Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867574
Yuan Gao, A. Padmanabhan, O. Chen, Ali C. Kheirabadi, R. Nagamune
This paper presents a baseline repositioning controller for floating offshore wind farms. It is assumed that each turbine in the farm receives a lateral position reference command by a high-level wind farm controller. A low-level controller for each wind turbine in the farm manipulates the aerodynamic force direction and magnitude to achieve the reference command, while either power maximization or power regulation at the rated power is sought depending on the generator speed feedback signal. The low-level repositioning controller is a combination of the existing controller of blade pitch angle and generator torque, and the gain-scheduled proportional-integral nacelle yaw controller. To validate the repositioning controller, a previously-developed low-fidelity dynamic wind farm simulator, called FOWFSimDyn, will be utilized. The simulation results demonstrate that repositioning wind turbines in a wind farm has a great potential in increasing the total energy capture of the wind farm with sufficiently long mooring lines. The result in this paper can be a baseline to be compared with other advanced wind farm repositioning control in future research.
{"title":"A Baseline Repositioning Controller for a Floating Offshore Wind Farm","authors":"Yuan Gao, A. Padmanabhan, O. Chen, Ali C. Kheirabadi, R. Nagamune","doi":"10.23919/ACC53348.2022.9867574","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867574","url":null,"abstract":"This paper presents a baseline repositioning controller for floating offshore wind farms. It is assumed that each turbine in the farm receives a lateral position reference command by a high-level wind farm controller. A low-level controller for each wind turbine in the farm manipulates the aerodynamic force direction and magnitude to achieve the reference command, while either power maximization or power regulation at the rated power is sought depending on the generator speed feedback signal. The low-level repositioning controller is a combination of the existing controller of blade pitch angle and generator torque, and the gain-scheduled proportional-integral nacelle yaw controller. To validate the repositioning controller, a previously-developed low-fidelity dynamic wind farm simulator, called FOWFSimDyn, will be utilized. The simulation results demonstrate that repositioning wind turbines in a wind farm has a great potential in increasing the total energy capture of the wind farm with sufficiently long mooring lines. The result in this paper can be a baseline to be compared with other advanced wind farm repositioning control in future research.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114983518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867203
A. Khalil, Khaled F. Aljanaideh, M. A. Janaideh
This paper investigates transmissibility operators for time-variant systems with bounded nonlinearities. Transmissibility operators are mathematical relations between a set of system responses to another set of responses within the same system. Both parameter variation and system nonlinearities are considered to be unknown. Transmissibility operators are shown in the literature to be independent of the system inputs. The bounded nonlinearities are considered as independent excitations on the system, which renders transmissibilities independent of these nonlinearities. To overcome the unknown parameter variation, we propose identifying transmissibilities using recursive least-squares in the form of noncausal FIR models. The recursive least squares algorithm is used to optimize what dynamics to include in the transmissibility operator, and what dynamics to exclude with the system nonlinearities. The identified transmissibilities then become robust against parameter variation and unknown nonlinearities. Next, transmissibilities are proposed for fault detection an autonomous multi-robotic system formulated to emulate connected autonomous vehicle platoons. The variant parameters are the robot mass and the ground friction coefficient.
{"title":"Transmissibility-based Fault Detection in Systems with Unknown Time-Varying Parameters","authors":"A. Khalil, Khaled F. Aljanaideh, M. A. Janaideh","doi":"10.23919/ACC53348.2022.9867203","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867203","url":null,"abstract":"This paper investigates transmissibility operators for time-variant systems with bounded nonlinearities. Transmissibility operators are mathematical relations between a set of system responses to another set of responses within the same system. Both parameter variation and system nonlinearities are considered to be unknown. Transmissibility operators are shown in the literature to be independent of the system inputs. The bounded nonlinearities are considered as independent excitations on the system, which renders transmissibilities independent of these nonlinearities. To overcome the unknown parameter variation, we propose identifying transmissibilities using recursive least-squares in the form of noncausal FIR models. The recursive least squares algorithm is used to optimize what dynamics to include in the transmissibility operator, and what dynamics to exclude with the system nonlinearities. The identified transmissibilities then become robust against parameter variation and unknown nonlinearities. Next, transmissibilities are proposed for fault detection an autonomous multi-robotic system formulated to emulate connected autonomous vehicle platoons. The variant parameters are the robot mass and the ground friction coefficient.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116650948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867143
Wankun Sirichotiyakul, N. Ashenafi, A. Satici
We synthesize controllers for underactuated robotic systems using data-driven approaches. Inspired by techniques from classical passivity theory, the control law is parametrized by the gradient of an energy-like (Lyapunov) function, which is represented by a neural network. With the control task encoded as the objective of the optimization, we systematically identify the optimal neural net parameters using gradient-based techniques. The proposed method is validated on the cart-pole swing-up task, both in simulation and on a real system. Additionally, we address questions about controller’s robustness against model uncertainties and measurement noise, using a Bayesian approach to infer a probability distribution over the parameters of the controller. The proposed robustness improvement technique is demonstrated on the simple pendulum system.
{"title":"Robust Data-Driven Passivity-Based Control of Underactuated Systems via Neural Approximators and Bayesian Inference","authors":"Wankun Sirichotiyakul, N. Ashenafi, A. Satici","doi":"10.23919/ACC53348.2022.9867143","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867143","url":null,"abstract":"We synthesize controllers for underactuated robotic systems using data-driven approaches. Inspired by techniques from classical passivity theory, the control law is parametrized by the gradient of an energy-like (Lyapunov) function, which is represented by a neural network. With the control task encoded as the objective of the optimization, we systematically identify the optimal neural net parameters using gradient-based techniques. The proposed method is validated on the cart-pole swing-up task, both in simulation and on a real system. Additionally, we address questions about controller’s robustness against model uncertainties and measurement noise, using a Bayesian approach to infer a probability distribution over the parameters of the controller. The proposed robustness improvement technique is demonstrated on the simple pendulum system.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127275004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867396
Spasena Dakova, Julia L. Heidingsfeld, M. Böhm, O. Sawodny
Adaptive civil engineering structures can actively counteract external disturbances by using actuators integrated in their elements, which reduces the structural weight roughly by half. However, the mass reduction leads to a higher susceptibility to vibrations and therefore increased wear of the structural elements. This publication presents an optimal control strategy that is able to redistribute external loads over the redundant actuators of the system by adaptation of the cost function. With this, the individual wear level of each element can explicitly be controlled, e.g. to realize a uniform wear of all elements. The proposed model predictive control (MPC) law increases the damping of vibrations and minimizes the elongation of worn elements. The proposed algorithm is evaluated in simulations by means of an exemplary wind disturbance. As a result, a significant decline of the elongation of worn elements compared to the application of the reference controller is achieved. Furthermore, the stress reduction of multiple elements is possible and the loads are distributed over elements with higher predicted life expectancy.
{"title":"An Optimal Control Strategy to Distribute Element Wear for Adaptive High-Rise Structures","authors":"Spasena Dakova, Julia L. Heidingsfeld, M. Böhm, O. Sawodny","doi":"10.23919/ACC53348.2022.9867396","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867396","url":null,"abstract":"Adaptive civil engineering structures can actively counteract external disturbances by using actuators integrated in their elements, which reduces the structural weight roughly by half. However, the mass reduction leads to a higher susceptibility to vibrations and therefore increased wear of the structural elements. This publication presents an optimal control strategy that is able to redistribute external loads over the redundant actuators of the system by adaptation of the cost function. With this, the individual wear level of each element can explicitly be controlled, e.g. to realize a uniform wear of all elements. The proposed model predictive control (MPC) law increases the damping of vibrations and minimizes the elongation of worn elements. The proposed algorithm is evaluated in simulations by means of an exemplary wind disturbance. As a result, a significant decline of the elongation of worn elements compared to the application of the reference controller is achieved. Furthermore, the stress reduction of multiple elements is possible and the loads are distributed over elements with higher predicted life expectancy.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124701171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867608
Takumi Hamada, S. Takai
We consider a reliable decentralized diagnosis problem for discrete event systems in the inference-based framework. This problem requires us to synthesize local diagnosers such that the occurrence of any failure string is correctly detected within a finite number of steps, even if local diagnosis decisions of some local diagnosers are not available for making the global diagnosis decision. In the case of single-level inference, we introduce a notion of reliable 1-inference-diagnosability and show that reliable 1-inference-diagnosability is a necessary and sufficient condition for the existence of a solution to the reliable decentralized diagnosis problem.
{"title":"Reliable Diagnosability for Decentralized Diagnosis of Discrete Event Systems with Single-Level Inference","authors":"Takumi Hamada, S. Takai","doi":"10.23919/ACC53348.2022.9867608","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867608","url":null,"abstract":"We consider a reliable decentralized diagnosis problem for discrete event systems in the inference-based framework. This problem requires us to synthesize local diagnosers such that the occurrence of any failure string is correctly detected within a finite number of steps, even if local diagnosis decisions of some local diagnosers are not available for making the global diagnosis decision. In the case of single-level inference, we introduce a notion of reliable 1-inference-diagnosability and show that reliable 1-inference-diagnosability is a necessary and sufficient condition for the existence of a solution to the reliable decentralized diagnosis problem.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124787025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867583
Xuan-Zhi ZHU, Pedro Casau, C. Silvestre
In this paper, we implement model-based event-triggered control on a hybrid controller by exploiting finite-time attractivity of the sensor dynamics. We provide conditions for the following properties of the closed-loop system: 1) asymptotic stability of a compact set; 2) no Zeno solutions; 3) robustness against small perturbations. The proposed implementation is demonstrated by applying it on top of an event-triggered controller.
{"title":"Finite-Time Model-Based Event-Triggered Implementation of Hybrid Controllers","authors":"Xuan-Zhi ZHU, Pedro Casau, C. Silvestre","doi":"10.23919/ACC53348.2022.9867583","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867583","url":null,"abstract":"In this paper, we implement model-based event-triggered control on a hybrid controller by exploiting finite-time attractivity of the sensor dynamics. We provide conditions for the following properties of the closed-loop system: 1) asymptotic stability of a compact set; 2) no Zeno solutions; 3) robustness against small perturbations. The proposed implementation is demonstrated by applying it on top of an event-triggered controller.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124919206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867392
H. Fathy
This presentation will briefly explore some recent developments in the areas of lithium-sulfur (Li-S) battery modeling, parameter estimation, state estimation, and control. Widely recognized for their potential to provide attractive specific energy levels safely and inexpensively, Li-S batteries are governed by chains of multiple nonlinear redox and self-discharge reaction dynamics. This makes it important to develop novel battery management algorithms specifically tailored for the unique characteristics of Li-S batteries, particularly in contrast to their more traditional lithium-ion counterparts. The presentation will highlight some of the progress, as well as some of the key challenges and opportunities, in this emerging research domain.
{"title":"Recent Progress on State and Parameter Estimation for Lithium-Sulfur Batteries","authors":"H. Fathy","doi":"10.23919/ACC53348.2022.9867392","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867392","url":null,"abstract":"This presentation will briefly explore some recent developments in the areas of lithium-sulfur (Li-S) battery modeling, parameter estimation, state estimation, and control. Widely recognized for their potential to provide attractive specific energy levels safely and inexpensively, Li-S batteries are governed by chains of multiple nonlinear redox and self-discharge reaction dynamics. This makes it important to develop novel battery management algorithms specifically tailored for the unique characteristics of Li-S batteries, particularly in contrast to their more traditional lithium-ion counterparts. The presentation will highlight some of the progress, as well as some of the key challenges and opportunities, in this emerging research domain.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125155934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867222
Amir-Salar Esteki, Solmaz S. Kia
A critical factor for expanding the adoption of networked solutions is ensuring local data privacy of in-network agents implementing a distributed algorithm. In this paper, we consider privacy preservation in the distributed optimization problem in the sense that local cost parameters should not be revealed. Current approaches to privacy preservation normally propose methods that sacrifice exact convergence or increase communication overhead. We propose PrivOpt, an intrinsically private distributed optimization algorithm that converges exponentially fast without any convergence error or using extra communication channels. We show that when the number of the parameters of the local cost is greater than the dimension of the decision variable of the problem, no malicious agent, even if it has access to all transmitted-in and -out messages in the network, can obtain local cost parameters of other agents. As an application study, we show how our proposed PrivOpt algorithm can be used to solve an optimal resource allocation problem with the guarantees that the local cost parameters of all the agents stay private.
{"title":"PrivOpt: an intrinsically private distributed optimization algorithm","authors":"Amir-Salar Esteki, Solmaz S. Kia","doi":"10.23919/ACC53348.2022.9867222","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867222","url":null,"abstract":"A critical factor for expanding the adoption of networked solutions is ensuring local data privacy of in-network agents implementing a distributed algorithm. In this paper, we consider privacy preservation in the distributed optimization problem in the sense that local cost parameters should not be revealed. Current approaches to privacy preservation normally propose methods that sacrifice exact convergence or increase communication overhead. We propose PrivOpt, an intrinsically private distributed optimization algorithm that converges exponentially fast without any convergence error or using extra communication channels. We show that when the number of the parameters of the local cost is greater than the dimension of the decision variable of the problem, no malicious agent, even if it has access to all transmitted-in and -out messages in the network, can obtain local cost parameters of other agents. As an application study, we show how our proposed PrivOpt algorithm can be used to solve an optimal resource allocation problem with the guarantees that the local cost parameters of all the agents stay private.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125859081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867228
Luis Felipe Florenzan Reyes, Francesco Smarra, A. D’innocenzo
In this work a complexity reduction methodology is proposed for a data-driven Switched Auto-Regressive eXoge-nous (SARX) model identification algorithm based on Regression Trees. In particular, we aim at reducing the number of submodels of a SARX dynamical model without compromising (and indeed improving) the model accuracy, and mitigating the overfitting problem. A validation procedure is addressed to compare the performance of the reduced model with respect to the original one. Results show an important reduction in the number of modes of the identified model that ranges between 96% and 99.74%. The accuracy of the reduced model is also tested in terms of closed-loop control performance in a Model Predictive Control (MPC) setup, on a benchmark consisting of a non-linear inverted pendulum on a cart: the comparison is provided with respect to an oracle, i.e. an MPC setup with perfect knowledge of the plant dynamics.
{"title":"Reduced SARX modeling and control via Regression Trees","authors":"Luis Felipe Florenzan Reyes, Francesco Smarra, A. D’innocenzo","doi":"10.23919/ACC53348.2022.9867228","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867228","url":null,"abstract":"In this work a complexity reduction methodology is proposed for a data-driven Switched Auto-Regressive eXoge-nous (SARX) model identification algorithm based on Regression Trees. In particular, we aim at reducing the number of submodels of a SARX dynamical model without compromising (and indeed improving) the model accuracy, and mitigating the overfitting problem. A validation procedure is addressed to compare the performance of the reduced model with respect to the original one. Results show an important reduction in the number of modes of the identified model that ranges between 96% and 99.74%. The accuracy of the reduced model is also tested in terms of closed-loop control performance in a Model Predictive Control (MPC) setup, on a benchmark consisting of a non-linear inverted pendulum on a cart: the comparison is provided with respect to an oracle, i.e. an MPC setup with perfect knowledge of the plant dynamics.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123255523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867616
Omidreza Ahmadzadeh, Renato Rodriguez, D. Soudbakhsh
This paper presents a data-driven model (DDM) of Li-ion batteries (LIBs). Accurate real-time modeling of LIBs allows for faster and more aggressive inputs and operations, improving their performance and safety. The DDM was developed using the enhanced single-particle model with electrolyte as the plant dynamics. A sparse model of the system was developed based on a library of potential terms. A sparsity-promoting optimization algorithm was used to balance the trade-off between the model accuracy and complexity. We compared the Sequentially Thresholded Ridge regression (STRidge) and a LASSO optimization and showed the advantages of using the STRidge. First, we developed a model using only voltage and current signals (Model I). Model I’s performance and robustness were assessed via validation and generalization tests, where the model achieved normalized root mean square error (NRMSE) values of less than 1.6%. Additionally, we evaluated Model I’s robustness to noise, achieving NRSME< 2%. We showed the trend of Model I parameters with the charge/discharge curves. However, this model required switching parameters as state-of-charge (SOC) changes. Therefore, we augmented the model by including terms related to the SOC in the library (Model II) to avoid switching. We showed the performance of Model II using the US06 highway driving cycle as the cell’s charge varied from 40%SOC to 20%SOC with small errors (NRMSE=4.78%). The results showed that the models accurately predict the dynamic response of the cells in the simulated scenarios. The models are interpretable as they have explicit input and output terms and allow for efficient control design.
{"title":"Modeling of Li-ion batteries for real-time analysis and control: A data-driven approach*","authors":"Omidreza Ahmadzadeh, Renato Rodriguez, D. Soudbakhsh","doi":"10.23919/ACC53348.2022.9867616","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867616","url":null,"abstract":"This paper presents a data-driven model (DDM) of Li-ion batteries (LIBs). Accurate real-time modeling of LIBs allows for faster and more aggressive inputs and operations, improving their performance and safety. The DDM was developed using the enhanced single-particle model with electrolyte as the plant dynamics. A sparse model of the system was developed based on a library of potential terms. A sparsity-promoting optimization algorithm was used to balance the trade-off between the model accuracy and complexity. We compared the Sequentially Thresholded Ridge regression (STRidge) and a LASSO optimization and showed the advantages of using the STRidge. First, we developed a model using only voltage and current signals (Model I). Model I’s performance and robustness were assessed via validation and generalization tests, where the model achieved normalized root mean square error (NRMSE) values of less than 1.6%. Additionally, we evaluated Model I’s robustness to noise, achieving NRSME< 2%. We showed the trend of Model I parameters with the charge/discharge curves. However, this model required switching parameters as state-of-charge (SOC) changes. Therefore, we augmented the model by including terms related to the SOC in the library (Model II) to avoid switching. We showed the performance of Model II using the US06 highway driving cycle as the cell’s charge varied from 40%SOC to 20%SOC with small errors (NRMSE=4.78%). The results showed that the models accurately predict the dynamic response of the cells in the simulated scenarios. The models are interpretable as they have explicit input and output terms and allow for efficient control design.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"28 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123579370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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