Real-time electrochemical state information of lithium-ion batteries attributes to a high-fidelity estimation of state-of-charge (SOC) and state-of-health (SOH) in advanced battery management systems. However, the consumption of recyclable lithium ions, loss of the active materials, and the interior resistance increase resulted from the irreversible side reactions cause severe battery performance decay. To maintain accurate battery state estimation over time, a scheme using the reduced-order electrochemical model and the dual nonlinear filters is presented in this article for the reliable co-estimations of cell SOC and SOH. Specifically, the full-order pseudo-two-dimensional model is first simplified with Padé approximation while ensuring precision and observability. Next, the feasibility and performance of SOC estimator are revealed by accessing unmeasurable physical variables, such as the surface and bulk solid-phase concentration. To well reflect battery degradation, three key aging factors including the loss of lithium ions, loss of active materials, and resistance increment, are simultaneously identified, leading to an appreciable precision improvement of SOC estimation online particular for aged cells. Finally, extensive verification experiments are carried out over the cell's lifespan. The results demonstrate the performance of the proposed SOC/SOH co-estimation scheme.

The growing demand for lithium-ion (Li-ion) battery in electric vehicles has expedited the need for new optimal charging approaches to improve the speed and reliability of the charging process without deteriorating battery performances. Many efforts have been deployed to develop optimal charging strategies for commercial Li-ion batteries over the last decade. The active optimal charging strategies have great potential to meet the requirement. This paper is a review of the studies on constructing the optimal charging algorithms for Li-ion batteries. The battery models on which these protocols rest are stated, the generalized structures are examined, the advantages and the drawbacks of the mathematical controller algorithms are discussed, and their applications are presented. Suggestions for overcoming the shortcomings of the proposed strategies are proposed. Challenges and future directions in the development of optimal charging strategies for commercial Li-ion batteries are also discussed.

Battery fast charging strategies have gained an increasing interest towards the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifespan, and charging time, this paper proposes a novel health-aware multi-objective optimal charging strategy to simultaneously shorten the charging time and relieve the battery degradation. The multi-objective optimal charging problem is formulated based on a coupled electrochemical-thermal-aging battery model. Constraints are explicitly imposed on physically meaningful state variables to avoid hazardous operations. Charging duration and battery aging process are well traded-off. Strategies for minimum-time and health-aware fast charging are investigated using different input current bounds, subject to both side reaction and temperature constraints. The experimental results validate that the presented multi-objective health-aware optimal charging algorithm is capable of reducing the charging time from its benchmarks largely without sacrificing the state-of-health of the battery.

Accurately identifying the aging-related parameters of a lithium-ion electrochemical model is crucial for the advanced battery management systems over the cells' service life. However, the multiparametric and highly nonlinear mathematical structures of the physical model heighten the difficulty for parameterization. Thus, analyzing the influence of degraded parameters on model output holds the key to efficient identification. In this article, a statistics-based global sensitivity analysis of overall 16 aging parameters in the pseudo-2-D model of lithium-ion batteries is investigated under both the charge process and dynamic driving cycles at 10°, 25°, and 45°. First, massive samples of the parameters are generated synchronously with the Latin hypercubes method. Then the model is simulated with the Monte Carlo technique. Finally, the sensitivity of each parameter is ranked with the partial correlation coefficient which quantifies the relation between the parameter variations and the voltage residuals. The results turn out that the sensitivity of each parameter varies at different operating conditions. Specifically, the resistance-related parameters are the most sensitive than capacity and diffusion-related parameters. To validate the effectiveness of the proposed approach, 16 aging parameters are clustered for identification. The identified model achieves low voltage root-mean-square errors across 170 cycles at three temperatures.

State of charge (SOC) and state of health (SOH) are of critical importance to ensure safety, reliability, and prolong battery life. In this paper, a novel SOC & SOH estimation method considering side reactions over the lifespan of a Li-ion degraded battery is presented. To achieve this, an order-reduced electrochemical model considering side reactions is established first by simplification of the partial differential equations (PDEs) using the Laplace transform, inverse Laplace transform, Pade approximation, etc. The open-circuit-voltage (OCV) versus SOC correlation is utilized as the link between equivalent circuit model (ECM) and electrochemical model. Then the current induced accumulated aging effects of loss of ions and active materials on the electrode stoichiometry could be calculated to obtain a modified OCV- SOC curve. The updated OCV-SOC relationship is applied to an ECM based SOC estimator. The SOH monitoring is achieved by quantifying the capacity fade with the help of equilibrium potentials. Finally, the robustness and effectiveness of the SOC and SOH estimation scheme integrated with the presented OCV curve are validated by simulated and experimental results. The results show that the SOC estimation precision of the presented scheme regarding battery interior properties has been improved compared to the existing method. Future work is also highlighted.