•The international trend for PEV technologies and industry are reviewed.•A triple-perspective review method is presented to evaluate Chinese PEVs industry.•The accurate results are presented by ...feasible detailed classification method for PEV.•Industrialization evolution rules are obtained by massive data for PEVs.•The industrialization and policy impact of five types of PEV are clarified.
Recently, China has been facing energy security and urban air pollution challenges. The development of new energy vehicles (NEVs) is considered an optimal technological route for solving such problems. By the end of 2015, China had become world’s largest plug-in electric vehicle (PEV) market; however, the core technologies associated with PEVs remain less competitive in the world marketplace. Thus, determining the global trend and national development laws is very important for the Chinese government to draft long-term technological strategies and lead the NEV industry. In this study, the international technological trend is analyzed and industrialization progresses of top global countries are compared. NEV development is reviewed through a detailed classification and a triple-perspective method to determine the industrialization rules. The review indicates the following. (i) China’s NEV market penetration, particularly for commercial electric vehicles, is dominated by state policies. The subsidy policy has a significant influence on powertrain options; therefore, the current incentive polices should be optimized. (ii) The range-extended-type plug-in hybrid electric cars have been verified as the optimal roadmap, and plug-in hybrid electric sports utility vehicles hold great promise in the future Chinese market. (iii) Micro-electric cars dominate the electric car market and are expected to be commercialized first when the government subsidy phases out. (iv) The industry has grown rapidly and the charging infrastructure construction can keep up with the progress of PEV market penetration. The post-EV market (such as battery and vehicle recycling) must be considered in advance.
To promote the market penetration of electric vehicles (EV), China launched the Electric Vehicle Subsidy Scheme (EVSS) in Jan 2009, followed by an update in Sep 2013, which we named phase I and phase ...II EVSS, respectively. In this paper, we presented the rationale of China’s two-phase EVSS and estimated their impacts on EV market penetration, with a focus on the ownership cost analysis of battery electric passenger vehicles (BEPV). Based on the ownership cost comparison of five defining BEPV models and their counterpart conventional passenger vehicle (CPV) models, we concluded that in the short term, especially before 2015, China’s EVSS is very necessary for BEPVs to be cost competitive compared with CPVs. The transition from phase I to phase II EVSS will generally reduce subsidy intensity, thus resulting in temporary rise of BEPV ownership cost. However, with the decrease of BEPV manufacturing cost, the ownership cost of BEPV is projected to decrease despite of the phase-out mechanism under phase II EVSS. In the mid term of around 2015–2020, BEPV could become less or not reliant on subsidy to maintain cost competitiveness. However, given the performance disadvantages of BEPV, especially the limited electric range, China’s current EVSS is not sufficient for the BEPV market to take off. Technology improvement associated with battery cost reduction has to play an essential role in starting up China’s BEPV market.
•China’s phase I and phase II electric vehicle subsidy schemes were reviewed.•Major electric vehicle models in China’s vehicle market were reviewed.•The ownership costs of five defining electric passenger vehicle models were compared.•Policies to promote electric vehicle deployment in China were discussed.
In view of the influence of different cell state parameters on the estimation of power battery packs’ state of charge (SOC), based on the travel data of electric vehicles in Beijing, random forest is ...used to reduce dimensionality, and the aging and thermoelectric characteristic parameters which have high correlation are selected as the input features of long short-term memory (LSTM). Then, using grid search to optimize the LSTM structure. Finally construct a data-driven method for high robustness prediction of battery SOC. The results show that the maximum absolute error (MaxAE) of the proposed SOC prediction method is only 1.539% under different temperatures, battery aging degrees and operating conditions. Compared with the two SOC prediction methods of gate recurrent unit (GRU) and recurrent neural network (RNN) , the MaxAE is reduced by 71.8% and 26.1% respectively. The research results provide a method basis for improving the robustness of power battery SOC estimation.
Reference electrodes (REs) implanted in lithium-ion batteries are essential indicators in the fields of health monitoring and safety management. The non-destructive charging profiles, for example, ...are usually determined by electrode potential measurements performed with the RE. However, errors in RE potential measurements, resulting in seriously flawed conclusions, are seldom discussed in real lithium-ion batteries. This study investigates the reliability of anode potentials measured with a Li/Cu RE implanted in commercial cells. Artefacts are advanced in RE measurements based on the inconsistency of measured anode potentials and lithium plating behaviors, and further validated by the excess anode overpotential while charging to high state of charge at high rates. Furthermore, artefact phenomenon is reflected in the electrochemical model highlighting the RE blocking of the Li-ion flow. Inhomogeneous lithium intercalation currents at the anode-separator interface are revealed to bring out the excess anode overpotential in RE measurements. Finally, the impact of critical parameters on potential artefacts is examined, and proper RE sizes and battery operating conditions are proposed to ensure the reliability of potential measurements. This work emphasizes the existence of artefacts in RE potential measurements, and provides a useful guide on eliminating errors and improving accuracy of RE in real lithium-ion batteries.
•Errors in measuring individual electrode potentials with reference electrode (RE).•RE blocks the transport of Li-ions and leads to non-uniform potential distributions.•Anode overpotential is over-estimated by RE during the charging of Li-ion cells.•Decreasing the RE width is beneficial to the accuracy of the measured potential.
With the development of electric vehicles in recent years, lithium-ion batteries have been widely used. Accurate state of charge (SOC) estimation plays an important role in the safety of electric ...vehicles. Since the temperature has the significant influence on charge and discharge performance of the battery, it is critical to achieve accurate SOC estimation over the wide temperature range. In this paper, a polymer ternary lithium-ion battery is focused, and a Thevenin equivalent circuit model with temperature compensation is established. The validity of the established battery model was verified by the dynamic stress test. On this basis, the ternary lithium-ion battery SOC was estimated using the unscented Kalman filter (UKF). The New European Driving Cycle is used to verify the effectiveness of the proposed algorithm. The simulation and experimental results show that the established Thevenin equivalent circuit model with temperature compensation can accurately represent the battery dynamics. Based on this model, the SOC was estimated using the UKF and the maximum errors are within 3%. Therefore, the proposed SOC estimation method is verified to be effective and robust.
Lithium battery for electric vehicle exhibits poor performance in durability and discharging efficiency under cold environment, therefore the traction battery must be heated to some suitable ...operation temperature before charging process begins. Self-heating by discharging current of the battery is recognized as a high-efficient and cost-effective method. However, the discharging current affects both the capacity degradation rate and heating time for lithium-ion battery greatly charged at low temperatures. Therefore, the discharging strategy should be optimized based on the parameters of the battery capacity fade rate and heating time, and it's the motivation of this research. The parameters of the Thevenin equivalent circuit model are set up and the temperature-rise model is identified by test data. To determine the optimal battery discharging current for heating, the dynamic programming algorithm is adopted The capacity fade rate and heating time is analyzed by setting different weighting factors in the heating process of battery temperatures rising from −10°C to +5°C. Compared with constant current discharging method, the multi-objective optimization self-heating method can decrease the battery capacity fade by 5.65%, the heating time is by 1.82%, and the power consumption by 3.04%. Therefore, it can be concluded that the proposed multi-objective optimization can optimize heating time and energy consumption at same time, with minimize capacity degradation.
Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods ...suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data.
Direct current to direct current (DC/DC) converters are required to have higher voltage gains in some applications for electric vehicles, high-voltage level charging systems and fuel cell electric ...vehicles. Therefore, it is greatly important to carry out research on high voltage gain DC/DC converters. To improve the efficiency of high voltage gain DC/DC converters and solve the problems of output voltage ripple and robustness, this paper proposes a double-boost DC/DC converter. Based on the small-signal model of the proposed converter, a double closed-loop controller with voltage–current feedback and input voltage feedforward is designed. The experimental results show that the maximum efficiency of the proposed converter exceeds 95%, and the output voltage ripple factor is 0.01. Compared with the traditional boost converter and multi-phase interleaved DC/DC converter, the proposed topology has certain advantages in terms of voltage gain, device stress, number of devices, and application of control algorithms.
All-wheel drive is an important technical direction for the future development of pure electric vehicles. The difference in the efficiency distribution of the shaft motor caused by the optimal load ...matching and motor manufacturing process, the traditional torque average distribution strategy is not applicable to the torque distribution of the all-wheel drive power system. Aiming at the above problems, this paper takes the energy efficiency of power system as the optimization goal, proposes a dynamic allocation method to realize the torque distribution of electric vehicle all-wheel drive power system, and analyzes and verifies the adaptability of this optimization algorithm in different urban passenger vehicle working cycles. The simulation results show that, compared with the torque average distribution method, the proposed method can effectively solve the problem that the difference of the efficiency distribution of the two shaft motors in the power system affects the energy consumption of the power system. The energy consumption rate of the proposed method is reduced by 5.96% and 5.69%, respectively, compared with the average distribution method under the China urban passenger driving cycle and the Harbin urban passenger driving cycle.
PDF
