This paper discusses the integrated design problem related to determining the power specifications of the main subsystems (sizing) and the supervisory control (energy management). Different bi-level optimization methods, with the outer loop using algorithms as Genetic Algorithms, Sequential Quadratic Programming, Particle Swarm Optimization or Pattern Search (DIRECT) and the inner loop using Dynamic Programming, are benchmarked to optimally size a parallel topology of a heavy duty vehicle. Since the sizing and control of a hybrid vehicle is inherently a mixed-integer multi-objective optimization problem, the Pareto analyses are also addressed. The results shows significant fuel reduction by hybridization and engine downsizing and offer insights in the usability of these nested optimization approaches.

The performance of Hybrid Electric Vehicles (HEVs) is strongly affected by their powertrain control strategies, in particular when complex architectures are concerned.Therefore the purpose of this paper is to analyze, through numerical simulation, different methodologies to develop an energy management strategy aiming to minimize the overall CO 2 emissions of the vehicle.In order to perform a comprehensive comparison, different optimization algorithms were selected among the available solutions in the control theory.Foremost a global optimization strategy, the Dynamic Programming (DP), was used to benchmark the performance of the energy management systems.Then a local optimization strategy, the Equivalent Consumption Minimization Strategy (ECMS), was evaluated, to prove its suboptimal performance and to evaluate the possibility to be implemented on a real Engine Control Unit (ECU).Finally, the potential of heuristic control techniques was evaluated due to their low computational requirements and since they represent the most common solution in real applications.The analysis focused on the case study architecture of the Chevrolet Volt, for which a Simulink model was built and tested on both regulatory driving cycles and real world driving conditions, emphasizing pro and cons of each method.