This paper investigates power management algorithms that optimally manage lithium-ion battery pack health, in terms of anode-side film growth, for plug-in hybrid electric vehicles (PHEVs). Specifically, we integrate a reduced electrochemical model of solid electrolyte interface (SEI) film formation into a stochastic dynamic programming formulation of the PHEV power management problem. This makes it possible to optimally trade off energy consumption cost versus battery health. A careful analysis of the resulting Pareto-optimal set of power management solutions provides two important insights into the tradeoffs between battery health and energy consumption cost in PHEVs. First, optimal power management solutions that minimize energy consumption cost tend to ration battery charge, while the solutions that minimize battery health degradation tend to deplete charge aggressively. Second, solutions that balance the needs for minimum energy cost and maximum battery health tend to aggressively deplete battery charge at high states of charge (SOCs), then blend engine and battery power at lower SOCs. These results provide insight into the fundamental tradeoffs between battery health and energy cost in PHEV power management.
- Dynamic Systems and Control Division
Battery-Health Conscious Power Management for Plug-In Hybrid Electric Vehicles via Stochastic Control
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Moura, SJ, Stein, JL, & Fathy, HK. "Battery-Health Conscious Power Management for Plug-In Hybrid Electric Vehicles via Stochastic Control." Proceedings of the ASME 2010 Dynamic Systems and Control Conference. ASME 2010 Dynamic Systems and Control Conference, Volume 1. Cambridge, Massachusetts, USA. September 12–15, 2010. pp. 615-624. ASME. https://doi.org/10.1115/DSCC2010-4089
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