Discrete-time battery models for system-level low-power design

Benini, Luca; Castelli, G.; Macii, Alberto; Macii, Enrico; Poncino, Massimo; Scarsi, R.

doi:10.1109/92.953497

Cited by 124 publications

(67 citation statements)

References 11 publications

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“…On the other hand, the mathematical model is not appropriate for representing physical characteristics [17]. The circuit-based model has often been used [18]- [21] since the battery characteristics are represented adequately and it is easy to use for analysis and simulation. For the battery model with nonlinear characteristics, the calculation time for the estimation algorithm increases and the real-time implementation is complex.…”

Section: A Lithium Battery Modelsmentioning

confidence: 99%

Condition Monitoring of Lithium Polymer Batteries Based on a Sigma-Point Kalman Filter

Seo¹,

Nguyễn²,

Lee³

et al. 2012

Journal of Power Electronics

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In this paper, a novel scheme for the condition monitoring of lithium polymer batteries is proposed, based on the sigma-point Kalman filter (SPKF) theory. For this, a runtime-based battery model is derived, from which the state-of-charge (SOC) and the capacity of the battery are accurately predicted. By considering the variation of the serial ohmic resistance (R o ) in this model, the estimation performance is improved. Furthermore, with the SPKF, the effects of the sensing noise and disturbance can be compensated and the estimation error due to linearization of the nonlinear battery model is decreased. The effectiveness of the proposed method is verified by Matlab/Simulink simulation and experimental results. The results have shown that in the range of a SOC that is higher than 40%, the estimation error is about 1.2% in the simulation and 1.5% in the experiment. In addition, the convergence time in the SPKF algorithm can be as fast as 300 s.

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Section: A Lithium Battery Modelsmentioning

confidence: 99%

Condition Monitoring of Lithium Polymer Batteries Based on a Sigma-Point Kalman Filter

Seo¹,

Nguyễn²,

Lee³

et al. 2012

Journal of Power Electronics

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“…In this work we propose a behavioral black-box modeling technique similar to the one introduced in [3]. Such modeling contains no information on the internal structure of the electronic components, but just tries to mimic the I/O characteristics (see Figure 3).…”

Section: Modeling Backgroundmentioning

confidence: 99%

“…In general, other levels of abstractions can be used to analyze systems [3]. Transistor-level models contain the complete switch-level structure of the electronic components.…”

Section: Modeling Backgroundmentioning

confidence: 99%

Stochastic Modeling and Analysis for Environmentally Powered Wireless Sensor Nodes

Şuşu¹,

Acquaviva²,

Atienza³

et al. 2008

Proceedings of the 6th Intl Symposium on Modeling and Optimization

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Abstract-Environmental energy is becoming a feasible alternative for many low-power systems, such as wireless sensor nodes. Designing an environmentally powered device faces several challenges: choosing the exact type of the energy harvester, the energy storage elements and determining the duty cycle of the application. With harvesting, the design process becomes even more difficult because it also has to take into account the unpredictability of the energy source.The contribution of this paper is a methodology that facilitates the analysis of energy harvesting nodes. The existing modeling strategies for battery powered systems are not suitable because they do not capture the uncertainty of the power source. Also, the metrics of interest for battery powered devices are different, as opposed to the harvesting powered ones: in the former case we search to maximize the system lifetime, while in the latter case a more expressive goal is to increase the system availability.

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“…Among those studies, the models based on electrochemical process modeling and analysis [9,10], although accurate, are too complicated to be used for the systemlevel design of electronics. As we develop an mathematical formulation of the migration efficiency, a battery model in the form of an electric circuit is suitable for our purpose [11,12].…”

Section: Ees Banksmentioning

confidence: 99%

Charge migration efficiency optimization in hybrid electrical energy storage (HEES) systems

Wang¹,

Kim²,

Xie³

et al. 2011

IEEE/ACM International Symposium on Low Power Electronics and Design

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Electrical energy is high-quality form of energy, and thus it is beneficial to store the excessive electric energy in the electrical energy storage (EES) rather than converting into a different type of energy. Like memory devices, no single type of EES element can fulfill all the desirable requirements. Despite active research on the new EES technologies, it is not likely to have an ultimate high-efficiency, highpower/energy capacity, low-cost, and long-cycle life EES element in the near future. We propose an HEES system that consists of two or more heterogeneous EES elements, thereby realizing the advantages of each EES element while hiding their weaknesses. The HEES management problems can be broken into charge allocation into different banks of EES elements, charge replacement (i.e., discharge) from different banks of EES elements, and charge migration from one bank to another bank of EES elements.In spite of the optimal charge allocation and replacement, charge migration is mandatory to leverage the EES system efficiency. This paper is the first paper that formally describes the charge migration efficiency and its optimization. We first define the charge migration architecture and the corresponding charge migration problem. We provide a systematic solution for a single source and single destination charge migration considering the efficiency of the charger and power converter, the rate capacity effect of the storage element, the terminal voltage variation of the storage element as a function of the state of charge (SoC), and so on. Experimental results for an HEES system comprising of banks of batteries and supercapacitors demonstrate a migration efficiency improvement up to 51.3%, for supercapacitor to battery and supercapacitor to supercapacitor charge migration.

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Discrete-time battery models for system-level low-power design

Cited by 124 publications

References 11 publications

Condition Monitoring of Lithium Polymer Batteries Based on a Sigma-Point Kalman Filter

Condition Monitoring of Lithium Polymer Batteries Based on a Sigma-Point Kalman Filter

Stochastic Modeling and Analysis for Environmentally Powered Wireless Sensor Nodes

Charge migration efficiency optimization in hybrid electrical energy storage (HEES) systems

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