesEffectiveness of the CG&PS algorithmCG&PS:the proposed methodGurobi:Gurobi 9.1.0 solverBP:Teo&Shu(2005,OR),Ni et al.(2021,JOC)cannot solve root problem within 1800 secondsHeuristic:Shen et al.(2011,JOC)Force()=2+(21)/6,0 to ensure that Gurobi can solve model.Yuli Zhang60

69、BITManagerial Insights-ScalabilityScaling Up EV Battery Swapping Services in Cities001234567MainBenchmarkBenchmark,SlowMain,60%-Q005540MainBenchmarkBenchmark,SlowMain,60%-QMain:Swap-locally,charge-centrally.Benchmark:On-site charging.Observation 1.Centralized chargin

70、g is LESS scalable than decentralized on-site charging(if all else being equal).Yuli Zhang61BITManagerial Insights-ScalabilityScaling Up EV Battery Swapping Services in CitiesObservation 1.Centralized charging is LESS scalable than decentralized on-site charging(if all else being equal).This is cont

71、rary to the wisdom in traditional supply chains in which pooling demands is economicalReason:The favorable pooling effect is dominated by two unfavorable effects with centralized charging:Order batching effect Transportation lead time effect Yuli Zhang62BITManagerial Insights-ScalabilityScaling Up E

72、V Battery Swapping Services in Cities001234567MainBenchmarkBenchmark,SlowMain,60%-Q005540MainBenchmarkBenchmark,SlowMain,60%-QSlow AC charging:7kW vs.Standard DC charging:42kWObservation 2.Centralized charging is MORE scalable than decentralized on-site charging(if o

73、nly slow charging permits).Yuli Zhang63BITFlexibilitiesScaling Up EV Battery Swapping Services in Cities000246810-80%-40%0+40%+80%0246Slow AC ChargeOptimalBaseline DC ChargeObservation 3.Centralized charging allows remarkable flexibilities in:-abating battery quantities-adjusting charging

74、 station deployment.Yuli Zhang64BITFlexibilitiesScaling Up EV Battery Swapping Services in CitiesObservation 4.Order splitting is only advantageous when the demand is scaled up by reducing fixed ordering cost.Yuli Zhang65BITSummaryScaling Up EV Battery Swapping Services in CitiesWe study swap-locall

75、y,charge-centrally service networks to address the phase-II infrastructural challenges Developed models of battery stocking,charging and circulating operationsProposed an effective algorithm for solving this special joint location-inventory optimization problemManagerial insights:Scalability depends on grid capacity Flexibilities in operations and design