With the growing demand for energy resources and rising environmental risks, the deployment of electric vehicles has been recognized as effective countermeasures to the global energy crisis and climate change. Lithium-ion batteries, thanks to their high efficiency, high energy/power density, and long lifespan, are widely used as critical energy storage devices for electric vehicles. Meanwhile, traction batteries play a deciduous role in the reliability, availability, maintainability, and safety of electric vehicles. Therefore, they should be meticulously designed and managed.

The development of advanced battery management systems has become the forefront of research worldwide. Upgraded modeling, estimation, control, and optimization of battery systems are researched in order to improve the battery performance while guaranteeing its safety. Interdisciplinary knowledge, including electrical engineering, control theory, electrochemistry, AI, and machine learning, needs to be integrated in an appropriate way to seek high-performance battery management.

Four articles have been collected in this feature topic with salient contributions to the field of battery management. The feature topic highlights the most recent advances in battery modeling, health and safety management. The four articles, as listed below, cover the topics of battery fault prognosis and diagnosis, data-driven modeling of the electrode, state of health estimation, and non-invasive characteristic analysis.

Reliable and timely detection of an internal short circuit (ISC) in lithium-ion batteries is important to ensure safe and efficient operation. This paper investigates ISC detection of parallel-connected battery cells by considering cell non-uniformity and sensor limitation (i.e., no independent current sensors for individual cells in a parallel string). To characterize ISC-related signatures in battery string responses, an electro-thermal model of parallel-connected battery cells is first established that explicitly captures ISC. By analyzing the data generated from the electro-thermal model, the distribution of surface temperature among individual cells within the battery string is identified as an indicator for ISC detection under the constraints of sensor limitations. A convolutional neural network (CNN) is then designed to estimate the ISC resistance by using the cell surface temperature and the total capacity of the string as inputs. Based on the estimated ISC resistance from CNN, the strings are classified as faulty or non-faulty to guide the examination or replacement of the battery. The algorithm is evaluated in the presence of signal noises in terms of accuracy, false alarm rate, and missed detection rate, verifying the effectiveness and robustness of the proposed approach.

Lithium-ion batteries have become one of the most promising technologies for speeding up clean automotive applications, where electrode plays a pivotal role in determining battery performance. Due to the strongly-coupled and highly complex processes to produce battery electrode, it is imperative to develop an effective solution that can predict the properties of battery electrode and perform reliable sensitivity analysis on the key features and parameters during the production process. This paper proposes a novel tree boosting model-based framework to analyze and predict how the battery electrode properties vary with respect to parameters during the early production stage. Three data-based interpretable models including AdaBoost, LPBoost, and TotalBoost are presented and compared. Four key parameters including three slurry feature variables and one coating process parameter are analyzed to quantify their effects on both mass loading and porosity of battery electrode. The results demonstrate that the proposed tree model-based framework is capable of providing efficient quantitative analysis on the importance and correlation of the related parameters and producing satisfying early-stage prediction of battery electrode properties. These can benefit a deep understanding of battery electrodes and facilitate to optimizing battery electrode design for automotive applications.

In the long-term prediction of battery degradation, the data-driven method has great potential with historical data recorded by the battery management system. This paper proposes an enhanced data-driven model for Lithium-ion (Li-ion) battery state of health (SOH) estimation with a superior modeling procedure and optimized features. The Gaussian process regression (GPR) method is adopted to establish the data-driven estimator, which enables Li-ion battery SOH estimation with the uncertainty level. A novel kernel function, with the prior knowledge of Li-ion battery degradation, is then introduced to improve the modeling capability of the GPR. As for the features, a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high efficiency. In the first stage, an optimal partial charging voltage is selected by the grid search; while in the second stage, the principal component analysis is conducted to increase both estimation accuracy and computing efficiency. Advantages of the proposed method are validated on two datasets from different Li-ion batteries: Compared with other methods, the proposed method can achieve the same accuracy level in the Oxford dataset; while in Maryland dataset, the mean absolute error, the root-mean-squared error, and the maximum error are at least improved by 16.36%, 32.43%, and 45.46%, respectively.

Power battery technology is essential to ensuring the overall performance and safety of electric vehicles. Non-invasive characteristic curve analysis (CCA) for lithium-ion batteries is of particular importance. CCA can provide characteristic data for further applications such as state estimation and thermal runaway warning without disassembling the batteries. This paper summarizes the characteristic curves consisting of incremental curve analysis, differential voltage analysis, and differential thermal voltammetry from the perspectives of exploring the aging mechanism of batteries and constructing the data-driven model. The process of quantitative analysis of battery aging mechanism is presented and the steps of constructing data-driven models are induced. Moreover, the recent progress and application of the main features and methodologies are discussed. Finally, the applicability of battery CCA is discussed by converting non-quantifiable battery information into transportable data covering macrostate and micro-reaction information. Combined with the cloud-based battery management platform, the above-mentioned battery characteristic curves could be used as a valuable dataset to upgrade the next-generation battery management system design.

In-wheel motor-drive electric vehicles have the advantage of independently controllable wheel torque and the disadvantages of unsprung mass rise and power restriction. To address the disadvantages, a centralized layout electric torque vectoring drive-axle system (E-TVDS) with dual motors is proposed, which can realize arbitrary distribution of driving torque between the left and right wheels. First, the speed and torque distribution principle of E-TVDS based on velocity diagram are analyzed, and a virtual prototype of the whole vehicle with basic gear ratio relation model of the E-TVDS is built for simulation to verify the theoretical results and the basic effect of E-TVDS on the steering performance of the vehicle. Second, the characteristics of 36 types of the novel E-TVDS topology structure are compared and analyzed, and the optimal structure scheme is selected. Third, the accurate multiple degrees of freedom dynamic model for the optimal structure is established by using the bond graph method, and its dynamic response characteristics are analyzed. The results show that the vehicle equipped with the proposed E-TVDS can distribute the driving torque with the almost identical amount but opposite sign between the left and right wheels in any direction, and varying amount according to different chassis dynamics control requirements, and the torque response performance is great with little delay and overshoot. The function and dynamic response of the proposed E-TVDS show that it has potential application value for various performance improvements of electric vehicles.

Software-defined vehicles have been attracting increasing attentions owing to their impacts on the ecosystem of the automotive industry in terms of technologies, products, services and enterprise coopetition. Starting from the technology improvements of software-defined vehicles, this study systematically combs the impact of software-defined vehicles on the value ecology of automotive products and the automotive industrial pattern. Then, based on the current situation and demand of industrial development, the main challenges hindering the realization of software-defined vehicles are identified, including that traditional research and development models cannot adapt to the iterative demand of new automotive products; the transformation of enterprise capability faces multiple challenges; and many contradictions exist in the industrial division of labor. Finally, suggestions are put forward to address these challenges and provide decision-making recommendations for enterprises on strategy management.

As intelligent vehicles usually have complex overtaking process, a safe and efficient automated overtaking system (AOS) is vital to avoid accidents caused by wrong operation of drivers. Existing AOSs rarely consider longitudinal reactions of the overtaken vehicle (OV) during overtaking. This paper proposed a novel AOS based on hierarchical reinforcement learning, where the longitudinal reaction is given by a data-driven social preference estimation. This AOS incorporates two modules that can function in different overtaking phases. The first module based on semi-Markov decision process and motion primitives is built for motion planning and control. The second module based on Markov decision process is designed to enable vehicles to make proper decisions according to the social preference of OV. Based on realistic overtaking data, the proposed AOS and its modules are verified experimentally. The results of the tests show that the proposed AOS can realize safe and effective overtaking in scenes built by realistic data, and has the ability to flexibly adjust lateral driving behavior and lane changing position when the OVs have different social preferences.

Energy security planning is fundamental to safeguarding the traffic operation in large-scale events. To guarantee the promotion of green, zero-carbon, and environmental-friendly hydrogen fuel cell vehicles (HFCVs) in large-scale events, a five-stage planning method is proposed considering the demand and supply potential of hydrogen energy. Specifically, to meet the requirements of the large-scale events’ demand, a new calculation approach is proposed to calculate the hydrogen amount and the distribution of hydrogen stations. In addition, energy supply is guaranteed from four aspects, namely hydrogen production, hydrogen storage, hydrogen delivery, and hydrogen refueling. The emergency plan is established based on the overall support plan, which can realize multi-dimensional energy security. Furthermore, the planning method is demonstrative as it powers the Beijing 2022 Winter Olympics as the first “green” Olympic, providing both theoretical and practical evidence for the energy security planning of large-scale events. This study provides suggestions about ensuring the energy demand after the race, broadening the application scenarios, and accelerating the application of HFCVs.