Energy Management System Design for Plug-in Hybrid Electric Vehicle Based on the Battery Management System Applications

Ding, Ning
Prasad, Krishnamachar
Lie, Tek Tjing
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Doctor of Philosophy
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Auckland University of Technology

The Energy Management System (EMS) applied in battery management system (BMS) plays the decisive role in effectiveness and proper operation of any hybrid energy storage system. Without significant advances in the state-of-the-art of BMS techniques, the future uptake of hybrid electric/electric vehicle applications are not feasible. Therefore, this thesis aims to provide a coherent body of work on the enhancement of the most important tasks performed by a modern BMS, which includes the hybrid EMS design and State of Charge (SoC) estimation. The premeditated EMS adopted groups of rules to determine the operation state of different components. The main advantage of premeditated EMS is less computational burden and easy to apply. A novel rule-based control strategy is proposed throughout this thesis to decrease the emission and increase the fuel economy. Then, an optimization using genetic algorithm (GA) is applied on the designed rule-based control strategy to improve the vehicle performance and achieve reduction of fuel consumption and emissions at the same time. This hybrid EMS combined the rule-based control strategy and its optimization is verified through a bi-directional simulation. The hybrid electric vehicle (HEV) model and the input of load condition adopted in the simulation are based on the real data to close to the practical implementations. In addition, an improved extended Kalman Filter (iEKF) is designed to provide high-accuracy SoC estimation of battery. The SoC estimation is considered as a dynamic identification process of the parameters of battery model and it significantly relies on the battery model. This estimator with iEKF algorithm adopts a composite battery model, which combines the method of open-circuit voltage (OCV) (to obtain an initial value of parameters), Amp-hour (Ah) method (to dynamically identify the parameters), and the extended Kalman Filter (to improve the accuracy) . There are five groups of experiments conducted on Lithium-based cell, to provide the data for parameters identification. Finally, the proposed estimator with iEKF algorithm is simulated in MATLAB_Simulink to show the effectiveness of the proposed SoC estimation in BMS.

Energy management system , Battery management system , Genetic Algorithm , State of Charge estimation , Extended Kalman Filter , Plug-in Hybrid Electric Vehicle
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