Tool/software:
Hello
We want to validate: the Qmax and FCC predicted by FuelgaugeIC after FuelgaugeIC learnt by self-learning (Qmax*37%) for batteries with different degrees of aging.
Currently validated for batteries with >70% aging.
Batteries with SOH = 76% have a Qmax value = 1086mAh.
Batteries with less than 60% aging are not able to meet Qmax*37% according to the charge/discharge threshold set by our application.
Reserve capacity in the app = 350mAh.
So, wondering:
Q1, What is the maximum allowable battery aging by performing the self-learning process (passedcharge>Qmax*37%)?
Q2, Is it possible to modify the value of Qmax by directly modifying the Qmax Cell 0 register value so that the capacity needed for Qmax*37% can be appropriately reduced? What is the effect after modification?
Q3, if Q2 is possible, then if Qmax=1000mAh is modified, are there any other registers that need to be modified together? How to modify?
我们要验证:针对不同程度的老化电池,FuelgaugeIC通过自学习(Qmax*37%)后,FuelgaugeIC预测的Qmax和FCC。
当前验证了老化程度>70%的电池。
SOH=76%的电池,Qmax值=1086mAh。
电池老化程度低于60%的电池,按照我们应用设定的充放电阈值,无法满足Qmax*37%。
应用中储备容量=350mAh。
所以,想知道:
Q1、执行自学习过程(passedcharge>Qmax*37%),最大允许的电池老化度是多少?
Q2、是否可以通过直接修改Qmax Cell 0寄存器值的方式,修改Qmax的值,使Qmax*37%需要的容量可以适当减小?修改后有什么影响?
Q3、Q2如果可以,那么修改Qmax=1000mAh,是否有其它寄存器需要一起修改?如何修改?