Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

In this document, we present the main ideas and results concerning high-gain observers and some of their applications in control. The introduction gives a brief history of the topic. Then, a motivating second-order example is used to illustrate the key features of high-gain observers and their use in feedback control. This is followed by a general presentation of high-gain-observer theory in a unified framework that accounts for modeling uncertainty, as well as measurement noise. The paper concludes by discussing the use of high-gain observers in the robust control of minimum-phase nonlinear systems.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rnc.3156

High-gain observers in nonlinear feedback control

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Stochastic Processes And Filtering Theory

A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.

/pdf/parameter-estimation-and-model-selection-in-computational-3xkar8hh7p.pdf

Parameter Estimation and Model Selection in Computational Biology

High-gain observers in nonlinear feedback control: HIGH-GAIN OBSERVERS IN NONLINEAR FEEDBACK CONTROL

Brief paper: An adaptive high-gain observer for nonlinear systems

We distinguish two kinds of observers for nonlinear systems which are used by scientists and engineers: empirical observers and converging observers.

Adaptive-gain observers and applications

In this paper, we present a general theory of motion planning for kinematic systems. In particular, the theory deals with e-approximations of non-admissible paths by admissible ones in a certain optimal sense. The need for such an approximation arises for instance in the case of highly congested configuration spaces. This theory has been developed by one of the authors in a previous series of papers. It is based upon concepts from subriemannian geometry. Here, we summarize the results of the theory, and we improve on, by developing in details an intricate case: the ball with a trailer, which corresponds to a distribution with flag of type 2, 3, 5, 6.

Motion Planning for Kinematic Systems

High-gain observers and Kalman filtering in hard real-time

This paper investigates an adaptation of the high-gain Kalman filter for nonlinear continuous-discrete system with multirate sampled outputs under an observability normal form. The contribution of ...

/pdf/high-gain-extended-kalman-filter-for-continuous-discrete-46c1m7ykib.pdf

Nicolas Boizot

Papers

Brief paper: An adaptive high-gain observer for nonlinear systems

Adaptive-gain observers and applications

Motion Planning for Kinematic Systems

High-gain observers and Kalman filtering in hard real-time

High-gain extended Kalman filter for continuous-discrete systems with asynchronous measurements