Intelligent energy management system for hybrid electric vehicles -

Abstract

Hybrid electric vehicles positively influence the transportation industry with regards to reducing the use of fossil fuels and minimizing emissions. A class of such vehicles incorporates fuel cells and energy storage systems as alternatives to the internal combustion engines. The energy management system in these vehicles locates the power split between the available sources while adhering to operational and component requirements. This dissertation develops an efficient energy management system for fuel cell hybrid vehicles for the purpose of achieving a sub-optimal power allocation between the energy sources while adhering to component requirements and maintaining the required operational performance. A power train configuration model based on a Simulink model of the electric vehicle is used for testing the energy management system. The dissertation addresses two stage control methodologies, pre-driving off-line optimization using an improved dynamic programming algorithm and on-line optimization using PID controller. In the first stage, the optimization strategies depend on the degree of knowledge of the driving cycle. If the cycle is known before hand, then the improved dynamic programming technique is used to find the sub-optimum power allocation for the whole cycle. Weighted improved dynamic programming algorithm analyses the effect of changing the relative cost of the battery with respect to the fuel cell. Stochastic estimation of the driving cycle is adopted if apriori knowledge of the cycle is not accurately known. On-line optimization is performed using a complete Simulink designed model of the fuel cell hybrid vehicle. The numerical outcomes of the off-line optimization are used to test the efficiency of the improved dynamic programming algorithm in lowering operational cost while ensuring drivability. PID controller is used to minimize the error between the actual and the approximated vehicle speeds during on-line optimization. A looped improved dynamic programming technique is tested during on-li