Budget impact analysis of apixaban to treat and prevent venous thromboembolism in Italy

BACKGROUND: Venous thromboembolism (VTE), a collective term for deep vein thrombosis (DVT) and pulmonary embolism (PE), is a serious vascular condition associated to high economic and clinical burden. Apixaban, a Novel Oral Anticoagulant (NOAC) has shown non-inferiority efficacy versus the current standard of care (low molecular weight heparin [LMWH]/vitamin K antagonist [VKA]) in the acute treatment and prevention of VTE and a significant reduction in the risk of bleeding.AIM: Evaluate the economic impact of the use of apixaban for treatment and prevention of DVT and PE from the perspective of the Italian National Health System (NHS).METHODS: A budget impact model was adapted in order to compare clinical outcomes and economic consequences associated to apixaban vs. LMWH/VKA and others NOACs over a three-year time horizon in the Italian setting. In the analysis two scenario were compared: status quo scenario without apixaban and an alternative scenario with apixaban. Only direct healthcare costs have been considered.RESULTS: Assuming a population of patients receiving apixaban over the first 3 years equal to 20,957, the introduction of apixaban is associated to an incremental saving of € 821,748 in the first years, € 1,250,454 in the second year, and € 1,866,466 in the third year. The total net saving over the 3-year period is € 3,938,668, which is a 2.47% decrease from the total budget for the status quo scenario without apixaban. This saving is mainly due to reduced VTE events and bleeds by apixaban. Indeed apixaban is associated with less VTE events (both fatal and non-fatal), less major bleeding and less Clinical Relevant Non Major (CRNM) bleeding with a total of 52 fatal events avoided.CONCLUSIONS: The listing of apixaban for the treatment of VTE (both DVT and PE) and the prevention of recurrent VTE provides both significant clinical advantages, in terms of deaths and events avoided, and economical advantages, consisting in a reduction in the total expenditure on the Italian NHS

System on chip battery state estimator: E-bike case study

This paper discusses the hardware implementation and experimental validation of a model-based battery state estimator. The model parameters are identified online using the moving window least squares method. The estimator is implemented in a field programmable gate array device as a hardware block, which interacts with the embedded processor to form a system on a chip battery management system (BMS). As a case study, the BMS is applied to the battery pack of an e-bike. Road tests show that the implemented estimator may provide very good performance in terms of maximum and rms estimation errors. This work also proposes a new methodology to assess the performance of a battery state estimator

Investigation of series-parallel connections of multi-module batteries for electrified vehicles

Large-format Lithium-ion battery packs consist of the series and parallel connection of elemental cells, usually assembled into modules. The required voltage and capacity of the battery pack can be reached by various configurations of the elemental cells or modules. It is thus worth investigating if different configurations lead to different performance of the battery pack in presence of a mismatch in the cell characteristics. A simulation tool is developed in this work and applied to a battery pack consisting of standard 12 V modules connected with various serial/parallel topologies. The results show that battery configurations with modules directly connected in parallel and then assembled in series are more robust against variation of the cell capacity through the battery. Moreover, given the cells and the battery configuration, we show that changing the position of the cells has a significant impact on the usable capacity of the battery

On the Sizing of the DC-Link Capacitor to Increase the Power Transfer in a Series-Series Inductive Resonant Wireless Charging Station

Wireless inductive-coupled power transfer is a very appealing technique for the battery recharge of autonomous devices like surveillance drones. The charger design mainly focuses on lightness and fast-charging to improve the drone mission times and reduce the no-flight gaps. The charger secondary circuit mounted on the drone generally consists of a full-bridge rectifier and a second-order filter. The filter cut-off frequency is usually chosen to make the rectifier output voltage constant and so that the battery is charged with continuous quantities. Previous works showed that an increase in power transfer is achieved, if compared to the traditional case, when the second-order filter resonant frequency is close to the double of the wireless charger excitation and the filter works in resonance. This work demonstrates that the condition of resonance is necessary but not sufficient to achieve the power increment. The bridge rectifier diodes must work in discontinuous-mode to improve the power transfer. The paper also investigates the dependence of the power transfer increase on the wireless excitation frequency. It is found the minimum frequency value below which the power transfer gain is not possible. This frequency transition point is calculated, and it is shown that the gain in power transfer is obtained for any battery when its equivalent circuit parameters are known. LTSpice simulations demonstrate that the transferred power can be incremented of around 30%, if compared to the case in which the rectifier works in continuous mode. This achievement is obtained by following the design recommendations proposed at the end of the paper, which trade off the gain in power transfer and the amplitude of the oscillating components of the wireless charger output

Hardware-in-the-Loop Platform for Assessing Battery State Estimators in Electric Vehicles

The development of new algorithms for the management and state estimation of lithiumion batteries requires their verification and performance assessment using different approaches and tools. This paper aims at presenting an advanced hardware in the loop platform which uses an accurate model of the battery to test the functionalities of battery management systems (BMSs) in electric vehicles. The developed platform sends the simulated battery data directly to the BMS under test via a communication link, ensuring the safety of the tests. As a case study, the platform has been used to test two promising battery state estimators, the Adaptive Mix Algorithm and the Dual Extended Kalman Filter, implemented on a field-programmable gate array based BMS. Results show the importance of the assessment of these algorithms under different load profiles and conditions of the battery, thus highlighting the capabilities of the proposed platform to simulate many different situations in which the estimators will work in the target application

Implementation of the fast charging concept for electric local public transport: The case-study of a minibus

This paper shows an effective implementation of the fast charging concept in the electric local public transport context. An electric minibus powered with a lead-acid battery is considered as a case-study. Its traction battery is redesigned using 12 V standard lithium-iron-phosphate modules to benefit from the higher performance of the lithium battery technology compared to the lead-acid one. The minibus can achieve a continuous operation characterised by 20 min of traveling alternated with 10 min of standstill for fast recharging of the battery. Experiments performed on a single module of the battery show that the load profile is sustained without appreciable issues both in temperature and life degradation of the lithium cells

Comparison of State and Parameter Estimators for Electric Vehicle Batteries

A Battery Management System (BMS) is needed to ensure a safe and effective operation of a Lithium-ion battery, especially in electric vehicle applications. An important function of a BMS is the reliable estimation of the battery state in a wide range of operating conditions. To this end, a BMS often uses an equivalent electrical model of the battery. Such a model is computationally affordable and can reproduce the battery behaviour in an accurate way, assuming that the model parameters are updated with the actual operating condition of the battery, namely its state-of-charge, temperature and ageing state. This paper compares the performance of two battery state and parameter estimation techniques, i.e., the Extended Kalman Filter and the classic Least Squares method in combination with the Mix algorithm. Compared to previous ones, this work focuses on the concurrent estimation of battery state and parameters using experimental data, measured on a Lithium-ion cell subject to a current profile significant for an electric vehicle application

Experimental Analysis of an Electric Minibus with Small Battery and Fast Charge Policy

The lead-acid battery of an electric minibus has been replaced with a smaller size lithium-ion battery system consisting of standard 12 V modules and a hierarchical battery management system. The minibus has experimentally been tested to show that the reduced battery capacity, which also cuts costs, does not affect the daily operational mission. This is assuming that the driving phases are alternated with fast charging periods. Experiments show that fast charging of 8 min guarantees up to 1 h of operation

Hardware-in-the-loop simulation of FPGA-based state estimators for electric vehicle batteries

This paper describes a hardware-in-the-loop (HiL) simulation platform specifically designed to test state estimators for Li-ion batteries in electric vehicle applications. Two promising estimators, the Mix algorithm combined with the moving window least squares and the dual extended Kalman filter, are implemented in hardware on a field-programmable gate array (FPGA) and evaluated using the developed HiL platform. The simulation results show the effectiveness of using FPGAs for hardware acceleration of battery state estimators and the importance of their assessment under different operating conditions, i.e., driving schedules, which can be simulated by the HiL platform