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This paper proposes a multi-objective hybrid energy management system that minimizes both the electricity expenses and the household greenhouse gas emissions released due to consumption, considering the entire life cycle of the generation assets used to provide energy. The global warming potential indicator is used to decide if it is more sustainable to purchase electricity from the grid or use the household's flexible generation sources like photovoltaic panels and the energy storage system. Results prove that it is possible to reduce greenhouse gas emissions without incurring expensive electricity bill costs thanks to the hybrid-based home energy management system approach. This method gives the end-users a more influential role in the climate change solution, allowing them to give more or less importance to the economic or environmental component, according to their preferences.

Electric vehicles (EV) in the power grid are not necessarily an additional burden, but can also act grid relieving. This is possible by storing energy from renewable sources and shaving the volatility of generation. For EV battery owners it is important to predict the aging effects resulting from secondary battery use. For a future energy system, it is therefore important to address the topic of EV battery aging for the group of stakeholders: EV owners, EV manufacturers, energy providers and grid operators. A physico-chemically motivated, impedance-based lithium-ion battery model has recently been presented in [6] and is available as open source [5]. Additionally, the authors present a new set of quantitative measurement data for parameterization of a battery pack of Daimler’s Smart electric drive. A battery pack was disassembled to gather all information from inside the battery pack. The cells have been aged as part of an extensive aging testing. Therefore, the simulation setup enables in-situ and operando analysis of cell behavior and helps understanding aging effects in electric vehicle operation. For model parameterization, the research contributions for electrical modeling based on electrical impedance spectroscopy from [1], as well as studies on aging behavior of lithium-ion cells with graphite anode from [2] and [3] are considered and used to adapt the method presented in [4]. The model is parameterized with electrical, thermal and aging parameters for an automotive battery pack that consists of 93 high-energy lithium-ion pouch cells with NMC cathode and graphite anode with a capacity of 50 Ah. A 3D-geometrical representation of the battery pack topology including cooling allows a detailed assessment of the simulated cell temperatures. It therefore helps to generate a better understanding of the incremental battery aging effects that are caused by vehicle-to-grid (V2G) operation. Controlled charging and discharging of EVs could help the grid in terms of stability and avoid additional cost for grid expansion. At the same time, V2G services could generate additional revenues to the EV battery owners from service payment. Nevertheless, calculation of EV battery aging is one major purpose for EV battery owners before they agree to the active use of their EV battery by third parties. Sources: [1] Dissertation H. Witzenhausen, https://doi.org/10.18154/RWTH-2017-03437 [2] Dissertation A. Warnecke, https://doi.org/10.18154/RWTH-2017-09646 [3] Dissertation M. Lewerenz https://doi.org/10.18154/RWTH-2018-228663 [4] Schmalstieg et al., https://doi.org/10.1016/j.jpowsour.2014.02.012 [6] Dissertation F. Hust, “Physico-Chemically Motivated Parameterization and Modelling of Real-Time Capable Lithium-Ion Battery Models - a Case Study on the Tesla Model S Battery”, January 2019 [5] www.openbat.de

The emergence of electric vehicles offers the opportunity to decarbonize the transportation and mobility sector. With smart charging strategies and the use of electricity generated from renewable sources, electric vehicle owners can reduce their electricity bill as well as reduce their carbon footprint. We investigated smart charging strategies for electric vehicle charging at household and workplace sites with photovoltaic systems. Furthermore, we investigated the participation of an electric vehicle in the provision of positive automatic frequency restoration reserve (aFRR) in Germany from 30 October 2018 to 31 July 2019. We find that the provision of positive aFRR in Germany returns a positive net return. The positive net return is, however, not sufficient to cover the current investment cost for a necessary control unit. For home charging, we find that self-sufficiency rates of up to 48.1% and an electricity cost reduction of 17.6% for one year can be reached with unidirectional smart charging strategies. With bidirectional strategies, self-sufficiency rates of up to 56.7% for home charging and electricity cost reductions of up to 26.1% are reached. We also find that electric vehicle (EV) owners who can charge at their workplace can reduce their electricity cost further. The impact of smart charging strategies on battery aging is also discussed.

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A comprehensive electric vehicle model is developed to characterize the behavior of the Smart e.d. (2013) while driving, charging and providing vehicle-to-grid services. To facilitate vehicle-to-grid strategy development, the EV model is completed with the measurement of the on-board charger efficiency and the charging control behavior upon external set-point request via IEC 61851-1. The battery model is an electro-thermal model with a dual polarization equivalent circuit electrical model coupled with a lumped thermal model with active liquid cooling. The aging trend of the EV’s 50 Ah large format pouch cell with NMC chemistry is evaluated via accelerated aging tests in the laboratory. Performance of the model is validated using laboratory pack tests, charging and driving field data. The RMSE of the cell voltage was between 18.49mV and 67.17mV per cell for the validation profiles. Cells stored at 100% SOC and 40 °C reached end-of-life (80% of initial capacity) after 431–589 days. The end-of-life for a cell cycled with 80% DOD around an SOC of 50% is reached after 3634 equivalent full cycles which equates to a driving distance of over 420,000 km. The full parameter set of the model is provided to serve as a resource for vehicle-to-grid strategy development.