Battery energy management plays a crucial role in fuel economy improvement ofcharge-sustaining parallel hybrid electric vehicles. Currently available control strategiesconsider battery state of charge (SOC) and driver’s request through the pedal input indecision-making. This method does not achieve an optimal performance for saving fuelor maintaining appropriate SOC level, especially during the operation in extremedriving conditions or hilly terrain. The objective of this thesis is to develop a controlalgorithm using forthcoming traffic condition and road elevation, which could be fedfrom navigation systems. This would enable the controller to predict potential ofregenerative charging to capture cost-free energy and intentionally depleting batteryenergy to assist an engine at high power demand.The starting point for this research is the modelling of a small sport-utility vehicle bythe analysis of the vehicles currently available in the market. The result of the analysisis used in order to establish a generic mild hybrid powertrain model, which issubsequently examined to compare the performance of controllers. A baseline isestablished with a conventional powertrain equipped with a spark ignition directinjection engine and a continuously variable transmission. Hybridisation of this vehiclewith an integrated starter alternator and a traditional rule-based control strategy ispresented. Parameter optimisation in four standard driving cycles is explained, followedby a detailed energy flow analysis.An additional potential improvement is presented by dynamic programming (DP),which shows a benefit of a predictive control. Based on these results, a predictivecontrol algorithm using fuzzy logic is introduced. The main tools of the controllerdesign are the DP, adaptive-network-based fuzzy inference system with subtractiveclustering and design of experiment. Using a quasi-static backward simulation model,the performance of the controller is compared with the result from the instantaneouscontrol and the DP. The focus is fuel saving and SOC control at the end of journeys,especially in aggressive driving conditions and a hilly road. The controller shows agood potential to improve fuel economy and tight SOC control in long journey and hillyterrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gapbetween the baseline controller and the DP. However, there is little benefit in short tripsand flat road. It is caused by the low improvement margin of the mild hybrid powertrainand the limited future journey information.To provide a further step to implementation, a software-in-the-loop simulation model isdeveloped. A fully dynamic model of the powertrain and the control algorithm areimplemented in AMESim-Simulink co-simulation environment. This shows smalldeterioration of the control performance by driver’s pedal action, powertrain dynamicsand limited computational precision on the controller performance.
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