While large scale deployment of Plug-in Electric Vehicles (PEVs) offers promising advantages such as environmental benefits, energy security and economic stability, it also poses certain cybersecurity related challenges. Unlike power grid security, PEV cybersecurity is significantly under-explored. However, cyber attacks on PEVs may lead to disastrous situations such as out-of-service EVs via Denial-of-Charging (DOC) or battery pack damage via overcharging. In this work, we attempt to address this issue by exploring control-oriented approaches for PEV cybersecurity. Specifically, we focus on designing algorithms for detecting cyber attacks that can potentially affect PEV battery packs during charging. We discuss two algorithms: (i) Static Detector which utilizes only measured variables, and (ii) Dynamic Detector which utilizes the knowledge of system dynamics along with the measurements. Furthermore, we propose a filter-based design approach for the Dynamic Detector that considers a multi-objective criteria including stability, robustness, and attack sensitivity. We perform theoretical analysis and simulation studies to evaluate the effectiveness of the algorithms under DOC and overcharging attacks which indicate the superiority of Dynamic Detector in terms of attack detectability.

The commercialization of renewable energy sources is often obstructed due to the cost of energy storages such as batteries, which are unavoidable due to intermittent nature of these energy sources. Average power density and low cycle life of batteries leads to frequent replacement and put additional economic burden towards the replacement cost at its End-Of-Life. The Battery Supercapacitor-Hybrid Energy Storage System (BS-HESS) with efficient energy management system (EMS) has shown potential to improve the battery life in various applications. Here, we exploit the Pontryagin's Minimum Principle (PMP) for optimal power-split between battery and supercapacitor in HESS for a stand-alone photovoltaic (PV) assisted agricultural feed mill such that the battery life is improved. Also, we employ a new technique called hybrid maximum power point tracking, by introducing an extra term in the performance index, to take care of the effects of partial shading and provide fast response to sudden change in solar irradiance. We verify the effectiveness of the proposed PMP-based EMS via simulation and comparison with conventional methods. Simulation results show that the HESS along with PMP-based EMS can improve the battery cycle life compared to rule-based EMS and battery-only storage. Finally, we implement the proposed PMP-based EMS in hardware prototype to prove its practicality. The results obtained are satisfactory.