Powertrain Fuel Consumption Modeling and Benchmark Analysis of a Parallel P4 Hybrid Electric Vehicle Using Dynamic Programming

The goal of this work is to develop a hybrid electric vehicle model that suitable for use in dynamic programming algorithm provides the benchmark optimal control powertrain. analysis employs by backward induction determine globally solution solving energy management problem starting at final timestep and proceeding backwards time. This method requires development facing propagates wheel speed given drive cycle through driveline components operating points Although only searches space within feasible regions operation, benchmarking must be solved every admissible state leading strict requirements runtime memory. quasi-static assumption powertrain operation reduce fidelity accommodate these requirements. Verification validation testing conducted ensure successful assess validity determined policy against high-fidelity forward-facing with percent difference fuel consumption 1.2%. over multiple cycles real-time determines trends can used improve existing algorithms. combined charge sustaining economy 32.99 MPG, 52.6% increase stock 3.6 L 2019 Chevrolet Blazer.