• Energy Research

Regenerative braking can extend the driving range and reduce PM emissions from abrasion for battery electric heavy-duty trucks (BETs). The composite braking control strategy including torque distribution and dynamic coordinated control for the four-axle BET equipped with the electromechanical braking system is studied. A segmented torque distribution strategy is proposed to maximize energy recovery while ensuring braking stability. The simulation results reveal that the strategy shows better comprehensive braking performance than the two benchmark strategies, and the energy recovery rate in different load states under CHTC-D is above 40%. The proposed coordinated control strategy takes advantage of regenerative braking’s rapid response and precise control to compensate for torque deviations caused by the hysteresis of friction braking. For two common braking mode transition conditions, regenerative braking torque correction and advance of the mode switching timing are adopted to enable the motor to obtain the torque compensation ability. This method leads to a slight loss of braking energy, and the maximum torque deviation during the mode switching process is suppressed to less than 1.4 kN·m, and the jerk and braking distance is reduced accordingly, which is of great importance in improving driving comfort and braking safety.

AbstractTo further improve the energy‐saving potential and robustness of the energy management strategy (EMS) for plug‐in hybrid electric vehicles (PHEVs) in a real‐time application, this paper proposes a modified model‐free‐adaptive‐control‐based (MFAC‐based) EMS to overcome the disadvantages in our previous MFAC‐based EMS. First, the influence of external disturbance on MFAC‐based EMS is discussed, and the results show that both the vehicle velocity and load change have a significant impact on its performance. Second, a modified MFAC‐based real‐time EMS is designed based on history driving data obtained from a repeated route in which a state‐of‐charge (SOC)‐constraint‐based reference SOC planning method is firstly proposed to simultaneously consider the vehicle velocity and changing load. Then, global SOC constraints are incorporated in Pontryagin's minimum principle (PMP) to enhance the adaptive capability of the proposed method. Finally, the optimal solution of PMP (i.e., optimal constant) is deployed as a benchmark, and the performance of the modified MFAC‐based EMS (namely MFAC‐II and MFAC‐III) is in contrast to the previous one (MFAC for short) under various real‐world driving cycles. The results demonstrate that the MFAC‐III has a remarkable improvement in both economic performance and robustness. Particularly, the energy‐saving effectiveness is close to the global optimum one in some driving conditions.

Great concerns have been raised on the driving cycle due to its critical importance in vehicle design, energy management strategy, and energy consumption forecast of new energy vehicles. Taking Xi'an city as a case, a novel method of driving cycle development for battery electric vehicles is proposed in this paper. First, the chase car method and on-board measurement method are combined to collect sufficient real driving data, which are randomly divided into two parts for developing and validating the target cycle. Then the nonlinear dimension reduction of characteristic parameters with respect to the micro-trips is achieved by employing kernel principal component analysis, and an improved clustering method is developed for constructing candidate cycles, in which the K-means clustering algorithm is applied in the training of random forest. The target cycle is selected from the candidate cycles by determining the assessment criteria with consideration of the characteristic parameters and the speed-acceleration distribution probability. Finally, a comparative study of different methods is implemented to illustrate the effectiveness of the proposed method. The typicality of the target cycle is revealed by analyzing the discrepancies between the target cycle and other legislative cycles.

Abstract This paper develops a systematic and practical construction methodology of a representative urban driving cycle for electric vehicles, taking Xi’an as a case study. The methodology tackles four major tasks: test route selection, vehicle operation data collection, data processing, and driving cycle construction. A qualitative and quantitative comprehensive analysis method is proposed based on a sampling survey and an analytic hierarchy process to design test routes. A hybrid method using a chase car and on-board measurement techniques is employed to collect data. For data processing, the principal component analysis algorithm is used to reduce the dimensions of motion characteristic parameters, and the K-means and support vector machine hybrid algorithm is used to classify the driving segments. The proposed driving cycle construction method is based on the Markov and Monte Carlo simulation method. In this study, relative error, performance value, and speed-acceleration probability distribution are used as decision criteria for selecting the most representative driving cycle. Finally, characteristic parameters, driving range, and energy consumption are compared under different driving cycles.

For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life. This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity. After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum. Then the battery model parameters are optimized by IALO algorithm. As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation. However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by “trial‐and‐error” method which is irregular and time‐consuming. In this paper, noise information is optimized by IALO algorithm. The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness. The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation. After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.

Vehicle mass and road grade information is important to improve the control capability and further intellectualization of vehicles. With the aim of real-time estimation of mass and grade without additional sensors, a two-step estimator is proposed in this paper. In the first-step estimator, the recursive least square with dual forgetting factors is used to estimate the vehicle mass with the consideration of the time-varying rolling friction coefficient and system error. In the second-step estimator, the road grade is estimated using an extended Kalman particle filter. Based on the data of CarSim/MATLAB co-simulation, the proposed approach has faster convergence rate and better tracking accuracy on the premise of meeting the real-time requirements by comparison with other estimation algorithms. The performance of the estimator is finally validated by the vehicle road test, and the results show that the mass and grade are estimated with great accuracy and robustness under different road conditions.

Accurate estimation of lithium battery state of charge is very important for ensuring the operation of battery management system, realizing the energy management strategy of electric vehicles, reducing mileage anxiety and promoting the sustainable development of electric vehicles. In this paper, several studies are carried out for state of charge estimation of lithium-ion battery: (1) Aiming at the problem of parameter identification of battery model, an optimal identification method of model parameters based on ant lion optimization algorithm is proposed. (2) An adaptive weighting Cubature particle filter (AWCPF) method is proposed for SOC estimation. The proposed AWCPF method is based on particle filter (PF) algorithm, while the Cubature Kalman filter (CKF) algorithm is utilized to generate the proposal distribution for PF algorithm, which can retrain the particles degradation problem in PF algorithm. To solve the problem that the CKF algorithm is sensitive to noise, comparing with fixed sigma point weights of the conventional CKF, the weights of sigma points are adaptively adjusted based on state and measurement residual vectors. Furthermore, the process noise and measurement noise are estimated iterative. In this paper, experimental verification of different initial values of SOC under various working conditions is carried out. The results show that the proposed AWCPF algorithm based SOC estimation method has high estimation accuracy, strong robustness, fast convergence speed, with the maximum SOC estimation error is less than 1%.

This paper proposes a novel driving cycle construction method in consideration of velocity, road slope, and passenger load, based on a real-world bus route with a plug-in hybrid electric bus (PHEB). The main purpose is to address the disadvantage that an inaccurate reflection of the real-world driving characteristics for city buses will be caused when ignoring the passenger load in the course of a driving cycle synthesis. Two contributions are supplemented to distinguish from the previous research. Firstly, a novel station-based method is proposed aiming at developing a driving cycle with high accuracy. The kinematic segments are partitioned according to the distance of adjacent bus stops, while a two-dimensional Markov chain Monte Carlo method is employed to synthesize driving cycle between each interval of adjacent bus stops. Secondly, the random passenger load for different bus stops is treated as a discrete Markov chain model, according to the correlation analysis of the measured passenger data which are distinguished for off-peak and peak hours. Meanwhile, Monte Carlo simulation and maximum likelihood estimation are utilized to determine the most likely number of passengers for each bus stop. At last, the fuel consumption of the PHEB is simulated with the best-synthesized driving cycle and contrasted to the mean fuel consumption of the later measured data which is composed of the velocity, road slope, and the passenger load. The results demonstrate that the synthesized driving cycle has a higher accuracy on fuel consumption estimation.