Incremental capacity (IC) analysis is an effective and widely used approach to evaluate the lithium-ion battery remaining capacity. A limitation for IC based capacity estimation is that it requires a consistent constant charging current for each test and demands a large amount of data to be stored, which hinder its use in practical conditions. To address the issues, this paper proposes an online capacity estimation method for battery module with incremental capacity curves processed by tracking differentiator. Specifically, the IC features, such as peak voltage, peak amplitude, are analyzed for the battery module empirically and theoretically. The analytic linear relationship between the IC features and capacity are formulated. As a basis, the calculation of dQ/dV is converted to the derivative of terminal voltage, which is obtained by a designed tracking differentiator. For the iteratively derived IC curve, a realtime peak detection algorithm is proposed to detect the second peak voltage without the requirement to store any historical data. The proposed method is not sensitive to noise and applicable to common charging conditions. Experimental results with cycle data for a commercial 206Ah battery module reveals the superiority of the proposed method.
Cell inconsistency affect battery life and driving safety. In order to solve the accuracy problem of online prediction of cell inconsistency of power battery, battery characteristic analysis based on of vehicle network big data is proceeded, health indicator(HI), based on the cell terminal voltage difference,is proposed through the degradation model; As the similar distribution of cell terminal voltage difference between battery discharge conditions, the health indicator sequence based on SOC(State of Charge) is constructed, and the next health indicator is predicted by Gaussian process regression. The prediction results show that the method requires less training samples and less hardware resources, and the overall prediction accuracy is not less than 85%, which can meet the practical requirements.
KEYWORDS: Fuzzy logic, System on a chip, Control systems, Performance modeling, Energy efficiency, Mathematical modeling, Picosecond phenomena, Computer simulations, Hydrogen, Process control
The core of the overall fuel cell vehicle control is energy distribution strategy. This study studies fuel cell buses and aims to extend the fuel cell lifespan to guarantee battery lifespan and to enhance the vehicle's overall performance. We proposed a fuzzy method for energy distribution and state of charge feedback and designed a fuel cell bus energy distribution model based on the Takagi–Sugeno fuzzy control. Secondary development based on these results was carried out through the ADVISOR simulation platform. Comparison of simulation results shows that the proposed control strategy not only satisfies the full vehicle dynamic performance requirements, but also enhances the fuel cell lifespan and guarantees the battery lifespan, while remains relatively economically competitive.
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