This paper aims to study the time-varying configuration energy management strategy for hybrid distributed drive heavy vehicles based on real vehicle test data. Firstly， based on the real vehicle test data and by using the dynamic programming algorithm， the chassis configuration with optimal energy consumption is found， and the long short-term memory （LSTM） neural network is trained to complete the configuration optimization in three typical scenes. Then， the RULE_LSTM algorithm is proposed based on rule judgment. Its matching accuracy is 11.76% higher than that using LSTM network configuration with optimal energy consumption， and its frequency of configuration switching is reduced by 33.3%. Next， based on traffic flow the prediction on long-scale operating condition information is completed and the optimal chassis configuration matching and reference SOC trajectory generation are fulfilled. Based on radial basis function neural network， a short-scale operating condition prediction sequence is generated as the input of the subsequent algorithm. Finally， time-varying configuration is adopted to optimize the control variables， meanwhile the strong speed change rate constraint and SOC reference trajectory guidance are also introduced to implement the guided multi-APU predictive energy management strategy. The results show that with above-mentioned measures taken， the fuel consumption is reduced by 10.60%， 3.95%， and 2.06% respectively.
For the power split hybrid electric vehicle integrated with multi-clutch， the transient mode switching behavior and optimal dynamic coordinated control strategy from pure electric mode to hybrid driving mode involving the state collaboration of two clutches are studied. Based on the lever analogy and the matrix method， the dynamic models of the system at different switching stages are established. The feasibility analysis of the dual-clutch working sequence is conducted according to the engine start-stop control and transient mode switching requirements， and the mode switching logic is further formulated. On this basis， for the mode switching quality degradation caused by the collaboration slipping of the two clutches， the weighted sum of the vehicle longitudinal jerk， clutch friction work and mode switching time is taken as the optimization objective， and the simulated annealing algorithm is applied to optimize the slipping behavior of different clutch engagement and disengagement processes. In order to address the problem that the fixed engine speed adjustment strategy is difficult to adapt to the requirements of different acceleration conditions， the adaptive engine speed adjustment strategy in hybrid driving mode is constructed to realize adaptive torque regulation of motor MG1 according to the driving torque requirements under different conditions. Simulation and hardware-in-the-loop （HIL） test results show that the proposed optimal dynamic coordinated control can not only reduce the mode switching jerk of the power split HEV effectively for the dual clutch collaboration process， but also enables good working condition adaptability， which can ensure good transient mode switching quality under different acceleration conditions.
In order to study the dynamic characteristics of electric drive system of pure electric vehicle， considering spatio-temporal electromagnetic excitation and system structure flexibility， an electromechanical coupling dynamic model of integrated electric drive system suitable for variable speed conditions is proposed and verified by simulation. By means of dynamic analysis， the effect of motor torque fluctuation， gear error and housing flexibility on the dynamic characteristics of electric drive system is studied under steady-state and acceleration conditions. The study shows that the influence of motor torque fluctuation on the bearing force is not obvious under the steady-state condition， the gear error will significantly increase the amplitude of gear dynamic meshing torque and bearing reaction force， and the influence of housing flexibility on gear dynamic meshing torque is slight. Under the acceleration condition， the gear error is easy to stimulate the system high-frequency resonance， and the low-order resonance related to rotating frequency is also easy to be excited after coupling the housing.
The over-modulation method used in the drive motor controller of electric vehicle can increase the maximum output power of motor drive system， and enhance the capabilities of torque output and speed regulation in high-speed region. However， the traditional over-modulation method has the problem of voltage mutation in the over-modulation zone II， leading to a large jitter of the output torque of motor drive system and affecting the power and NVH performances of the vehicle. In view of this problem， a superposition-based variable-weight over-modulation method is proposed in this paper. The method eliminates the voltage mutation and hence reduces the total harmonic distortion by introducing the phase angle of the reference voltage into the calculation of the superposed weighting factor. The results of simulation and test show that this method can enhance the current stability in drive motor control， reduce the output torque jitter of the drive motor system， and enable the motor drive system of electric vehicle to output the maximum torque and maximum speed in the high-speed region.
Shift schedule is a key factor affecting the economy and power performance of vehicle.For the independently developed two gear dry dual clutch transmission （2DCT） of pure electric vehicle， taking the shifting speed under different pedal opening as the optimization variable， the Pareto front of the shifting speed is obtained by NSGA-Ⅱ algorithm，and the shift schedule considering both economy and power performance of pedal opening and speed is obtained by linear weighting method. A three-parameter fuzzy controller is constructed by introducing in acceleration to dynamically adjust the shifting speed， and the comprehensive shifting schedule is obtained. The results show that the comprehensive shift schedule can take into account the economy and power performance of the vehicle simultaneously， and reduce the shift frequency greatly compared with the economic shift schedule.
Based on a large number of real vehicle data in China， this paper analyzes the impact of energy structure changes， battery capacity degradation， operating conditions， temperature， purpose， battery charging efficiency， vehicle transmission efficiency and other factors on carbon emission reduction of electric vehicles （EVs）， and establishes a carbon emission model of EV driving stage. According to the national and provincial dimensions， this paper analyzes the reasons for the regional differences in carbon emission reduction of EVs， and studies the emission reduction benefits of different types of EVs. Sensitivity analysis is carried out on the influence of electricity carbon emission factor， climate and temperature on carbon emission reduction of EVs. Finally， based on the analysis results， suggestions are put forward for the low-carbon development path in the field of road transportation.
To optimize the fuel economy and traction battery performance of series hybrid electric tracked vehicle （SHETV）， an energy management strategy （EMS） based on twin delayed deep deterministic policy gradient with prioritized experience replay （TD3-PER） is proposed. The TD3 algorithm can achieve more precise continuous control and prevent training from falling into over-assessment. The PER algorithm can accelerate strategy training and obtain higher optimization performance. Based on the model of the SHETV including longitudinal and lateral dynamics， the framework construction and simulation verification of EMS based on TD3-PER is completed. The results show that compared with deep deterministic policy gradient algorithm， the strategy proposed reduces the fuel consumption of SHETV by 3.89%， making its fuel economy reaching 95.05% of DP algorithm as a benchmark， with a better battery SOC retention ability and working condition adaptability.
Torque distribution optimization is carried out for an electric vehicle equipped with a novel dual-motor multi-mode drive system in this paper. According to the features of the dual-motor multi-mode drive system， a vehicle model is established， with the working range of different modes divided. On the premise of meeting the requirements of power performance， the system efficiency oriented strategies for torque distribution and mode switching based on particle swarm optimization algorithm are formulated， and the offline and online methods are combined to enhance the real-time response speed of the system. The simulation model is established in Matlab / Simulink with a simulation conducted and a hardware-in-the-loop test is carried out for verification. The results show that the average efficiency of the system is 3% higher than that with the traditional mode switching strategy， and the energy consumption is 11.28% lower than that with the torque distribution strategy based on genetic algorithm.
To ensure that the rotor core of automotive permanent magnet synchronous motor （PMSM） has the ability to withstand large peeling forces， while avoiding destructive peeling test， two stacking processes， i.e. stacked riveting and adhesive process are modeled based on the principle of force-energy equivalence and the bilinear cohesion model introduced， with the peeling forces of rotor core obtained through simulation in this paper. Firstly， a peeling test platform for motor rotor core is constructed to compare the maximum peeling force of two stacking processes by test. Then the no-load radial magnetic density of PMSM under two processes is analyzed by simulation with ANASY Maxwell. The results show that the adhesive process adopted can greatly enhance the peel strength of the iron core， and meanwhile increase the operation efficiency of motor and the closeness of air-gap magnetic field to sine function， and reduce the operating current and losses of motor.
In order to improve the shifting performance when the parameters of the automatic transmission shifting actuator are time-varying， a hierarchical state estimation and parameter identification method based on the nonlinear H∞ algorithm is proposed， considering both the highly nonlinear system model and the unknown system noise characteristics. Firstly， the problem of time-varying parameters of the shifting actuator is found through experiments， and a nonlinear model is established for the shifting actuator. Then， the hierarchical state estimation layer and parameter identification layer are designed， both of which are based on the nonlinear H∞ algorithm. The upper estimation layer estimates the state of the actuator and transfers the result to the lower identification layer； the lower identification layer applies the state results as the measurement， and uses the system model as the measurement equation to identify the system parameters. Then， the cooperative operation of the two layers estimate the state of the shifting actuator， and identifies the structural and electrical performance parameters. Finally， a state estimation and parameter identification process based on automatic calibration is designed， which realizes the parameter identification of the shift actuator while calibrating and correcting the shift position value. The test results show that the hierarchical state estimation and parameter identification method proposed in this paper can accurately estimate and identify the state and parameters of the shifting actuator. After modifying the parameters， the shifting performance of the system is improved.
Ultra-high-speed permanent magnet motor （UhsPM） has the advantages of small size， high efficiency and high power density， and is widely used in automotive application fields such as fuel cell air compressors and electrically assisted turbochargers. The features of small inductance and high fundamental frequency make its drive control more difficult than that of normal-speed permanent magnet motor. In this paper， the research status quo of automotive UhsPM drive control technology in terms of circuit topology， voltage modulation strategy matching and position-sensor-less control is elaborated， the research highlights of various technologies are summarized， and the evaluations by advantage and disadvantage comparison are given. Finally， the future development trends are forecasted.
Aiming at the handling and stability problems of distributed electric vehicles under normal and faulty driving motor conditions， a driving force distribution control method combining front wheel steering and driving force reconstruction is proposed. Firstly， a sliding mode weighted controller is designed based on yaw rate and the side slip angle of mass center to calculate the additional yaw moment required； and the optimal distribution model of driving force for the normal and faulty conditions of drive motor is established respectively， in which， the yaw moment is compensated by coordinating the steering of front wheels for limiting the output capacity of drive motor in faulty conditions. Then the optimal driving force distribution value is solved out based on quadratic programming theory. Finally， a Carsim/Simulink joint simulation is conducted to verify the effectiveness of the coordinated control method proposed. The results show that this method can make full use of the redundant characteristics of distributed drives to ensure the distributed electric vehicles can meet the requirements of handling stability under normal and faulty driving motor conditions.
In view of the problem of insufficient flexibly regulating resources the new-type of electricity system faced due to high proportional penetration of renewable energy， a transportation-energy-electricity integrated technology scheme with the new energy vehicle as its core is proposed， coupling energy storage， hydrogen energy and intelligence， with the corresponding technical feasibility， development map and policy suggestion given. The results of predictive calculation show that the interaction between onboard traction battery and electric grid is a distributed short-period energy storage way with high safety， low cost and large scale. In the year of 2040， there will be some 300 million electric vehicles carrying 20 TW·h batteries， in which the flexible adjustable capacity exceeds 10 TW·h， being able to meet the requirements of short-period peak-valley adjustment. The hydrogen energy multi-utilization promoted by hydrogen energy traffic is a long-term concentrated ideal way of energy transformation， the combination of both can meet the daily and seasonal peak adjustment requirements of electricity in 2040 in China， providing forceful supports for achieving double carbon targets.
For the problem that the design of the fuzzy energy management strategy is difficult to adapt to complex driving cycles owing to only dependent on expert experience， a fuzzy energy management strategy is proposed for extended-range electric vehicles with working condition identification based on neural network. Firstly， the working condition identification model is designed based on the data of Chinese high truck driving cycle （CHTC-HT） with back propagation neural network optimized by improved genetic algorithm. Subsequently， combining the identified working condition with battery state of charge and vehicle power demand， an adaptive fuzzy energy management strategy is developed to implement optimized energy distribution by real time acquisition of engine output power. Finally， the proposed method has been verified by hardware in the loop test. The results show that the proposed adaptive fuzzy energy management strategy can reduce fuel consumption by 9.67% compared with CD-CS strategy and by 7.84% compared with fuzzy energy management strategy， which effectively improves the fuel economy for extended-range electric vehicles.
In view of the challenges to the economy and stability of micro-grid’s operation brought about by the uncertainty of renewable energy electricity generation and electric vehicles’ batteries connected to the grid， the economic operation model of micro-grid is constructed in this paper， on the basis of setting different charging scenes with consideration of the uncertainty of photovoltaic electricity generation and taxi charging. A simulation is conducted by adopting the hybrid particle swarm optimization algorithm to solve out the optimum power output of each distributed power source and the lowest operation cost of system in different charging scenes， with minimizing the total cost of electricity generation in micro-grid as objective， and the power limits of each distributed power source and power balance as constraints. The results of optimization show that comprehensively optimizing the charging behavior can enhance the dispatchability of the isolated micro-grid， reducing the operation cost of the micro-grid by 21.2%.
In order to improve the dc bus quality of the high voltage system of electric vehicles， the load restriction requirements of the driving system are put forward based on the principle of stability analysis. Based on the impedance analysis method， the source/load side impedance model is established. According to the Middlebrook stability criterion， the stability conditions in the full frequency domain are deduced， and the power restriction for the load connected to the DC bus is clearly defined. Moreover， the system stability improvement by the source/load side impedance matching method is studied， which provides a theoretical basis for system parameters design. Finally， the analysis conclusion is verified by experiments combined with a specific electric vehicle.
It is increasingly important to study the collision safety of the battery system with the development and popularization of new energy vehicles. As a key part of the high voltage electrical system of new energy vehicle， it is particularly important to study its electrical safety performance under crash condition of the high voltage harness. Based on the typical extrusion load that the HV-cable of the battery system may suffer under the crash conditions， the harness with a diameter of 15.8 mm is selected to design the dynamic and static compression tests under two working conditions of the D5 cylindrical surface and V60 wedge surface. During the test， real-time monitoring of short circuit between the punch and the internal conductor of the harness is conducted， and the tensile and compression tests are carried out on the three component materials of sheath， insulation layer and conductor to calibrate the corresponding material model. Through the harness structure test and component material test， the one component homogenization model of HV-cable and the two-component model of conductor-equivalent insulation layer are calibrated respectively. The results show that there is a strong dynamic effect of HV-cable under mechanical load， and the dynamic mechanical response increases. The short circuit behavior of HV-cable is highly related to the external loading condition and loading speed. The simulation results of the two HV-cable simulation models using element deletion to predict the cable short circuit are basically consistent with the test results， and the two models can accurately predict the short circuit risk under extrusion condition.
In order to reduce the energy consumption and extend the driving range of battery electric vehicles， the cause of the poor efficiency of low-voltage electric system is systematically investigated for improving its energy management strategy. Firstly， the efficiency model of the low-voltage system， adapted to longtime fast operation is built by using forward simulation method，in which， the efficiency model of DC/DC converter consists of a first-order inertia loop and an efficiency-interpolation function， while the system control model covers two strategies for floating control and rule control. Then， test bench is constructed to measure the performances of the components and system with model parameters extracted. Finally， both simulation and test are conducted to study the influences of load power， converter efficiency， ambient temperature， battery types， and control strategies on system efficiency. The results show that the system efficiency will fall with the reduction of mean power， the reduction of converter efficiency and the increase of battery inner resistance， and will rise with the shortening of idle period， the improvement of control strategies and the change of battery types， and in light load condition， using rule control can enhance the efficiency of low-voltage electric system by 10%.
The integration， high-frequency， and high-efficiency for SiC MOSFET devices set a higher requirement on the packaging form and processes of power module. In this paper， the structural optimization and technological innovations of packaging forms in recent years are summarized， including the influence of the length， width， and number of metal bonding wires of bonded power modules on parasitic inductance， and the effects of the area and height of the ceramic layer in direct bonded copper ceramic substrates on parasitic capacitance， and the achievements in parasitic parameter optimization by using stacked commutation technology. The influences of the thickness and shape of the buffer layer of the double-sided heat dissipation structure on the heat dissipation indicator， stress and deformation are reviewed. The failure mechanism and solving measures of power modules are summed up， providing references for the safe operation of the module. Finally， the requirements and key issues of advanced silver sintering technologies are discussed， with the development direction of sintering packaging technologies and materials forecasted.
In order to ensure the accuracy of road load simulation and realize the hardware-in-the-loop test of electric drive system， a dynamic compensation algorithm of bench test is proposed in this paper. Firstly， based on the analysis on load simulation problem， the test bench inverse model algorithm is adopted. Then， for improving torque dynamic response， Kalman filter is adopted to achieve accurate compensation of model errors. Finally， on the basis of the analysis on bench accuracy， accuracy coupling compensation algorithm is put forward and verified by bench test. The results show that compensation algorithm can well achieve speed following in cycle conditions and enhance the accuracy of bench test in starting condition.
