A novel procedure combining computational fluid dynamics and evolutionary approach to minimize parasitic power loss in air cooling of Li‐ion battery for thermal management system design

Abstract

Air‐cooling‐based battery thermal management system (BTMS) is a research hotspot for electric vehicles because of lower cost and simpler design. Past research works have immensely concentrated on the enhancement of heat removal from Li‐ion battery, but the minimum consideration has been given to minimize the parasitic power. In this paper, a novel procedure is proposed to predict the operating parameters (inlet velocity, working time of fan, and range of heat generation rates) of air‐cooling design for minimizing parasitic power and ensuring the battery temperature do not exceed the upper threshold limit simultaneously. Based on findings via computational fluid dynamics analysis, an empirical model of air‐cooling BTMS is further developed using an evolutionary approach of model selection criteria approximated genetic programming (MSC‐GP). The model is then optimized to determine operating parameters of air cooling which causes minimum parasitic power while keeping the average temperature of battery cells within the limit. Further, sensitivity analysis and parameter interaction (2D and 3D) analysis is also performed to study the effect and identify the contribution of various operating parameters on parasitic power. Operating time of fan has 49% influence on final temperature while inlet velocity has 36% influence only. However parasitic power is more sensitive to inlet velocity (77%) while operating time has 23% influence only. Finally, the optimal values of operating parameters for various heat generation rate per cell (function of discharge rate) are obtained. The optimized parasitic power was observed to be nonlinearly increasing with heat generation rate. Operating time of fan has 49% influence on final temperature while inlet velocity has 36% influence only. However, parasitic power is more sensitive to inlet velocity (77%) while operating time has 23% influence only.

The significance of detecting imperceptible physical/chemical changes/reactions in lithium-ion batteries: a perspective

“…This heat flow source is represented by ÀI Z . Further, eqn (18) gives the calculation method 29,34 (note: this method is dependent on the good CE of greater than 99%):…”

“…This heat flow source is represented by − I η . Further, eqn (18) gives the calculation method 29,34 (note: this method is dependent on the good CE of greater than 99%):The d Q d /d t and d Q c / d represent the thermal power during the discharging and charging processes, respectively. The second bracketed term in eqn (18) is the integration of voltage hysteresis in the same cycle.…”

“…25,26 Isothermal microcalorimetry is also a sensitive technique indicating parasitic reaction, since the parasitic heat flow is voltage-and time-dependent, and the testing is relatively simple and quick. [27][28][29] Extracting each component (e.g., parasitic reactions, polarization and entropy) from the total heat flow has proven to be valuable. This approach will help understand the function of electrolyte additives and provide some insight into choosing the right mixture of battery chemistry and electrolyte additives under the desired operating conditions.…”

“…Isothermal microcalorimetry is also a sensitive technique indicating parasitic reaction, since the parasitic heat flow is voltage- and time-dependent, and the testing is relatively simple and quick. 27–29 Extracting each component ( e.g. , parasitic reactions, polarization and entropy) from the total heat flow has proven to be valuable.…”

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

“…For higher discharge rates, liquid cooling is preferred. There is power consumption for performing real-time thermal management based on the pressure difference, flowrate and inlet temperature of air/liquid [32]. Therefore, the reconfiguration optimisation problem should also take into account heat generation due to switches and cells and its corresponding power consumption as additional factors in its objective function of the reconfiguration optimization problem.…”