Battery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods for early failure detection and reducing safety risks from failing high energy lithium-ion batteries has become a major challenge for industry. In this article, a real-time early fault diagnosis scheme for lithium-ion batteries is proposed. By applying both the discrete Fréchet distance and local outlier factor to the voltage and temperature data of the battery cell/module that measured in real time, the battery cell that will have thermal runaway is detected before thermal runaway happens. Compared with the widely used single parameter based diagnosis approach, the proposed one considerably improve the reliability of the fault diagnosis and reduce the false diagnosis rate. The effectiveness of the proposed method is validated with the operational data from electric vehicles with/without thermal runaway in daily use.

Thermal runaway of an electric vehicle (EV) battery can cause severe loss of property and human life. With the increasing market share of EVs, this issue becomes more critical since one single cell short circuit could easily cause thermal runaway in a few minutes. Therefore, battery short-circuit detection systems are important for the prevention and limitation of EV fire incidents. This article proposes a short-circuit detection and isolation method for lithium-ion battery packs based on relative entropy and the Z-score method, which identifies the cell voltage dropping behaviors caused by a short circuit with the sliding window processing method. Taking the optimal sliding window width, the proposed detection algorithm can be performed in real time without any significant delay. The effectiveness and efficiency of the proposed method are verified using real-world data measured from EVs that experienced fire incidents caused by thermal runaways. Results indicate the method in this article is capable to recognize the data pattern of the potential threat in real time and send an early alarm to the driver.

To combat the impacts of uncertain noise on the estimation of vehicle state parameters and the high cost of sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The design equation of the state observer is derived by establishing the vehicle’s three degrees-of-freedom (DOF) model. On this basis, the Sage–Husa algorithm and unscented Kalman filter (UKF) are combined to form the AUKF algorithm to adaptively update the statistical feature estimation of measurement noise. Finally, a co-simulation using Carsim and Matlab/Simulink confirms the algorithm is effective and reasonable. The simulation results demonstrate that the proposed algorithm, compared with the UKF algorithm, increases estimation accuracy by 19.13%, 32.8%, and 39.46% in yaw rate, side-slip angle, and longitudinal velocity, respectively. This is because the proposed algorithm adaptively adjusts the measurement noise covariance matrix, which can estimate the state parameters of the vehicle more accurately.

In order to evaluate the influence of unsprung mass on ride comfort for an in-wheel motor electric vehicle, an improved genetic algorithm based on fitness evaluation was proposed to optimize the suspension system. The simulation model was established in ADAMS software for the target sports utility vehicle with front McPherson and rear unequal length double arm suspension systems. The ride comfort of the target vehicle was analyzed by the developed simulation model. The stiffness and damping of the suspensions were optimized by the root mean square values of the vehicle weighted vertical acceleration and the pitching angle acceleration with the help of the multi-disciplinary and multi-objective optimization software, ISIGHT. The results show that, the proposed multi-objective optimization algorithm is helpful to achieve the ride comfort improvement and the computation time reduction.