Battery electrode particle fracture due to stress generation is a critical mechanism causing capacity fade, and thus reducing battery life. This paper develops a nonlinear adaptive observer for lithium-ion battery state of charge (SOC), electrode particle stress, and solid phase diffusivity estimation using a high-fidelity coupled single particle--mechanical stress model, where the stress submodel captures stress development during lithium-ion intercalation and deintercalation. Simultaneous state and parameter estimation based on coupled single particle and mechanical stress model is extremely challenging because the coupled model is given by highly nonlinear partial differential equations. We address this problem by reducing the coupled model to a nonlinear finite dimensional system. The key novelty of this paper is a nonlinear internal state and parameter estimation methodology, from which the internal stress and the state of health-related parameters are monitored from real-time electric current and terminal voltage measurements. Numerical studies on simulation and experimental data have been conducted to illustrate the performance of the proposed estimation scheme.

A constrained feedback control strategy designed on the basis of a simplified electrochemical–thermal model is considered for the fast and healthy charging of a lithium-ion battery cell. The constraints ensure avoidance of side reactions and operating modes that yield premature aging (healthier charging). They are enforced through a reference governor approach, hence requiring a low computational burden. A systematic approach is presented for model identification and control law design. The method is first validated on a detailed battery simulator based on the Doyle–Fuller–Newman model combined with a thermal model. Next, it is validated experimentally through battery-in-the-loop long-term aging campaigns, and the results show that the charging time is reduced by 22% while simultaneously ensuring a longer lifetime (26% less capacity degradation) compared to a 2C constant-current/constant-voltage (CCCV) approach. Compared to recommended C/2 CCCV, our method charges the battery 70% faster and degrades it similarly despite more demanding operating conditions.

A state-space model for Li-ion battery packs with parallel-connected cells is introduced. The key feature of the model is an explicit solution to Kirchhoff’s laws for parallel-connected packs, which expresses the branch currents directly in terms of the model’s states, applied current, and cell resistances. This avoids the need to solve these equations numerically. To illustrate the potential of the proposed model for pack-level control and estimation, a method to bound the error of a state-estimator is introduced and the modeling framework is generalized to a class of electrochemical models. It is hoped that the insight brought by this model formulation will allow the wealth of results developed for series-connected packs to be applied to those with parallel connections.

Lithium-ion battery electrode-level online state estimation using high-fidelity nonlinear electrochemical models remains a key challenge. This is particularly due to weak observability inherited from the complex model structure, even for reduced-order electrochemical models. This manuscript presents a systematic and rigorous strategy to analyze the local observability of a single particle model (SPM) with both electrodes, which is commonly known to be locally unobservable from current-voltage measurements. Estimating the essential states, e.g. state of charge (SOC) and solid-phase lithium surface concentration, is crucial for battery charge and health monitoring since different degradation mechanisms affect each electrode individually. In this manuscript, the proposed observability analysis approach based on the Kalman decomposition enables provably convergent estimates. Ultimately, using the observability analysis, we propose a state estimator based on the nonlinear SPM dynamics and prove estimation error system stability. The observability analysis and state estimation scheme exploits the conservation of lithium property. Simulations demonstrate the effectiveness of the electrode-level state estimator as opposed to the cell-level estimator.