A comparison and accuracy analysis of impedance-based temperature estimation methods for Li-ion batteries

In order to guarantee safe and proper use of Lithium-ion batteries during operation, an accurate estimate of the battery temperature is of paramount importance. Electrochemical Impedance Spectroscopy (EIS) can be used to estimate the battery temperature and several EIS-based temperature estimation methods have been proposed in the literature. In this paper, we argue that all existing EIS-based methods implicitly distinguish two steps: experiment design and parameter estimation. The former step consists of choosing the excitation frequency and the latter step consists of estimating the battery temperature based on the measured impedance resulting from the chosen excitation. By distinguishing these steps and by performing Monte-Carlo simulations, all existing methods are compared in terms of accuracy (i.e., mean-square error) of the temperature estimate. The results of the comparison show that, due to different choices in the two steps, significant differences in accuracy of the estimate exist. More importantly, by jointly selecting the parameters of the experiment-design and parameter-estimation step, a more-accurate temperature estimate can be obtained. In case of an unknown State-of-Charge, this novel method estimates the temperature with an average absolute bias of 0.4. °C and an average standard deviation of 0.7. °C using a single impedance measurement for the battery under consideration

Decentralised robust controller synthesis for discrete-time polytopic systems with additive uncertainty using an Iterative-LMI approach

In this paper, we study robust and decentralised controller synthesis for discrete-time polytopic systems with norm-bounded additive uncertainty. To do so, we aim at finding a local solution to an optimisation problem involving bilinear matrix inequality constraints. The local solution is found using an iterative scheme that involves solving an optimisation problem with linear matrix inequality constraints. We will also briefly present and adapt a non-iterative but conservative solution strategy based on linear matrix inequalities and a solution strategy based on a so-called cone complementarity linearisation to compare our proposed method to. Finally, we will illustrate the solution strategy and show that it reduces conservatism in the controller synthesis on two decentralised controller synthesis examples, and on an example of networked control of a chemical batch reactor

H_inf optimal sampled-data controller synthesis with generalised disturbance and performance channels

Discrete-time controllers, implemented on digital platforms, are generally used to control continuous-time plants using sampled measurements. In this paper, a tractable sampled-data controller synthesis method is proposed for linear time-invariant plants. The proposed method gives guarantees for stability and performance of the closed-loop system in terms of the ${\mathcal{H}_\infty }$-norm, while taking the effect of sampling explicitly into account. This is done by taking a hybrid systems approach, which allows formulating linear matrix inequalities using the explicit solution to a Riccati differential equation. Furthermore, the sampled-data problem formulation is extended so that continuous-time design techniques like ${\mathcal{H}_\infty }$ loop-shaping can be used in a sampled-data context. To do so, it is essential to consider generalised disturbance and performance channels, where both discrete and continuous signals are weighted using weighting filters. The controller design method is demonstrated on an academic example and on a more practical example of reference tracking of a two-mass-spring-damper system

Joint state and parameter estimation for discrete-time polytopic linear parameter-varying systems

\u3cp\u3eLinear parameter-varying systems are very suitable for modelling nonlinear systems, since well-established methods from the linear-systems domain can be applied. Knowledge about the scheduling parameter is an important condition in this modelling framework. In case this parameter is not known, joint state and parameter-estimation methods can be employed, e.g., using interacting multiple-model estimation methods, or using an extended Kalman filter. However, these methods cannot be directly used in case the parameters lie in a polytopic set. Furthermore, these existing methods require tuning in order to have convergence and stability. In this paper, we propose to solve the joint-estimation problem in a two-step, Dual Estimation approach, where we first solve the parameter-estimation problem by solving a constrained optimisation problem in a recursive manner and secondly, employ a robust polytopic observer design for state estimation. Simulations show that our novel method outperforms the existing joint-estimation methods and is a promising first step for further research.\u3c/p\u3

Model-based periodic event-triggered control for linear systems

Periodic event-triggered control (PETC) is a control strategy that combines ideas from conventional periodic sampled-data control and event-triggered control. By communicating periodically sampled sensor and controller data only when needed to guarantee stability or performance properties, PETC is capable of reducing the number of transmissions significantly, while still retaining a satisfactory closed-loop behavior. In this paper, we will study observer-based controllers for linear systems and propose advanced event-triggering mechanisms (ETMs) that will reduce communication in both the sensor-to-controller channels and the controller-to-actuator channels. By exploiting model-based computations, the new classes of ETMs will outperform existing ETMs in the literature. To model and analyze the proposed classes of ETMs, we present two frameworks based on perturbed linear and piecewise linear systems, leading to conditions for global exponential stability and l2-gain performance of the resulting closed-loop systems in terms of linear matrix inequalities. The proposed analysis frameworks can be used to make tradeoffs between the network utilization on the one hand and the performance in terms of l2-gains on the other. In addition, we will show that the closed-loop performance realized by an observer-based controller, implemented in a conventional periodic time-triggered fashion, can be recovered arbitrarily closely by a PETC implementation. This provides a justification for emulation-based design. Next to centralized model-based ETMs, we will also provide a decentralized setup suitable for large-scale systems, where sensors and actuators are physically distributed over a wide area. The improvements realized by the proposed model-based ETMs will be demonstrated using numerical examples

Modeling and control of a radio-controlled model racing car

This paper presents an experimental platform and a modeling and control challenge posed to second-year Bachelor students in Automotive Engineering at Eindhoven University of Technology. The experimental platform consists of a customized radio-controlled 1:5-scale model racing car. The car consists of a digital signal processor, which can be programmed using Simulink, two motors, each driving one rear wheel, and sensors to measure the wheel speeds and the jaw rate. The radio-controlled car is used to give students hands-on experience in modeling and control, which is essential for a well-balanced control education. In this paper, it is shown that the radio-controlled car can be modeled using a bicycle model, which shows that this simple model can capture the essential vehicle dynamics. Furthermore, both a solution for torque vectoring and traction control are presented and demonstrated in this paper using the developed experimental platform

LMI-based robust observer design for battery state-of-charge estimation

\u3cp\u3eEstimating the battery State-of-Charge (SoC) is often done using nonlinear extensions of the Kalman filter. These filters do not explicitly address convergence of the estimation error and robustness with respect to model uncertainty, and make nonrealistic assumptions on the noise. Therefore, these filters require extensive tuning of the covariance matrices, which is a non-intuitive and tedious task. In this paper, a robust Luenberger estimator is proposed that explicitly addresses the requirements on estimation-error convergence, robustness and noise attenuation and shows their inherent trade-off. Different observers are synthesised using polytopic embeddings of the nonlinear battery model and using linear matrix inequalities that provide bounds on the {ell-{2,infty}-, ell-{infty,infty}-} or the ell-{2,2}-gains between input and output (to accommodate for model uncertainty and sensor noise). This guarantees a robustly converging SoC observer and makes its design more intuitive. The proposed observers are validated and compared with an Extended Kalman Filter (EKF) using experimental data. The results show that the performance of two out of three proposed observers is similar to the EKF, while the implementation is simpler and tuning is more intuitive and more straightforward.\u3c/p\u3

Periodic event-triggered control for linear systems

Event-triggered control (ETC) is a control strategy that is especially suited for applications where communication resources are scarce. By updating and communicating sensor and actuator data only when needed for stability or performance purposes, ETC is capable of reducing the amount of communications, while still retaining a satisfactory closed-loop performance. In this paper, an ETC strategy is proposed by striking a balance between conventional periodic sampled-data control and ETC, leading to so-called periodic event-triggered control (PETC). In PETC, the event-triggering condition is verified periodically and at every sampling time it is decided whether or not to compute and to transmit new measurements and new control signals. The periodic character of the triggering conditions leads to various implementation benefits, including a minimum inter-event time of (at least) the sampling interval of the event-triggering condition. The PETC strategies developed in this paper apply to both static state-feedback and dynamical output-based controllers, as well as to both centralized and decentralized (periodic) event-triggering conditions. To analyze the stability and the ${cal L}_{2}$-gain properties of the resulting PETC systems, three different approaches will be presented based on 1) impulsive systems, 2) piecewise linear systems, and 3) perturbed linear systems. Moreover, the advantages and disadvantages of each of the three approaches will be discussed and the developed theory will be illustrated using a numerical example