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.

Two of the main challenges for electric vehicle (EV) adoption include limited range and long recharge times. Ultra-fast charging can help to mitigate both of these concerns. However, for typical 400 V battery EVs, the charging rate is limited by the practical cable size required to carry the charging current. To reach ultra-high charge rates of 350 or 400 kW, 800 V battery EVs are a promising alternative. However, the design of an 800 V EV requires careful new considerations for all electrical systems. This paper reviews the current state of 800 V vehicle powertrain electrical design, and performs an analysis of benefits, challenges, and future trends regarding multiple vehicle powertrain components. Specifically, detailed benefits and challenges related to the battery, propulsion motor, inverter, auxiliary power unit, and on- and off-board chargers are discussed.

Lithium-Ion battery packs are an essential component for electric vehicles (EVs). These packs are configured from hundreds of series and parallel connected cells to provide the necessary power and energy for the vehicle. An accurate, adaptable battery management system (BMS) is essential to monitor and control such a large number of cells. Series and parallel connected cells also experience different production and operational conditions, which makes it challenging for the BMS to ensure the safe operation of each individual cell. The main functions of the BMS include battery state estimation, cell balancing, thermal management, and fault diagnosis. Robust estimation of the state of charge (SOC) is crucial for providing the driver with an accurate indication of the remaining range. This paper presents the state of art of battery pack SOC estimation methods along with the impact of cell inconsistency on pack performance and SOC estimation. Cell balancing methods, which are necessary due to cell inconsistencies, are discussed as well. Four categories of pack SOC estimation methods are presented, including individual cell, lumped cell, reference cell, and mean cell and difference estimation methods. The SOC estimation methods are compared in terms of algorithm type, computational load, and engineering effort to help practitioners decide which method best fits their application.

In the last decade, the automotive industry has undergone a paradigm shift towards electrification. Electric vehicles have become increasingly popular, but so far, they have almost solely utilized single-ratio gearboxes. The use of multiple gear ratios has several potential benefits, including enabling the electric traction machine and inverter to operate in a more efficient region, increasing vehicle acceleration, gradeability, and top speed, and reducing overall traction system mass and volume. Performance vehicles, light to heavy-duty trucks, and buses may especially benefit from multi-speed gearboxes due to their high torque and power requirements. This paper covers the fundamentals of applying multi-speed gearboxes to EVs, the latest designs, and future trends. The efforts of both academia and industry in this field are covered. A range of topics are discussed, including gearbox topologies, gear ratio selection, gearbox losses, noise vibration and harshness, gearbox control, shift scheduling, and regenerative braking. Prior studies are presented showing that depending on the drive cycle, vehicle type, and gearbox configuration, drivetrain energy consumption may be reduced slightly or increased anywhere from a few percent to thirty percent when utilizing a multi-speed configuration. While multi-speed EV traction systems do show considerable promise, more investigation is needed to conclusively determine in what cases they can outperform highly optimized single-speed systems.

Intelligent and pragmatic state-of-health (SOH) estimation is critical for the safe and reliable operation of Li-ion batteries, which recently have become ubiquitous for applications such as electrified vehicles, smart grids, smartphones, as well as manned and unmanned aerial vehicles. This paper introduces a convolutional neural network (CNN)-based framework for directly estimating SOH from voltage, current, and temperature measured while the battery is charging. The CNN is trained with data from as many as 28 cells, which were aged at two temperatures using randomized usage profiles. CNNs with between 1 and 6 layers and between 32 and 256 neurons were investigated, and the training data was augmented with noise and error as well to improve accuracy. Importantly, the algorithm was validated for partial charges, as would be common for many applications. Full charges starting between 0 and 95% SOC as well as for multiple ranges ending at less than 100% SOC were tested. The proposed CNN SOH estimation framework achieved a mean average error (MAE) as low as 0.8% over the life of the battery, and still achieved a reasonable MAE of 1.6% when a very small charge window of 85% to 97% SOC was used. While the CNN algorithm is shown to estimate SOH very accurately with partial charge data and two temperatures, further studies could also investigate a wider temperature range and multiple different charge currents or constant power charging.