This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. In low-temperature environments, EVs suffer a sharp driving range loss resulting from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as the temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating the battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO₄ battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then, given a certain ambient temperature, a target preheating temperature is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with decreasing ambient temperature, however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save on total vehicle operation costs.

Charging performance affects the commercial application of electric vehicles (EVs) significantly. This paper presents an optimal charging strategy for Li-ion batteries based on the voltage-based multistage constant current (VMCC) charging strategy. In order to satisfy the different charging demands of the EV users for charging time, charged capacity and energy loss, the multi-objective particle swarm optimization (MOPSO) algorithm is employed and the influences of charging stage number, charging cut-off voltage and weight factors of different charging goals are analyzed. Comparison experiments of the proposed charging strategy and the traditional normal and fast charging strategies are carried out. The experimental results demonstrate that the traditional normal and fast charging strategies can only satisfy a small range of EV users’ charging demand well while the proposed charging strategy can satisfy the whole range of the charging demand well. The relative increase in charging performance of the proposed charging strategy can reach more than 80% when compared to the normal and fast charging dependently.

Both the battery/supercapacitor (SC) and SC/battery are two common semi-active configurations of hybrid energy storage systems (HESSs) in hybrid electric vehicles, which can take advantage of the battery’s and supercapacitor’s respective characteristics, including the energy ability, power ability and the long lifetime. To explore in depth the characteristics and applicability of the two kinds of HESS, an analysis and comparison study is proposed in this paper. Based on the data collected from public transit hybrid electric bus (PTHEB) with battery-only on-board energy storage, the range and distribution probability of electric power/energy demand is comprehensively statistically analyzed with the decomposing and normalizing methods. Accordingly, the performance of each topology under different parameter matching conditions but same mass, volume and cost values with battery-only energy storage, are presented and compared quantitatively. The results show that both HESS configurations can meet the electric power demand of the hybrid electric vehicle (HEV) through reasonable design. In particular, the SC/battery can make better use of the SC features resulting in high efficiency and long life cycles compared with the battery/SC. Equally, it proves that the SC/battery topology is a better choice for the HEV. Finally, an experimental validation of a real HEV is carried out, which indicated that a 7% fuel economy improvement can be achieved by a SC/battery system compared with battery-only topology.

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%.

Range anxiety and battery cycle life are two major factors which restrict the development of electric vehicles. Battery degradation can be reduced by adding supercapacitors to create a Hybrid Energy Storage System. This paper proposes a systematic approach to configure the hybrid energy storage system and quantifies the battery degradation for electric vehicles when using supercapacitors. A continuous power-energy function is proposed to establish supercapacitor size based on national household travel survey statistics. By analyzing continuous driving action in standard driving cycles and special driving phases (start up and acceleration), the supercapacitor size is calculated to provide a compromise between the capacitor size and battery degradation. Estimating the battery degradation after 10 years, the battery capacity loss value decreases 17.55% and 21.6%, respectively, under the urban dynamometer driving schedule and the US06. Furthermore, the battery lifespan of the continuous power-energy configured system is prolonged 28.62% and 31.39%, respectively, compared with the battery alone system.

This paper proposes a long short-term memory (LSTM) network to predict the power degradation of proton exchange membrane fuel cells (PEMFCs), and in order to promote the performance of the LSTM network, the ant colony algorithm (ACO) is introduced to optimize the hyperparameters of the LSTM network. First, the degradation mechanism of PEMFCs is analyzed. Second, the ACO algorithm is used to set the learning rate and dropout probability of the LSTM network combined with partial aging data, which can show the characteristics of the dataset. After that, the aging prediction model is built by using the LSTM and ACO (ACO-LSTM) method. Moreover, the convergence of the method is verified with previous studies. Finally, the fuel cell aging data provided by the Xiangyang Da’an Automotive Testing Center are used for verification. The results show that, compared with the traditional LSTM network, ACO-LSTM can predict the aging process of PEMFCs more accurately, and its prediction accuracy is improved by about 35%, especially when the training data are less. At the same time, the performance of the model trained by ACO-LSTM is also excellent under other operating conditions of the same fuel cell, and it has strong versatility.