Implementazione su FPGA di un algoritmo per la stima online dello stato di batterie al litio

Nell'elaborato viene descritto inizialmente lo stato dell'arte nel campo dei circuiti di monitoraggio (BMS) e degli algoritmi più diffusi per la stima dello stato delle batterie al litio. Successivamente, viene presentato un algoritmo che ne stima lo stato di carica e viene descritta la sua implementazione su FPGA, realizzata grazie ad un flusso di progetto alternativo

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

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

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

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

Mantle Cell Lymphoma of Mucosa-Associated Lymphoid Tissue:A European Mantle Cell Lymphoma Network Study

While classical nodal mantle cell lymphoma (cMCL) is often associated with involvement of multiple extranodal sites, isolated extranodal disease (ED) at the time of diagnosis is a rare event; data on the outcome of these forms are lacking. On behalf of the European MCL Network, we conducted a retrospective analysis on the clinical characteristics and outcomes of MCL presenting with isolated or predominant ED (MALT MCL). We collected data on 127 patients with MALT MCL diagnosed from 1998 to 2015: 78 patients (61%) were male with a median age of 65 years. The involved sites include: upper airways + Waldeyer ring (40; 32%), gastrointestinal tract (32; 25%), ocular adnexa (17; 13%), oral cavity and salivary glands (17; 13%) and others (13; 1%); 7 patients showed multiple extranodal sites. The median follow-up was 80 months (range: 6–182), 5-year progression-free survival (PFS) was 45% (95% CI: 35–54) and 5-year overall survival (OS) was 71% (95% CI: 62–79). In an explorative setting, we compared MALT MCL with a group of 128 cMCL patients: MALT MCL patients showed a significantly longer PFS and OS compared with nodal cMCL; with a median PFS of 4.5 years vs 2.8 years (p = 0.001) and median OS of 9.8 years vs 6.9 years (p = 0.018), respectively. Patients with MALT MCL at diagnosis showed a more favorable prognosis and indolent course than classical nodal type. This clinical variant of MCL should be acknowledged to avoid possible over-treatment

Advances in algorithms and management systems for lithium-ion batteries

Nowadays, the lithium-ion battery technology allows the development of many battery-powered applications, such as electric mobility, thanks to its high energy and power densities. However, this kind of batteries needs an electronic system, the so called battery management system, to work in a safe and effective way. This work aims at analysing the most widespread battery management system architectures and battery state estimation algorithms and at presenting two new architectures based on a field-programmable gate array provided with advanced estimation algorithms implemented as hardware co-processors. These battery management systems have been implemented and tested both with simulated data, using a hardware in the loop platform, and with real cells in two different applications