Long-distance heavy-duty electric vehicles, e.g., electric trucks, expect demanding requirements from the electric drive system, including high starting torque, high torque density, high power factor, high efficiency, etc. Permanent magnet synchronous machines (PMSMs) have been widely used for traction applications. However, the high cost and associated environmental issues due to the rare-earth permanent magnet material intensify researchers to explore new solutions. Accordingly, a brushless electrically-excited synchronous machine (EESM) is designed and investigated in this paper. The brushless EESM is also quantitatively compared with a PMSM candidate for the aforementioned application. Differing from conventional EESMs in which the brushes and slip rings are used for rotor field winding excitation, a contactless/brushless rotating transformer is adopted for the presented EESM to feed the rotor field winding. It is shown that the interior PMSM exhibits higher efficiency in low-speed region, but lower efficiency in high-speed region. By contrast, the brushless EESM exhibits lower efficiency in low-speed region, but much higher efficiency in high-speed region. Hence, the EESM is more suitable for long-distance heavy-duty electric vehicle applications where significant highspeed operations are required. Moreover, compared to the PMSM, the EESM is more cost-effective, sustainable, and environmentally friendly due to the absence of the rare-earth permanent magnet material. Both the PMSM and the EESM are prototyped and manufactured. Experimental results are provided to validate the effectiveness of the presented designs.

Electrically excited synchronous machines have become an attractive solution to electric vehicles. The excitation of the machine is controllable by adjusting the field current. However, due to the magnetic mutual couplings between stator and rotor windings, a voltage will be induced in the stator winding in case of a current rise in the field winding and vice-versa. In this article, a dynamic current control algorithm is proposed in which magnetic mutual couplings are comprehensively taken into consideration. To achieve this, first, the expected current derivatives are determined according to the error. Then, the voltages across all self- and mutual inductances are calculated correspondingly. In the end, the resistive voltages, inductive voltages, and the cross-coupling terms are summed up to construct the total controller output. To make sure that the control still works when the voltage output limit is reached, an antiwindup algorithm with adaptive bandwidth is proposed to cooperate with the dynamic current control algorithm. The results from simulations and experiments show that smoother responses can be achieved with the proposed control method.

Electrically excited synchronous machines (EESMs) are becoming more prevalent in electric vehicles (EVs) due to their high power density and independence of rear-earth materials. Additionally, the use of brushless excitation systems can further improve the efficiency and reliability of EESMs. However, the brushless design also makes the field current not directly measurable. Therefore, a new Extended-Kalman-Filter (EKF) based field current estimation method is proposed in this paper. The proposed method relies on the modelling of the EESM stator and rotor, and utilizes the stator current measurements and parameter information which are already available in the current controller. Hence, this method is easy to implement and requires no additional sensors or parameter calibration work. Experimental results show that, through using the proposed method, the EESM field current can be accurately estimated in both steady states and transients. In addition, the estimation converges fast even with significant initial state errors.

Real-time simulations refer to the simulations of a physical system where model equations for one time-step are solved within the same time period as in reality. An FPGA/CPU-based real-time simulation platform is presented in this paper, with a full-electric vehicle model implemented in a central processing unit (CPU) board and an electric drive model implemented in a field programmable gate arrays (FPGA) board. It has been a challenge to interface two models solved with two different processors. In this paper, one open-loop and three closed-loop interfaces are proposed. Real-time simulation results show that the best method is to transmit electric machine speed from the vehicle model to the electric derive model, with feedback electric machine torque calculated in FPGA. In addition, a virtual vehicle testing tool (CarMaker) is used when building the vehicle model, achieving more accurate modeling of vehicle subsystems. The presented platform can be used to verify advanced vehicle control functions during hardware-in-the-loop (HIL) testing. Vehicle anti-slip control is used as an example here. Finally, experiments were performed by connecting the real-time platform with a back-to-back electric machine test bench. Results of torque, rotor speed, and d&q axis currents are all in good agreement between simulations and experiments.

Multi-motor drivetrain systems utilizing more than one electric machine for propulsion can provide possibilities to improve energy efficiency and vehicle dynamic performance of battery electric vehicles (BEV). However, laboratory testing of such drivetrain systems using a dyno test bench can be costly. A solution can be to use a mechanical-hardware-in-the-loop (MHIL) test bench, which combines real-time simulations of the intended working environment with the dyno test bench. To utilize the MHIL approach for multi-motor drivetrain systems, one drivetrain is implemented in the dyno test bench, while the remaining are simulated using a real-time simulator. Therefore, providing a less expensive solution for laboratory testing of drivetrain components and control methods in their intended environment. In this work, an MHIL test bench for a multi-motor drivetrain system is designed and experimentally verified. A BEV with two independently driven front wheels is considered for modeling. To interface the dyno test bench with real-time simulation, two different methods, namely open-loop and closed-loop, are proposed and verified in experiments by prototyping an MHIL test bench. In addition, an anti-slip control is implemented and evaluated experimentally to demonstrate the suitability of the proposed MHIL test bench in the verification of control methods.

Traction control is one of the most important functions in vehicle drivetrain systems. When a vehicle is driven on a low‐friction road surface, loss of traction force can cause the driven wheels to spin. This reduces vehicle acceleration performance and can even cause the driver to lose control of the vehicle. The high bandwidth of electric machine control in electric vehicles gives more possibilities to regulate driving torque on wheels and prevent wheel spin. An acceleration‐based wheel slip control is designed and investigated. Compared to traditional slip‐based traction control, the proposed method does not depend on the estimation of the vehicle speed and only relies on the driven wheel rotational acceleration. The control method is verified using the simulation of an electric vehicle with a decentralised electric drivetrain system. The vehicle and the electric drive are modelled in CarMaker and PLECS, respectively. The simulation results show that the proposed method is able to prevent the driven wheel from spinning when the vehicle is accelerated on an ice road. In addition, the control is fast enough and requires only half a second to reduce the wheel acceleration to a normal range.