As the only power source of pure electric vehicles, lithium-ion batteries play an important role in vehicle powertrain systems. However, lithium-ion batteries have a significant reduction in capacity and power capability at low temperatures, which results in a greatly shortened driving range and poor acceleration of the vehicle. In this study, a rule-based battery external heating control strategy was developed to heat the battery during driving. An electrothermal film was affixed to the surface of each cell as an external heating material that was powered by the battery. An equivalent circuit model combined with a thermal model was established to simulate the electrical and thermal dynamics of the system with sufficiently high accuracy, and control rules were developed based on the model. The optimal solution was obtained by adopting the dynamic programming algorithm to optimize the trade-off between temperature rise and energy consumption and maximize the total driving range under different low temperature driving conditions. Simulation results on the experimentally validated model show that the vehicle with the proposed control algorithm increased the total driving range by 18.6% to 220% for different driving conditions at cold to extremely cold temperatures compared with the vehicle without external heating. Furthermore, the rule-based control showed a 1.1% to 4.4% improvement compared with the maximum (constant) power heating method.

Thermal monitoring is critical to the safety of electric vehicles. Due to the uneven surface temperature of large-format lithium-ion (Li-ion) batteries, traditional lump-based thermal monitoring methods can’t capture two-dimensional (2-D) spatiotemporal thermodynamics. This paper proposes a distributed thermal monitoring framework to solve this problem. It contains two parts. In the modeling part, the spectral method is utilized to establish the globally distributed temperature field of the pouch cell using only four sensors. In the fault detection part, there are two stages: 1) In the offline training stage, the 2-D battery thermal process is first decomposed into basis functions and time coefficients using the spectral method. The time coefficients are further decomposed by independent component analysis. Then, the dominant temporal components are formed as the monitoring statistic, which is used to derive the confidence bound through kernel density estimation. 2) In the online detection stage, the thermal fault can be detected in real-time by comparing the updated monitoring statistic with the confidence bound. Simulations and experiments on a pouch-type Li-ion battery are conducted to verify the effectiveness of the proposed method.

The reduction in driving range and the degradation of vehicle performance in cold weather has become one of the challenges in vehicle electrification in recent years. The root cause of this phenomenon is the property of lithium-ion batteries with capacity and power capability reduction at low temperatures. In this study, an external battery heating system was developed by employing an electrothermal film affixed to the surface of each cell, and the heating process was performed during driving. An equivalent circuit model combined with a thermal model was established for the simulation and control design. A fuzzy logic control strategy was developed to optimize the external heating power provided by the battery pack, and to achieve the maximum range by the end of discharge. A global optimal control strategy obtained by dynamic programming and a constant maximum power heating strategy were used for comparison. Simulation and experimental validations show that the proposed fuzzy logic control algorithm can achieve a 3.6% to 5.3% improvement in driving range than the maximum power heating method, and has close performance to the global optimal solution. Furthermore, the vehicle equipped with the proposed heating system can have up to 150.4% of the range recovery under different driving conditions.

Energy management strategies (EMSs) are a core technology in hybrid electric vehicles (HEVs) and have a significant impact on their fuel economy. Optimal solutions for EMSs in the literature usually focus on improving fuel efficiency by operating the engine within a high efficiency range, without considering the drivability, which is affected by noise–vibration–harshness (NVH) constraints at low vehicle speeds. In this paper, a dual-mode combustion engine was implemented in a plug-in series hybrid electric vehiclethat could operate efficiently either at low loads in homogeneous charge compression ignition (HCCI) mode or at high loads in spark ignition (SI) mode. An equivalent consumption minimization strategy (ECMS) combined with a dual-loop particle swarm optimization (PSO) algorithm was designed to solve the optimal control problem. A MATLAB/Simulink simulation was performed using a well-calibrated model of the target HEV to validate the proposed method, and the results showed that it can achieve a reduction in fuel consumption of around 1.3% to 9.9%, depending on the driving cycle. In addition, the operating power of the battery can be significantly reduced, which benefits the health of the battery. Furthermore, the proposed ECMS-PSO is computationally efficient, which guarantees fast offline optimization and enables real-time applications.

“Principles of Automatic Control” is an introductory course for engineering majors, requiring high fundamental physics and mathematics knowledge. However, the traditional theoretical teaching methods based on PPT or blackboard writing have the following problems: 1) For students who are not good at mathematics, it is difficult to understand the content taught by the teacher in class, and self-study after class is challenging to understand; 2) For students who are good at mathematics, they can cope with exams, but they cannot use theoretical knowledge to solve practical professional problems. In response to the above considerations, this paper puts forward two suggestions for curriculum reform. 1) Interactive teaching: teachers design half-blank handouts in advance, where the essential knowledge points are included and core mathematical derivations are completed by students and teachers together; 2) Professional case-based teaching: all knowledge points of automatic control can be studied through 2–3 professional cases according to the major. In this way, not only the theoretical level of students can be improved, and their ability to solve practical professional problems can be strengthened.