Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

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

Design and Optimization of In-Cycle Closed-Loop Combustion Control with Multiple Injections

With the increasing demand of transportation, biofuels play a fundamental role in the transition to sustainable powertrains. For the increased uncertainty of biofuel combustion properties, advanced combustion control systems have the potential to operate the engine with high flexibility while maintaining a high efficiency and robustness. For that purpose, this thesis investigates the analysis, design, implementation, and application of closed-loop Diesel combustion control algorithms. By fast in-cylinder pressure measurements, the combustion evolution can be monitored to adjust a multi-pulse fuel injection within the same cycle. This is referred to as in-cycle closed-loop combustion control.The design of the controller is based on the experimental characterization of the combustion dynamics by the heat release analysis, improved by the proposed cylinder volume deviation model. The pilot combustion, its robustness and dynamics, and its effects on the main injection were analyzed. The pilot burnt mass significantly affects the main combustion timing and heat release shape, which determines the engine efficiency and emissions. By the feedback of a pilot mass virtual sensor, these variations can be compensated by the closed-loop feedback control of the main injection. Predictive models are introduced to overcome the limitations imposed by the intrinsic delay between the control action (fuel injection) and output measurements (pressure increase). High prediction accuracy is possible by the on-line model adaptation, where a reduced multi-cylinder method is proposed to reduce their complexity. The predictive control strategy permits to reduce the stochastic cyclic variations of the controlled combustion metrics. In-cycle controllability of the combustion requires simultaneous observability of the pilot combustion and control authority of the main injection. The imposition of this restriction may decrease the indicated efficiency and increase the operational constraints violation compared to open-loop operation. This is especially significant for pilot misfire. For in-cycle detection of pilot misfire, stochastic and deterministic methods were investigated. The on-line pilot misfire diagnosis was feedback for its compensation by a second pilot injection. High flexibility on the combustion control strategy was achieved by a modular design of the controller. A finite-state machine was investigated for the synchronization of the feedback signals (measurements and model-based predictions), active controller and output action. The experimental results showed an increased tracking error performance and shorter transients, regardless of operating conditions and fuel used.To increase the indicated efficiency, direct and indirect optimization methods for the combustion control were investigated. An in-cycle controller to reach the maximum indicated efficiency increased it by +0.42%unit. The indirect method took advantage of the reduced cyclic variations to optimize the indicated efficiency under constraints on hardware and emission limits. By including the probability and in-cycle compensation of pilot misfire, the optimization of the set-point reference of CA50 increased the indicated efficiency by +0.6unit at mid loads, compared to open-loop operation.Tools to evaluate the total cost of the system were provided by the quantification of the hardware requirements for each of the controller modules

FPGA design methodology for industrial control systems—a review

This paper reviews the state of the art of fieldprogrammable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic

MLP neural network based gas classification system on Zynq SoC

Systems based on Wireless Gas Sensor Networks (WGSN) offer a powerful tool to observe and analyse data in complex environments over long monitoring periods. Since the reliability of sensors is very important in those systems, gas classification is a critical process within the gas safety precautions. A gas classification system has to react fast in order to take essential actions in case of fault detection. This paper proposes a low latency real-time gas classification service system, which uses a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) to detect and classify the gas sensor data. An accurate MLP is developed to work with the data set obtained from an array of tin oxide (SnO2) gas sensor, based on convex Micro hotplates (MHP). The overall system acquires the gas sensor data through RFID, and processes the sensor data with the proposed MLP classifier implemented on a System on Chip (SoC) platform from Xilinx. Hardware implementation of the classifier is optimized to achieve very low latency for real-time application. The proposed architecture has been implemented on a ZYNQ SoC using fixed-point format and achieved results have shown that an accuracy of 97.4% has been obtained

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%

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

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

Fuzzy Control Based Renewable Energy Sources for DC Microgrid Applications using FPGA Platform with EMS

The main objective of this proposed system is to provide uninterruptible power supply to the load.  This proposed system mainly deals with the Energy Management System (EMS) of the DC microgrid systems, using the fuzzy logic control.  This proposed system consists of the power sources, which obtains its power from the PV panels, Wind turbine, and fuel cells stack.  The EMS incorporates the fuzzy control that is responsible for the Energy Management and Battery Management.  The fuzzy maintains the State of Charge (SoC) parameters of the battery.  The fuzzy logic implementation of this system was done by using the Field Programmable Gate Array (FPGA)

A scalable, portable, FPGA-based implementation of the Unscented Kalman Filter

Sustained technological progress has come to a point where robotic/autonomous systems may well soon become ubiquitous. In order for these systems to actually be useful, an increase in autonomous capability is necessary for aerospace, as well as other, applications. Greater aerospace autonomous capability means there is a need for high performance state estimation. However, the desire to reduce costs through simplified development processes and compact form factors can limit performance. A hardware-based approach, such as using a Field Programmable Gate Array (FPGA), is common when high performance is required, but hardware approaches tend to have a more complicated development process when compared to traditional software approaches; greater development complexity, in turn, results in higher costs. Leveraging the advantages of both hardware-based and software-based approaches, a hardware/software (HW/SW) codesign of the Unscented Kalman Filter (UKF), based on an FPGA, is presented. The UKF is split into an application-specific part, implemented in software to retain portability, and a non-application-specific part, implemented in hardware as a parameterisable IP core to increase performance. The codesign is split into three versions (Serial, Parallel and Pipeline) to provide flexibility when choosing the balance between resources and performance, allowing system designers to simplify the development process. Simulation results demonstrating two possible implementations of the design, a nanosatellite application and a Simultaneous Localisation and Mapping (SLAM) application, are presented. These results validate the performance of the HW/SW UKF and demonstrate its portability, particularly in small aerospace systems. Implementation (synthesis, timing, power) details for a variety of situations are presented and analysed to demonstrate how the HW/SW codesign can be scaled for any application