Nima Lotfi

Reduced-Order Electrochemical Model-Based SOC Observer With Output Model Uncertainty Estimation

As an integral part of energy storage systems, Li-ion batteries require extensive management to guarantee their safe and efficient operation. Estimation of the remaining energy capability of the battery, usually expressed in terms of state of charge (SOC), plays an important role in any battery-powered application. Electrochemical model-based estimation techniques have proven very attractive for this purpose due to the additional information they provide regarding the internal battery operating conditions. A modified reduced-order model based on the single particle approximation of the electrochemical model, suitable for the real-time implementation of SOC estimation, is employed in this paper. This model, while maintaining some of the physical insights about the battery operation, provides a basis for an output-injection observer design to estimate the SOC. Output model uncertainties, originating primarily from the electrolyte-phase potential difference approximation and encountered mainly at higher discharge rates, are handled by incorporating an adaptation algorithm in the observer. Therefore, the proposed method, while being suitable for online implementation, provides an electrochemical model-based solution for battery SOC estimation over a wide range of operations. System stability and the robustness of the estimates given measurement noise are proved analytically using Lyapunov stability. Finally, accurate performance of the proposed SOC estimation technique is illustrated using simulation data obtained from a full-order electrochemical model of a lithium manganese oxide battery.

Emulating electric vehicle regenerative and friction braking effect using a Hardware-in-the-Loop (HIL) motor/dynamometer test bench

This paper provides a new approach for emulating electric vehicle (EV) braking performance on a motor/dynamometer test bench. The brake force distribution between regenerative braking and friction braking of both the front and rear axles are discussed in detail. A brake controller is designed, which represents a very close model of an actual EV braking system and takes into account both regenerative and friction braking limitations. The proposed brake controller is then integrated into the existing controller of an EV Hardware-in-the-Loop (HIL) test bench, and its performance is validated in real-time using the same experimental setup.

Robust Nonlinear Observer for State of Charge Estimation of Li-Ion Batteries

In this paper, a robust nonlinear observer is proposed to estimate the State of Charge (SOC) of a Li-ion battery, a problem which is critical in designing efficient Li-ion battery management systems and energy management systems in battery-powered applications. An equivalent circuit is used to model the battery behavior. The advantage of this model is that a straightforward identification process can be utilized for parameter identification. Although this model can capture battery dynamics very well for various operating conditions, modeling errors and also unknown disturbances will still be present; therefore, the battery management system should be able to take these uncertainties into consideration. To this end, the proposed estimation algorithm is designed to be robust against uncertainties. Furthermore, the observer does not impose any constraints on the battery current or the SOC relationship with Open Circuit Voltage (OCV). In other words, this algorithm does not require the battery current to be constant or the SOC-OCV relationship to be linear. Global asymptotic convergence of the estimated SOC to its true value is proved via the Lyapanov Stability Theorem. Simulation and experimental results demonstrate the effectiveness of the proposed method.Copyright

Development of an Educational Small-Scale Hybrid Electric Vehicle (HEV) Setup

In this paper, development of an educational small scale hybrid electric vehicle (HEV) learning module is discussed. The module is comprised of all the key components of an actual HEV that have been scaled down to provide an ideal test platform to evaluate and study hybrid power trains as well as simulate both electric and HEV systems. The test platform consists of low cost off-the-shelf RC parts controlled by Arduino microcontroller boards. LabVIEW is used as an interface to interact with the user, allowing power flow and energy analysis while simulating different drive cycles.

Development of an Educational Small Scale Hybrid Electric Vehicle (HEV) Setup

In this paper, the development of an educational small-scale hybrid electric vehicle (HEV) system is discussed. It consists of all the key components of a real HEV that have been scaled down to provide an ideal test platform to evaluate and study hybrid powertrains. The overall configuration is designed to allow the scaled vehicle to emulate the behavior of a series, parallel, or series-parallel HEV, as well as all-electric operation with minor modifications to the powertrain. The developed test bench not only facilitates hands-on experience but can be considered a safe and affordable solution to providing practical aspects of education in the field of electric and hybrid vehicle technology. Considering the potential of such clean transportation alternatives and their continuous growth, the learning experience obtained using the developed test bench will prove invaluable in preparing next generation professionals for further advancement of this field.

Li-ion Battery State of Health Estimation based on an improved Single Particle model

Health-conscious battery management is one of the main facilitators for widespread commercialization of Li-ion batteries as the primary power source in electrified transportation and portable electronics and as the backup source in stationary energy storage systems. The majority of the existing Battery Management Systems (BMSs) define battery State of Health (SOH) in terms of internal resistance increase or battery capacity decay and use various open-loop criteria based on the battery cycle number and/or operating conditions to determine its SOH. However, considering the wide range of operating conditions and current profiles for Li-ion batteries, the use of a closed-loop SOH estimation approach based on the measureable quantities of the battery along with a battery model is of great importance. In this work, the battery internal resistance increase which can be attributed to various chemical and mechanical degradation mechanisms is considered as the measure of the battery SOH. In order to estimate the SOH, a modified reduced-order electrochemical model based on the Single Particle (SP) Li-ion battery model is proposed to improve the traditional SP model accuracy. This model not only incorporates an analytical expression for the electrolyte-phase potential difference, it is also capable of accurately predicting the battery performance over a wide range of operating currents by considering the effects of the unmodeled dynamics. Finally, this model integrated with an adaptive output-injection observer to estimate the SP model states and the output model uncertainties, can be used to estimate the internal resistance increase during the battery lifetime. The modeling and estimation results are validated via a comparison to the full-order electrochemical model simulations.

Thermal Management and Voltage Stabilization in Air-Forced Open-Cathode Fuel Cells

Temperature control is undoubtedly one of the important challenges in open-cathode fuel cell systems. Due to cost considerations, it is traditionally achieved by constant-speed operation of the fans. In this paper, a state feedback temperature controller, combined with a Kalman filter to mitigate the noisy temperature measurements is designed and implemented. The controller-filter set facilitates robust thermal management with respect to model uncertainties and measurement noise. The proposed temperature control not only manages to track the fuel cell temperature reference, it can also be used to stabilize the output voltage. Voltage regulation is of great importance for open-cathode fuel cells as it guarantees a predictable and fixed fuel cell output voltage for given current values in spite of internal and external disturbances. The controllers were implemented experimentally and the results show promising performances in regulating the reference temperature and voltage despite model uncertainties and disturbances.Copyright

Electrochemical Model-Based Adaptive Estimation of Li-Ion Battery State of Charge

Electrochemical model-based estimation techniques have attracted increasing attention in the past decade due to their inherent insight about the internal battery operating conditions and limits while being able to monitor important li-ion battery states. The applicability of these methods is, however, limited due to the implementation complexity of their underlying models. In order to facilitate online implementation while maintaining the physical insight, a reduced-order electrochemical model is used in this work. This model, which is based on the commonly-used single particle model, is further improved by incorporating the electrolyte-phase potential. Furthermore, an output-injection observer, suitable for online implementation, is proposed to estimate SOC. The observer convergence is proved analytically using Lyapunov theory. Although the proposed observer shows great performance at low C rates, its accuracy deteriorates at high C-rates. To overcome this issue and achieve accurate SOC estimates for such charge/discharge rates, an adaptation algorithm is augmented to the observer. The adaptation algorithm, which can be implemented online, is used to compensate for model uncertainties, especially at higher C rates. Finally, simulation results based on a full-order electrochemical model are used to validate the observer performance and effectiveness.Copyright

A globally convergent observer for velocity estimation in robotic manipulators with uncertain dynamics

We present a method for global estimation of joint velocities in robot manipulators. A non-minimal model of a robotic manipulator is used to design an adaptive observer capable of handling uncertainties in robot dynamics. Dimension of the proposed observer is shown to be at least 3n where n stands for the manipulator degrees of freedom. This number is less than the dimension of most of existing globally convergent adaptive observers. Global asymptotic convergence of system state estimates to their true values is achieved under no persis-tency of excitation condition. Smoothness of the dynamics of the proposed observer allows its easy implementation in comparison with non-smooth observers. Simulation results illustrate low noise sensitivity of the proposed observer in comparison with non-smooth observers.