Mechanical abuse can lead to internal short circuits and thermal runaway in lithium-ion batteries, causing severe harm. Therefore, this paper systematically investigates the thermal runaway behavior and safety assessment of lithium iron phosphate (LFP) batteries under mechanical abuse through experimental research. Mechanical abuse experiments are conducted under different conditions and battery state of charge (SOC), capturing force, voltage, and temperature responses during failure. Subsequently, characteristic parameters of thermal runaway behavior are extracted. Further, mechanical abuse conditions are quantified, and the relationship between experimental conditions and battery characteristic parameters is analyzed. Finally, regression models for battery safety boundaries and the degree of thermal runaway risk are established. The research results indicate that the extracted characteristic parameters effectively reflect internal short circuit (ISC) and thermal runaway behaviors, and the regression models provide a robust description of the battery's safety boundaries and thermal runaway risk degree. This work sheds light on understanding thermal runaway behavior and safety assessment methods for lithium-ion cells under mechanical abuse.

This article proposes a handling-stability control strategy for distributed drive electric vehicles (EVs) to improve motion performance. A motion supervisor, using only front steering angle feedback, is developed to evaluate the driving state and generate a unified yaw rate reference for handling-stability coordination. To ensure tracking convergence, a Lyapunov-based nonlinear model predictive control (LNMPC) strategy is proposed for direct yaw moment control (DYC), incorporating a contraction constraint to guarantee closed-loop stability, with rigorous proofs provided. For rapid problem-solving, a modified iterative linear quadratic regulator (iLQR) algorithm is developed, leveraging a relaxed log barrier function and double-loop iteration to handle inequality constraints, preventing violations and theoretically ensuring convergence to the original problem’s solution. Additionally, an auxiliary control law is applied to generate the initial solution in iLQR, reducing sensitivity. Using a Karush-Kuhn-Tucker (KKT) conditions-based approach, the virtual control distribution is optimized efficiently, and the torque command of in-wheel motors (IWMs) can be gained. Simulations and hardware-in-the-loop (HIL) experiments demonstrate superior handling-stability performance and high computational efficiency with the proposed strategy.

Obstacle avoidance problem for the autonomous vehicle has received more and more attention these years, especially for the autonomous heavy vehicle, which has the high gravity center, large carrying capacity and promising commercial prospect. However, the vehicle dynamic properties have been rarely considered for path planning, which is a crucial factor for driving safety. An integrated collision avoidance framework that consists of a new path planner and a tracking controller considering multiple dynamic constraints is proposed for an autonomous multi-axis distributed vehicle. In the upper level, a new optimal path generator based on the B-splines is designed for safety and stability purposes, and the collision avoidance path is optimized considering multiple dynamic constraints, including the tire nonlinear dynamic, lateral stability limits and the anti-slip constraints. Meanwhile, a safety assessment evaluator according to the time to collision calculation is designed for risk level recognition and path planning weight determination. In the lower level, a path tracking controller based on the nonlinear model predictive control (NMPC) is designed to improve the control accuracy, and a high-fidelity nonlinear dynamic predictive vehicle model with 16 degrees of freedom is established to depict the vehicle dynamic properties comprehensively. To solve the nonlinear optimization problem and get the optimal control steering angle output, an adaptive weight adjustment strategy and a varying predictive duration method are formulated. The optimization constraints related to steering actuator ability, plane stability limits, rollover prevention thresholds and tire adhesion requirements are constructed and converted as the bounds of the vehicle dynamic states. To verify the proposed framework, the simulation and HIL test platform are built and different driving scenarios are validated. The test results show the satisfactory performance of path planning and path tracking considering the dynamic constraints for safety and stability

This paper proposes a positive temperature coefficient (PTC) self-heating method, in which EVs can be operated independently of external power source at low temperature, with a lithium-ion battery (LIB) pack discharging electricity to provide PTC material with power. Three comparative heating experiments have been carried out respectively. With charge/discharge tests implemented, results demonstrate the superiority of the self-heating method, proving that the discharge capability, especially the discharge capacity of the self-heated pack is better than that of the external power heated pack. In order to evaluate the heating effect of this method, further studies are conducted on temperature distribution uniformity in the heated pack. Firstly, a geometric model is established, and heat-generation rate of PTC materials and LIB are calculated. Then, thermal characteristics of the self-heating experiment processes are numerically simulated, validating the accuracy of our modeling and confirming that temperature distributions inside the pack after heating are kept in good uniformity. Therefore, the PTC self-heating method is verified to have a significant effect on the improvement of performance of LIB at low temperature.

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

Data-driven methods have been widely employed for capacity estimation of lithium-ion batteries through exploiting machine learning models to build a mapping relationship between extracted health features and capacity. However, existing machine learning based approaches require plentiful and intricate data processing for feature extraction. To remedy this limitation, this paper presents a deep learning method for online capacity estimation of lithium-ion batteries. A predictive model, namely deep convolutional time memory network with the properties of automatic feature abstraction and target estimation, is established via fusing the convolutional neural network and long short-term memory unit. Partial charging voltage and current data are selected from the raw charging data and can be directly fed into the proposed model without complicated pre-processing, resulting in easier training preparation and lower computational intensity. Experimental results demonstrate that the proposed algorithm can precisely estimate the battery capacity with the absolute error of less than 0.021 Ampere-hour and 0.11 Ampere-hour for two types of batteries. The proposed method requires only short charging voltage and current profiles and pledges high estimation accuracy, contributing to fast online capacity estimation.