A Review of Production Multi-Motor Electric Vehicles and Energy Management and Model Predictive Control Techniques

Abstract

<div class="section abstract"><div class="htmlview paragraph">This paper presents the characteristics of more than 260 trim levels for over 50 production electric vehicle (EV) models on the market since 2014. Data analysis shows a clear trend of all-wheel-drive (AWD) powertrains being increasingly offered on the market from original equipment manufacturers (OEMs). The latest data from the U.S. Environmental Protection Agency (EPA) shows that AWD EVs have seen a nearly 4 times increase in production from 21 models in 2020 to 79 models in 2023. Meanwhile single axle front-wheel-drive (FWD) and rear-wheel-drive (RWD) drivetrains have seen small to moderate increases over the same period, going from 9 to 11 models and from 5 to 12 models, respectively. Further looking into AWD architectures demonstrates dual electric machine (EM) powertrains using different EM types on each axle remain a small portion of the dual-motor AWD category. However, these architectures have been shown to have energy savings of 1 % to 5 % over that of identical dual-motor permanent magnet (PM) machine or dual-motor induction machine (IM) architectures. Further work shows dual motor architectures with an IM powering the front axle and a PM machine powering the rear axle under mathematical optimization-based controls to be less energy consuming than the same architecture subjected to a rule-based energy management strategy (EMS). This leads to a review of electrified vehicle EMSs, with the two main methods of rule-based and optimization-based controls being presented. The pros and cons of each control method are stated with optimization-based methods showing the most benefit. The optimal control method of model predictive control (MPC) is then presented by covering its’ background, structure, variations, and mechanics. Finally, the use of MPC as a viable EMS for multi-motor EVs is reviewed with motor thermal regulation as part of the control objective.</div></div>