Accurate remaining useful life (RUL) prediction and state-of-health (SOH) diagnosis are of extreme importance for safety, durability, and cost of energy storage systems based on lithium-ion batteries. It is also a crucial challenge for energy storage systems to predict RUL and diagnose SOH of batteries due to the complicated aging mechanism. In this paper, a novel method for battery RUL prediction and SOH estimation is proposed. First, a novel support vector regression-based battery SOH state-space model is established to simulate the battery aging mechanism, which takes the capacity as the state variable and takes the representative features during a constant-current and constant-voltage protocol as the input variables. The estimated impedance variables are taken as the output due to the correlation between battery capacity and the sum of charge transfer resistance and electrolyte resistance. Second, in order to suppress the measurement noises of current and voltage, a particle filter is employed to estimate the impedance degradation parameters. Furthermore, experiments are conducted to validate the proposed method. The results show that the proposed SOH estimation method can provide an accurate and robustness result. The proposed RUL prediction framework can also ensure an accurate RUL prediction result.

The prognostics and health management of lithium-ion batteries is extremely important for the working performance and cost of energy storage systems. Accurately forecasting battery state of health (SOH) and remaining useful life (RUL) plays an important role in ensuring reliable system operation and minimizing maintenance costs. This paper is, thus, concerned with online short-term SOH estimation and long-term RUL prediction using Brownian motion (BM) based degradation model and particle filtering (PF). The proposed model tackles the capacity degradation as the traveling distance of a Brownian particle in a given time interval. Then, the PF is used to estimate the drift parameter of the BM. This framework leads an accurate short-term SOH estimation result and gives a clear explanation for long-term RUL prediction because the first hitting time of BM follows the inverse Gaussian distribution. The predictive capability and effectiveness of the method are demonstrated by degradation datasets from different types of lithium-ion batteries. Through comparisons with other methods, the experimental results show the superiority of the proposed method in battery health prognosis and it can provide accurate and robust SOH and RUL forecasting.

Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, and so forth. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications, multiple battery cells have to be connected in series and/or parallel. While battery technology has advanced significantly during the past decade, existing battery management systems (BMSs) mainly focus on the state monitoring and control of battery systems packed in fixed configurations. In fixed configurations, though, battery system performance is, in principle, limited by the weakest cells, which can leave large parts severely underutilized.