针对目前电池荷电状态(stage of charge,SOC)估计算法存在稳定性差、误差大等缺点,提出一种基于实车云端放电数据的自适应扩展卡尔曼滤波(adaptive extended Kalman filter,AEKF)与长短时记忆(long short term memory,LSTM)融合的算法,...针对目前电池荷电状态(stage of charge,SOC)估计算法存在稳定性差、误差大等缺点,提出一种基于实车云端放电数据的自适应扩展卡尔曼滤波(adaptive extended Kalman filter,AEKF)与长短时记忆(long short term memory,LSTM)融合的算法,预测小动力电动车的电池SOC。首先采用自适应遗忘因子最小二乘法(adaptive forgetting factor recursive least squares,AFFRLS)辨识电池的二阶RC等效电路模型参数。其次,将云端实时采集到的放电数据作为研究目标,通过AEKF-LSTM融合算法对小动力电动车的电池SOC进行预测实验,实验过程中AEKF-LSTM融合算法将当前时刻的端电压、电流、温度以及上一时刻电池的SOC作为输入,以更新的SOC作为输出训练估计模型。最后,将AEKF-LSTM融合算法和单一AEKF算法预测电池SOC的结果与实际SOC值进行比较,实验结果表明,AEKF-LSTM融合算法的均方根误差(root mean square error,RMSE)为0.0058 V,平均绝对误差(mean absolute error,MAE)为0.0041 V,比AEKF算法的RMSE减小0.0087 V,MAE减小0.1164 V,且AEKF-LSTM融合算法的RMSE和MAE均在0.6%以内,证明了该融合算法有较高的估计精度和较强的鲁棒性。展开更多
Naturally degradable capsule provides a platform for sustained fragrance release.However,practical challenges such as low encapsulation efficiency and difficulty in sustained release are still limited in using fragran...Naturally degradable capsule provides a platform for sustained fragrance release.However,practical challenges such as low encapsulation efficiency and difficulty in sustained release are still limited in using fragranceloaded capsules.In this work,the natural materials sodium alginate and gelatine are dissolved and act as the aqueous phase,lavender is dissolved in caprylic/capric triglyceride(GTCC)as the oil phase,and SiO_(2) nanoparticles with neutralwettability as a solid emulsifier to form O/W Pickering emulsions simultaneously.Finally,multi-core capsules are prepared using the drop injection method with emulsions as templates.The results show that the capsules have been successfully prepared with a spherical morphology and multi-core structure,and the encapsulation rate of multi-core capsules can reach up to 99.6%.In addition,the multi-core capsules possess desirable sustained release performance,the cumulative sustained release rate of fragrance at 25℃over 49 days is only 32.5%.It is attributed to the significant protection of multi-core structure,Pickering emulsion nanoparticle membranes,and hydrogel network shell for encapsulated fragrance.This study is designed to deliver a new strategy for using sustained-release technology with fragrance in food,cosmetics,textiles,and other fields.展开更多
文摘针对目前电池荷电状态(stage of charge,SOC)估计算法存在稳定性差、误差大等缺点,提出一种基于实车云端放电数据的自适应扩展卡尔曼滤波(adaptive extended Kalman filter,AEKF)与长短时记忆(long short term memory,LSTM)融合的算法,预测小动力电动车的电池SOC。首先采用自适应遗忘因子最小二乘法(adaptive forgetting factor recursive least squares,AFFRLS)辨识电池的二阶RC等效电路模型参数。其次,将云端实时采集到的放电数据作为研究目标,通过AEKF-LSTM融合算法对小动力电动车的电池SOC进行预测实验,实验过程中AEKF-LSTM融合算法将当前时刻的端电压、电流、温度以及上一时刻电池的SOC作为输入,以更新的SOC作为输出训练估计模型。最后,将AEKF-LSTM融合算法和单一AEKF算法预测电池SOC的结果与实际SOC值进行比较,实验结果表明,AEKF-LSTM融合算法的均方根误差(root mean square error,RMSE)为0.0058 V,平均绝对误差(mean absolute error,MAE)为0.0041 V,比AEKF算法的RMSE减小0.0087 V,MAE减小0.1164 V,且AEKF-LSTM融合算法的RMSE和MAE均在0.6%以内,证明了该融合算法有较高的估计精度和较强的鲁棒性。
文摘Naturally degradable capsule provides a platform for sustained fragrance release.However,practical challenges such as low encapsulation efficiency and difficulty in sustained release are still limited in using fragranceloaded capsules.In this work,the natural materials sodium alginate and gelatine are dissolved and act as the aqueous phase,lavender is dissolved in caprylic/capric triglyceride(GTCC)as the oil phase,and SiO_(2) nanoparticles with neutralwettability as a solid emulsifier to form O/W Pickering emulsions simultaneously.Finally,multi-core capsules are prepared using the drop injection method with emulsions as templates.The results show that the capsules have been successfully prepared with a spherical morphology and multi-core structure,and the encapsulation rate of multi-core capsules can reach up to 99.6%.In addition,the multi-core capsules possess desirable sustained release performance,the cumulative sustained release rate of fragrance at 25℃over 49 days is only 32.5%.It is attributed to the significant protection of multi-core structure,Pickering emulsion nanoparticle membranes,and hydrogel network shell for encapsulated fragrance.This study is designed to deliver a new strategy for using sustained-release technology with fragrance in food,cosmetics,textiles,and other fields.
