Integration of Energy Storage in Solar-powered EV Smart Charging Systems

Research output: ThesisDissertation (TU Delft)

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This thesis investigates the integration of electric vehicle (EV) charging, photovoltaic (PV) power, and battery energy storage (BES), using a direct current (DC) integrated multi-port power converter. The goal is to aid the energy transition using the intelligent operation of the aforementioned components to provide a more cost-effective system that helps increase the penetration of small-scale local PV system and increase the sustainability of local loads, such as EV charging. To achieve this, this work focuses on
two parts: the power electronic converter and the smart charging control, including battery degradation.

A. Power Electronics
In this thesis a modular DC-integrated multi-port converter is developed. The DC integration allows to reduce the amount of power converters hereby reducing its costs, while increasing efficiency and power density. All converters ports are developed for bidirectional operation to maximize its flexibility. a two level DC-AC converter is used for the bidirectional AC grid connection. Next, a 4-phase interleaved flyback converter is used for isolated EV charging. Finally, two interleaved four-switch buck-boost (FSBB) converters are used for both the PV and BES ports. All DC-DC converters utilize quasi-resonant boundary conduction mode (QR-BCM), combined with silicon carbide semiconductors to achieve efficiencies up to above 99%. A novel control method for the interleaved FSBB converter is proposed to enable multi-mode QR-BCM operation. Based on an experimental comparison with three other soft-switching modulation schemes it is shown that the proposed modulation and control achieve the highest efficiency (up to 99.5%) with little to no compromise in power density and control complexity.

B. Smart Charging
Next, a two-level smart charging structure is proposed to utilize the flexibility obtained from the multi-directional power electronic hardware. The first level is a non-linear programming (NLP) model that optimizes the charging powers of the EV and BES in a moving horizon context, to minimize the operational costs, including primary frequency control market participation and battery degradation. To minimize the battery degradation, a literature survey study has been done on lithium-ion ageing mechanisms and how to model it. Based on this survey the best suited degradation model is chosen and integrated in the NLP model. The second level of the proposed smart charging structure recalculates the setpoints based on grid frequency deviation, and PV forecasting errors. Both the theoretical and experimental results show that the proposed control method is effective in reducing the lifetime system costs. In combination with optimal sizing of the components the total lifetime system costs can be reduced up to 460% compared to conventional non-optimal charging methods.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
  • Bauer, P., Supervisor
  • Chandra Mouli, G.R., Advisor
Award date4 Oct 2023
Print ISBNs978-94-6366-737-1
Publication statusPublished - 2023


  • Energy Storage
  • Smart charging
  • Power electronic converter


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