Electric and hybrid vehicles have become widespread in large cities due to the desire for environmentally friendly technologies, reduction of greenhouse gas emissions and fuel, and economic advantages over gasoline and diesel vehicles. In electric vehicles, overheating, vibration, or mechanical damage due to collision with an object or another vehicle can lead to the failure of lithium-ion batteries up to thermal runaway and fire. Therefore, the development of battery safety control systems is one of the most important factors contributing to the large-scale electrification of public and private transport. This review examines the design features of the location and management of the battery pack to achieve maximum safety and operational efficiency when using an electric vehicle. The power characteristics and life-cycles of various types of lithium-ion batteries depending on the chemical nature of their electrodes are considered, using the example of commercial vehicles’—Tesla, Nissan Leaf, Porsche Taycan, Zeekr, and Chevrolet Volt—strategic technologies for the placement and packaging of batteries, and battery cooling and monitoring systems (State of Health and State of Charge) are also discussed. In conclusion, the current challenges in the field are summarized and promising research directions are proposed.

An equivalent circuit model (ECM) is a highly practical tool for simulating Li-ion battery behavior. There are many relevant studies which compare different ECM variants or suggest algorithms to extract model parameters from the experimental data. However, little attention has been given to the battery tests used for identification of the ECM parameters. Therefore, here the influence of experimental test pulse characteristics on the parameterized ECM accuracy was systematically studied. The test pulse duration was varied in a wide range from 9 s to about 2.5 min. The portion of the relaxation phase data used by the parameter optimization algorithm was also varied in an even wider range. Total 168 ECM parameter sets were obtained. Each parameter set was validated using nine diverse current profiles representing different battery operation conditions, including one based on Urban Dynamometer Driving Schedule (UDDS). The validation results prove that the impact of the test pulse choice on the parameterized ECM accuracy is great to the point that it can overshadow the use of a higher-order Thevenin model. By choosing the optimal parameter set, the simulated voltage root mean square error (RMSE) was reduced to as low as 3.0 mV and 1.2 mV for first- and second-order ECM, respectively, while the second-order model based on arbitrary chosen test pulse on average yields RMSE value above 5 mV.

An equivalent circuit model (ECM) is a highly practical tool for simulating Li-ion battery behavior. There are many relevant studies which compare different ECM variants or suggest algorithms to extract model parameters from the experimental data. However little attention has been given to the battery tests used for identification of the ECM parameters. Therefore, we systematically studied the influence of experimental test pulse characteristics on the parameterized ECM accuracy. Test pulse duration and amplitude were varied along with the portion of the relaxation phase data used by the parameters fitting algorithm. That resulted in 168 parameter sets, each validated using 9 diverse current profiles including one based on a realistic drive cycle. The validation results prove that the impact of the test pulse choice on the parameterized ECM accuracy is great to the point it can overshadow the use of a higher order Thevenin model. By choosing the optimal parameter set the simulated voltage root mean square error was reduced to as low as 1.2 mV.