A MATLAB graphical user interface for battery design and simulation; from cell test data to real-world automotive simulation

This paper describes a graphical user interface (GUI) tool designed to support cell design and development of manufacturing processes for an automotive battery application. The GUI is built using the MATLAB environment and is able to load and analyze raw test data as its input. After data processing, a cell model is fitted to the experimental data using system identification techniques. The cell model's parameters (such as open-circuit-voltage and ohmic resistance) are displayed to the user as functions of state of charge, providing a visual understanding of the cell's characteristics. The GUI is also able to simulate the performance of a full battery pack consisting of a specified number of single cells using standard driving cycles and a generic electric vehicle model. After a simulation, the battery designer is able to see how well the vehicle would be able to follow the driving cycle using the tested cells. Although the GUI is developed for an automotive application, it could be extended to other applications as well. The GUI has been designed to be easily used by non-simulation experts (i.e. battery designers or electrochemists) and it is fully automated, only requiring the user to supply the location of raw test data

Advances in Electric Drive Vehicle Modeling with Subsequent Experimentation and Analysis

A combination of stricter emissions regulatory standards and rising oil prices is leading many automotive manufacturers to explore alternative energy vehicles. In an effort to achieve zero tail pipe emissions, many of these manufacturers are investigating electric drive vehicle technology. In an effort to provide University of Kansas students and researchers with a stand-alone tool for predicting electric vehicle performance, this work covers the development and validation of various models and techniques. Chapter 2 investigates the practicality of vehicle coast down testing as a suitable replacement to moving floor wind tunnel experimentation. The recent implementation of full-scale moving floor wind tunnels is forcing a re-estimation of previous coefficient of drag determinations. Moreover, these wind tunnels are relatively expensive to build and operate and may not capture concepts such as linear and quadratic velocity dependency along with the influence of tire pressure on rolling resistance. The testing method explained here improves the accuracy of the fundamental vehicle modeling equations while remaining relatively affordable. The third chapter outlines various models used to predict battery capacity. The authors investigate the effectiveness of Peukert's Law and its application in lithium-based batteries. The work then presents the various effects of battery temperature on capacity and outlines previous work in the field. This paper then expands upon Peukert's equation in order to include both variable current and temperature effects. The method proposed captures these requirements based on a relative maximum capacity criterion. Experimental methods presented in the paper provide an economical testing procedure capable of producing the required coefficients in order to build a high-level, yet accurate state of charge prediction model. Moreover, this work utilizes automotive grade lithium-based batteries for realistic outcomes in the electrified vehicle realm. The fourth chapter describes an advanced numerical model for computing vehicle energy usage performance. This work demonstrates the physical and mathematical theories involved, while building on the principles of Newton's second law of motion. Moreover, this chapter covers the equipment, software, and processes necessary for collecting the required data. Furthermore, a real world, on-road driving cycle provides for a quantification of accuracy. Multiple University of Kansas student project vehicles are then studied using parametric studies applicable to the operating requirements of the vehicles. Further investigation demonstrates the accuracy and trends associated with the advanced models presented in Chapters 2 and 3. Lastly, chapter 5 investigates the design and building of a graphical user interface (GUI) for controlling the models created in the previous three chapters. The chapter continues to outline the creation of a stand-alone GUI as well as instructions for implementation, usage, and data analysis

Estimation of State of Charge of Battery Used In Electric Vehicles With Wireless Battery Management System

This research paper presents a comprehensive investigation into the development and analysis of a wireless battery management system (BMS) using MATLAB Simulink. The primary objective of this study is to create an efficient, reliable, and scalable BMS that caters to the demands of various applications, such as electric vehicles, grid energy storage, and portable electronics. Our methodology involves designing and simulating key BMS components, including state estimation algorithms, fault detection mechanisms, and communication protocols, within the MATLAB Simulink environment. The paper first elucidates the motivation for adopting wireless technology in BMS, emphasizing its advantages over traditional wired systems. Subsequently, we explore the intricacies of the proposed wireless BMS architecture, detailing the implementation of essential features such as state-of-charge estimation, fault diagnosis, and thermal management. We also address the challenges associated with wireless communication, including latency, security, and energy efficiency, by incorporating robust communication protocols and power management strategies. Through rigorous simulations, we demonstrate the efficacy of the proposed wireless BMS, showcasing its ability to ensure optimal performance, safety, and longevity of battery packs. The outcomes of this research not only contribute to the advancement of BMS technology but also pave the way for further improvements in battery-powered systems. In conclusion, this paper offers a holistic perspective on wireless BMS design, emphasizing its potential to revolutionize energy management and extend the applications of battery technology in various domains

Development of a modular dual engine hybrid electric vehicle simulation model

Depleting resources of fossil fuel, climate change impacts, high oil prices, and strict emission requirements are leading to the research on efficient, environmentally friendly, and lowered fossil fuel dependent solutions in the transportation field. While a number of studies used computer modeling and simulation tools to investigate hybrid electric vehicles (HEVs), very few attempted to model and simulate a dual-engine hybrid vehicle. Designing a vehicle engine to meet energy needs in the fully loaded condition is not an optimal solution for manufacturers and customers. The larger the engine, the higher the manufacturing costs for companies, and higher fuel consumption for customers. The integration of dual-engine hybrid technology can help to solve this problem. The objective of this study was to design and simulate a dual-engine hybrid electric vehicle (DE-HEV) model to investigate whether it can be a fuel efficient and environmentally friendly solution without sacrificing vehicle performance. The simulated DE-HEV uses two small engines instead of one large engine. In the simulated design, a smaller single engine supplies the power if the energy need is not more than a single engine can provide. The second engine turns on when the power demand is greater than the single engine can supply. Working models for the DE-HEV components, such as an electric motor, generator, battery, and the controller have been developed using the Matlab/Simulink™ simulation package. Each model was validated with test data from the literature. Appropriate power management strategy has been developed to accommodate the dual engine design. Fuel-efficiency, overall performance, and manufacturing cost for the simulated DE-HEV model have been compared against current commercial models. Simulation results showed that DE-HEV has between a 2% to 6% higher efficiency than comparable HEVs. Cost analysis results showed that the manufacturing cost of DE-HEV is 11% higher. Performance of the vehicle was tested with standard drive cycles. Test results are satisfactory; although there was significant increase in fuel-efficiency, because of its higher initial manufacturing cost, maintenance, and complexity, DE-HEVs may have challenges in the short term. However, with expected decreases in manufacturing costs of battery storage and power electronics technology, the implementation of DE-HEVs can be feasible transportation options in the near future

STUDY OF STRATEGIES FOR AN OPTIMAL ENERGY MANAGEMENT ON ELECTRIC AND HYBRID VEHICLES

Questa tesi di dottorato è focalizzata sull’identificazione di strategie di gestione dell’energia a bordo di veicoli elettrici e ibridi, con l’obiettivo di ottimizzare la gestione dell’energia e, quindi, consentire un risparmio di risorse. Infatti, l’ottimizzazione della fase d’uso del veicolo, attraverso una più efficiente gestione dell’energia, consente di dimensionare in modo ridotto i principali componenti, come il pacco batterie. Innanzitutto, viene presentato un tool di simulazione denominato TEST (Target-speed EV Simulation Tool). Questo strumento consente di effettuare simulazioni di dinamica longitudinale per veicoli completamente elettrici o ibridi e, quindi, di monitorare tutti i dati rilevanti necessari per effettuare un corretto dimensionamento del gruppo propulsore, inclusi il/i motore/i elettrico/i ed il pacco batterie. Inoltre, è possibile testare anche diversi layout di propulsori, compresi quelli che utilizzano celle a combustibile, le cosiddette “fuel cell”. Viene poi presentata una strategia di frenata rigenerativa, adatta per veicoli FWD, RWD e AWD. L’obiettivo principale è quello di recuperare la massima energia frenante possibile, mantenendo il veicolo stabile, con buone prestazioni in frenata. La strategia è stata testata sia attraverso un consolidato software di simulazione della dinamica del veicolo (VI-CarRealTime), sia attraverso simulazioni “driver-in-the-loop” utilizzando un simulatore di guida. Inoltre, la strategia proposta è stata integrata nel tool TEST per valutarne l’influenza sull’autonomia e sui consumi del veicolo. Gli strumenti sopra menzionati sono stati utilizzati per studiare uno scenario di casi reali, per valutare la fattibilità dell’utilizzo di una flotta alimentata a fuel cell a metano per svolgere attività di raccolta rifiuti porta a porta. I risultati mostrano un’elevata fattibilità in termini di autonomia del veicolo rispetto alle missioni standard di raccolta dei rifiuti, a condizione che i componenti siano adeguatamente dimensionati. Il dimensionamento dei componenti è stato effettuato attraverso iterazioni, utilizzando diversi componenti nelle stesse missioni. Infine, è stata riportata un’analisi approfondita degli studi LCA (Life Cycle Assessment) relativi ai veicoli elettrici, con particolare attenzione al pacco batterie, evidenziando alcune criticità ambientali. Questo studio sull’LCA sottolinea quindi l’importanza di una corretta gestione dell’energia per ridurre al minimo l’impatto ambientale associato al consumo stesso di energia.This PhD thesis is focused on identifying energy management strategies on board electric and hybrid vehicles, to optimize energy management and thus allow for resource savings. In fact, vehicle’s operational phase optimisation through a more efficient energy management allows main components downsizing, such as battery pack. First of all, a simulation tool called TEST (Target-speed EV Simulation Tool), is presented. This tool allows to carry out longitudinal dynamics simulations on pure electric or hybrid-electric vehicles, and therefore monitoring all the relevant data needed to carry out a proper powertrain sizing, including the electric motor(s) and the battery pack. Furthermore, several powertrain layouts can be also tested, including those using fuel cells. Then a regenerative braking strategy, suitable for FWD, RWD and AWD vehicles, is presented. Its main target is to recover the maximum possible braking energy, while keeping the vehicle stable with good braking performance. The strategy has been tested both through a state-of-art vehicle dynamics simulation software (VI-CarRealTime) and through driver-in-the-loop simulations using a driving simulator. Furthermore, the proposed strategy has been integrated into TEST to evaluate its influence on vehicle range and consumptions. The above-mentioned tools have been used to evaluate a real-world case scenario to assess the feasibility of using a methane fuel cell powered fleet to carry out door to door waste collection activities. Results show high feasibility in terms of vehicle range compared to standard waste collection missions, provided that components are properly sized. Components sizing has been done through iterations using different components on the same missions. Finally, an in-depth analysis of the LCA (Life Cycle Assessment) studies related to electric vehicles has been reported, with particular focus to the battery pack, highlighting some environmental critical issues. This LCA study therefore emphasizes the importance of a correct energy management to minimize the environmental impact associated with energy consumption

Design and creation of different simulation architectures for hybrid and electric vehicles

PFC del programa Erasmus EPSTreball desenvolupat dins el marc del programa 'European Project Semester'.Development of electric vehicle architectures requires complex analysis and innovative designs in order to produce a highly efficient mode of personal transportation acceptable to the target demographic. Using computer-aided modeling and simulation has been proven to decrease the development time of conventional vehicles while increasing overall success of the product design. Computer-aided automotive development also allows a fast response to the testing and inclusion of developing technologies in individual systems. Therefore, it follows to use this technique in the research and development of electric vehicles for consumer markets. This paper presents a system level model development and simulation for an electric vehicle using the Matlab-Simulink platform and its associated process. The current state of the art technologies for electric and plug-in hybrid electric vehicles are given to provide an introduction into the subject. Following, the project development is briefly described, detailing the specific goals for the project and the methods by which results were achieved. Next the paper discusses the analytical and simulation models for each key component as divided by the following systems: battery, charging, and traction. Model assembly and the development of a graphic user interface follows. Finally, the testing procedures for model validation, along with results, and future project works are provided

Characterization of heavy duty engine fuel maps used for model based simulation tools

Characterization of fuel consumption is of critical importance for framing or modifying federal regulations for trucking industry. Due to its complexity, fuel consumption is often only known for a few test cycles which generally represent limited types of vehicle activity. It is known that vehicle fuel consumption strongly depends on the vehicle activity, chassis design and engine model year (MY), and hence poses a significant challenge while predicting fuel consumption of heavy-duty vehicles over real-world vehicle activity.;Upcoming Greenhouse Gas (GHG) regulation for 2017, engine manufacturers are required to assess heavy-duty engine fuel economy using vehicle simulation tools. With recent focus on fuel economy and GHG emissions, regulatory agencies are progressively relying on vehicle simulation tools that allow prediction of the fuel consumption for a variety of vehicles over different test cycles.;Autonomie simulation tool developed by Argonne National Laboratory was used in this study to predict the fuel consumption over different cycles and then the prediction of simulation tool was compared with chassis and engine dynamometer data to check the accuracy of the simulation tool.;Autonomie simulation results were compared with the chassis dynamometer test data and the results showed a 5.93% and 11.53% difference in engine work and brake-specific fuel consumption (bsfc) respectively. When Autonomie simulation results were compared with engine dynamometer test data, the difference in work done, integrated fuel consumption and bsfc were found to be 13.21%, 4.92%, and 8.32% respectively.;Autonomie generated fuel consumption simulation data was compared with a dynamic vehicle simulator, Greenhouse Gas Emissions Model (GEM). The method was able to predict ARB transient cycle within 10% error, with an absolute error of 6.38%

Model Based Automotive System Integration: Fuel Cell Vehicle Hardware-In-The-Loop

abstract: Over the past decade, proton exchange membrane fuel cells have gained much momentum due to their environmental advantages and commutability over internal combustion engines. To carefully study the dynamic behavior of the fuel cells, a dynamic test stand to validate their performance is necessary. Much attention has been given to HiL (Hardware-in-loop) testing of the fuel cells, where the simulated FC model is replaced by a real hardware. This thesis presents an economical approach for closed loop HiL testing of PEM fuel cell. After evaluating the performance of the standalone fuel cell system, a fuel cell hybrid electric vehicle model was developed by incorporating a battery system. The FCHEV was tested with two different control strategies, viz. load following and thermostatic. The study was done to determine the dynamic behavior of the FC when exposed to real-world drive cycles. Different parameters associated with the efficiency of the fuel cell were monitored. An electronic DC load was used to draw current from the FC. The DC load was controlled in real time with a NI PXIe-1071 controller chassis incorporated with NI PXI-6722 and NI PXIe-6341 controllers. The closed loop feedback was obtained with the temperatures from two surface mount thermocouples on the FC. The temperature of these thermocouples follows the curve of the FC core temperature, which is measured with a thermocouple located inside the fuel cell system. This indicates successful implementation of the closed loop feedback. The results show that the FC was able to satisfy the required power when continuous shifting load was present, but there was a discrepancy between the power requirements at times of peak acceleration and also at constant loads when ran for a longer time. It has also been found that further research is required to fully understand the transient behavior of the fuel cell temperature distribution in relation to their use in automotive industry. In the experimental runs involving the FCHEV model with different control strategies, it was noticed that the fuel cell response to transient loads improved and the hydrogen consumption of the fuel cell drastically decreased.Dissertation/ThesisMasters Thesis Engineering 201

A BAYESIAN NETWORK APPROACH TO BATTERY AGING IN ELECTRIC VEHICLE TRANSPORTATION AND GRID INTEGRATION

Nowadays, batteries in electric vehicles (EVs) are facing a variety of tasks in their connection to the power grid in addition to the main task, driving. All of these tasks play a very significant role in the battery aging, but they are highly variable due to the change in the driver behavior, grid connection availability and weather conditions. The effect of these external factors in the battery degradation have been studied in literature by mostly deterministic and some stochastic approaches, but limited to specific cases. In this dissertation, first, a large-scale deterministic approach is implemented to evaluate the effect of variations in the EV battery daily tasks. To do so, a software tool named REV-Cycle is developed to simulate the EV powertrain and studied the effect of driving behavior, recharging facilities and timings, grid services and temperature/weather change effects, one by one. However, there are two main problems observed in the deterministic aging evaluation: First, the battery capacity fade factors such as temperature, cycling current, state of charge (SOC) … are dependent to the external variables such as location, vehicle owner’s behavior and availability of the grid connection. Therefore, it is not possible to accurately evaluate the battery degradation with a deterministic model, while its inputs are stochastic. Second, the battery aging factors’ dependency is hierarchical and it is not easy to follow and implement this hierarchy with deterministic models. Therefore, using a hierarchical probabilistic framework is proposed that can better represent the problem and realized that the Bayesian statistics with Markov Chain Monte Carlo (MCMC) can provide the problem solving structure needed for this purpose. A comprehensive hierarchical probabilistic model of the battery capacity fade is proposed using Hierarchical Bayesian Networks (HBN). The model considers all uncertainties of the process including vehicle acceleration and velocity, grid connection for charging and utility services, temperatures and all unseen intermediate variables such as battery power, auxiliary power, efficiencies, etc. and estimates the capacity fade as a probability distribution. Metropolis-Hastings MCMC algorithm is applied to generate the posterior distributions. This modeling approach shows promising result in different case studies and provides more informative evaluation of the battery capacity fade