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

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

FPGA Implementation of an Ant Colony Optimization Based SVM Algorithm for State of Charge Estimation in Li-Ion Batteries

Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques in general can provide real-time SoC estimation without the need to design a cell model. In this work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained model was 10-fold cross-validated and then designed in Hardware Description Language to be run on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good performance was further verified when applied to a dataset acquired from different driving cycles to the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%

Intelligent and Efficient Transport Systems

The aim of this book is to present a number of digital and technology solutions to real-world problems across transportation sectors and infrastructures. Nine chapters have been well prepared and organized with the core topics as follows: -A guideline to evaluate the energy efficiency of a vehicle -A guideline to design and evaluate an electric propulsion system -Potential opportunities for intelligent transportation systems and smart cities -The importance of system control and energy-power management in transportation systems and infrastructures -Bespoke modeling tools and real-time simulation platforms for transportation system development This book will be useful to a wide range of audiences: university staff and students, engineers, and business people working in relevant fields

Secure Control and Operation of Energy Cyber-Physical Systems Through Intelligent Agents

The operation of the smart grid is expected to be heavily reliant on microprocessor-based control. Thus, there is a strong need for interoperability standards to address the heterogeneous nature of the data in the smart grid. In this research, we analyzed in detail the security threats of the Generic Object Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV) protocol mappings of the IEC 61850 data modeling standard, which is the most widely industry-accepted standard for power system automation and control. We found that there is a strong need for security solutions that are capable of defending the grid against cyber-attacks, minimizing the damage in case a cyber-incident occurs, and restoring services within minimal time. To address these risks, we focused on correlating cyber security algorithms with physical characteristics of the power system by developing intelligent agents that use this knowledge as an important second line of defense in detecting malicious activity. This will complement the cyber security methods, including encryption and authentication. Firstly, we developed a physical-model-checking algorithm, which uses artificial neural networks to identify switching-related attacks on power systems based on load flow characteristics. Secondly, the feasibility of using neural network forecasters to detect spoofed sampled values was investigated. We showed that although such forecasters have high spoofed-data-detection accuracy, they are prone to the accumulation of forecasting error. In this research, we proposed an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed algorithms was experimentally verified on the Smart Grid testbed at FIU. The test results showed that the proposed techniques have a minimal detection latency, in the range of microseconds. Also, in this research we developed a network-in-the-loop co-simulation platform that seamlessly integrates the components of the smart grid together, especially since they are governed by different regulations and owned by different entities. Power system simulation software, microcontrollers, and a real communication infrastructure were combined together to provide a cohesive smart grid platform. A data-centric communication scheme was selected to provide an interoperability layer between multi-vendor devices, software packages, and to bridge different protocols together

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies

Control of active cell balancing systems : innovation report

Lithium-ion battery packs are increasingly being used for high power and energy applications such as electric vehicles and grid storage. These battery packs consist of many individual cells connected in series and/or parallel. Manufacturing tolerances and varied operating conditions mean that each cell will be different one from another, being able to store different amounts of energy and deliver different amounts of power. This also means some cells will finish charging or discharging before others, resulting in unutilised energy in the remaining cells. Passive balancing systems are often used in multi-cell battery packs to ensure that all of the cells can be fully charged. However, this does not account for differences in cell capacity, meaning that not all cells will be fully discharged. Active balancing systems have been developed to transfer energy between the cells, in theory allowing for stronger cells to compensate for weaker ones. However, their perceived cost and complexity have prevented them from being widely adopted in commercial applications. In this work, an innovative control strategy was developed to determine how and when to energy balance a set of battery cells, with the aim of maximising battery pack energy utilisation. A model-based control system was designed, using state of charge to evaluate the level of energy imbalance between cells. Real-time implementation using second-hand electric vehicle cells and commercial balancing hardware demonstrated that the control strategy can decrease the amount of unused charge in the battery pack from 8% with passive balancing to 1% with active balancing, which has significant impact for battery pack energy throughput, physical size, mass, and long-term health

Sliding mode control rotor flux MRAS based speed sensorless induction motor traction drive control for electric vehicles

Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics