Temperature-Dependence in Battery Management Systems for Electric Vehicles: Challenges, Criteria, and Solutions

Abstract

Owing to a global effort towards reducing carbon emissions, electric vehicles (EVs) have emerged to replace the petroleum-fueled vehicles. However, the battery is a bottleneck restricting EVs from being utilized in the same way as petroleum-fueled vehicles. Lithium-ion batteries (LiBs) are commonly used in EVs, but have an optimal temperature range, and operation outside this range causes accelerated aging in the form of capacity fading and power fading, especially in cold climates. We propose that both state parameter estimation and thermal management are interconnected problems and should be addressed together: Battery health and performance depends on temperature, while temperature depends on operational conditions, battery health, structural design and thermal management. Temperature dependent decay accounting for heat generation in cells, temperature variation between cells and heat transfer with surroundings, can allow more accurate state parameter estimation and guide thermal management strategies. This review investigates how the dynamics of temperature dependence and heat generation are addressed in the literature related to estimation of battery state parameters. Approaches involving temperature were divided into three categories: 1) maintain constant ambient temperature and omit battery temperature, 2) verify at different ambient temperatures, and 3) use available data for cell and/or ambient temperature. A valid solution to the problem in real applications, must satisfy three criteria: a) suitable for online applications, b) scalable to battery packs, and c) applicable to dynamic battery cycling occurring during normal use. The most promising methods include coupled thermal and electric models with adaptive filtering, and recurrent neural network methods.

Degradation of Lithium-Ion Batteries in an Electric Transport Complex

“…The battery capacity is 2900 mAh. Typical battery performance is show in Figure 7 [111]. When testing the cell at different depths of discharge at a temperature of 250 C, it was determined that with a decrease in the used capacity, the number of cycles increases significantly.…”

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

“…Different approaches for more or less direct measurements of state of health exist and are proposed for online SOH estimation. Some of these can be based on continuous measurements such as time series of currents, voltages and temperatures, whereas others are based on measurements collected during particular experiments or procedures (Karlsen, Dong, Yang, & Carvalho, 2019). For example, the annual test currently required for maritime battery systems used for propulsion utilizes a coulomb counting technique and a controlled charging/discharging procedure.…”

“…The effect of temperature may be included in such models by coupling the ECM with an energy balance or thermal model, see e.g. (Karlsen et al, 2019;Bian, Liu, Yan, Zou, & Zhao, 2020).…”

Review on Battery Management System for Electric Vehicle Application

“…This has the major concern towards the power quality issues such as Voltage sag, Voltage Swell, harmonics and power demand by employing the ac charging system. The fault diagnosis(Battery Prognostics and Health Management) also plays a vital role in determine the performance and safety of the battery system [88][89][90][91][92][93][94].Even though different power quality mitigation techniques are found, there is a problem in the grid. The peak demand should not coincide with battery charging of an electric vehicle.…”