Modeling and control of battery systems. Part II: A model predictive controller for optimal charging

Suresh, Resmi; Rengaswamy, Raghunathan

doi:10.1016/j.compchemeng.2018.08.017

Cited by 18 publications

(9 citation statements)

References 10 publications

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“…Song et al [31] and Wang et al [10] used the derivative of capacity over time applied at a certain point in time during the battery life, using Equations (25) and (26).…”

Section: Degradation Costmentioning

confidence: 99%

“…Electrochemical models are usually very complex, so they are unsuitable for optimization problems because there are many possible solutions, a high number of evaluations, and therefore excessive calculation times. The models of Astaneh et al [21,22], Xiong et al [23], Wijewardana et al [24], Suresh et al [25,26], and Ashwin et al [27,28] are examples. A simplified electrochemical model is provided by Rechkemmer et al [29].…”

Section: Introductionmentioning

confidence: 99%

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Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries

2020

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Estimating the degradation costs of lithium-ion batteries is essential to the designs of many systems because batteries are increasingly used in diverse applications. In this study, cyclic and calendar degradation models of lithium batteries were considered in optimization problems with randomized non-cyclic batteries use. Such models offer realistic results. Electrical, thermal, and degradation models were applied for lithium nickel cobalt manganese oxide (NMC) and lithium iron phosphate (LFP) technologies. Three possible strategies were identified to estimate degradation costs based on cell models. All three strategies were evaluated via simulations and validated by comparing the results with those obtained by other authors. One strategy was discarded because it overestimates costs, while the other two strategies give good results, and are suitable for estimating battery degradation costs in optimization problems that require deterministic models.

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“…Song et al [31] and Wang et al [10] used the derivative of capacity over time applied at a certain point in time during the battery life, using Equations (25) and (26).…”

Section: Degradation Costmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries

2020

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“…Model predictive control (MPC) appears to be the most used optimization-based methodology for charging of Liion batteries [25][26][27][28][29]. MPC is selected due to its robustness and adaptability.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, ref. [25] has proposed MPC strategies based on pseudo-two-dimension (P2D) electrochemical model, while the works in [26][27][28][29] have suggested the use of reduced-order electrochemical models to simplify model complexity. For MPC configurations, most of the research in this area utilizes general MPC with the exception of [28] which is based on the sensitivitybased MPC and [29] which adopts nonlinear MPC.…”

Section: Introductionmentioning

confidence: 99%

A Novel Charging Algorithm for Lithium-Ion Batteries Based on Enumeration-Based Model Predictive Control

et al. 2020

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Lithium-ion (Li-ion) batteries play a substantial role in energy storage solutions for modernday technologies such as hand-held consumer electronics, aerospace, electric vehicles, and renewable energy systems. For Li-ion batteries, designing a high-quality battery charging algorithm is essential since it has significant influences on the performance and lifetime of Li-ion batteries. The objectives of a highperformance charger include high charging efficiency, short charging time, and long cycle life. In this paper, a model predictive control based charging algorithm is proposed, the presented technique aims to simultaneously reduce the charging time, and the temperature rise during charging. In this study, the coulomb counting method is utilized to calculate the future state-of-charge and an artificial neural network trained by experimental data is also applied to predict the future temperature rise. Comparing with the widely employed constant current-constant voltage charging method, the proposed charging technique can improve the charging time and the average temperature rise by 1.2 % and 4.13 %, respectively.

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“…In this regard, Bandpey et al [19] used a Fuzzy Logic Controller (FLC) to smooth the load profile and obtain an optimum charging strategy when considering the State of Charge (SoC) and the Urgency Level (UL) as inputs, which refers to battery charging urgency, and as output the Preference Factor (PF), which indicates the batteries charging/discharging priority. Moreover, Suresh et al [20] developed a battery capacity fade minimization model by introducing the model predictive control (MPC) framework, which has also been developed in [21], for the identification and realization of optimal charge-discharge cycles for Lithium-ion batteries. The proposed approach can be used to improve the battery life and charge carrying capacity of Li-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

et al. 2019

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This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices.

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Modeling and control of battery systems. Part II: A model predictive controller for optimal charging

Cited by 18 publications

References 10 publications

Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries

Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries

A Novel Charging Algorithm for Lithium-Ion Batteries Based on Enumeration-Based Model Predictive Control

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

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