采用976 nm锁波长激光二极管(LD)双向泵浦掺Yb全光纤激光器,单谐振腔输出1.2 k W近单模激光,总光光转换效率为70.8%,光束质量M_x^2≈1.03,M_y^2≈1.55,实验验证在千瓦功率量级内,正、反泵浦相互影响不明显。光纤激光器从阈值电流到最大...采用976 nm锁波长激光二极管(LD)双向泵浦掺Yb全光纤激光器,单谐振腔输出1.2 k W近单模激光,总光光转换效率为70.8%,光束质量M_x^2≈1.03,M_y^2≈1.55,实验验证在千瓦功率量级内,正、反泵浦相互影响不明显。光纤激光器从阈值电流到最大电流范围内,输出功率随泵浦功率曲线基本线性,在1 k W功率下做8小时稳定性测试,稳定度在2%以下。激光器可在宽温度范围内工作,温度循环试验表明,输出功率随温度变化具有较好的一致性。展开更多
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
文摘采用976 nm锁波长激光二极管(LD)双向泵浦掺Yb全光纤激光器,单谐振腔输出1.2 k W近单模激光,总光光转换效率为70.8%,光束质量M_x^2≈1.03,M_y^2≈1.55,实验验证在千瓦功率量级内,正、反泵浦相互影响不明显。光纤激光器从阈值电流到最大电流范围内,输出功率随泵浦功率曲线基本线性,在1 k W功率下做8小时稳定性测试,稳定度在2%以下。激光器可在宽温度范围内工作,温度循环试验表明,输出功率随温度变化具有较好的一致性。
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.
