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To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

The energy used for cabin cooling and heating can drastically reduce the operating range of electric vehicles. The energy efficiency and performance of the cabin heating, ventilation and air conditioning (HVAC) system depend on the system configuration and ambient conditions. The presented research investigates the energy efficiency and performance of cabin thermal management in electric vehicles. A simulation model of cabin heating and cooling systems was developed in the AMESim software. Simulations were carried out in the standard test cycles and one real-world driving cycle to take into account different driving behaviors and environments. The cabin thermal management performance was analyzed in relation to ambient temperature, system efficiency and cabin thermal balance. The simulation results showed that the driving range can shorten more than 50% in extreme cold conditions.

Hybrid, plug-in hybrid, and electric vehicles have enthusiastically embraced rechargeable Li-ion batteries as their primary/supplemental power source of choice. Because the state of charge (SoC) of a battery indicates available remaining energy, the battery management system of these vehicles must estimate the SoC accurately. To estimate the SoC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. The Bayesian algorithm is obtained by modifying Potter's squareroot filter and named the Potter SoC tracker (PST) in this paper. We test the PST in challenging test cases including high-rate charge/discharge cycles with outlier cell voltage measurements. The simulation results reveal that the PST can estimate the SoC with accuracy above 95% without experiencing divergence.

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.

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.

Due to their high energy density, power density, and durability, lithium-ion (Li-ion) batteries are rapidly becoming the most popular energy storage method for electric vehicles. Difficulty arises in accurately estimating the amount of left capacity in the battery during operation time, commonly known as battery state of charge (SOC). This paper presents a comparative study between six different Equivalent Circuit Li-ion battery models and two different state of charge (SOC) estimation strategies. The Battery models cover the state-of-the-art of Equivalent Circuit models discussed in literature. The Li-ion battery SOC is estimated using non-linear estimation strategies i.e. Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF). The models and the state of charge estimation strategies are compared against simulation data obtained from AVL CRUISE software.

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.

This paper presents a dynamic model for an interior permanent magnet (IPM) machine with a space-vector-modulation-based voltage source inverter. The dynamic model considers spatial harmonics, cross-coupling and magnetic saturation. In order to include the nonlinear electromagnetic characteristics of the IPM machine, the dynamic model is built based on the current-flux look-up tables obtained from finite element analysis (FEA). The model is co-simulated with the drive system, which considers the effects of the modulation technique and the switching frequency. The dynamic performance of a 60/8 IPM machine is analyzed using the dynamic model at different operating conditions and then validated with the torque waveforms obtained from FEA. The results show that dynamic performance can be analyzed accurately and more quickly using the dynamic model presented in this paper.

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.

Battery state-of-charge (SOC) is critical information for the vehicle energy management system and must be accurately estimated to ensure reliable and affordable electrified vehicles (xEV). However, due to the nonlinear temperature, health, and SOC dependent behaviour of Li-ion batteries, SOC estimation is still a significant automotive engineering challenge. Traditional approaches to this problem, such as electrochemical models, usually require precise parameters and knowledge from the battery composition as well as its physical response. In contrast, neural networks are a data-driven approach that requires minimal knowledge of the battery or its nonlinear behaviour. The objective of this work is to present the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The method includes a description of data acquisition, data preparation, development of an FNN, FNN tuning, and robust validation of the FNN to sensor noise.

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 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 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.

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.

This paper presents a Differential Flatness-Based Control (FBC) approach for the current control of Switched Reluctance Machines (SRMs), a potential candidate for the automotive industry. The main challenges in SRM control methods stem from motor nonlinearity. In electrical drives, FBC has been applied in doubly-fed induction generators, permanent magnet motors, and magnet-assisted synchronous reluctance motors. Among the few papers that have used FBC for SRM, this research distinguishes itself by addressing current control and considering both current and flux-linkage separately as a flat output, an approach not found in previous literature. The performance of the proposed controls is assessed in a three-phase 12/8 SRM against the conventional hysteresis current controller (HCC) and PI controller. Additionally, it is integrated into a torque-sharing function based on a maximum torque per ampere control strategy.