Dynamic EV Battery Health Recommendations

Abstract

Prolonging the lifetime of batteries in Electric Vehicles (EVs) becomes a more and more important issue for private users and fleet operators. In addition to the environmental point of view, a better battery health results in less cost, higher battery capacities and higher performance. To achieve this, the EV drivers or the fleet operators need to get proper information, which kind of actions will increase or decrease the batteries health. To this point, various tips and recommendations exist distributed over literature. Unfortunately, those kind of recommendations are hard to follow in the day-today routine. This paper suggests so called dynamic recommendations for battery health that are able to advise the user in specific situations with respect to battery use. Recommendations from literature are broken down into a list, which can be automatically computed. Recommendations will then be dynamically created in the current context of the EV and displayed to the user just in time. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models; Activity centered design; • Applied computing → Physics; • Information systems → Data analytics;

Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology

“…To extend its uptime, the lithium NMC battery should be operating in the “battery life safe zone” rather than using the full range from 0% to 100%. Based on research [ 4 , 5 , 27 , 28 ], it has been proven that the optimal discharge level for batteries is between around 20% to 90%. Charging the battery to 90% and not to 100% significantly reduces the battery charging time.…”

“…Predictive monitoring on the other hand focuses on optimizing the production in real time, based on the monitored parameters in industrial applications [ 2 , 3 ]. In the context of battery management, predictive monitoring is widely used in electric vehicles, where the battery state of charge data can be used to plan the optimal route of the vehicle [ 4 ] but also to optimize the health of the battery [ 5 ] and its charging process [ 6 , 7 ]. Predictive monitoring does not have to be limited to industrial applications.…”

Quantitative Ultrasound Spectroscopy for Screening Cylindrical Lithium‐Ion Batteries for Second‐Life Applications

“…[7,8] The end of life for LIBs in electric vehicles is reached at approximately 70-80 % of their State of Health (SOH) (defined as the remaining percentage of the capacity compared to the pristine state) [9] because this has been defined as a point where batteries would not satisfy the mobility needs of consumers. [10] This threshold has been revised by some authors, [11,12] but, regardless, retired LIBs from EVs present substantial opportunities for repurposing as second-life batteries (SLBs) in lower power demand applications. [13] Such life extension reduces the pressure to mine high-value and hard-toextract minerals used to manufacture new LIBs, and further avoids early recycling.…”

Electrical Vehicle Fleet Routing Accounting for Dynamic Battery Degradation

“…In parallel, recent research has also focused on how to address such factors, analyzing driving techniques for predictive energy management of EVs [6], or dynamic recommendations based on driving patterns [7]. However, even the most recent works [5] only addresses the degradation modeling within routing problems to a limited extent.…”