This paper proposes a co-estimation scheme of state of charge (SOC), state of health (SOH), and state of function (SOF) for lithium-ion batteries in electric vehicles. The co-estimation denotes that the SOC, SOH and SOF are estimated simultaneously in real-time application. The model-based SOC estimation is fulfilled by the Extended Kalman Filter (EKF). The battery parameters related with the battery SOH and SOF are online identified using the Recursive Least Square (RLS) algorithm with a forgetting factor. The capacity and the maximum available output power are then estimated based on the identified parameters. The online update of the capacity and correlated parameters help improve the accuracy of the state estimation but with limited increase in the computation load, by making good use of the correlations among the states. The co-estimation scheme is validated in a real Battery Management System (BMS) with good real-time performance and convincible estimation accuracy.

The online estimation of battery state-of-health is an ever significant issue for the intelligent energy management of the autonomous electric vehicles. Machine-learning based approaches are promising for online state-of-health estimation. This paper proposes a machine-learning based algorithm for the online state-of-health estimation of Li-ion battery. A predictive diagnosis model used in the algorithm is established based on support vector machine (SVM). The support vectors, which reflects the intrinsic characteristics of the Li-ion battery, are determined from the charging data of fresh cells. Furthermore, the coefficients of the SVMs for cells at different state-of-health are identified once the support vectors are determined. The algorithm functions by comparing partial charging curves with the stored SVMs. Similarity factor is defined after comparison to quantify the state-of-health of the data under evaluation. The operation of the algorithm only requires partial charging curves, e.g. 15min charging curves, making fast on-board diagnosis of battery state-of-health into reality. The partial charging curves can be intercepted from a wide range of voltage section, thereby relieving the pain that there is little chance that the driver charges the battery pack from a predefined state-of-charge. Train, validation, and test are conducted for two commercial Li-ion batteries with Li(NiCoMn)1/3O2 cathode and graphite anode, indicating that the algorithm can estimate the battery state-of-health with less than 2% error for 80% of all the cases, and less than 3% error for 95% of all the cases.

Concentrated electrolytes usually demonstrate good electrochemical performance and thermal stability, and are also supposed to be promising when it comes to improving the safety of lithium-ion batteries due to their low flammability. Here, we show that LiN(SO₂F)₂-based concentrated electrolytes are incapable of solving the safety issues of lithium-ion batteries. To illustrate, a mechanism based on battery material and characterizations reveals that the tremendous heat in lithium-ion batteries is released due to the reaction between the lithiated graphite and LiN(SO₂F)₂ triggered thermal runaway of batteries, even if the concentrated electrolyte is non-flammable or low-flammable. Generally, the flammability of an electrolyte represents its behaviors when oxidized by oxygen, while it is the electrolyte reduction that triggers the chain of exothermic reactions in a battery. Thus, this study lights the way to a deeper understanding of the thermal runaway mechanism in batteries as well as the design philosophy of electrolytes for safer lithium-ion batteries.

Lithium-ion batteries connected in series are prone to be overdischarged. Overdischarge results in various side effects, such as capacity degradation and internal short circuit (ISCr). However, most of previous research on the overdischarge of a cell was terminated when the cell voltage dropped to 0 V, leaving the further impacts of overdischarge unclear. This paper investigates the entire overdischarge process of large-format lithium-ion batteries by discharging the cell to −100% state of charge (SOC). A significant voltage platform is observed at approximately −12% SOC and ISCr is detected after the cell is overdischarged when passing the platform. The scanning electron microscopy (SEM) and X-ray diffraction (XRD) results indicate that the overdischarge-induced ISCr is caused by Cu deposition on electrodes, suggesting possible Cu collector dissolution at the voltage platform near −12% SOC. A prognostic/mechanistic model considering ISCr is used to evaluate the resistance of ISCr (R_ISCr), the value of which decreases sharply at the beginning of ISCr formation. Inducing the ISCr by overdischarge is effective and well controlled without any mechanical deformation or the use of a foreign substance.