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  • 1.
    Streb, Moritz
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry. KTH Royal Inst Technol, Sch Engn Sci Chem Biotechnol & Hlth, Dept Chem Engn, Appl Electrochem, SE-10044 Stockholm, Sweden..
    Ohrelius, Mathilda
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry.
    Siddiqui, Aamer
    Scania CV AB, Granparksvagen 10, S-15148 Södertälje, Sweden.;Uppsala Univ, Dept Chem, Angstrom Lab, Struct Chem, Lagerhyddsvagen 1, S-75121 Uppsala, Sweden..
    Klett, Matilda
    Scania CV AB, Granparksvagen 10, S-15148 Södertälje, Sweden..
    Lindbergh, Göran
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry.
    Diagnosis and prognosis of battery degradation through re-evaluation and Gaussian process regression of electrochemical model parameters2023In: Journal of Power Sources, ISSN 0378-7753, E-ISSN 1873-2755, Vol. 588, article id 233686Article in journal (Refereed)
    Abstract [en]

    Lithium-ion battery degradation is complex, and many mechanisms occur concurrently. In-depth degradation is traditionally investigated by post-mortem characterization in lab-settings. If mechanisms could instead be identified in-operando, utilization could be adjusted, and battery lifetime extended. We investigate changes in electrochemical model parameters during battery testing and their correlation with degradation observed in a traditional post-mortem characterization. Commercial batteries are cycle-aged using different stationary storage service cycles and a novel reference performance test is applied intermittently. This test is based on current profiles optimally designed with respect to maximized sensitivity for individual electrochemical parameters and embedded within a charging procedure. Usage dependency of parameter trajectories over the course of ageing is demonstrated and coupled to observed micro-structural changes. Subsequently, the parameter trajectories are extrapolated using Gaussian Process Regression for physics-based state-of-health estimation and remaininguseful-life prediction. We demonstrate and validate estimation of full cell performance under constant load at a later state in life.

  • 2.
    Ohrelius, Mathilda
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry.
    Berg, Magnus
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Process Technology. Vattenfall AB, SE-16287 Stockholm, Sweden..
    Lindström, Rakel
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry.
    Lindbergh, Göran
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry.
    Lifetime Limitations in Multi-Service Battery Energy Storage Systems2023In: Energies, E-ISSN 1996-1073, Vol. 16, no 7, article id 3003Article in journal (Refereed)
    Abstract [en]

    A reliable power grid system based on renewable energy sources is a crucial step to restrict the climate crisis. Stationary battery energy storage systems (BESS) offer a great potential to repel power fluctuations in the grid at different timescales. However, for a reliable operation and cost estimation, the degradation in the batteries needs to be understood. We present an accelerated battery degradation study, on single as well as multi-service applications, of NCM532/Gr lithium-ion battery cells. Frequency regulation (FR) was the least harmful for the battery, with an expected lifetime of 12 years, while peak shaving (PS) resulted in an expected lifetime of 8 years. The combined cycle (FRPS) accelerated the capacity loss, and degradation of the positive electrode was induced from the start of cycling, causing power limitations after only 870 equivalent full cycles (EFC). Tracking the 1C-rate discharge capacity was proven to be a good indication of the accelerated cell polarization, and it can serve as a useful method to evaluate the internal battery state of health (SOH).

  • 3.
    Streb, Moritz
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry.
    Ohrelius, Mathilda
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry.
    Klett, Matilda
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry. Scan CV AB, Granparksvagen 10, S-15148 Södertälje, Sweden..
    Lindbergh, Göran
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry.
    Improving Li-ion battery parameter estimation by global optimal experiment design2022In: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 56, article id 105948Article in journal (Refereed)
    Abstract [en]

    Li-ion batteries are a key enabling technology for electric vehicles and determining their properties precisely is an essential step in improving utilization and performance. Batteries are highly complex electrochemical sys-tems, with processes occurring in parallel on many time-and length-scales. Models describing these mechanisms require extensive parametrization efforts, conventionally using a combination of ex-situ characterization and systems identification. We present a methodology that algorithmically designs current input signals to optimize parameter identifiability from voltage measurements. Our approach uses global sensitivity analysis based on the generalized polynomial chaos expansion to map the entire parameter uncertainty space, relying on minimal prior knowledge of the system. Parameter specific optimal experiments are designed to maximize sensitivity and simultaneously minimize interactions and unwanted contributions by other parameters. Experiments are defined using only three design variables making our approach computationally efficient. The methodology is demon-strated using the Doyle-Fuller-Newman battery model for eight parameters of a 2.6 Ah 18,650 cell. Validation confirms that the proposed approach significantly improves model performance and parameter accuracy, while lowering experimental burden.

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