Transient cooling of a lithium-ion battery module during high-performance driving cycles using distributed pipes - A numerical investigation

Transient effects are often excluded from the design and analysis of battery thermal management systems (BTMS). However, electric vehicles are subjected to significant dynamic loads causing transient battery heating that is not encountered in a steady state. To evaluate the significance of such effects, this paper presents a time-dependent analysis of the battery cooling process, based on an existing cooling system that satisfactorily operates in steady conditions. To resemble realistic conditions, the temporal variations in the battery power withdrawal are inferred from different standard driving cycles. Computational fluid dynamics is then utilized to predict the coolant and battery temperatures inside a battery module for a period of 900 s. It is shown that, for air cooling, the batteries temperature can exceed the safe limit. For example, in a high-performance driving cycle, after 200 s, the battery temperature goes beyond the critical value of 308 K. Nonetheless, the temperatures are always within the safe region when liquid is used to cool the battery module. Also, during a high-performance cycle where the flow rate is 1.230 g/s, the battery temperature decreased below the critical threshold and reached 304 K. In addition, to maintain the temperature of the batteries below the critical threshold during NYCC traffic and US06 driving cycles, a maximum coolant pressure inlet of 1.52 and 0.848 g/s, equivalent to 100 Pa and 50 Pa, respectively, are required. The temporal changes in Nusselt number distribution over the battery module, induced by the acceleration of the vehicle during the driving cycles, are also discussed. It is concluded that the assumption of a steady state might lead to the non-optimal design of BTMSs

Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles

Abstract—This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence—an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack

A Study on the Integration of a High-Speed Flywheel as an Energy Storage Device in Hybrid Vehicles

The last couple of decades have seen the rise of the hybrid electric vehicle as a compromise between the outstanding specific energy of petrol fuels and its low-cost technology, and the zero tail-gate emissions of the electric vehicle. Despite this, considerable reductions in cost and further increases in fuel economy are needed for their widespread adoption. An alternative low-cost energy storage technology for vehicles is the high-speed flywheel. The flywheel has important limitations that exclude it from being used as a primary energy source for vehicles, but its power characteristics and low-cost materials make it a powerful complement to a vehicle's primary propulsion system. This thesis presents an analysis on the integration of a high-speed flywheel for use as a secondary energy storage device in hybrid vehicles. Unlike other energy storage technologies, the energy content of the flywheel has a direct impact on the velocity of transmission. This presents an important challenge, as it means that the flywheel must be able to rotate at a speed independent of the vehicle's velocity and therefore it must be coupled via a variable speed transmission. This thesis presents some practical ways in which to accomplish this in conventional road vehicles, namely with the use of a variator, a planetary gear set or with the use of a power-split continuously variable transmission. Fundamental analyses on the kinematic behaviour of these transmissions particularly as they pertain to flywheel powertrains are presented. Computer simulations were carried out to compare the performance of various transmissions, and the models developed are presented as well. Finally the thesis also contains an investigation on the driving and road conditions that have the most beneficial effect on hybrid vehicle performance, with a particular emphasis on the effect that the road topography has on fuel economy and the significance of this

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

Real-time Energy Management System of Battery-Supercapacitor in Electric vehicles

This thesis presents the design, simulation and experimental validation of an Energy Management System (EMS) for a Hybrid Energy Storage System (HESS) composed of lithium ion batteries and Supercapacitors (SCs) in electric vehicles. The aim of the EMS is to split the power demand considering the weaknesses and strengths or the power sources. The HESS requires an EMS to determine power missions for the battery and SC in real time, where the SC is commanded to assist the battery during high power demand and recover the energy generated during braking. Frequency sharing techniques have been proposed by researchers to achieve this objective, including the Discrete Wavelet Transform (DWT) and conventional filtration methods (low and high pass filters). However, filtration approaches can introduce delay (milliseconds to tens of seconds) in the frequency components which undermines the hybridisation advantages. Hence, the selection of the filtration technique and filter design are crucial to the system's performance. Researchers have proposed power demand prediction methodologies to deal with time delay, however, the advantages and drawbacks of using such methods have not been investigated thoroughly, particularly whether time delay compensation and its inherent prediction error improves the system performance, efficiency, and timely SC contribution during the motoring and braking stages. This work presents a fresh perspective to this research field by introducing a novel approach that deals with delay without complicated prediction algorithms and improves the SC contribution during the motoring and braking stages while reducing energy losses in the system. The proposed EMS allows the SC to provide timely assistance during motoring and to recover the braking energy generated. A charging strategy controls energy circulation between the battery and SC to keep the SC charge availability during the whole battery discharge cycle. The performance and efficiency of the HESS is improved when compared to the traditional use of conventional filtration techniques and the DWT. Results show that the proposed EMS method improves the energy efficiency of the HESS. For the US06 driving cycle, the energy efficiency is 91.6%. This is superior to the efficiency obtained with an EMS based on a high pass filter (41.3%), an EMS based on DWT high frequency component (30.3%) and an EMS based on the predicted DWT high frequency component (41%)

COLOMBO Deliverable 4.2: Extended Simulation Tool PHEM coupled to SUMO with User Guide

This public deliverable is an extension of the draft deliverable D4.1. The first part covers the extensions performed on PHEM’s database for modelling the vehicle fleet in the year 2020. The document extension describes the second work item which was to allow using PHEM as an emission model directly within the COLOMBO overall simulation system (COSS). Both possibilities – an off-line connection with SUMO output files fed into PHEM, and an on-line approach by embedding the derivative PHEMlight into SUMO – are presented in detail

investigation of the energy requirements for the on board generation of oxy hydrogen on vehicles

Abstract The present study investigates the energy needs for the on-board generation of oxyhydrogen (HHO) used as fuel additive on vehicles. HHO production is performed through the use of an alkaline electrolyzer, directly taking energy from the equipped internal combustion engine. A longitudinal vehicle dynamic model is used to evaluate the driving power to be supplied by the engine for two reference speed profiles, NEDC and WLTC. The performed investigation determines the engine brake thermal efficiency gain required to ensure HHO production without increase in fuel consumption. The results can be used as guidelines for the development of on-board control strategies

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

Electrification of Urban Waste Collection: Introducing a Simulation-Based Methodology for Feasibility, Impact and Cost Analysis

We introduce a multi-agent-based simulation methodology to investigate the feasibility and evaluate environmental and economic sustainability of an electrified urban waste collection. Electrification is a potential solution for transport decarbonization and already widely available for individual and public transport. However, the availability of electrified commercial vehicles like waste collection vehicles is still limited, despite their significant contribution to urban emissions. Moreover, there is a lack of clarity whether electric waste collection vehicles can persist in real word conditions and which system design is required. Therefore, we present a synthetic model for waste collection demand on a per-link basis, using open available data. The tour planning is solved by an open-source algorithm as a capacitated vehicle routing problem (CVRP). This generates plausible tours which handle the demand. The generated tours are simulated with an open-source transport simulation (MATSim) for both the diesel and the electric waste collection vehicles. To compare the life cycle costs, we analyze the data using total cost of ownership (TCO). Environmental impacts are evaluated based on a Well-to-Wheel approach. We present a comparison of the two propulsion types for the exemplary use case of Berlin. And we are able to generate a suitable planning to handle Berlin’s waste collection demand using battery electric vehicles only. The TCO calculation reveals that the electrification raises the total operator cost by 16-30 %, depending on the scenario and the battery size with conservative assumptions. Furthermore, the greenhouse gas emissions (GHG) can be reduced by 60-99%, depending on the carbon footprint of electric power generation.DFG, 398051144, Analyse von Strategien zur vollständigen Dekarbonisierung des urbanen Verkehr

An investigation into hybrid power trains for vehicles with regenerative braking

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