Acrl

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  • Acrl

    1. 1. Improving Electric Vehicle Pack Efficiency and Longevity Through Accurate Prediction of Power Demands Alex Styler, ACRL S2011 1
    2. 2. ChargeCar Optimization• Electric vehicle optimization is dependent on accurate prediction of immediate future duty• This project predicts immediate future duty using a weighted k-Nearest Neighbor approach• Rich driver history allows greater accuracy of prediction and improved performance, car improves over time as data is gathered• Although many subsystems can be optimized, including the BMS and thermal management, for the scope of this project we optimize the use of a cache in a heterogeneous power store 2
    3. 3. Heterogeneous Power Store 3
    4. 4. • Battery pack: high energy density, vehicle range, bad with high current• Supercapacitor: high power density, very limited energy, great with high current, expensive• Analogous to a computer system with cheap memory (RAM or HDD) and expense, fast memory (L1/L2 cache)• With intelligent management, the supercapacitor acts as a cache to serve high-power cycles such as rapid acceleration and regenerative breaking 4
    5. 5. Project Execution• Developed an optimal controller, given foreknowledge of the upcoming duty• Analyzed the lookahead horizon using this optimal controller to determine what was useful for the learning system to predict• Implemented a k-Nearest Neighbor algorithm (kd-Tree for speed, delay from computing is expensive in the real system)• Added useful features and manually adjusted feature weights to improve performance• Analyzed sensitivity to history data coverage 5
    6. 6. − −i2 integral over all trips for: No Capacitor, Best Static Policy, Optimal Controller 6
    7. 7. Project Execution• Developed an optimal controller, given foreknowledge of the upcoming duty• Analyzed the lookahead horizon using this optimal controller to determine what was useful for the learning system to predict• Implemented a k-Nearest Neighbor algorithm (kd-Tree for speed, delay from computing is expensive in the real system)• Added useful features and manually adjusted feature weights to improve performance• Analyzed sensitivity to history data coverage 7
    8. 8. Optimal Controller• The cumulative sum of charge taken from the battery must always be greater than the cumulative sum of charge spent by the motor• Any excess cumulative charge moved from the battery, not spent by the motor, is stored by the capacitor• This gives a small window of opportunity for taking power from the battery, with the lower bound being the charge spent by the motor, and the upper bound being charge spent by the motor + the total charge the capacitor can hold• We fit a piecewise linear function inside these bounds that minimizes the sum of the rate squared 8
    9. 9. Lookahead Horizoni2 integral reduction of Optimal Policy given varying limits of lookahead “Knee” occurs between 90 and 120 seconds 9
    10. 10. k-Nearest Neighbor• Very straightforward algorithm for this problem• Natural choice given the relationship of features to upcoming duty• Points near some intersection facing north will have similar upcoming duties, but vastly different than points facing south at that intersection• Computationally expensive, potentially bad for this problem if delays in power management result in poor performance, in practice controls are very low frequency• Implemented as kd-Tree for sanity in runtime, also as controls must be calculated at least as frequently as new GPS data comes in (about one per second)• Ground truth value is a neighbor-distance weighted average of upcoming power demand curve (averaged at each time slice independently) 10
    11. 11. Feature Weighting and Selection• Some features are very telling: GPS, time of day, charge spent so far• Others are very high frequency and have little relation to the long-term upcoming demand such as acceleration• Final features were: GPS location, elevation, velocity, bearing, charge spent so far, time of day, day of week (class based)• Day of week feature was 0 distance for same day, 0.5 distance for same class of days (weekday or weekend), and 1 distance otherwise• Latitude and Longitude are heavily weighted, current position is vital to upcoming prediction due to the difference of duty from traffic signals, road type (highway, urban), usual traffic• Charge spent thus far with a medium weight, captures a bit of traffic encountered and direction, seemingly better than time of day or day of week 11
    12. 12. Results and Sensitivity• Using a very low history set for each driver (10,000 points, about a week of driving), with an N of 5 neighbors (similar results from 3-9 neighbors, not as impressive N=1), and tested versus two weeks of driving• Overall performance yields a 54.4% reduction in current squared. The previous best static algorithms yield a 40.2% reduction, and the upper bound is 74.1% reduction• The best case, for a driver performing only regular commutes, we see a 76.3% reduction, compared to an upper bound of 79.0%, due to the history providing complete coverage for prediction• The average case, with drivers performing only a few irregular trips not in the history, we see between 66-72% reduction• The worst case, when the driver gave the car to his wife, we still see a 34.3% reduction, likely from the overlap of driving near the home base, or similar driving styles 12
    13. 13. Questions/Comments? 13

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