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Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation

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  • Anselma, Pier Giuseppe
  • Kollmeyer, Phillip
  • Lempert, Jeremy
  • Zhao, Ziyu
  • Belingardi, Giovanni
  • Emadi, Ali

Abstract

Achieving a satisfactory high-voltage battery lifetime while preserving fuel economy is a key challenge in the design of hybrid electric vehicles (HEVs). While several battery state-of-health (SOH) sensitive control approaches for HEVs have been presented in the literature, these approaches have not typically been experimentally validated. This paper thus aims at illustrating an optimal, multi-objective battery SOH sensitive off-line HEV control approach, which is based on dynamic programming (DP) and is experimentally validated in terms of prediction capability of the battery lifetime. An experimental campaign is conducted which ages cells with current profiles for three different predicted lifetime cases. The predictive accuracy of the battery ageing model is subsequently improved by including the effect of temperature and updating the empirical ageing characterization curve. The improved ageing model is then used to assess HEV performance in terms of fuel economy and battery lifetime for various high-voltage battery pack sizes and control goals. Results suggest that, thanks to the proposed multi-objective battery SOH sensitive control approach, the battery pack may be downsized by 35% with no impact on battery lifetime and a fuel consumption increase of just 1.1%. Engineers and designers could thus potentially adopt the proposed control approach to design HEVs which take tradeoffs between fuel economy and battery lifetime into consideration. Considerable reductions in battery pack cost, weight and production related CO2 emissions could be achieved in this way.

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  • Anselma, Pier Giuseppe & Kollmeyer, Phillip & Lempert, Jeremy & Zhao, Ziyu & Belingardi, Giovanni & Emadi, Ali, 2021. "Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation," Applied Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:appene:v:285:y:2021:i:c:s0306261921000088
    DOI: 10.1016/j.apenergy.2021.116440
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    References listed on IDEAS

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    6. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
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    9. Giuseppe Di Luca & Gabriele Di Blasio & Alfredo Gimelli & Daniela Anna Misul, 2023. "Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles," Energies, MDPI, vol. 17(1), pages 1-34, December.
    10. Chang, Long & Ma, Chen & Zhang, Chenghui & Duan, Bin & Cui, Naxin & Li, Changlong, 2023. "Correlations of lithium-ion battery parameter variations and connected configurations on pack statistics," Applied Energy, Elsevier, vol. 329(C).
    11. Edoardo Lelli & Alessia Musa & Emilio Batista & Daniela Anna Misul & Giovanni Belingardi, 2023. "On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles," Energies, MDPI, vol. 16(12), pages 1-21, June.
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