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Battery Aging and Fuel Efficiency as Optimization Objectives as Part of a Real-time Operating System of a Multi-source HEV



Modeling and effecting battery health is a crucial issue in the electrified transportation field. Beside enormous battery cost, also the competence of modern transportation systems strongly depends on the functionality of batteries. Beside diagnosing the battery state/health, an integration into control-/power management systems is important to effect the battery status. Power management strategies designed for Li-Ion battery powered hybrid electric vehicles (HEVs) should consider battery health as an optimization objective as battery health can be severely affected by charge/discharge patterns. For model-based estimation of battery health, various models have been proposed in the literature. These models can be grouped into two categories: one emphasizing on internal chemical reactions in the battery for example electrochemical models; and another focusing on experimental laws. The aim of this paper is to explicitly show how aging can be modeled and therefore considered within operation and control systems. Here aging-related parameters are integrated in a multi-objective optimization algorithm to generate solutions that satisfy the conflicting objectives of fuel minimization and battery life extension. The rule-based power management developed previously is modified to include batteries state of health (SoH) in designing rules. A dynamic battery model that integrates the effects of temperature and capacity fading is used. Some of the main factors responsible for aging are temperature, time, and cycling. Both number of cycles and depth of cycles play a key role. In this paper the power management strategy is designed to manage the battery health degradation in some optimal sense. First, formulating an optimization problem for maximizing the usable capacity of the battery, second, utilizing a supercapacitor to take over the peaks in demand while limiting the battery current. An aging model and a suitable algorithm is used to analyze the cycling effects of an irregular load profile on remaining battery capacity


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