Predicting the state of charge and health of batteries using data-driven machine learning

Abstract

W ith rising concerns about global warming, electrification of transport has recently emerged as an important vision in many countries. The successful development of electric vehicles (EVs) depends highly on the cycling performance, cost and safety of the batteries. Rechargeable lithium-ion (Li-ion) batteries are currently the best choice for EVs due to their reasonable energy density and cycle life 1 . Further research and development on Li-ion batteries will lead to even higher energy density and more complicated battery dynamics, where the efficiency and safety of such batteries will become a concern. An advanced battery management system (BMS) that can monitor and optimize battery behaviour and safety is thus essential for the entire electrification system 2 .Today, one of the major barriers to widespread adoption of EVs is range anxiety. The ability of a BMS to accurately determine the state of charge (SOC) and state of health (SOH) of batteries, and hence the estimated driving range, will alleviate this problem. In addition, reliable prediction of remaining useful life (RUL) will allow batteries to be used to their fullest potential and maximum life expectancy before replacement or disposal. Knowledge of the RUL of spent batteries will also enable their redeployment in less demanding, second-life applications such as stationary grid storage. If we are able to sort manufactured cells based on their expected lifetime using early-cycle data, we can further accelerate the testing, validation and development process of new batteries. In summary, accurate prediction of the current and future state of batteries will open up vast opportunities in battery manufacturing, usage and optimization 3,4 . SOC and SOH are the two most important parameters in battery management and are generally defined as:where C curr is the capacity of the battery in its current state, C full is the capacity of the battery in its fully charged state, C nom is the nominal capacity of the brand-new battery 2 . In essence, SOC denotes the capacity of the battery in its current state compared to the capacity in its fully charged state (equivalent of a fuel gauge), while SOH describes the capacity of the battery in its fully charged state compared to the nominal capacity when brand new. By convention, SOC is 100% when the battery is fully charged and 0% when it is empty, while SOH is 100% at the time of manufacture and reaches 80% at end of life (EOL). In the battery manufacturing industry, EOL is often defined as the point at which the actual capacity at full charge drops to 80% of its nominal value 2 . The remaining number of charge/discharge cycles until the battery reaches EOL is the RUL of the battery. Current BMSs can determine the SOC of Li-ion batteries within 0.6% to 6.5% 5 , but are unable to predict the SOH and RUL of batteries accurately 6 .The traditional methods for SOC estimation include ampere hour counting estimation, open-circuit voltage-based estimation, impedance-based estimation, model-based estimation, fuzzy logic, and Kal...

An Overview of Artificial Intelligence Applications for Power Electronics

“…In addition to its expected value, the uncertainty metrics with the lower and upper confidence interval (l lo , l up ) are also of great importance. AI methods in RUL prediction is typically dealing with a nonlinear regression between the degradation information and the corresponding RUL based on the training dataset [167]. In this way, degradation patterns can be characterized.…”

An overview of artificial intelligence applications for power electronics

“…In addition to its expected value, the uncertainty metrics including the lower and upper confidence interval (l lo , l up ) are also of great importance. AI methods in RUL prediction is typically dealing with a nonlinear regression between the degradation information and the corresponding RUL based on the training dataset [166]. In this way, degradation patterns can be characterized.…”

A Taillight Matching and Pairing Algorithm for Stereo-Vision-Based Nighttime Vehicle-to-Vehicle Positioning

“…When the pixel coordinates of each taillight in the left and right images have been determined, the world coordinates of this taillight can be calculated in stage (4). Finally, given the world coordinates of each pair of taillights, the world coordinates of the corresponding vehicle are estimated in stage (5). An overview of this process is presented in Figure 1.…”

“…Therefore, recent work on both scientific and industrial sides has focused on constructing an intelligent transport system (ITS), in which autonomous vehicles are arguably the most attractive part [1][2][3]. It is partly due to the rapid development of artificial intelligent approaches that their contributions have been shown in several fields in recent years [4][5][6]. However, since the final target is completely replacing the traditional vehicles with the autonomous ones, there are still many challenges ahead that need to be resolved.…”