As a favorable energy storage component, lithium-ion (Li-ion) battery has been widely used in the battery energy storage systems (BESS) and electric vehicles (EV). Data driven methods estimate the battery state-of-health (SOH) with the features extracted from the measurement. However, excessive features may reduce the estimation accuracy and also increases the human labor in the lab. By proposing an optimization process with nondominated sorting genetic algorithm II (NSGA-II), this article is able to establish a more efficient SOH estimator with support vector regression (SVR) and the short-term features from the current pulse test. NSGA-II optimizes the entire process of establishing a SOH estimator considering both the measurement cost of the feature and the estimation accuracy. A series of nondominated solutions are obtained by solving the multiobjective optimization problem, which also provides more flexibility to establish the SOH estimator at various conditions. The degradation features in this article are the knee points at the transfer instants of the voltage in the short-term current pulse test, which is fairly convenient and easy to be obtained in real applications. The proposed method is validated on the measurement from two LiFePO4/C batteries aged with the mission profile providing the primary frequency regulation (PFR) service to the grid.

State-of-health (SOH) of lithium-ion batteries plays a vital role in the safe and reliable operation of electric vehicles. However, most of the existing SOH estimation methods still require a large number of battery aging data while the established model usually lacks generalization. Here, we build a data-driven transfer learning model to obtain more generality on the SOH estimation. First, potential health features are extracted from battery charging data and then pruned via the importance function. Second, support vector regression (SVR) is employed to establish source models with different battery data, which takes selected features as the input and capacity as the output. Third, the transfer-stacking (TS) method is utilized to combine all source models. A TS-SVR method for SOH estimation is then established only using the first 30 $\%$ of target battery data after solving the optimization problem of assigning weight to each source model. Finally, the proposed algorithm is verified by three different battery datasets and shows better estimation performance than the comparative algorithms. It is proved that the proposed method uses only a small amount of target battery data while together with the source battery data can achieve an accurate SOH estimation during its life cycle.

With the widespread use of Lithium-ion (Li-ion) batteries in Electric Vehicles (EVs), Hybrid EVs and Renewable Energy Systems (RESs), much attention has been given to Battery Management System (BMSs). By monitoring the terminal voltage, current and temperature, BMS can evaluate the status of the Li-ion batteries and manage the operation of cells in a battery pack, which is fundamental for the high efficiency operation of EVs and smart grids. Battery capacity estimation is one of the key functions in the BMS, and battery capacity indicates the maximum storage capability of a battery which is essential for the battery State-of-Charge (SOC) estimation and lifespan management. This paper mainly focusses on a review of capacity estimation methods for BMS in EVs and RES and provides practical and feasible advice for capacity estimation with onboard BMSs. In this work, the mechanisms of Li-ion batteries capacity degradation are analyzed first, and then the recent processes for capacity estimation in BMSs are reviewed, including the direct measurement method, analysis-based method, SOC-based method and data-driven method. After a comprehensive review and comparison, the future prospective of onboard capacity estimation is also discussed. This paper aims to help design and choose a suitable capacity estimation method for BMS application, which can benefit the lifespan management of Li-ion batteries in EVs and RESs.

Temperature monitoring is of paramount importance for guaranteeing the safety and proper operation of lithium-ion batteries. Traditional temperature sensors suffer from heat transfer delay, where internal battery temperature cannot be measured directly. Motivated by this, this letter proposes a novel sensorless temperature estimation method based on broadband impedance spectroscopy. In this letter, pseudorandom sequence (PRS) with finite signal levels is utilized for impedance measurements. The measured impedance information is merged into an impedance-temperature model, which cooperates with a specially designed least-square method for temperature estimation. The proposed framework is robust against interference, whereas simple enough for online implementation. Experimental results suggest excellent estimation accuracy of the proposed method under different circumstances.

Current pulses are convenient to be actively implemented by a battery management system. However, the short-term features (STF) from current pulses originate from various sensors with uneven qualities, which hinder one powerful and strong learner with STF for the battery state of health (SOH) estimation. This article, thus, proposes an optimized weak learner formulation procedure for lithium-ion battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto front-based selection strategy is designed to select the representative solutions from the nondominated solutions fed by a knee point driven evolutionary algorithm, which guarantees both the diversity and accuracy of the weak learners. Afterward, the weak learners, whose coefficients are obtained by self-adaptive differential evolution, are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of the SOH estimation. The validation of the proposed method is proved by LiFePO ${_4}$ /C batteries under accelerated cycling ageing test including one mission profile providing primary frequency regulation service to the grid and one constant current profile.