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.

The complicated coupling of component design together with energy management has brought a significant challenge to the design, optimization, and control of plug-in hybrid electric buses (PHEBs). This paper proposes an integrated optimization methodology to ensure the optimum performance of a PHEB with a view toward designing and applications. First, a novel co-optimization method is proposed for redesigning the driveline parameters offline, which combines a nondominated sorting genetic algorithm-II (NSGA-II) with dynamic programming to eliminate the impact of the coupling between the component design and energy management. Within the new method, the driveline parameters are optimally designed based on a global optimal energy management strategy, and fuel consumption and acceleration time can be respectively reduced by 4.71% and 4.59%. Second, a model-free adaptive control (MFAC) method is employed to realize the online optimal control of energy management on the basis of Pontryagin’s minimum principle (PMP). Particularly, an MFAC controller is used to track the predesigned linear state-of-charge (SOC), and its control variable is regarded as the co-state of the PMP. The main finding is that the co-state generated by the MFAC controller gradually converges on the optimal one derived according to the prior known driving cycles. This implies that the MFAC controller can realize a real-time application of the PMP strategy without acquiring the optimal co-state by offline calculation. Finally, the verification results demonstrated that the proposed MFAC-based method is applicable to both the typical and unknown stochastic driving cycles, meanwhile, and can further improve fuel economy compared to a conventional proportional-integral-differential (PID) controller.

To 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.

This paper proposes a robust design approach based on the Design for Six Sigma (DFSS), to promote the robustness of our previous model-free-adaptive-control-based (MFAC-based) energy management strategy (EMS) for the plug-in hybrid electric vehicles (PHEVs) in real-time application. First, the multi-island genetic algorithm (MIGA) is employed for a deterministic design of the MFAC-based EMS, and the Monte Carlo simulation (MCS) is utilized to evaluate the sigma level of the strategy with the deterministic design results. Second, a DFSS framework is formulated to reinforce the robustness of the MFAC-based EMS, in which the velocity and the vehicle mass are considered external disturbances whilst the terminal state of charge (SOC) of the battery and the fuel consumption (FC) are conducted as responses. In addition, real-time SOC constraints are incorporated into Pontryagin’s minimum principle (PMP) to confine the fluctuation of battery SOC in MFAC-based EMS to make it closer to the solution of the dynamic programming (DP). Finally, the effectiveness of the robust design results is assessed by contrasting with other strategies for various combined driving cycles (including velocity, vehicle mass, and road slope). The comparisons demonstrate the remarkable promotion of the robust design in terms of the energy-saving potential and the performance against external disturbance. The average improvement of the FCs can reach up to a considerable 19.66% and 9.79% in contrast to the charge-depleting and charge-sustaining (CD-CS) strategy as well as the deterministic design of MFAC-based EMS. In particular, the energy-saving performance is comparable to DP, where there is only a gap of −1.68%.

This paper proposes an adaptive shift schedule design framework based on dynamic programming (DP) algorithm and fuzzy logical control to promote the shift schedule’s adaptability whilst improving the comprehensive performance of the multi-gear automated manual transmission (AMT) electric vehicles in real-time application. First, the DP algorithm is employed to extract an offline optimal gear-shift schedule based on a set of driving conditions, including 11 groups of typical driving cycles. Second, a fuzzy logical controller is formulated considering the variation in the vehicle load and acceleration, where a velocity increment is exported online to adjust the gear-shift velocity of the predesigned DP-based schedule to develop a Fuzzy-DP shift schedule. In addition, multi-objective particle swarm optimization (MOPSO) is utilized to construct a comprehensive shift schedule by simultaneously considering the dynamic and economic performance of the vehicle. Then, the dynamic and economic shift schedules are deployed as the benchmark to examine the performance of the proposed shift schedule. Finally, the effectiveness of the Fuzzy-DP shift schedule is evaluated by comparison with others under various combined driving cycles (including vehicle load and velocity). The comparisons demonstrate the remarkable promotion in the adaptability of the Fuzzy-DP shift schedule in terms of acceleration time, energy-saving potential, and shift frequency. The most significant improvements in the dynamic, economic, and shift frequency can reach 8.86%, 10.12%, and 8.56%, respectively, in contrast to the MOPSO-based shift schedule.

Accurate estimation of vehicle states is extremely crucial for vehicle stability control. As a reliable estimation methodology, the unscented Kalman filter (UKF) has been widely utilized in vehicle control. However, the estimation accuracy still needs to be improved caused by the unpredictable measurement and process noise. In this paper, a novel modified UKF state estimation methodology combined with the ant lion optimization (ALO) is proposed for the stability control of a four in-wheel motor independent drive electric vehicle (4WIDEV). First, the optimal performance of the ALO algorithm is analyzed, where both unimodal and multimodal optimization test functions are selected and optimized by GA, PSO, and ALO, respectively. The results indicate that the ALO algorithm has good global optimization capability and applicability. Second, the ALO algorithm is merged into the UKF to adjust the statistical properties of noise information for the ALOUKF estimator design without extra sensor signals. At last, the simulations on the Matlab/Simulink-CarSim co-simulation platform and the road test based on an A&D 5435 rapid prototyping experiment platform (RPP) are carried out to verify the proposed method. The simulation and experiment results demonstrate that the ALOUKF estimator can improve state estimation accuracy and resist the vehicle nonlinearity even in the case of the complicated and emergency maneuvers.