A low temperature environment will lead to the decrease of chemistry reaction rate and increase of the internal resistance of the lithium battery. In addition, the excessive charging current will cause the lithium to separate out and even the permanent attenuation of battery capacity. In order to solve these problems, this paper proposes a low-temperature charging heating combined control strategy, which takes the temperature acceptable charging current of the battery at low temperature as the charging current constraint and the maximum output power of the system as the power constraint. Firstly, a scheme of combined charging and heating control system is put forward. Secondly, the low temperature charging control strategy based on adaptive fuzzy control is established and then the model is simulated and analyzed in MATLAB software. At last, a Chroma 72,001 charge and discharge tester is used to conduct a low temperature test on 18,650 lithium iron phosphate battery monomers. The results show that the low-temperature charging control strategy proposed in this paper has a more stable temperature control effect on the battery, the constant current charging time of the battery is reduced by 14% compared with the traditional threshold control method, and the overall charging energy consumption is reduced by 5.6%.

With the popularity of electric vehicles (EV), the charging technology has become one of the bottleneck problems that limit the large-scale deployment of EVs. In this paper, a charging method using multi-stage constant current based on SOC (MCCS) is proposed, and then the charging time, charging capacity and temperature increase of the battery are optimized by multi-objective particle swarm optimization (MOPSO) algorithm. The influence of the number of charging stages, the cut-off voltage, the combination of different target weight factors and the ambient temperature on the charging strategy is further compared and discussed. Finally, according to the ambient temperature and users’ requirements of charging time, a charging strategy suitable for the specific situation is obtained by adjusting the weight factors, and the results are analyzed and justified on the basis of the experiments. The results show that the proposed strategy can intelligently make more reasonable adjustments according to the ambient temperature on the basis of meeting the charging demands of users.

ABSRACT An autonomous emergency braking (AEB) algorithm for snow-asphalt joint pavement is proposed. Based on machine vision and kinetic analysis, we realize the identification of road information, including the road type, slope, and road section lengths. A new safety model, the reference velocity model, is proposed to solve the problem of determining the braking time on the joint pavement to achieve collision avoidance. In the asphalt section, we design the desired deceleration considering the comfort and safety, while in the snow section, we use the estimated maximum deceleration that the pavement can provide. To meet the desired deceleration requirement, we choose a single-neuron proportion integration differentiation (PID) controller with a Kalman filter. The joint simulation with CarSim and Simulink shows that the host vehicle successfully realizes collision avoidance in various working conditions and verifies the proposed AEB algorithm. Benefitting by the recognition of the forefront road conditions, our proposed model performs better than the traditional AEB model.

The development of connected and automated vehicles (CAV) technology not only helps to reduce traffic accidents and improve traffic efficiency, but also has significant potential for energy saving and emission reduction. Using the dynamic traffic flow information around the vehicle to optimize the vehicle trajectory is conducive to improving the energy efficiency of the vehicle. Therefore, an energy-efficient driving method for CAVs based on reinforcement learning is proposed in this paper. Firstly, a set of vehicle trajectory prediction models based on long and short-term memory (LSTM) neural networks are developed, which integrate driving intention prediction and lane change time prediction to improve the prediction accuracy of surrounding vehicle trajectories. Secondly, an energy-efficient driving model is built based on Proximity Policy Optimization (PPO) reinforcement learning. The model takes the current states and predicted trajectories of surrounding vehicles as input information, and outputs energy-saving control variables while taking into account various constraints, such as safety, comfort, and travel efficiency. Finally, the method is tested by simulation on the NGSIM dataset, and the results show that the proposed method can save energy consumption by 9–22%.

Power distribution and battery thermal management are important technologies for improving the energy efficiency of plug-in hybrid electric vehicles (PHEVs). In response to the global optimization of integrated energy thermal management strategy (IETMS) for PHEVs, a dynamic programming algorithm based on adaptive grid optimization (AGO–DP) is proposed in this paper to improve optimization performance by reducing the optimization range of SOC and battery temperature, and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The simulation results indicate that through AGO–DP optimization, the reduction ratio of the state feasible region is more than 30% under different driving conditions. Meanwhile, the algorithm can obtain better global optimal driving costs more rapidly and accurately than traditional dynamic programming algorithms (DP). The computation time is reduced by 33.29–84.67%, and the accuracy of the global optimal solution is improved by 0.94–16.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO–DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68–23.74% under various driving conditions, which increases the energy-saving potential by 0.55–3.26% compared to just doing the energy management.

Durability is the key issue for the proton exchange membrane fuel cell application and its commercialization. Current research usually uses the accelerated stress test to decrease the experiment time, whereas the performance evolution—especially the internal state evolution—under real use may be different from that under the accelerated stress test. In addition, studies rarely report this kind of durability in real decay scenarios. This paper investigates the seldom-reported impact of dry–wet cycles on durability in terms of open circuit voltage (OCV), inner resistance, and hydrogen crossover current at the condition of 20,000 cycles or the equivalent 400 h, while simultaneously running the test for the same time interval in the control experiment. The mechanical and chemical test is independent. Frequent dry–wet cycles make the OCV decay over 14% compared to 6.9% under the normal decay. Meanwhile, the dry–wet cycle helps to alleviate deterioration in terms of the inner resistance decline (61% vs. 37%) and in terms of the hydrogen crossover current increase (−64% vs. 15%). The inner state evolution is irregular and against common sense. The relationship between the crack, platinum transfer, and the moisture which heals the crack is the potential reason for the above-mentioned phenomena. These findings are beneficial to navigating fuel cell storage.