Identification of lithium-ion (Li-ion) battery models is essential for enhancing the operation of electrical vehicles. This paper develops a novel approach for estimating the equivalent circuit model (ECM) of Li-ion batteries and reconstructing the open-circuit voltage (OCV) and state of charge (SOC) relationship. We formulate the OCV-SOC relation as a piecewise affine (PWA) function and estimate its coefficients and the Markov parameters (impulse response) of the ECM via l₁-regularized least squares. The state space model of the ECM is derived through the Ho-Kalman algorithm. Experiments with simulated and real-life battery data demonstrate the method's effectiveness and advantages with respect to the state of the art.

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This paper develops a novel approach for online Lithium-ion (Li-ion) battery model identification and state of charge (SOC) estimation. To account for the SOC-dependent battery dynamics and the static nonlinearity between the open-circuit voltage (OCV) and SOC, we formulate a grey box nonlinear state-space model, in which elements depend on SOC in a polynomial way. For model identification, we propose an online concurrent state and parameter estimation by alternating the recursive least squares algorithm and particle filter; the SOC is computed via Coulomb counting during the modeling. The identified grey box model is then applied for SOC estimation using the particle filter. Simulation with real-world battery measurements demonstrates the effectiveness of the model structure and the estimation approach, which is reflected in accurate terminal voltage estimation and nonlinear OCV-SOC relation, and superior performance regarding SOC estimation compared to state-of-the-art approaches.

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To solve unmodeled optimization problems with hard constraints, this paper proposes a novel zeroth-order approach called Safe Zeroth-order Optimization using Linear Programs (SZO-LP). The SZO-LP method solves a linear program in each iteration to find a descent direction, followed by a step length determination. We prove that, under mild conditions, the iterates of SZO- LP have an accumulation point that is also the primal of a KKT pair. We then apply SZO-LP to solve an Optimal Power Flow (OPF) problem on the IEEE 30-bus system. The results demonstrate that SZO-LP requires less computation time and samples compared to state-of-the-art approaches.@en