Development of optimal energy management topology for battery electric vehicle with load segmentation

Sustainable transportation has been widely explored as a result of fossil fuels depletion and pollution emissions released by conventional vehicles. Among the alternatives, hybrid and plug-in hybrid electric vehicles manage to reduce but incompletely remove the carbon impacts. Battery electric vehicles (BEVs) instead, offer zero carbon footprint solution with outstanding drivetrain performance and energy efficiency, however they are confined by the driving range due to constraints in batteries capacity and volume. The increase in power requirement and number of electrical loads on-board, due to the transportation electrification has complicated the situation further. Primarily, the challenges in BEV having batteries as the only energy storage but multiple loads to be fulfilled lie in eliminating the „range anxiety‟ by developing stringent control rules and management strategy that could further extend the driving range. In this thesis, an attempt has been made to modularly design a power and energy management system (PEMS) for BEV by modelling the plant that comprises the modules of energy management system (EMS) and power management system (PMS). Several simulation tests performed on BEV model have verified its control robustness, effectiveness in satisfying the targeted performances and suggested load distribution profiles for the corresponding driving cycles. The area of PEMS in the application field of BEV is relatively new and incorporates several different disciplines. Two levels of control; low level component control (LLCC) and high level supervisory control (HLSC) have been implemented, adapting load segmentation strategy from large scale power distribution systems. Four auxiliary load segments have been modelled and ranked for prioritization task via energy distribution strategy algorithm, operated within three distribution regions of battery state-of-charge (SOC). The incorporation of load segmentation into EMS topology has significantly improved the organization of energy flow management between supply and load. The simulation tests in New European urban and extra urban driving (NEDC) has successfully verified the optimal energy consumption with a saving of 18.6% in energy or an increase of 28.5% (17.22 km) in driving range cumulatively. Subsequently, the development of three driving modes in PMS via power scheme management has successfully represented the diversity in driving between the most-comfortable-driving with highest-power-usage (Comfort Mode) and the least-comfort-driving with lowest-power-usage (Economic Mode). The combination of PMS-EMS has been proven in satisfying all cost functions during simulation tests. An integrated driving mode i-FUZZY has also been proposed using fuzzy logic control to overcome the manual mode selection in PMS. The simulation tests have verified the robustness of i-FUZZY in making quick decision on selecting the best adaptive driving mode while satisfying the predefined cost functions. In conclusion, the simulation results with the proposed PEMS strategy have proven the effectiveness and potential of BEV as the future sustainable transportation.

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