Accurate electrical battery model capable of predicting runtime and I-V performance
Abstract
Low power dissipation and maximum battery runtime are crucial in portable electronics. With accurate and efficient circuit and battery models in hand, circuit designers can predict and optimize battery runtime and circuit performance. In this paper, an accurate, intuitive, and comprehensive electrical battery model is proposed and implemented in a Cadence environment. This model accounts for all dynamic characteristics of the battery, from nonlinear open-circuit voltage, current-, temperature-, cycle number-, and storage time-dependent capacity to transient response. A simplified model neglecting the effects of self-discharge, cycle number, and temperature, which are nonconsequential in low-power Li-ion-supplied applications, is validated with experimental data on NiMH and polymer Li-ion batteries. Less than 0.4% runtime error and 30-mV maximum error voltage show that the proposed model predicts both the battery runtime and I-V performance accurately. The model can also be easily extended to other battery and power sourcing technologies.
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The PNGV model is a modified version of the Thevenin model that incorporates a fictive capacitor to account for changes in OCV.
“To account for changes in OCV, the Partnership for a New Generation of Vehicles (PNGV) model, also known as the Freedom Car model, is a modified version of the Thevenin model. It incorporates a fictive capacitor [21,22].”
From Recent developments and challenges in state-of-charge estimation techniques for electric vehicle batteries: A review (2024) · cites this paper as [21]
The extent of cell degradation in capacity was not correlated with the ageing or cycling conditions.
“Moreover, according to our experimental results and that in [38], the extent of the cell degradation in capacity was not correlated with the ageing or cycling conditions.”
From A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects (2018) · cites this paper as [38]
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References 32 · Cited by 2,427
- Authors
- Chen, M., & Rincon-Mora, G. A.
- Affiliations
- Georgia Institute of Technology (US)
- ORCID
- Chen, Min
- Venue
- IEEE Transactions on Energy Conversion
- Published
- 2006-06
- Volume
- 21
- Issue
- 2
- Pages
- 504-511
- ISSN
- 0885-8969 (ISSN-L)
- 1558-0059
- OpenAlex
- W2133496267
- Cited by
- 2,427
- References
- 32
- Citations by year
- 2026: 108 · 2025: 112 · 2024: 131 · 2023: 142 · 2022: 150 · 2021: 146 · 2020: 119 · 2019: 166 · 2018: 158 · 2017: 174 · 2016: 169 · 2015: 145 · 2014: 187 · 2013: 180 · 2012: 122
- FWCI
- 58.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Energy Conversion (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Keywords
- battery modeling, lithium-ion batteries, nickel-metal hydride batteries, current-voltage characteristics, open-circuit voltage, capacity fade, temperature dependence, transient response, portable electronics, low power design
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