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Dual three-phase (DTP) permanent magnet synchronous machines (PMSMs) are becoming attractive for electric vehicle (EV) propulsion systems in the automotive industry. Flux-weakening (FW) control technique is important to ensure DTP-PMSMs operating in high-speed range. This paper proposes an adaptive FW control algorithm to ensure better performance and stability in variant speeds. A small-signal model is developed to obtain the adaptive gain for a constant controller bandwidth regardless of the speeds. The proposed FW controller is implemented, tuned, and validated on a DTP-PMSM experiment setup. The proposed method improves the FW performances in terms of torque and system stability, compared with the non-adaptive FW controller. Moreover, the harmonics analysis shows an inevitable xy-components affecting the overall performances. The effect of xy controller gain is further investigated for the FW operation.

Battery terminal voltage modelling is crucial for various applications, including electric vehicles, renewable energy systems, and portable electronics. Terminal voltage models are used to determine how a battery will respond under load and can be used to calculate run-time, power capability, and heat generation and as a component of state estimation approaches, such as for state of charge. Previous studies have shown better voltage modelling accuracy for long short-term memory (LSTM) recurrent neural networks than other traditional methods (e.g., equivalent circuit and electrochemical models). This study presents two new approaches – sequence training and data shuffling – to improve LSTM battery voltage models further, making them an even better candidate for the high-accuracy modelling of lithium-ion batteries. Because the LSTM memory captures information from past time steps, it must typically be trained using one series of continuous data.

This paper reviews battery cooling systems in production fast-charging electric vehicles and the characteristics of different cooling channel pathways discussed in literature. In production fast charging electric vehicles, the predominant cooling method was found to be liquid edge cooling, where battery modules sit on top of a cooling manifold which cools one edge of each cell. Based on this, four main classes of cooling channel pathways are identified with examples of real-life implementation. A battery module from a Porsche Taycan electric vehicle is also instrumented with temperature sensors to observe the thermal characteristics across the cell surface during fast charging, and the results are presented. With fast charging, the Taycan module charged from 0 to 80% SOC within 24.27 minutes. The maximum temperature rise of the battery cells during the fast charge was 28.14°C and the temperature deviation across the cell surface was ±2.06°C.

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.

This paper investigates different regenerative braking strategies applied to Battery Electric Vehicles, such as series and parallel brake blends. The comparison includes energy efficiency assessment using homologation and real-world drive cycle and objective and subjective drivability evaluation. Multiple simulations are performed using a one-dimensional (1D) vehicle model developed in Simulink and a static driving simulator. The driving simulator provides a fair comparison of real-world driving since it creates repeatable highway and urban traffic conditions. These simulations compare the system energy efficiency by looking at the battery's state of charge (SOC). The drivability is assessed on top of consumption by using the static driving simulator. It is objectively measured by calculating the longitudinal acceleration change ratio over time, which occurs during the regeneration ramp-in and ramp-out, for different pedal positions and pedal gradients.

Transport electrification is pushing the automotive and aerospace industries to enhance the power density of their powertrains further and further. One of the technologies currently pursued by some companies is high-speed electric motors. For instance, the new Model S Plaid motor by Tesla has a carbon-fiber wrapped IPM (Interior Permanent Magnet) rotor which can exceed 20,000rpm. The SPX88-120 made by Helix company shows a power density of about 18kW/kg at 50,000rpm. However, such high rotating speeds result is huge mechanical stresses in the entire rotating assembly, thus making the structural design of these parts extremely challenging. The primary goal of this paper is to provide a scientific rationale for the effective Finite Element Modeling (FEM) and integration strategies to maximize the rotating assembly durability of a 150kW radial flux SPMSM (surface-mounted permanent magnet synchronous motor) considered as a case-study.

This paper presents an improved full-order stator flux-linkage observer for the Permanent Magnet Synchronous Machine (PMSM) drives employed for electromagnetic power conversion in the Electric Vehicle (EV) powertrain. The parameters of a typical PMSM are influenced by constantly changing operating conditions leading to significant errors when torque estimation is performed using an a-priori parametric model, also known as a current model. This issue is usually addressed using a voltage model-based flux-linkage estimation. However, this approach suffers from inaccuracy due to the inverter-generated disturbances. The significance of this disturbance also grows as the operating speed reduces. A conventional full-order flux-linkage observer relies upon a current model for low operating speed and gradually shifts to the voltage model as the machine accelerates.

Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality.

Instantaneous optimization-based energy management systems (EMS) are getting popular since they can yield near-optimal performance in unknown driving situations with minimalistic tuning parameters. However, they often disregard the drivability score of the powertrain as a performance assessment criterion, and this leads to too frequent or even infeasible mode-transitions during the multi-mode operation of a hybrid electric powertrain. Aiming to bring down the mode-transition frequency below a feasible limit, this paper proffers an instantaneous optimization-based EMS, which also accounts for the energy lost during mode-transitions into the cost function along with the electrical and chemical energy losses. The energy lost during a single mode-transition event refers to the summation of change in rotational energy for all the prime-movers, i.e., internal combustion engine and electric machines.

Predicting the fuel economy capability of hybrid electric vehicle (HEV) powertrains by solving the related optimal control problem has been available for a few decades. Dynamic programming (DP) is one of the most popular techniques implemented to this end. Current research aims at integrating further powertrain modeling criteria that improve the fidelity level of the optimal HEV powertrain control behavior predicted by DP, thus corroborating the reliability of the fuel economy assessment. Dedicated methodologies need further development to avoid the curse of dimensionality which is typically associated to DP when increasing the number of control and state variables considered. This paper aims at considerably reducing the overall computational effort required by DP for HEVs by removing the state term associated to the battery state-of-charge (SOC).

A large amount of heat is generated in electric vehicle battery packs during high rate charging, resulting in the need for effective cooling methods. In this paper, a prototype liquid cooled large format Lithium-ion battery module is modeled and tested. Experiments are conducted on the module, which includes 31Ah NMC/Graphite pouch battery cells sandwiched by a foam thermal pad and heat sinks on both sides. The module is instrumented with twenty T-type thermocouples to measure thermal characteristics including the cell and foam surface temperature, heat flux distribution, and the heat generation from batteries under up to 5C rate ultra-fast charging. Constant power loss tests are also performed in which battery loss can be directly measured.

This paper benchmarks three different lithium-ion (Li-ion) battery voltage modelling approaches, a physics-based approach using an Extended Single Particle Model (ESPM), an equivalent circuit model, and a recurrent neural network. The ESPM is the selected physics-based approach because it offers similar complexity and computational load to the other two benchmarked models. In the ESPM, the anode and cathode are simplified to single particles, and the partial differential equations are simplified to ordinary differential equations via model order reduction. Hence, the required state variables are reduced, and the simulation speed is improved. The second approach is a third-order equivalent circuit model (ECM), and the third approach uses a model based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)). A Li-ion pouch cell with 47 Ah nominal capacity is used to parameterize all the models.

Accurate battery state of charge (SOC) estimation is essential for safe and reliable performance of electric vehicles (EVs). Lithium-ion batteries, commonly used for EV applications, have strong time-varying and non-linear behaviour, making SOC estimation challenging. In this paper, a processor in the loop (PIL) platform is used to assess the execution time and memory use of different SOC estimation algorithms. Four different SOC estimation algorithms are presented and benchmarked, including an extended Kalman filter (EKF), EKF with recursive least squares filter (EKF-RLS) feedforward neural network (FNN), and a recurrent neural network with long short-term memory (LSTM). The algorithms are deployed to two different NXP S32Kx microprocessors and executed in real-time to assess the algorithms' computational load. The algorithms are benchmarked in terms of accuracy, execution time, flash memory, and random access memory (RAM) use.

A comparative analysis of overmodulation methods is performed in the generalized form in this paper. The generalized form is based on four segmented formulae, which streamlines the execution of the PWM module. The comparative analysis considers five aspects: actual modulation index, harmonic content, transition to six-step operation, modulation index linearization, and execution complexity. The main contributions of this paper are twofold. Firstly, a thorough assessment of conventional overmodulation strategies for dual three-phase PMSM drives is undertaken. Secondly, a modified Minimum Phase Error (MPE) overmodulation method is proposed to extend the overmodulation to six-step operation. The modified MPE is introduced with advantages of wider modulation index range, low harmonic components in voltages and currents, smooth transition to six-step operation, and simple implementation.

This paper presents a model for predicting the energy consumption of a 2017 Chevrolet Bolt electric vehicle. The model is validated using 93 measured drive cycles covering in excess of 10,600 kilometres of driving and temperatures from −8 to 32 °C. The mechanical road load acting on the vehicle is calculated via ABC parameters from the publicly available US Environmental Protection Agency (EPA) Annual Certification Data database. The vehicle model includes wheel diameter, gear ratio, rated electric machine torque and power, 12V accessory load based off measurements, measured electric machine efficiency obtained from a publication from General Motors, and modelled inverter efficiency. Assumptions are made regarding gearbox losses as well. To ensure accuracy under real-world conditions, road grade, temperature effects, and heating and cooling energy are included as well. The model predicts an EPA range of 380 km, which is very close to the 383 km rating.

The assessment and control of driveline dynamics is only possible if a representative model is available. A driveline model enables engineers to estimate the system’s reactions for different torque inputs and shows how those inputs impact drivability and comfort. Modelling methods in literature are frequently designed only for internal combustion engine vehicles, disregarding electrified powertrains. To remedy that, a modelling method for electrified drivelines is presented. It simplifies the inclusion of dynamic factors such as road resistances, flexibility, friction, and inertias. The method consists in drawing a vertical diagram of the drivetrain topology where each key component is represented as a block. Newton’s second law is used to balance torque in each block connection, from propelling systems to the wheels. State variables and inputs are defined accounting for the powertrain topology.

Multiphase machines continue to increase in popularity in high power applications due to their proven benefits compared to their three-phase counterparts. However, with the increased phase number and, therefore, the increased number of degrees of freedom, the complexity of both modelling and control strategies significantly increases. This paper proposes a dynamic modelling method for six-phase interior permanent magnet machines using the vector space decomposition transformation, which can be extended to machines with any number of phases. The proposed technique considers the nonlinear characteristics of the machine, such as spatial harmonics, magnetic saturation, and cross-coupling, which are based on flux linkage look-up tables from finite element analysis. The main contribution of this paper is the consideration of the effect of harmonic components and asymmetries within the machine windings on losses.

Lithium-ion battery (LIB) terminal voltage models are investigated using two modelling approaches. The first model is a third-order Thevenin equivalent circuit model (ECM), which consists of an open-circuit voltage in series with a nonlinear resistance and three parallel RC pairs. The parameters of the ECM are obtained by fitting the model to hybrid pulse power characterization (HPPC) test data. The parametrization of the ECM is performed through quadratic-based programming. The second is a novel modelling approach based on long short-term memory (LSTM) recurrent neural networks to estimate the battery terminal voltage. The LSTM is trained on multiple vehicle drive cycles at six different temperatures, including −20°C, without the necessity of battery characterization tests. The performance of both models is evaluated with four automotive drive cycles at each temperature. The results show that both models achieve acceptable performance at all temperatures.

Hybrid electric vehicle (HEV) powertrains are characterized by a complex design environment as a result of both the large number of possible layouts and the need for dedicated energy management strategies. When selecting the most suitable hybrid powertrain architecture at an early design stage of HEVs, engineers usually focus solely on fuel economy (directly linked to tailpipe emissions) and vehicle drivability performance. However, high voltage batteries are a crucial component of HEVs as well in terms of performance and cost. This paper introduces a multitarget assessment framework for HEV powertrain architectures which considers both fuel economy and battery lifetime. A multi-objective formulation of dynamic programming is initially presented as an off-line optimal HEV energy management strategy capable of predicting both fuel economy performance and battery lifetime of HEV powertrain layout options.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.