Sources of Big Data in EVs • Cars are generating lots of data every second: – Acceleration/Braking/g forces – Idle / Number of stops – Electric Consumption/Battery state – Ambient temperature – HVAC temperature – Tire preassures – GPS traces – Charging time periods – Charging rated power
How to use these data? Drivers Fleet Generators managers Electric Vehicle DATA TSO Retailers DSOs
What Big Data can do for drivers? • Improving driving effi fici cien ency cy • Allowing to detect ct anomali alies and problems in their own vehicles • Optimizing charging electr trici icity ty costs sts
Improving driver behavior. Big Data for drivers • Provi viding ing feedb dback ack to drivers ers on how they are currently doing. • Comparing to personal historical data • Comparing to other similar drivers – Same mobility patterns (same route or area) – Same type of EVs
Improving driver behavior. Big Data for drivers • Providing feedback to drivers on how how to do it it bett tter er. – Avoiding aggresive aceleration/braking events – Time spent idling
Detecting problems in the Vehicles. Big Data for drivers • Sharing and comparing different measurements will allow to id identify ify anomalo alous us vehic icle le behavior vior. – Anor orma mal range ange reduct eduction ion or higher gher averag erage battery ery tempera perature ure can lead to battery problems (accelerated ageing of the battery) – Diagn gnos ostics ics trouble uble codes odes
Optimizing charging electricity costs. Big Data for driver • Optimizing charging costs, taking into account: – Vari riab able le electric ctricity ity pric ice – The persona rsonal daily ly sche hedule ule • Determining posib sible le charging arging locati ation ons • Determining posib sible le charging arging period riods
What can Big Data do for fleets? • Comprenhensive analysis of: – Fuel el/elec electrici tricity ty econo conomy repor eporting ing • Measuring real-world consumption from all fleet vehicles. – Idle monit nitori oring ng and d mana nagem gement ent • Reporting idle periods and allowing to quantify savings and identify drivers that may require additional route adjustments
What can Big Data do for fleets? • Driver behavi vior or feedb dback ack • Di Diagnostic stic troubl ouble e codes • Distribution of charging ging times es • Ve Vehic icle le lo locati tion n tracking
What can Big Data do for Elec. Retailers? • EV Load fore recast casting – Improving their offer bids and increasing their benefits • Segmentation entation-dri driven ven marketin keting g offers • Special al tariff iff designs
What can Big Data do for DSO? • More e effe fecti ctive ve monitor toring ng and proactive maintenance – Obtaining operation conditions for charging EVs on local household distribution grid. • Power losses • Power quality (voltage and current profiles, unbalance and harmonics) – Modell delling ing large ge scale ale (spatial-temporal) deployment of EVs and quantifying the impacts on distribution operation conditions and infrastructures.
What Bid Data can do for DSO? • Investigating optimal EV charging profiles that result in maximal economic, environmental benefits and minimal operation disturbance. • Reducing or postponing the need for network reinforcement through charging active demand management.
What can Big Data do for TSO? • Oper erati ation on (sh short ort term rm) – Predict ict network ork flow over the next minute utes, s, hours, , days and weeks. – Optimize imize the power system tem accordingly (tradeoff between reliability-economy) • Asset et mana nagem gement ent (mid id term) – Understandi rstanding facto ctors rs driving aging and failures lures of components – Undestan stand critically tically of componen onents ts ’ availability lability for system operation – Optimi imize ze the repairing ring and replacemen acement of equipment accordingly • Inves estm tment ent (long ong term) – Predict ict usage of the power system tem over the next years – Accordingly, take highly strate rategi gic importan rtant decision sions From BD in the management of EES. Louis Wehenkel
What can Big Data do for Generators? • EV Load fore recast casting – Improving their supply bids and increasing their benefits • Integration of intermittent generation • Combined generation bids: – Wind energy + distributed storage capacity of EVs
EV EV A AND TAX ND TAXI FL FLEE EETS TS
Benefits of EV in taxi fleet • Improvements in Air Quality – EVs have zero tailpipe emissions. – Highest GHG concentrations is found in areas with high traffic rate (also high density of taxi trips). – 20% of total generated electric energy in California comes from renewables. – Using cleaner energy sources will reduce the emissions asssociated with powering EVs.
Benefits of EV in taxi fleet • Reduced Carbon footprint – Even after accounting for the energy- production-level emissions associated with Evs, electrification of taxis would lower the fleet’s carbon emissions. • Resiliency – EV can also be designed to be usable as mobile power storage units in the event of an emergency
Benefits of EV in taxi fleet • Visibility • Price consistency – Electricity prices are much less volatile. • Energy security – Reducing the country petroleum imports.
Economics of EV ownership • Factors that determine the adoption of an EV: – Vehicle icle pur urchase chase price ice • Battery price is the key driver of purchase price • Price is projected to decline over the time – Maint intena enance nce and d repa pairs irs • Lower maintenance (savings) – Batter ery replacem placement ent • Battery is degraded over time, more quickly if is quick charged (need to be replaced) • Oppor portunity tunity: battery reuse for static applications. – Year ars vehic hicle le in ser ervice vice – Resid sidua ual value lue – Cost st of elec ectric tricity ty • Taxi operators, to maximize revenues and minimize downtime, limiting time available for charging.
DA DATA TA INFO NFORMA RMATI TION ON
Data information • General information: – GPS traces 466 Vehicles of Yellow Taxi Cap. – Collected: May-June 2008. – Data provides: • Lat-Lon • Time stamp (Unix time) • Ocupation
Data information • It is assumed: – We are focus on on the taxi xi (no on the driver). • Taxis can be driven en by by differ ferent nt drivers ers. – Drivers have same skills (similar knowledge of the city).
Consumption model • EV consumption model Consumption model Input ut: GPS trac ack Consider: Terrain elevat evation ion Output put: • Auxi xili liary ary loads ds (lights/heating) Power • • Occu cupat ation ion (increasing mass Cons nsumed med energy ergy • • for vehicle occupied with SoC evolut olution ion • customer)
Consumption model • Equations: a F F F F F F 1 . 05 M M te rr a hc la la car d te P F v te 1 2 F AC v a d 2 F M M g sin hc car d cos F R M M g rr car d
Consumption model • Forward driving: te P P P F v P P P te P mot _ out te P bat mot _ in aux mot _ out mot _ in gear mot
Consumption model • Regenerative braking: P R P P P P P P P P _ te _ reg gen _ ratio te bat mot in aux mot _ out gear te _ reg mot _ out gear te _ reg
Consumption model • Battery dynamics: – Discharging process (moving forward) – Charging process (regenerative braking)
Consumption model • Results 100 100 Car Stopped State of Charge (%) (Speed=0 kph & SoC=58%) speed (kph) 80 80 60 60 40 40 20 20 Battey SoC (%) 0 0 0 0 2000 2000 4000 4000 6000 6000 8000 8000 10000 10000 12000 12000 14000 14000 16000 16000 18000 18000 Time [s]
Consumption model validation • We have tuned our model based on real test consumption: – 111.4 km (69.2 mile) – 3.9 miles/kWh – 0.159 kWh/km Our consumption model: 0.165 kWh/km http://insideevs.com/real-world-test-2013-nissan-leaf-range-vs-2012-nissan-leaf-range/
Consumption model • More complex Consumption models are available Transmission Battery Inverter (Power Electronics) Electric Machine http://vbn.aau.dk/files/55733132/Electric_Vehicles_Modelling_and_Simulations.pdf
Consumption model http://vbn.aau.dk/files/55733132/Electric_Vehicles_Modelling_and_Simulations.pdf
RE RESU SULT LTS
Results • Analyzing the spatio-temporal mobility of a single taxi vehicle.
Results • Vehicle: 1 • Number of days: 24 • Number of movements: 49 • Number of stops ps (>30 min): 48 > 30 min
Results Occupied Empty >30 min
Results
Results • How is the distribution of distance travelled between two consecutive stops (> 30 min)?
Results • Electrification Rate: 63.27%
Results • St Stop locati ation on and durati ation:
Results • Best location for charging points
Results • How long are they stopped? Histogram stop p time e durati ation
Results • St Stop locati ation on and St Stop Initi itial al Ti Time:
Results • When are they parked? Histogram stop op initia tial time
Results • How much energy are they demanding?
Results • Energy demanded during the recharging process: 297.21 kWh
Results ELECT CTRIC RIC TAXI CONVENTIONAL ENTIONAL TAX AXI
Results ELECT CTRIC RIC TAXI CONVEN ENTIONA TIONAL TAXI • Total energy demanded: 297.211 kWh • Gasoline Price: $3.692/gallon • Electricity Price: 23.3 cents/kWh • Consumption: 16 miles/gallon • Total distance: 1,325.5 miles • Total distance: 1,325.5 miles • Total distance: 2,132.8 km • Total distance: 2,132.8 km • Total cost: $69.25 9.25 • Total cost: $244 44.56 56 • Saving: $175.31 http://www.bls.gov/regions/west/news-release/averageenergyprices_sanfrancisco.htma
Results Pick up points • Centroid: (Lat, Lon): 37°46'42.2"N 122°25'01.6"W • Radius of gyration: 2.56 km (1.6 mil)
Results Drop off points • Centroid: (Lat, Lon): 37 37°46 46'27. '27.5"N 5"N 122 122°24'59 4'59.8" .8"W • Radius of gyration: 3.18 km (1.97 mil)
Results • When are taxis occupied?
Results Empty taxi • Average speed: 16.13 km/h
Results Occupied taxi • Average speed: 26.92 km/h
Results Empty taxi • Average distance: 3.52 km Occupied taxi • Average distance: 4.2 km
Results
Results • Analyzing the spatio-temporal mobility of a taxi fleet.
Results • Number of analyzed Vehicles: 466 Taxis. • Average number of days analyzed: 23 days • Average number of stops > 30 min: 60
Results • Gyration radius distribution for empty and occupied situation. occupied r 3 . 91 0 . 785 km gyr empty r 3 . 51 1 . 4 km gyr
Results • Time duration of the stops. – Max: 17 days (413.5 hours) – Stop (>30 min) less than 24 hours: 99.36% • Average time duration: 2 hours 34 minutes.
Results • Starting time to recharge EV taxis:
Results • Energy demanded by all vehicles:
Results • Energy recharged during the stops: 170 170 MWh • Electrificability rate: 65.3% of the total journeys Battery Capacity: 24 kWh
Results • California daily electricity demand http://www.caiso.com/outlook/SystemStatus.html
Results • Impact on the California daily electricity demand: 0.002% in the peak (14:00)
AD ADDI DITI TIONA ONAL RE RESE SEAR ARCH CH
New developments at ETSIT-UPM • A mobile application for identifying the potential for EV adoption in company fleets. • The app records: – Distance traveled. – Average speed. – It is posible to record energy consumption. • The app provides the user with information about the daily driving distances can be cover using an electric vehicle. • A database with the technical specifications of different EVs are used to advice users.
Mobile app description • Initia tial screen een: 1. Start to register 1 1 2. Analyzing a track 2 3. Analyzing all tracks 3 4. Share the track 4 2 3 4
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