Linearizing Battery Degradation for Health-aware Vehicle Energy Management

Shuangqi Li, Pengfei Zhao, Chenghong Gu, Da Huo, Jianwei Li, Shuang Cheng

Research output: Contribution to journalArticlepeer-review

Abstract

The utilization of battery energy storage systems (BESS) in vehicle-to-grid (V2G) and plug-in hybrid electric vehicles (PHEVs) benefits the realization of net-zero in the energy-transportation nexus. Since BESS represents a substantial part of vehicle total costs, the mitigation of battery degradation should be factored into energy management strategies. This paper proposes a two-stage BESS aging quantification and health-aware energy management method for reducing vehicle battery aging costs. In the first stage, a battery aging state calibration model is established by analyzing the impact of cycles with various Crates and depth of discharges based on a semi-empirical method. The model is further linearized by learning the mapping relationship between aging features and battery life loss with a linear-in-the-parameter supervised learning method. In the second stage, with the linear battery life loss quantification model, a neural hybrid optimization-based energy management method is developed for mitigating vehicle BESS aging. The battery aging cost function is formulated as a linear combination of system states, which simplifies model solving and reduces computation cost. The case studies in an aggregated EVs peak-shaving scenario and a PHEV with an engine-battery hybrid powertrain demonstrate the effectiveness of the developed method in reducing battery aging costs and improving vehicle total economy. This work provides a practical solution to hedge vehicle battery degradation costs and will further promote decarbonization in the energy-transportation nexus.

Original languageEnglish
Article number9932019
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Power Systems
Early online date28 Oct 2022
DOIs
Publication statusE-pub ahead of print - 28 Oct 2022

Keywords

  • Aging
  • Analytical models
  • Batteries
  • battery aging
  • battery energy storage system
  • Costs
  • Electric vehicle
  • Energy management
  • energy management
  • model-data-driven method
  • US Department of Defense
  • vehicle to grid
  • Vehicle-to-grid

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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