In this work, a real-time energy management problem for a parallel hybrid electric vehicle (HEV) is proposed. The considered powertrain is built from a commercial HEV model. First, a non-linear optimal control problem under model predictive control scheme is formulated. The designed controller aims to generate the optimal power split and gear ratio schedule with respect to minimise the energy consumption of fuel and electricity. Moreover, the multiple shooting algorithm is introduced to decouple the dynamic constraints with the ability of avoiding the strong non-linearity while solving the optimisation problem. After that the optimisation problem is solved using sequential quadratic programming solver. Then, to evaluate the performance of the proposed real-time optimisation strategy on different traffic scenarios, the controller is applied to an adaptive cruise control (ACC) under connected environment. In this case, a solution of ACC with consideration of minimising energy consumption and maintaining string stability is provided. Finally, the proposed controller can be implemented in the traffic-in-the-loop platform without the knowledge of the predefined driving route. Simulations reveal that the proposed real-time control scheme shows great optimisation performance under the designed scenarios.

Contrary to other in-car engineering systems where the use of simulation tools is highly extended prior to a prototyping stage, the simulation of vehicular electrical distribution systems (EDSs) is not still a common practice as manufacturers so far have mainly relied on laborious empirical procedures for technical validation. However, to provide flexibility in EDS design and procure even faster endorsement, the development of computation tools on this subject is compelling considering the intricacy of these networks. To face this challenge, this work provides guidelines and experiences to develop a customised platform for EDS visualisation and simulation within the automotive industry context. The use of agile techniques for software development, visual analytics, and tailored power flow methods is highlighted among other aspects. Realistic case studies are presented to discuss the attributes of the implemented computational tool. To also provide relevant perspectives on how future EDS visualisation and simulation platforms will be developed, the latest research is discussed in topics such as new electric/electronic architectures, electro-thermal analysis, electronic fuses, mild hybrid power trains, hardware in the loop, and high-voltage networks.

Electric vehicle (EV) traction drives should be associated with flux-weakening (FW) techniques wide-range speed demands. In this study, the EV performance with an optimal FW strategy is studied in relation to the battery voltage variation caused by cell state-of-charge and temperature changes. The results show that the battery suffers from a voltage reduction by larger internal resistance as the temperature decreases. Moreover, the higher current is required for activating the FW process. However, the inner resistance growth produces more heat inside the cell that affects the battery electrical parameters as well as the system. To assess this effect by simulation, an improved electro-thermal model of lithium-ion battery ls dynamically coupled to the optimal FW strategy. In this model, all the electrical parameters are temperature-dependent deduced from experimental measurements of an off-road EV. The simulation results confirm the effect of the cell self-heating on the battery voltage at sub-zero temperatures. The higher battery voltage can support the FW operation at −10°C for more 1200 s under the modified NEDC driving cycle, whereas the motor drive voltage is saturated after 1118 s by using the simple battery model without thermal effects.

This study investigates the impact of battery and fuel cell (FC) degradation on energy management of a FC hybrid electric vehicle. In this respect, an online energy management strategy (EMS) is proposed considering simultaneous online adaptation of battery and FC models. The EMS is based on quadratic programming which is integrated into an online battery and proton exchange membrane FC (PEMFC) parameters identification. Considering the battery and PEMFC states of health, three scenarios have been considered for the EMS purpose, and the performance of the proposed EMS has been examined under two driving cycles. Numerous test scenarios using standard driving cycles reveal that the ageing of battery and PEMFC has a considerable impact on the hydrogen consumption. Moreover, the proposed EMS can successfully tackle the model uncertainties owing to the performance drifts of the power sources at the mentioned scenarios.