Improved Battery State Estimation Using Novel Sensing Techniques.

Lithium-ion batteries have been considered a great complement or substitute for gasoline engines due to their high energy and power density capabilities among other advantages. However, these types of energy storage devices are still yet not widespread, mainly because of their relatively high cost and safety issues, especially at elevated temperatures. This thesis extends existing methods of estimating critical battery states using model-based techniques augmented by real-time measurements from novel temperature and force sensors. Typically, temperature sensors are located near the edge of the battery, and away from the hottest core cell regions, which leads to slower response times and increased errors in the prediction of core temperatures. New sensor technology allows for flexible sensor placement at the cell surface between cells in a pack. This raises questions about the optimal locations of these sensors for best observability and temperature estimation. Using a validated model, which is developed and verified using experiments in laboratory fixtures that replicate vehicle pack conditions, it is shown that optimal sensor placement can lead to better and faster temperature estimation. Another equally important state is the state of health or the capacity fading of the cell. This thesis introduces a novel method of using force measurements for capacity fade estimation. Monitoring capacity is important for defining the range of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs). Current capacity estimation techniques require a full discharge to monitor capacity. The proposed method can complement or replace current methods because it only requires a shallow discharge, which is especially useful in EVs and PHEVs. Using the accurate state estimation accomplished earlier, a method for downsizing a battery pack is shown to effectively reduce the number of cells in a pack without compromising safety. The influence on the battery performance (e.g. temperature, utilization, capacity fade, and cost) while downsizing and shifting the nominal operating SOC is demonstrated via simulations. The contributions in this thesis aim to make EVs, HEVs and PHEVs less costly while maintaining safety and reliability as more people are transitioning towards more environmentally friendly means of transportation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120815/1/nassimab_1.pd

Hydro/Battery Hybrid Systems for frequency regulation

An innovative Hydro/Battery Hybrid System (HBHS), composed of a hydropower plant (HPP) and a Battery Energy Storage System (BESS) is proposed to provide frequency regulation services in the Nordic Power System (NPS). The HBHS is envisioned to have a faster and more efficient response compared to HPPs currently providing these services, whilst retaining their high energy capacity and endurance, thus alleviating stand-alone BESS operation constraints. This Thesis aims to explore the operation and optimization of such a hybrid system in order to make it efficient and economically viable. A power plant perspective is employed, evaluating the impact different control algorithms and parameters have on the HBHS performance. Providing Frequency Containment Reserves for Normal Operation (FCR-N), to the national TSO in Sweden, is defined from technology and market analyses as the use case for the HBHS. The characteristics of HPPs suitable for HBHS implementation are found theoretically, by evaluating HPP operational constraints and regulation mechanisms. With the aim of evaluating the dynamic performance of the proposed HBHS, a frequency regulation model of the NPS is built in MATLAB and Simulink. Two different HBHS architectures are introduced, the Hydro Recharge, in which the BESS is regulating the frequency and the HPP is controlling its state of charge (SoC), and the Frequency Split, in which both elements are regulating the frequency with the HPP additionally compensating for the SoC. The dynamic performance of the units is qualitatively evaluated through existing and proposed FCR-N prequalification tests, prescribed by the TSO and ENTSO-E. Quantitative performance comparison to a benchmark HPP is performed with regards to the estimated HPP regulation wear and tear and BESS degradation during 30-day operation with historical frequency data. The two proposed HBHS architectures demonstrate significant reductions of estimated HPP wear and tear compared to the benchmark unit. Simulations consistently report a 90 % reduction in the number of movements HPP regulation mechanism performs and a more than 50 % decrease in the distance it travels. The BESS lifetime is evaluated at acceptable levels and compared for different architectures. Two different applications are identified, the first being installing the HBHS to enable the HPP to pass FCR-N prequalification tests. The second application is increasing the FCR-N capacity of the HPP by installing the HBHS. The Frequency Split HBHS shows more efficient performance when installed in the first application, as opposed to the Hydro Recharge HBHS, which shows better performance in the second application. Finally, it is concluded that a large-scale implementation of HBHSs would improve the frequency quality in the NPS, linearly decreasing the amount of time outside the normal frequency band with increasing the total installed HBHS power capacity

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

Effects of Internal Resistance on Performance of Batteries for Electric Vehicles

ABSTRACT Effects of Internal Resistance on Performance of Batteries for Electric Vehicles by Rohit A Ugle The University of Wisconsin-Milwaukee, 2013 Under the Supervision of Professor Anoop K. Dhingra An ever increasing acceptance of electric vehicles as passenger cars relies on better operation and control of large battery packs. The individual cells in large battery packs do not have identical characteristics and may degrade differently due to their manufacturing variability and other factors. It is beneficial to evaluate the performance gain by replacing certain battery modules/cells during actual driving. The following are the objectives of our research. We will develop an on-line battery module degradation diagnostic scheme using the intrinsic signals of a battery pack equalization circuit. Therefore, a battery health map can be constructed and updated in real time. Next based on the derived battery health map, the performance of the battery pack will be evaluated a user specified trip so as to evaluate the worthiness of replacing certain modules/cells. Different electric vehicles have different performance for the same driving cycle. These variations are due to variation in driving patterns, traffic, different light patterns, random behavior of the drivers etc. To account for this random behavior of the electric vehicle performance we generate 100 random trip cycles. We aim to model the behavior of the driving cycle and battery behavior. Finally, the thesis also explores the possibility of energy exchange between the battery packs and the smart grid. In the smart grid scenario where we have the knowledge of the electricity price and the load patterns on the grid, it is beneficial for the user to schedule charging and discharging patterns for electric vehicles. Our research will define charging and discharging patterns throughout the life of the battery. We will optimize the charging and discharging times and define the opportunity cost for each day during summer and winter months. The objective is to maximize the profit earned by selling excess energy in the battery to the grid and minimize the charging cost for the electric vehicle

Dynamic Modelling and Control of Grid-Level Energy Storage Systems

The focus of this work is on two energy storage technologies, namely pumped storage hydroelectricity (PHS) and secondary batteries. Under secondary battery technologies, two potential technologies for grid-scale storage, namely high-temperature sodium-sulfur (NaS) battery and vanadium redox flow battery (VRFB), are investigated. PHS is a largescale (\u3e100 MW) technology that stores and generates energy by transporting water between two reservoirs at different elevations. The goal is to develop a detailed dynamic model of PHS and then design the controllers to follow the desired load trajectory accurately with high efficiency. The NaS battery and VRFB are advanced secondary batteries which can be charged and discharged rapidly. Since temperature excursion of high temperature NaS batteries especially under fast cycling conditions is a safety hazard and the temperature excursion can take place at some location within the cell where measurement is not feasible, the focus is on a model-based approach for transient analysis and development of novel thermal management techniques. A detailed thermo-electrochemical dynamic model of a single NaS has been developed. As a detailed cell model is computationally intractable for simulating large number of cells in the battery, various strategies such as coordinate transformation, orthogonal collocation, and model reformulation have been developed to obtain a reduced order model that solves significantly faster than the full, high-dimensional model but provides an accurate estimate of the key variables such as transient voltage/current/temperature profile in the cell. Sodium sulfur batteries need to be maintained within a temperature range of 300-4000C. Therefore, the focus was on developing thermal management strategies that can not only maintain the cell temperature near the optimum, but can effectively utilize the heat, improving the overall efficiency of the battery system. VRFBs can provide large amount of storage as the electrolytes are stored in separate tanks. However, the self-discharge reactions (due to crossover) along with the undesired side reactions and the dissolved water in the membrane, can significantly reduce the capacity. A dynamic model-based approach is developed for detection, identification, and estimation of capacity fade and SOC as a function of time. A model-based prognostic capability has been developed for estimating the remaining useful cell life

Minimizing Grid Interaction With A Residential Self-Consumption System That Includes PV

This study analyses the impact a self-consumption system with solar PV and battery storage has on the energy usage of a typical residential home in Boone, NC. This is quantified using two means, grid import reduction and demand reduction. Performance of the self-consumption system is compared to the performance of a PV only model system of an equivalent size

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

Forward converter current fed equalizer for lithium based batteries in ultralight electrical vehicles

In this paper, the concept of a forward balancing technique fed by a buck converter for lithium-based batteries in Electrical Vehicle (EV) applications is investigated. The proposed active topology equalizes eight cells in a series in a battery pack, by using a forward converter for each battery pack and the whole battery packs, using a buck converter. The battery bank consists of four battery packs, which are in series. Therefore, the proposed system will equalize 32 cells in series. In this paper, the proposed circuit employs a single transistor used in a Zero Voltage Switch (ZVS) for the forward converter. In practice, this means a capacitor in parallel with the switch at the same time a demagnetizing of the transformer is obtained. The circuit realizes a low Electromagnetic Interference (EMI) and reduces ringing. To overcome the problem of many pins on a coil former, the transformer secondary windings are made by using hairpin winding, on a ring core. It permits, e.g., having eight secondaries and uniform output voltages. Each secondary winding is made by two hairpin turns using two zero-Ohm resistors in series. The proposed topology has less components and circuitry, and it can equalize multiple battery packs by using a single buck converter and several forward converters for each battery pack. Experimental and simulation results are performed to verify the viability of the proposed topology

Study of space battery accelerated testing techniques. Phase 2 report - Ideal approaches towards accelerated tests and analysis of data

Ideal approaches to accelerated life tests and data analysis applied to space batterie