Battery safety is one of the most crucial issues in the utilization of lithium-ion batteries (LiBs) for all-climate electric vehicles (AEVs). Short circuit, overcharge, overheat are three common field failures of LiBs. In this study, online fault diagnosis for external short circuit (ESC) of LiB packs is investigated. The experiments are carried out to obtain and compare ESC characteristics of 18650-type NMC battery pack and single cell. Based on the analysis of experimental results, a two-step equivalent circuit model is established to describe ESC process and an online model-based scheme is proposed to diagnose ESC faults of battery packs. The proposed scheme is evaluated by experimental data. The results show that it can effectively diagnose ESC faults in 3.5s after their occurrences with the terminal voltage error less than 25mV. The proposed scheme has shown great generalization ability. ESC faults of battery packs under different number of cells connected in series and unavailable current information can also be diagnosed at the terminal voltage error less than 48mV and 60mV, respectively.

Plug-in hybrid electric vehicles (PHEVs) have been recognized as one of the most promising vehicle categories nowadays due to their low fuel consumption and reduced emissions. Energy management is critical for improving the performance of PHEVs. This paper proposes an energy management approach based on a particle swarm optimization (PSO) algorithm. The optimization objective is to minimize total energy cost (summation of oil and electricity) from vehicle utilization. A main drawback of optimal strategies is that they can hardly be used in real-time control. In order to solve this problem, a rule-based strategy containing three operation modes is proposed first, and then the PSO algorithm is implemented on four threshold values in the presented rule-based strategy. The proposed strategy has been verified by the US06 driving cycle under the MATLAB/Simulink software environment. Two different driving cycles are adopted to evaluate the generalization ability of the proposed strategy. Simulation results indicate that the proposed PSO-based energy management method can achieve better energy efficiency compared with traditional blended strategies. Online control performance of the proposed approach has been demonstrated through a driver-in-the-loop real-time experiment.

online estimation of the state of power (SoP) of lithium-ion batteries (LiBs) is crucial for both battery management system and energy management system in electric vehicles. In this study, the approach of online estimating the SoP is investigated with a concern of the impact of the imprecise state of charge (SoC). Firstly, the characteristics of lithium batteries under different state of health (SoH) conditions are experimented based on a typical vehicle driving cycle; then the SOP estimation algorithm using genetic algorithm (GA) is proposed to deal with the long time-scale estimation for power management application, on top of that, the sensitivity coefficient (δ) of the SoP estimation to the SoC precision is analyzed and the correlations of δ with the varying SoH, estimation time-scale are established. Finally, the presented algorithm is evaluated by a simulation study. The proposed GA-based estimation method can improve the SoP estimation accuracy by up to 7.2% in certain cases compared to the traditional Taylor method.

The regenerative braking control strategy of distributed drive electric vehicles (DDEVs) under the varying road slope is investigated in this study. Firstly, vehicle dynamic characteristics at the downhill driving condition are analyzed based on a vehicle dynamics model, and the specific impacts of the road slope on the braking control problem are disclosed. Since the estimate of the slope is related to the vehicle mass, an online co-estimation of the road slope and vehicle mass is proposed based on neural network and least square algorithm. The control lines are adjusted according to the estimation results, and the optimization of power allocation is conducted to achieve the optimal braking torque split among the front motor, rear motor, and hydraulic braking system. Finally, the control scheme of regenerative braking is proposed and evaluated by comparing with the Economic Commission of Europe (ECE)-based strategy and the I-curve strategy. The presented strategy provides better braking performance and higher energy recovery compared with that the traditional methods. The results indicate that energy recovery can be improved by up to 9.62% under certain driving conditions.

The implementation of each function of a battery management system (BMS) depends on sensor data. Efficient sensor fault diagnosis is essential to the durability and safety of battery systems. In this paper, a model-based sensor fault diagnosis scheme and fault-tolerant control strategy for a voltage sensor and a current sensor are proposed with recursive least-square (RLS) and unscented Kalman filter (UKF) algorithms. The fault diagnosis scheme uses an open-circuit voltage residual generator and a capacity residual generator to generate multiple residuals. In view of the different applicable state of charge (SOC) intervals of each residual, different residuals need to be selected according to the different SOC intervals to evaluate whether a sensor fault occurs during residual evaluation. The fault values of the voltage and current sensors are derived in detail based on the open-circuit voltage residual and the capacity residual, respectively, and applied to the fault-tolerant control of battery parameters and state estimations. The performance of the proposed approaches is demonstrated and evaluated by simulations with MATLAB and experimental studies with a commercial lithium-ion battery cell.