Lithium-ion (Li-ion) batteries have been utilized increasingly in recent years in various applications, such as electric vehicles (EVs), electronics, and large energy storage systems due to their long lifespan, high energy density, and high-power density, among other qualities. However, there can be faults that occur internally or externally that affect battery performance which can potentially lead to serious safety concerns, such as thermal runaway. Thermal runaway is a major challenge in the Li-ion battery field due to its uncontrollable and irreversible nature, which can lead to fires and explosions, threatening the safety of the public. Therefore, thermal runaway prognosis and diagnosis are significant topics of research. To efficiently study and develop thermal runaway prognosis and diagnosis algorithms, thermal runaway modeling is also important. Li-ion battery thermal runaway modeling, prediction, and detection can help in the development of prevention and mitigation approaches to ensure the safety of the battery system. This paper provides a comprehensive review of Li-ion battery thermal runaway modeling. Various prognostic and diagnostic approaches for thermal runaway are also discussed.

In order to keep the lithium-ion battery within the optimal temperature range to achieve excellent battery performance and extend its lifespan, it is necessary to have an effective control strategy for a low-temperature battery thermal management system (BTMS) consisting of electric pump, cooling plate and radiator. In this paper, a control-oriented model for BTMS is established, and an intelligent model predictive control (IMPC) strategy is developed by integrating a neural network-based vehicle speed predictor (VSP) and a target battery temperature adaptor based on Pareto boundaries. The strategy developed based on the BTMS model is applied to plug-in electric vehicles operating in electric vehicle (EV) mode, and the results show its superiority in terms of battery temperature control, battery lifespan extension and energy saving. Under the new European driving cycle, the average difference between the real-time battery temperature under the novel IMPC and its target temperature is 0.26 °C, and the maximum temperature difference among modules is 1.03 °C. Moreover, compared with the on-off controller, model predictive control (MPC), and MPC with VSP, the state of health under IMPC at the end of the driving cycle is 0.016%, 0.012%, and 0.008% higher, respectively. At the end of the driving cycle, the energy consumption of IMPC is 24.5% and 14.1% lower than that of the on-off controller and traditional MPC, respectively.

Lithium-ion (Li-ion) batteries have been extensively used in electric vehicles, portable electronics, cell phones, and laptops. The charging protocol, as one of the most critical technologies for Li-ion battery systems, has a significant impact on battery performance. Charging current affects battery degradation and charging time, and therefore, it needs to be carefully optimized. To this end, a novel charging protocol using a series of constant charging currents has been developed, which considers the charging time and the battery capacity fade simultaneously. These two conflicting charging objectives are traded off by solving a multi-objective optimization problem based on battery electro-thermal-aging behavior. Particle swarm optimization (PSO) has been applied to obtain the optimal charging current profile. Three optimal charging strategies for minimum charging time, minimum battery aging, and balanced charging performance are obtained by changing the weight factor. The proposed balanced charging is capable of reducing the charging time significantly with a negligible increase in capacity degradation compared to the 0.5C CC-CV strategy recommended by the manufacturer.

Lithium-ion batteries have been widely used in electric vehicles. To ensure safety and reliability, accurate prediction of the battery’s future degradation trajectory is critical. However, early prediction capability and adaptive prediction capability under various battery aging conditions remain two main challenges. Either physics-based or data-driven methods have their advantages and limitations. In this study, a novel hybrid method that combines the physics-based and data-driven approaches is proposed to achieve early prediction of the battery capacity degradation trajectory. This framework consists of three steps. First, to improve the generality of the method, a hybrid feature is extracted using an electrochemical model and measured voltage data. Second, the clustering algorithm is adopted to divide battery degradation data into different clusters, and the data augmentation technique is used to enrich the training dataset. Finally, the training dataset in each cluster is used to train the sequence-to-sequence deep neural network, and the future degradation trajectory can be predicted. The proposed method provides accurate predictions using only 20% training data, and it has strong robustness under noisy input. Validation results under different aging conditions show that the mean absolute percentage errors of capacity degradation trajectory and remaining useable cycle life are below 2.5% and 6.5%, respectively.