Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

Abstract

The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process.

Neuroevolution Application to Collaborative and Heuristics-Based Connected and Autonomous Vehicle Cohort Simulation at Uncontrolled Intersection

“…Therefore, this result shows that trying to find an equilibrium between the time at the intersection and acceleration-based energy consumption is unfeasible or at least leads to non-robust controllers-and that a two-level optimization would be required. This is compatible with the idea that local vehicle energy reduction can be achieved by using a local predictive energy management powertrain strategy that relies on the speed profile of the vehicle [17]-as opposed to an all-in-one solution purely relying on cohort speed control.…”

“…Indeed, mixing turning and non-turning operations inherently constrains the speed achievable for all vehicles and forces sub-optimal operation. These limitations prompted the authors to investigate the use of Artificial Intelligence, which demonstrated high energy savings with large heterogeneous cohorts operating at controlled intersection [16] and for local powertrain adaptive control [17]. Artificial intelligence offers a simple and fast development framework, low compute power requirements and high reusability thanks to its adaptiveness to a wide range of dynamics.…”