Electric vehicles(EVs)powered by lithium-ion batteries have emerged as a global development trend.To ensure the safe and stable driving of EVs,it is imperative to address battery safety and thermal management issues,w...Electric vehicles(EVs)powered by lithium-ion batteries have emerged as a global development trend.To ensure the safe and stable driving of EVs,it is imperative to address battery safety and thermal management issues,which rely heavily on the precise state-of-charge(SOC)estimation of the battery.However,estimating SOC under uncontrolled environmental temperatures remains an unresolved challenge.This study proposes a patch-level representation learning model based on domain knowledge to estimate the SOC over a wide temperature range.First,patches were adopted as inputs instead of traditional points,thereby mitigating error accumulation and capturing dynamic changes in the battery from these more informative representations.Second,the open-circuit voltage(OCV)-SOC-temperature relationship was incorporated to obtain the temperature-related SOC priors.Subsequently,the prior was updated recursively along the time dimension to obtain a more precise SOC estimate.The accuracy of the proposed model was confirmed experimentally for three driving cycles at six ambient temperatures,significantly reducing the root mean square error by 48.19%compared to popular existing models.Notably,the performance of the proposed method had an excellent improvement of 51.52% and 57.20% at-10℃ and-20℃,respectively.Moreover,the parameter size of the proposed method was 39.748 KB,which significantly promoted the deployment and application of data-driven models in the real world.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61973247)the National Postdoctoral Innovative Talents Support Program of China(Grant No.BX20200272)。
文摘Electric vehicles(EVs)powered by lithium-ion batteries have emerged as a global development trend.To ensure the safe and stable driving of EVs,it is imperative to address battery safety and thermal management issues,which rely heavily on the precise state-of-charge(SOC)estimation of the battery.However,estimating SOC under uncontrolled environmental temperatures remains an unresolved challenge.This study proposes a patch-level representation learning model based on domain knowledge to estimate the SOC over a wide temperature range.First,patches were adopted as inputs instead of traditional points,thereby mitigating error accumulation and capturing dynamic changes in the battery from these more informative representations.Second,the open-circuit voltage(OCV)-SOC-temperature relationship was incorporated to obtain the temperature-related SOC priors.Subsequently,the prior was updated recursively along the time dimension to obtain a more precise SOC estimate.The accuracy of the proposed model was confirmed experimentally for three driving cycles at six ambient temperatures,significantly reducing the root mean square error by 48.19%compared to popular existing models.Notably,the performance of the proposed method had an excellent improvement of 51.52% and 57.20% at-10℃ and-20℃,respectively.Moreover,the parameter size of the proposed method was 39.748 KB,which significantly promoted the deployment and application of data-driven models in the real world.
