Optimal Energy Management System Derivation

Vehicle Operation Prediction Model ADAS data, along with current GPS location and current vehicle velocity, was used to predict future vehicle operation within the perception subsystem. An artificial neural network combined the outputs from sensors and signals to generate a vehicle velocity prediction.

Planning Subsystem Model This vehicle operation prediction was then used in an optimal control algorithm that uses dynamic programming to derive the globally Optimal EMS for the given prediction. This optimal control is then issued as a control request to the vehicle's “running controller,” which evaluates component limitations before actuating the vehicle plant. The process of perception, planning, and vehicle actuation uses second-by-second feedback with the Optimal EMS, computed for 15-second predictions.

Vehicle Subsystem Model This inputs to the vehicle model are the Optimal EMS control requests and disturbances attributed to misprediction. Of particular interest is the fuel consumption, achieved engine power, and battery state of charge. These results can then be compared to the outputs from baseline EMS simulation.


ADAS Output Comparison:

For the vehicle-in-front state, the average accuracy was around 60% for ADAS1 and 70% for ADAS2. The ADAS2 accuracy was better due to the different weight and configuration files used for detection. However, the CNN in both ADAS1 and ADAS2 failed to identify objects at distances farther than 20m. The ground truth data labeled by hand identified vehicles at a much more significant range.

Fig. 4. ADAS percent accurate to ground truth — traffic light state. (©SAE International)

For traffic lights, the average accuracy for the highway drive cycle videos was around 70%, while the average accuracy for the city drive cycle videos was 40%, as shown in Figure 4. The inaccuracies were due to the changing ambient lighting and diverse positioning of the traffic light. Many frames of the traffic light were blocked by cars and were detected for ground truth at a much higher distance. Most traffic lights — being smaller than vehicles — only provided positive readings when the vehicle was within 5 meters. The accuracy of detection of stop light colors was also challenging to get correct. Relying on the average color of the pixels was found to be an ineffective predictor for these lights.

Fuel Economy Improvements with ADAS:

ADAS detection Optimal EMS fuel economy improvements must be assessed with respect to the globally Optimal EMS fuel economy improvements. Globally Optimal EMS fuel economy improvement is possible when the entire drive cycle is predicted 100% accurately from time zero. This serves as a reference point to understand the scope of the improvement that can be realized through ADAS detection. A new metric called “prediction window optimal,” was also defined: the optimal prediction of a 15-second window used for ADAS1 and ADAS2.

Fig. 5. City-focused drive cycle comparison of fuel economy for 2010 Toyota Prius with various strategies: Optimal EMS improvement in fuel economy realized through 100% accurate detection of the entire drive cycle (globally optimal fuel economy increase), 100% accurate ADAS detection (ground truth ADAS fuel economy increase), and the algorithms that compose ADAS1 and ADAS2. All results are relative to the baseline control strategy used in the 2010 Toyota Prius. (© SAE International)

Figure 5 shows the comparison for city-focused drive cycle between the globally Optimal EMS strategy, the two ADAS strategies (ADAS1, ADAS2), and the ground truth ADAS strategy. All results are presented relative to the baseline control strategy for the 2010 Toyota Prius. The globally Optimal EMS fuel economy improvement is 19.6%, which represents an upper bound. The prediction window optimal fuel economy improvement is 11.9%. With ground truth ADAS detection, approximately half of this amount is realized at 6.1%, which is a very promising result.

Using the actual algorithms, a 4.4% improvement was realized with ADAS1, and 5.3% with ADAS2. The difference between the ground truth ADAS and results from the actual ADAS deployments was due to the inaccuracies and limitations with the real-time computer vision algorithms. The failure to detect an object at great distances reduced the accuracy in many cases. Another source of error was with the traffic light state detection. There were also several difficulties in producing accurate results in time to make an impact on fuel economy. ADAS2 gave a slightly higher percentage improvement than ADAS1, primarily due to the different set of weighting and training parameters and the addition of vehicle brake light data.


Overall, these promising results suggest that modern commercially available ADAS technology could be repurposed to implement an Optimal EMS. As new sensing capabilities become commercially available, the fuel economy improvements possible through an Optimal EMS may start to approach the global Optimal EMS results.

This article was adapted from SAE Technical Paper 2018-010593, written by Jordan A. Tunnell, Zachary D. Asher, Sudeep Pasricha, Thomas H. Bradley, Colorado State University. To obtain the full technical paper and access more than 200,000 resources for the aerospace, automotive, and commercial vehicle industries, visit the SAE MOBILUS website here .

Photonics & Imaging Technology Magazine

This article first appeared in the May, 2019 issue of Photonics & Imaging Technology Magazine.

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