Energy Management Techniques for Hybrid Energy Storage Systems in Electric Vehicles

Abstract

Electric vehicles (EV) are considered as a strong alternative of internal combustion engine vehicles expecting lower cost per mile, higher energy efficiency and low carbon emission. However, their actual benefits are not yet clearly verified while its energy storage system (ESS) can be improved in many ways. First, low cost per mile of EV is largely diminished if we charge EV with electricity from fossil fuel power plants due to power loss during generation, transmission, conversion and charging. On the other hand, regenerative braking is direct power conversion from the wheel to battery and one of the most important processes that can enhance energy efficiency of EV. Power loss during regenerative braking can be reduced by hybrid energy storage system (HESS) such that supercapacitors accept high power as batteries have small rate capability. Second, low cost per mile claimed by EV manufacturers does not take battery depreciation into account. Battery cost takes up to 50% of the total EV price, and its life is generally guaranteed for only 8~10 years. Harsh charge and discharge profiles of a battery results in reduced cycle life. Use of HESS and systematic charge management algorithms gives potential to mitigate the problems and improve various metrics of ESS such as cycle efficiency, cycle life, and so on.

This dissertation discusses design-time and run-time issues in HESS for EVs in order to maximize the energy efficiency and minimize the operating cost. This dissertation performs extensive optimization based on elaborate component models to achieve the objectives. First, we proposed systematic algorithms to maximize the energy efficiency for a regenerative braking scenario, while most of the previous works relied on empirical and heuristic methods. We improve the energy efficiency by calculating the optimal charging power distribution between the supercapacitor bank and battery bank. Minimizing the cost per mile of an EV should consider optimization over a period of time including multiple acceleration and deceleration profiles. A little forecast on the future driving profiles helps prepare the supercapacitor state of charge (SOC) to the optimal level by systematic charge migration such that it can charge and discharge the electrical energy to enhance the energy efficiency. We also propose grid power source-aware EV charging technique to minimize the electricity bill from a home equipped with photovoltaic energy generation. Lastly, we implement an actual EV equipped with HESS to verify the proposed algorithms.