The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Battery-Health Conscious Power Management in Plug-In Hybrid Electric Vehicles via Electrochemical Modeling and Stochastic Control
Abstract
This paper develops techniques to design plug-in hybrid electric vehicle (PHEV) power management algorithms that optimally balance lithium-ion battery pack health and energy consumption cost. As such, this research is the first to utilize electrochemical battery models to optimize the power management in PHEVs. Daily trip length distributions are integrated into the problem using Markov chains with absorbing states. We capture battery aging by integrating two example degradation models: solid–electrolyte interphase (SEI) film formation and the “Ah-processed” model. This enables us to optimally tradeoff energy cost versus battery-health. We analyze this tradeoff to explore how optimal control strategies and physical battery system properties are related. Specifically, we find that the slope and convexity properties of the health degradation model profoundly impact the optimal charge depletion strategy. For example, solutions that balance energy cost and SEI layer growth aggressively deplete battery charge at high states-of-charge (SoCs), then blend engine and battery power at lower SoCs.
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Citations
Cites
Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell
Doyle, M., Fuller, T. F., & Newman, J. (1993) · Journal of The Electrochemical Society · doi:10.1149/1.2221597 · Cited by 4,328 · cited 1×
“As a case study, we examine a model particularly wellsuited for model reduction and control applications that account for lithium diffusion dynamics, intercalation kinetics, and electrochemical potentials developed by Doyle, Fuller, and Newman [12], [13].”
Simulation and Optimization of the Dual Lithium Ion Insertion Cell
Fuller, T. F., Doyle, M., & Newman, J. (1994) · Journal of The Electrochemical Society · doi:10.1149/1.2054684 · Cited by 2,057
Cited by
Battery State Estimation for a Single Particle Model With Electrolyte Dynamics
Moura, S. J., Argomedo, F. B., Klein, R., Mirtabatabaei, A., & Krstic, M. (2017) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2016.2571663 · Cited by 431
Self-citationBizeray, A. M., Zhao, S., Duncan, S. R., & Howey, D. A. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.07.019 · Cited by 269
Hu, X., Cao, D., & Egardt, B. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2675920 · Cited by 205
A Framework for Simplification of PDE-Based Lithium-Ion Battery Models
Zou, C., Manzie, C., & Nesic, D. (2016) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2015.2502899 · Cited by 196
Metadata
- Authors
- Moura, S. J., Stein, J. L., & Fathy, H. K.
- Venue
- IEEE Transactions on Control Systems Technology
- Published
- 2013-05
- Volume
- 21
- Issue
- 3
- Pages
- 679-694
- ISSN
- 1063-6536 (ISSN-L)
- 1558-0865
- OpenAlex
- W2155350901
- Cited by
- 211
- References
- 2
- FWCI
- 27.73
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Battery-Health Conscious Power Management in Plug-In Hybrid Electric Vehicles via Electrochemical Modeling and Stochastic Control. https://app.cochat.ai/papers/10.1109/tcst.2012.2189773
- The paper
- Moura, S. J., Stein, J. L., & Fathy, H. K. (2013). Battery-Health Conscious Power Management in Plug-In Hybrid Electric Vehicles via Electrochemical Modeling and Stochastic Control. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2012.2189773
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
