CHARGE MANAGER FOR AN ELECTRIC SCHOOL BUS FLEET Guillaume - PowerPoint PPT Presentation

INNOVATIVE VEHICLE INSTITUTE SMART PEAK SHAVING CHARGE MANAGER FOR AN ELECTRIC SCHOOL BUS FLEET Guillaume Fournier, P.Eng. Program manager (EV)

ABOUT IVI We develop electric, autonomous and connected vehicle prototypes

PROJECT PARTICIPANTS Sponsors Partners

PROBLEM Context 1 2 3 Peaks occurred when These excessive Autobus Laval had block heaters were peaks resulted in multiple instances of manually activated a significant cost excessive power peaks while electric buses increase were charging

Approximate block heater power (kW) at -10°C on a typical weekday Approximate block heater power (kW) PROBLEM Loads – Block Heaters Time of day (weekdays)

Approximate EVSE power (kW) at -10°C on a typical weekday Approximate EVSE power (kW) PROBLEM Loads – EVSE Time of day (weekdays)

PROBLEM Loads – Building Empirical data shows that building power demand can be roughly estimated by: For T > 7 ° C, W building = 50 kW W building = ( - 2.67 T + 70) kW For T ≤ 7 ° C, For example , at -10°C, building power demand is around 97 kW

Approximate EVSE power (kW) at -10°C on a typical weekday Approximate total power (kW) at -10°C on a typical weekday Block heaters Bus EVSEs Approximate EVSE power (kW) PROBLEM Loads – Sum of loads Building Time of day (weekdays)

Approximate EVSE power (kW) at -10°C on a typical weekday Approximate total power (kW) at -10°C on a typical weekday Manual activation Approximate EVSE power (kW) PROBLEM Loads – Sum of loads Time of day (weekdays)

PROJECT GOALS Prevent power peaks, smooth power as much as possible FROM THIS TO THIS P P t t SAME ENERGY

PROJECT GOALS Decrease electricity cost by reducing maximum power demand (MPD) P P $$$$ MPD MPD t t

PROJECT GOALS Maintain bus availability End SOC Start SOC Trip 85% 15% 65% 0%

PROJECT GOALS VERSATILITY MONITORING SAFE Realtime monitoring of Possibility to charge plan Besides schedule, disable at all time administration everything else by the end user + should be + It should be automatic SMS sent when possible to look problem occurs back and learn from our errors

PROJECT LOCATION

PROJECT LOCATION

PROJECT LOCATION Electric buses Diesel buses Building

TARGET CUSTOMERS 1 Small fleet operators that cannot afford large-scale custom solutions Having little knowledge on charging challenges 2 Heavily impacted on power peak increase (no masking loads) 3 Newcomers in the electric fleet management that want to gradually 4 increase their EV count

SOLUTION Overview – Information gathered Information Supplier Start and end time of all bus routes Google calendar Distance for each trip planned Google calendar State of charge of each bus Fleet management interface Presence or absence of a bus at each EVSE EVSE Bridge Temperature-compensated 24-hour power profile prediction of the building without the electric buses Electric utility interface including the profile from the diesel buses’ block heaters The instantaneous power reported by the meter Electric utility interface Demand Response Event list Electric utility interface Battery capacity of each bus Web administration interface Dedicated EVSE for each bus Web administration interface Estimation of power lost while charging* Web administration interface Maximum power target to aim for* Web administration interface Energy consumption per km traveled* Web administration interface Phone numbers of emergency contacts Web administration interface Operating mode: safe or normal Web administration interface *These values are entered for each month of the year, based on experimental data, since they are highly correlated to external temperature.

Cloud / Google Calendar (bus schedule) Cloud / Charge Manager Cloud / Utility Multi-stage linear 24-hour power profile prediction & optimizer line contactor drivers Charge plan On-site administration SOLUTION Line contactors for block heater circuits Smart MQTT broker Overview meter Diesel buses EVSE bridge EVSE local bridge 4G EVSE backend Cloud / EVSE Cloud / Fleet management 4G Interface to buses

SOLUTION Trip calendar

SOLUTION Trip calendar Unplugged Description Distance (m)

SOLUTION Data from Hydro-Quebec Energy accumulator (kWh) every 5 min 1 3 P Average power is calculated Block heaters Control loop on EVSE (between 2 and 4 AM) Building 2 Demand Response Event list Now Now +24h (3PM) (3PM)

SOLUTION Data from fleet management School bus Fleet Management Vehicle CANBus Our cloud Cloud REST Cellular 4G Backend Endpoint Modem SOC VCU From school bus State of charge

SOLUTION Data to/from EVSE Our cloud EVSE Cloud On site EVSE EVSE EVSE local 4G bridge backend bridge From EVSEs To EVSEs Instantaneous supplied current Current to propose to bus Voltage and energy meter Plugged status

SOLUTION Optimizing power demand – the charge plan 1 2 3 4 93 94 95 96 The output of each stage is a charge plan. Bus #1 More specifically, it is represented by 7 arrays (one per bus) of 96 power values to propose to each bus. Each square represents a 15-minute Bus #2 interval and its value (in kW) is the amount of power that is going to be offered for that bus … for this interval. The charge plan covers the next 24h. Bus #7

SOLUTION Stage 1: Minimize the “Maximum Power Demand” for the next 24 hours Constraints Charge power must be between 0 and 16.64 kW Charge allowed only when bus is plugged in Energy in a bus must be between 0 kWh and its max capacity Energy in buses must be sufficient to perform all trips P P MPD MPD t t

SOLUTION Stage 1.5:Find maximum power available per interval The objective is to compute, for each interval, the maximum power available that can be drawn without increasing the maximum power demand already registered for this billing cycle. P P t t

SOLUTION Stage 2:Minimize power demand on Demand Response Events Contraints All constraints from stage 1 Never go above the maximum power available (stage 1.5) Event P P t T

SOLUTION Stage 3:Maximize the time when the buses are ready for their next trip For each trip, for each bus, for the next 24h, calculate how long 1 before the trip the bus is ready 2 Maximize the smallest time Bus #1 trip #1: ready 30 min before trip Bus #2 trip #1: ready 15 min before trip Bus #3 trip #1: ready 58 min before trip MAXIMIZE ..... SMALLEST TIME Bus #7 trip #4: ready 35 min before trip Contraints All constraints from stage 2 The energy consumed on each Demand Response Event does not exceed what was calculated on stage 2

SOLUTION Stage 4:Maximize the amount of energy in the buses before each trip – Step 1 For every trip (all buses) for the next 24h, calculate how much 1 exceeding energy is available before the trip 2 Maximize the smallest energy Bus #1 trip #1: 17 kWh more than needed before trip Bus #2 trip #1: 15 kWh more than needed before trip Bus #3 trip #1: 10 kWh more than needed before trip MAXIMIZE ..... SMALLEST ENERGY Bus #7 trip #4: 5 kWh more than needed before trip Contraints All constraints from stage 3 For each trip, the amount of time before the bus is ready must be greater or equal than the result of stage 3

SOLUTION Stage 5:Maximize the amount of energy in the buses before each trip – Step 2 For every trip (all buses) for the next 24h, sum the amount of 1 remaining expected energy after each trip 2 Maximize this value Bus #1 trip #1: 14 kWh remaining after trip Bus #2 trip #1: 27 kWh remaining after trip Bus #3 trip #1: 36 kWh remaining after trip MAXIMIZE ..... 14+27+36+…+34=168kWh THIS VALUE Bus #7 trip #4: 34 kWh remaining after trip Contraints All constraints from stage 4 For each trip, the amount of excess energy must be greater or equal than the result of stage 4

SOLUTION Stage 6:Minimize power variations Contraints For the next 24 hours, minimize the variations 1 of power offered to the vehicles between two All constraints from stage 5 adjacent intervals The sum of excess energy before trips in the next 24 2 Probably unnecessary hours must be greater than or equal to the result of stage 5

RESULT Overview

RESULT Charge plan

RESULT Energy in batteries

RESULT Energy in batteries

UNIQUENESS OF OUR SOLUTION VS OTHERS A lot more than your typical load sharing solution Predicts other loads and plans the charging Considers past MPD to increase power profiles accordingly without increasing cost Retrieves state of Integrates a vehicle calendar (distance, charge from vehicles charge time) Solution is EVSE agnostic Realtime feedback on the meter to compensate for estimation Reduces energy drawn on Hydro- Quebec’s Uses linear optimization techniques for Demand Response Events optimal solutions

IMPLEMENTATION CHALLENGES What went wrong Selecting and controlling EVSEs Getting the information from the smart meter Getting the information from the buses

LESSONS LEARNED SO FAR What we uncovered Although the technology is available, it is not Aggregating information from multiple completely mature sources is complicated Getting all stars aligned is almost impossible Make sure partners have incentives (could be result, financial, etc.) to work toward a common goal