The mechanism revelation of performance decrease and fast-charging limitation of lithium-ion batteries at low temperatures is indispensable to optimize battery design and develop fast-charging methods. In this article, an electrochemical model-based quantitative analysis method is proposed to uncover the dominant reason for performance decrease and fast-charging limitation of batteries at low temperatures. The highly important dynamic parameters are carefully determined by the experimental data from the checked three-electrode battery and optimized by the genetic algorithm, rather than directly taken from the references. Validation results confirm that the electrochemical model can well reproduce battery behaviors under different conditions and that identified parameters are accurate. The quantitative analysis indicates that the sluggish diffusion in cathode and anode electrodes is the principal reason for battery available capacity loss. Battery available power attenuation is primarily attributed to the increased film resistance of anode and the reduced exchange current density of cathode, and it is substantially independent of the reduced diffusivity. The comparison result from the lithium-plating-prevention charging current reveals that the increased film resistance of the anode is responsible for the predominant limitation of low-temperature fast-charging, despite the most change in the exchange current density of the anode. This quantitative revelation breaks through the traditional understanding from the qualitative analysis that performance decrease and fast-charging limitation of batteries at low temperatures are highly associated with the degree of the change of characteristic parameters.

Power simulation of lithium ion battery through battery model is of great significance for dynamic response simulation, heat generation calculation and charge-discharge strategy development. The accuracy and applicability of the model become crucial. In order to demonstrate the battery transient characteristics more effectively, a novel identification method for parameters of the 2nd order RC equivalent circuit model was proposed. Based on the derived evolution law of battery transient characteristics under the continuous pulse excitation, four feature points are extracted for parameter identification in each cycle. The proposed method reduced the time cost of identification from 11796.88s to 0.06s while ensuring that the error of voltage doesn't exceed 2.2mV. In order to verify the power profiles applicability of the proposed method, applicability analysis of power profile for different identification methods was carried out including the methods using different amount of data (4N points, 200 points, 6000 points) under unidirectional current pulse excitation (UCPE), bidirectional current pulse excitation (BCPE) and unidirectional voltage pulse excitation (UVPE). It was illustrated that the identification process using data of multiple cycles could significantly reduce errors, including maximum error and average error. What's more, the proposed method under UCPE had the lowest maximum error of 0.420% in voltage simulation and -0.421% in the current simulation of power profiles. Compared with the conventional method (using 200 points of single pulse data for parameter identification), the proposed method can reduce the average voltage error and the maximum error by 62.5% and 11.8% respectively under the DST power profile.

The battery management system (BMS) is a core component to ensure the efficient and safe operation of electric vehicles, and the practical evaluation of key BMS functions is thus of great importance. However, the testing of a BMS with actual battery packs suffers from a poor testing repeatability and a long status transition time due to the uncontrollable degradation of battery systems and testing environment variations. In this paper, to overcome this challenge, we propose an efficient BMS testing framework that uses virtual battery packs rather than actual ones, thus enabling a rapid and accurate evaluation of a BMSs key functions. A series-connected virtual battery pack model through leveraging Copula’s method is formulated to capture the dynamics and inconsistency of individual batteries in the pack. The developed lithium iron phosphate model features low computational efforts and is experimentally validated with different dynamical profiles, implying a high-precision virtual battery pack that is capable of reproducing the actual one. Furthermore, this framework includes a closed-loop testing platform, which can provide the state-of-charge/state-of-power references and thus automatically test and evaluate the states of the battery packs estimated from the BMS. Particularly, we consider the initial polarization that often exists in the batteries during the operation to accurately calibrate the available state-of-power benchmark of battery packs in the real world. The performed BMS testing results using the proposed framework illustrate that the tested BMS cannot adapt to the varied operation conditions, thus leading to high state estimation errors, which may result in the over-charge/discharge or over-temperature of the batteries. Therefore, this work highlights the value of effective BMS testing, providing the promising potential to achieve reliability and durability for battery systems.

With the rapid growth of renewable energy and the DC fast charge pile of the electric vehicle, their inherent volatility and randomness increase a power system’s unbalance of instantaneous power. The need for power grid frequency regulation is increasing. The energy storage system (ESS) can be used to assist the thermal power unit so that a better frequency regulation result is obtained without changing the original operating mode of the unit. In this paper, a set of different charging/discharging control strategies of the lithium titanate battery (LTO) is proposed, which are chosen according to the interval of the State of energy (SOE) to improve the utilization rate of the ESS. Finally, the cost-benefit model of the ESS participating in automatic generation control ancillary service is established. Case analysis proves that after a 1.75 MWh ESS is configured for a 600 MW thermal power unit, K_p and D is increased from 1.42 to 6.38 and 2857 to 6895 MW. The net daily income is increased from 20,284 yuan to 199,900 yuan with a repayment period of 93 days. The results show that the control strategies and the energy configuration method can improve the performance and economic return of the system.