State of charge (SOC) estimation is critical to the safe and reliable operation of Li-ion battery packs, which nowadays are becoming increasingly used in electric vehicles (EVs), Hybrid EVs, unmanned aerial vehicles, and smart grid systems. We introduce a new method to perform accurate SOC estimation for Li-ion batteries using a recurrent neural network (RNN) with long short-term memory (LSTM). We showcase the LSTM-RNN's ability to encode dependencies in time and accurately estimate SOC without using any battery models, filters, or inference systems like Kalman filters. In addition, this machine-learning technique, like all others, is capable of generalizing the abstractions it learns during training to other datasets taken under different conditions. Therefore, we exploit this feature by training an LSTM-RNN model over datasets recorded at various ambient temperatures, leading to a single network that can properly estimate SOC at different ambient temperature conditions. The LSTM-RNN achieves a low mean absolute error (MAE) of 0.573% at a fixed ambient temperature and an MAE of 1.606% on a dataset with ambient temperature increasing from 10 to 25 °C.

Servo drives and drives for position control require a high dynamic on speed control. In this paper, model predictive direct speed control (MP-DSC) is proposed, which overcomes limitations of cascaded linear controllers. The novel concept predicts the future current and speed states in discrete steps and it selects plant inputs which depends mainly on the predicted speed error. Secondary control objectives, such as maximum torque per ampere tracking are included. MP-DSC uses the finite control set approach which makes it suitable for online predictions with a prediction horizon of a few sample periods. The concept has been developed by simulation and evaluated on an experimental test bench. The overall control behavior is evaluated applying reference and disturbance steps to the system, where MP-DSC shows promising results. A solution for disturbance (e.g., load toque) rejection is proposed, and the effectiveness to avoid control offsets is shown. Furthermore, the dynamic performance and the steady-state behavior of MP-DSC is evaluated and discussed.

In order to achieve lower fuel consumption and less greenhouse gas (GHG) emissions, we need higher efficiency vehicles with improved performance. Electrification is the most promising solution to enable a more sustainable and environmentally friendly transportation system. Electrified transportation vision includes utilizing more electrical energy to power traction and nontraction loads in the vehicle. In electrified powertrain applications, the efficiency of the electrical path, and the power and energy density of the components play important roles to improve the electric range of the vehicle to run the engine close to its peak efficiency point and to maintain lower energy consumption with less emissions. In general, the electrified powertrain architecture, design and control of the powertrain components, and software development are coupled to facilitate an efficient, high-performance, and reliable powertrain. In this paper, enabling technologies and solutions for the electrified transportation are discussed in terms of power electronics, electric machines, electrified powertrain architectures, energy storage systems (ESSs), and controls and software.

A novel type of Model Predictive Direct Torque Control (MP-DTC) is proposed in this paper. The future behavior of the plant is predicted using a discrete-time state-space model in the dq reference frame for a finite set of voltage vectors, which is pre-selected by a graph algorithm. The preferable input is chosen based on a cost function. Contrarily to conventional MP-DTC (and DTC), the stator flux is not explicitly controlled nor hysteresis bounds are used. The cost function is designed to meet multiple demands: torque reference tracking, Maximum Torque per Ampere (MTPA) tracking for high electrical efficiency, and limitation of the states to their maximum admissible values.

Recently, increased emissions regulations and a push for less dependence on fossil fuels are factors that have enticed a growth in the market share of alternative energy vehicles. Readily available energy storage systems (ESSs) pose a challenge for the mass market penetration of hybrid electric vehicles (HEVs), plug-in HEVs, and EVs. This is mainly due to the high cost of ESS available today. However, tremendous research efforts are going into reducing the cost of these storage devices, increasing their lifespan, and improving their energy density. This paper aims to give an overview of the current state of readily available battery and ultracapacitor (UC) technologies as well as a look ahead toward promising advanced battery chemistries and next generation ESS. Energy management systems and various battery balancing configurations are discussed in addition to battery state/parameter estimation and protection mechanisms. Finally, hybrid ESSs (HESS) are reviewed as a mitigation strategy to the shortcomings of traditional battery and UC technologies. Consideration is given to the combination of advanced battery chemistries with UCs to portray HESS performance, which can meet and exceed the performance of current ESS technologies.