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Register a new userAccurate Online Full Charge Capacity Modeling of Smartphone Batteries
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Full charge capacity (FCC) refers to the amount of energy a battery can hold. It is the fundamental property of smartphone batteries that diminishes as the battery ages and is charged/discharged. We investigate the behavior of smartphone batteries while charging and demonstrate that the battery voltage and charging rate information can together characterize the FCC of a battery. We propose a new method for accurately estimating FCC without exposing low-level system details or introducing new hardware or system modules. We also propose and implement a collaborative FCC estimation technique that builds on crowdsourced battery data. The method finds the reference voltage curve and charging rate of a particular smartphone model from the data and then compares the curve and rate of an individual user with the model reference curve. After analyzing a large data set, we report that 55% of all devices and at least one device in 330 out of 357 unique device models lost some of their FCC. For some models, the median capacity loss exceeded 20% with the inter-quartile range being over 20 pp. The models enable debugging the performance of smartphone batteries, more accurate power modeling, and energy-aware system or application optimization.
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This paper proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model proposed in [1]. It is known that the original model can address the rate capacity effect and energy recovery effect inherent to batteries better than other models. However, it is a purely linear model and includes no representation of a batterys nonlinear phenomena. Hence, this work transforms the original model by introducing a nonlinear-mapping-based voltage source and a serial RC circuit. The modification is justified by an analogy with the single-particle model. Two parameter estimation approaches, termed 1.0 and 2.0, are designed for the new model to deal with the scenarios of constant-current and variable-current charging/discharging, respectively. In particular, the 2.0 approach proposes the notion of Wiener system identification based on maximum a posteriori estimation, which allows all the parameters to be estimated in one shot while overcoming the nonconvexity or local minima issue to obtain physically reasonable estimates. An extensive experimental evaluation shows that the proposed model offers excellent accuracy and predictive capability. A comparison against the Rint and Thevenin models further points to its superiority. With high fidelity and low mathematical complexity, this model is beneficial for various real-time battery management applications.
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