A cost of ownership analysis of batteries in all-electric and plug-in hybrid vehicles

Exploring legal documents such as laws, judgments, and contracts is known to be a time-consuming task. To support domain experts in efficiently browsing their contents, legal documents in electronic form are commonly enriched with semantic annotations. They consist of a list of headwords indicating the main topics. Annotations are commonly organized in taxonomies, which comprise both a set of is-a hierarchies, expressing parent/child-sibling relationships, and more arbitrary related-to semantic links. This paper addresses the use of Deep Learning-based Natural Language Processing techniques to automatically extract unknown taxonomy relationships between pairs of legal documents. Exploring the document content is particularly useful for automatically classifying legal document pairs when topic-level relationships are partly out-of-date or missing, which is quite common for related-to links. The experimental results, collected on a real heterogeneous collection of Italian legal documents, show that word-level vector representations of text are particularly effective in leveraging the presence of domain-specific terms for classification and overcome the limitations of contextualized embeddings when there is a lack of annotated data

Energy-efficient coordinated electric truck-drone hybrid delivery service planning

Recent works have shown that a coordinated delivery strategy in which a drone collaborates with a truck using it as a moving depot is quite effective in improving the performance and energy efficiency of the delivery process. As most of these works come from the research community of logistics and transportation, they are instead focused on the optimality of the algorithms, and neglect two critical issues: (1) they consider only a planar version of the problem ignoring the geographic information along the delivery route, and (2) they use a simplified battery model, truck, and drone power consumption model as they are mostly focused on optimizing delivery time alone rather than energy efficiency.In this work, we propose a greedy heuristic algorithm to deter-mine the most energy-efficient sequence of deliveries in which a drone and an EV truck collaborate in the delivery process, while accounting for the two above aspects. In our scenario, a drone delivers packages starting from the truck and returns to the truck after the delivery, while the truck continues on its route and possibly delivers other packages. Results show that, by carefully using the drone’s energy along the truck delivery route, we can achieve 43-69% saving of the truck battery energy on average over a set of different delivery sets and different drone battery sizes. We also compared two "common-sense" heuristics, concerning which we saved up to 42%

Battery-aware electric truck delivery route exploration

The energy-optimal routing of Electric Vehicles (EVs) in the context of parcel delivery is more complicated than for conventional Internal Combustion Engine (ICE) vehicles, in which the total travel distance is the most critical metric. The total energy consumption of EV delivery strongly depends on the order of delivery because of transported parcel weight changing over time, which directly affects the battery efficiency. Therefore, it is not suitable to find an optimal routing solution with traditional routing algorithms such as the Traveling Salesman Problem (TSP), which uses a static quantity (e.g., distance) as a metric. In this paper, we explore appropriate metrics considering the varying transported parcel total weight and achieve a solution for the least-energy delivery problem using EVs. We implement an electric truck simulator based on the EV powertrain model and nonlinear battery model. We evaluate different metrics to assess their quality on small size instances for which the optimal solution can be computed exhaustively. A greedy algorithm using the empirically best metric (namely, distance × residual weight) provides significant reductions (up to 33%) with respect to a common-sense heaviest first package delivery route determined using a metric suggested by the battery properties. This algorithm also outperforms the state-of-the-art TSP heuristic algorithms, which consumes up to 12.46% more energy and 8.6 times more runtime. We also estimate how the proposed algorithms work well on real roads interconnecting cities located at different altitudes as a case study

A case for a battery-aware model of drone energy consumption

The market of small drones has been recently increasing due to their use in many fields of application. The most popular drones are multirotors, in particular quadcopters. They are usually supplied with batteries of limited capacity, and for this reason their total flight time is also limited.As a consequence of the non linear characteristics of batteries, estimation of the real flight time may become an issue, since most battery models do not include all the non idealities. Consequently, applications such as delivery service, search and rescue, surveillance might not be accomplished correctly because of inaccurate energy estimations.This paper describes a battery-aware model for an accurate analysis of the drone energy consumption; this model is then applied to a scenario of drone delivery. Results show an accuracy greater of about 16% with respect to the traditional estimation model

Battery-aware energy model of drone delivery tasks

Drones are becoming increasingly popular in the commercial market for various package delivery services. In this scenario, the mostly adopted drones are quad-rotors (i.e., quadcopters). The energy consumed by a drone may become an issue, since it may affect (i) the delivery deadline (quality of service), (ii) the number of packages that can be delivered (throughput) and (iii) the battery lifetime (number of recharging cycles). It is thus fundamental try to find the proper compromise between the energy used to complete the delivery and the speed at which the quadcopter flies to reach the destination. In order to achieve this, we have to consider that the energy required by the drone for completing a given delivery task does not exactly correspond to the energy requested to the battery, since the latter is a non-ideal power supply that is able to deliver power with different efficiencies depending on its state of charge. In this paper, we demonstrate that the proposed battery-aware delivery scheduling algorithm carries more packages than the traditional delivery model with the same battery capacity. Moreover, the battery-aware delivery model is 17% more accurate than the traditional delivery model for the same delivery scheme, which prevents the unexpected drone landing

SystemC-AMS Simulation of Energy Management of Electric Vehicles

Electric vehicles (EV) are rapidly invading the market, since they are clean, quiet and energy efficient. However, there are many factors that discourage EVs for current and potential customers. Among them, driving range is one of the most critical issues: running out of battery charge while driving results in serious inconvenience even comparable to vehicle breakdown, as an effect of long fuel recharging times and lack of charging facilities. As a result, the dimensioning of the energy subsystem of an EV is a crucial activity. The choice of the power components and of the adopted policies should thus be validated at design time through simulations, that estimate the vehicle driving range under reference driving profiles. It is thus necessary to build a simulation framework that takes into account an EV power consumption model, dependent on the characteristics of the vehicle and of the driving route, plus accurate models for all power components, including batteries and green power sources. The goal of this paper is to achieve early EV simulation, so that the designer can estimate at design time the driving range of the vehicle, validate the adopted components and policies and evaluate alternative configurations

Multi-Criteria Coordinated Electric Vehicle-Drone Hybrid Delivery Service Planning

According to recent works, a coordinated delivery strategy in which terrestrial and aerial electric vehicles work together effectively improves delivery throughput and energy efficiency. However, most research on logistics and transportation focuses on delivery performance and does not care about energy efficiency, with three main limitations: 1. Most of these works ignore geographic information along the delivery route, while road slope is one of the most critical energy consumption components. 2. Vehicle and drone power consumption models are simplified as driving mileage, while the delivery time is a significant concern. 3. The battery model is simplified as a linear model even though practical batteries have non-linearity properties. This work proposes a framework to provide energy- and time-efficient delivery schedules with a hybrid delivery service with terrestrial and aerial electric vehicles. We first implement accurate electric van and drone power models and a battery model based on manufacturers' system specifications and experimental data. Then, we propose a heuristic delivery scheduling algorithm to determine the electric van and drone delivery schedule. We also introduce various cost functions to evaluate the delivery scheduling results regarding time, energy, the weighted sum of time and energy, and the economic model. The proposed framework is validated on randomly implemented delivery missions and delivery scenarios in existing cities. Results indicate that the coordinated delivery saves delivery costs up to 27.25% in terms of the economic model compared with the electric van-only delivery schedule

A SystemC-AMS Framework for the Design and Simulation of Energy Management in Electric Vehicles

Driving range is one of the most critical issues for electric vehicles (EVs): running out of battery charge while driving results in serious inconvenience even comparable to a vehicle breakdown, as an effect of long fuel recharging times and lack of charging facilities. This may discourage EVs for current and potential customers. As an effect, the dimensioning of the energy subsystem of an EV is a crucial issue: the choice of the energy storage components and the policies for their management should be validated at design time through simulations, so to estimate the vehicle driving range under reference driving profiles. Thus, it is necessary to build a simulation framework that considers an EV power consumption model that accounts for the characteristics of the vehicle and the driving route, plus accurate models for all power components, including batteries and renewable power sources. The goal of this paper is to achieve such an early EV simulation, through the definition of a SystemC-AMS framework, which models simultaneously the physical and mechanical evolution, together with energy flows and environmental characteristics. The proposed solution extends the state-of-the-art framework for the simulation of electrical energy systems with support for mechanical descriptions and the AC domain, by finding a good balance between accuracy and simulation speed and by formalizing the new information and energy flows. The experimental results demonstrate that the performance of the proposed approach in terms of accuracy and simulation speed w.r.t. the current state-of-the-art and its effectiveness at supporting EV design with an enhanced exploration of the alternatives