Dynamic Range Prediction for an Electric Vehicle
Transcript of Dynamic Range Prediction for an Electric Vehicle
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Dynamic Range Prediction for an Electric
Vehicle
João C. Ferreira1,2, Vitor Monteiro2, João L. Afonso2
1ISEL, Lisbon, Portugal; 2Centro Algoritmi, Univ. Minho, Guimarães, Portugal
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The Problem
• EVs have limited autonomy (e.g., 160 km)
• EV charging process is slow
• Missing EV charging infrastructures
Range Anxiety Problem fear that an
electric vehicle (EV) will run out of charge
before it reaches its destination
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Range autonomy is based on
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• Personalized Range Prediction
• Range Presentation on a Map
• Information in Real Time on a
Mobile Device
• Creation of a Driver Profile
Main Idea
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Proposal
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EVA
Vehicle Interface System
(VIS)
Charging Functions
Extended Range Navigation
External
Interface
Driver
BehaviourRange Prediction
Process
HomePublic
GISTopology
Grade
Road Type
Traffic
ENVWind
Temperature
Charge cycle
Driving Style
Driver Profile
EV DataEV type
Battery type
Battery age
Consumption profile
Trips profile
EV energy parameters
Charge Prediction
Energy
Market
POI
Public
Transportation
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General Overview
Driver Profile
IVChargingRange
Prediction
EVElectricity
Market
Transportation &
Infrastructure
Interface to External SystemsCollaborative
Broker
-Range Representation
-Route Guidance
-Charge need to reach destination
-Smart Charging
-Reserve Charging slots
-Remote Interaction
-Energy transactions Logs
-Aggregation of EV for
marketparticipation
-DER
-Profit share
-Exchange Account
-Transportation information (price and schedules)
-Parking places
-Traffic Information
-PoI
-Prices
-Energy production information
-SoC
-Speed
-Temp…
Main Functions
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Driver Profile
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Analyses of transactions data can be
useful information for future charging
or discharging processes, taking into
account a smart charging strategy to
combine distribution network
limitation and low prices. All of this
information is stored on the
information repository on the central
server. If internet communication is
available, the driver can check
remotely the home charging process,
and interact with it, if he wants to.
Information Extraction from EV and
Charging Device
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Data mining
approach combines
past data of driver
profile, with current
information of EV
(speed, SOC level),
weather information
and traffic.
Range Prediction Process
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Average Energy Consumption key
influencing factors
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Data from ICE engine
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Data from ICE engine
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Range Representation Process
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EVA Server and Application
architecture.
EVA Portal in MVC (Model View
Controller) approach.
EVA Application
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Range Representation in a trip:
1) Starting point of the EV with full charge
2) After 25 km of trip
3) After 100 km of trip
4) After 150 km of trip
Results
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The red shadow is a range that is possible to achieve, but for which the driver needs to perform driving optimization (with air conditioner off and avoiding big accelerations). This could be a helpful information, because the driver can customize his behavior in function of the range he needs to achieve in his trip. This process can be continuously updated, and thus, when the S0C level is low, this uncertainty is also low.
Range estimation based on the
uncertainty factors
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Range Representation with
SOC level in 5%
Range Representation on a
Charging Process
Range Representation with
SOC level in 80%
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(a) EV position; (b) Available functions; (c) Range representation.
Results - Range prediction interaction
process:
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Results
Screens of the EVA mobile application
related with details about Charging
Stations (CS) and Points of Interest
(POI).
Screens of the EVA mobile application
of the route paths near the limit of
the EV range autonomy.
Results
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Results
Screens of the EVA mobile
application getting past
information from historical events
data.
Screens of the EVA mobile application:
(a) Guidance to Charging Stations (CS) and
Points of Interest (POI);
(b) Alert of insufficient charge to reach a
desired destination.
Results
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► The current work has as main goal to minimize the driver range anxiety
problem by:
(1) an accurate EV range prediction based on past driver behavior, batteries
SOC level and external parameters, like road characteristics, traffic
conditions and weather;
(2) range representation on a map taking into account current driver position
with an uncertainty associated with driver behavior.
► Other important work output is the historical driver profile data that can be
used to establish driver communities profiles (drivers with similar behavior),
and from this information start driver education towards energy savings.
Conclusions
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Thank you for your attention.