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Hybrid electric vehicles have been recognized as one of the promising countermeasures to realize carbon neutrality and mitigate energy issues, whose performance depends to a certain extent on energy management strategies. On the one hand, the power batteries are highly temperature sensitive, and working in high temperature will damage the battery life. On the other hand, energy management controller should be able to execute computation-intensive optimization task in real-time. Thus, besides fuel economy, battery temperature and algorithmic time efficiency should be of great concern in the development of practicality oriented EMS. In this work, a time-efficient battery temperature sensitive EMS for series hybrid electric vehicle is proposed. First, a multi-objective optimal control problem is mathematically formulated in model predictive control (MPC) framework. Then, the non-convex optimization problem caused by the non-linear battery state of energy and state of temperature models is transformed into convex optimization problem by convex reformulation. Finally, to improve computational efficiency, alternating direction method of multipliers is used to split the optimization problem into small-scale subproblems and solve the energy management problem. The simulation and Hardware-in-loop test results show that the proposed strategy is capable of not only improving the fuel economy but also effectively keeping the battery temperature as close as possible to the thermal comfort zone. It is observed that the proposed method produces a promising computational efficiency, relative to Pontryagin's minimization principle-MPC, close to that produced by rule-based strategy.

This study proposes a sequential clustering quadratic programming (SCQP) method for the energy management strategies of plug-in hybrid electric vehicles (PHEVs). In this method, the clustering algorithm is introduced to gather up the points with a smaller iteration step size in the iteration process. The clustering results are utilized to design the termination criterion based on the distance between the cluster centers of various iteration domains. In the case that the distance varies within the preset range, it indicates that the current iteration point is sufficiently close to the optimal point. So that the criterion turns to terminate the computation to reduce unnecessary iteration steps. To analyze the convergence of the method with the designed criterion, the mathematical illustrations are proposed. In the mathematical illustrations, the monotonicity of the clustering objective function is firstly given. Then, the theorem of feasibility for the solution obtained by the designed criterion is proved. On the basis of aforementioned conclusions, the convergence of the SCQP method is obtained. Finally, the performance of the proposed method is validated both in simulation test and hardware-in-loop (HIL) test. The simulation results reveal that the PHEV achieves 8.81% and 7.74% less fuel consumption under two driving cycles. And the average iteration number of the proposed method is obviously reduced compared with the conventional SQP. The HIL results reveal that the proposed strategy exhibits similar performance in both real controller and simulation. The energy saving and real-time performance can be verified.

In heavy-duty series hybrid electric vehicles (SHEVs), the engine-generator set (EGS) functions as the main power source for propulsion. However, the limitation of engine power per liter and delayed computation of control algorithm result in the hysteretic response of EGS to high demand power. It leads to deteriorating operation of powertrain. Thus, the challenging technical issue lies in achieving stable powertrain operation, which is difficult to describe precisely by real-time control. In this work, an efficient power control scheme for heavy-duty HEV with online optimized variable universe fuzzy system is proposed. First, a splitting sequential clustering quadratic programming algorithm is designed to solve power distribution and achieve real-time control. The original subproblem is split into two subproblems with smaller scale to obtain iterative points. And clustering algorithm is introduced to gather up the points to improve the termination criterion. It turns to skip unnecessary short step in the iteration which fails to obtain sufficient descent. Then, the online optimized variable universe fuzzy system is established to achieve rapid response of EGS by adjusting power distribution. In this system, online optimization of membership function distribution parameters is considered. The optimization is constructed on real-time membership overlap degree and central value of fuzzy system rather than the traditional offline optimization using posterior information of vehicle. Finally, the effectiveness of the proposed scheme is validated both in simulation test and hardware-in-loop test. The results reveal that stable power output is maintained and calculation time is decreased by 40.9%, 46.0% under two driving cycles.