TITLE:
Powertrain Fuel Consumption Modeling and Benchmark Analysis of a Parallel P4 Hybrid Electric Vehicle Using Dynamic Programming
AUTHORS:
Aaron R. Mull, Andrew C. Nix, Mario G. Perhinschi, W. Scott Wayne, Jared A. Diethorn, Dawson E. Dunnuck
KEYWORDS:
Hybrid Electric Vehicle, Dynamic Programming, Powertrain Modeling, Backwards Induction
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.12 No.4,
October
14,
2022
ABSTRACT: The goal of this work is to
develop a hybrid electric vehicle model that is suitable for use in a dynamic
programming algorithm that provides the benchmark for optimal control of
the hybrid powertrain. The benchmark analysis employs
dynamic programming by backward induction to determine the globally optimal solution by solving the energy
management problem starting at the
final timestep and proceeding backwards in time. This method requires the
development of a backwards facing model that propagates the wheel speed of the
vehicle for the given drive cycle through the driveline components to determine the operating points of
the powertrain. Although dynamic programming only searches the solution space
within the feasible regions of operation, the benchmarking model must be
solved for every admissible state at every timestep leading to strict
requirements for runtime and memory.
The backward facing model employs the quasi-static assumption of powertrain
operation to reduce the fidelity of the model to accommodate these
requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure
successful operation of the algorithm
and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle
model with a percent difference of fuel consumption of 1.2%. The
benchmark analysis is conducted over multiple
drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and
determines control trends that can be used to improve existing algorithms. The optimal
combined charge sustaining fuel economy of the vehicle is determined by the
dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock
3.6 L 2019 Chevrolet Blazer.