The efficiency of hybrid electric powertrains is heavily dependent on energy and power management strategies, which are sensitive to the dynamics of the powertrain components that they use. In this study, a Modified Particle Swarm Optimization (Modified PSO) methodology, which incorporates novel concepts such as the Vector Particle concept and the Seeded Particle concept, has been developed to minimize the fuel consumption and NOx emissions for an extended-range electric vehicle (EREV). An optimization problem is formulated such that the battery state of charge (SOC) trajectory over the entire driving cycle, a vector of size 50, is to be optimized via a control lever consisting of 50 engine/generator speed points spread over the same 2 h cycle. Thus, the vector particle consisted of the battery SOC trajectory, having 50 elements, and 50 engine/generator speed points, resulting in a 100-D optimization problem. To improve the convergence of the vector particle PSO, the concept of seeding the vector particles was introduced. Additionally, further improvements were accomplished by adapting the Time-Varying Acceleration Coefficients (TVAC) PSO and Frankenstein’s PSO features to the vector particles. The MATLAB/SIMULINK platform was used to validate the developed commercial vehicle hybrid powertrain model against a similar ADVISOR powertrain model using a standard rule-based PMS algorithm. The validated model was then used for the simulation of the developed, modified PSO algorithms through a multi-objective optimization strategy using a weighted sum fitness function. Simulation results show that a fuel consumption reduction of 12% and a NOx emission reduction of 35% were achieved individually by deploying the developed algorithms. When the multi-objective optimization was applied, a simultaneous reduction of 9.4% fuel consumption and 7.9% NOx emission was achieved when compared to the baseline model with the rule-based PMS algorithm.

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Energy and Power Management Strategies play vital role in improving efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used is the given system. A mathematical model for hybrid powertrain of Range Extended Electric Vehicle (REEV) has been developed in this study and is further optimized to reduce the level of fuel consumption and NOx emissions individually by optimizing the control strategy of Power Management System (PMS). A modified Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the optimum PMS strategy. In performing these optimizations, the control signal consisting of genset speed and power for a full 2-hour cycle was used as the controllable decision parameters input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours giving slightly more than 2 minutes per point, noting that the values used in the models were interpolated between the points for each time step. With the control signal consisting of 2 distinct signals viz. speed and power, as 50 element time variant signals, a 100-D problem was formulated for the optimizer. The developed algorithms were simulated on the REEV model on MATLAB/SIMULINK platform. Simulation results show that the fuel consumption reduction of 12% and NOx emission reduction of 35% was achieved individually by deploying the optimal PMS strategy when compared with the baseline results.