The diffusion of electric vehicles suffers from immature and expensive battery technologies. Repurposing electric vehicle batteries for second-life application scenarios may lower the vehicles' total costs of ownership and increases their ecologic sustainability. However, identifying the best-or even a feasible-scenario for which to repurpose a battery is a complex and unresolved decision problem. In this exaptation research, we set out to design, implement, and evaluate the first decision support system that aids decision-makers in the automobile industry with repurposing electric vehicle batteries. The exaptation is done by classifying decisions on repurposing products as bipartite matching problems and designing two binary integer linear programs that identify (a) all technical feasible assignments and (b) optimal assignments of products and scenarios. Based on an empirical study and expert interviews, we parameterize both binary integer linear programs for repurposing electric vehicle batteries. In a field experiment, we show that our decision support system considerably increases the decision quality in terms of hit rate, miss rate, precision, fallout, and accuracy. While practitioners can use the implemented decision support system when repurposing electric vehicle batteries, other researchers can build on our results to design decision support systems for repurposing further products.
Proof-of-concept projects have demonstrated that used electric vehicle batteries (EVBs), after their removal from electric vehicles due to insufficient performance, can be repurposed for less demanding applications. It is expected that numerous batteries will be available for repurposing in the 2020s. However, the information asymmetries and transaction costs of trading used EVBs have remained unexplored, as have principles that guide the design of information systems that support the trade. Based on existing literature and in-depth interviews with battery experts, we conceptualize two key transactions for trading used EVBs. We then identify potential information asymmetries and transaction costs based on new institutional economic theory and propose five design principles that information Electronic supplementary material The online version of this article (https://doi.org/10.systems should implement to address these information asymmetries and transaction costs. Subsequent research can build on our results to further frame the economic properties of trading used EVBs and to design information systems in line with new institutional economic theory.
Demand Response (DR) facilitates the monitoring and management of appliances in energy grids by employing methods that, for example, increase the reliability of energy grids and reduce users' cost. Within energy grids, Smart Home scenarios can be characterized by a unique combination of appliances and user preferences. To increase their impact, a scenario-specific selection of the best performing DR methods is necessary. As the user faces a multitude of heterogeneous DR methods to choose from, a complex decision problem is present. The primary goal of this study is to develop a decision support framework that can determine the bestperforming DR methods. Building on literature analyses, expert workshops and expert interviews, we identify seven requirements, derive solution concepts addressing these requirements, and develop the framework by combining the concepts using a benchmarking process as a template. To demonstrate the framework's applicability, we conduct a simulation study that uses artificial (simulated) data for seven types of households. Within this study, we employ four DR methods, assume changing appliances over time and cost minimization as primary objective. The study indicates, that by using the framework and thus by identifying and using the best DR method for each scenario, the users can achieve further cost benefits. The application of the framework allows practitioners to increase the efficiency of the DR method selection process and to further enhance DR-related benefits, such as cost minimization, load profile flattening, and peak load reduction. Researchers benefit from guidance for benchmarking and evaluating DR methods.
Abstract.A battery management system (BMS) is an embedded system for monitoring and controlling complex battery systems in high-tech goods, such as electric vehicles or military communication devices. BMSs are often designed for simplicity and cost efficiency, storing few crucial data on the condition of batteries. With an increasing trend to reuse batteries, BMSs face a need to implement additional functionality to support decision-making tasks. This functionality requires rich data on the structure, usage history, and condition of a battery that is not supported by current BMS type series. Based on expert interviews and document analyses, we sketch a design theory for implementing BMSs that supply the data required for making decisions on how to best reuse battery systems.
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