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| 1 | Gastrodin blocks neural stem cell differentiation into glial cells mediated by kainic acid显示文摘Kainic acid can simulate excitatory amino acids in vitro.Neural stem cells,isolated from newborn Wistar rats,were cultured in vitro and exposed to 100-4 000 μM kainic acid for 7 days to induce neuronal cell differentiation,causing the number of astrocytes to be significantly increased.Treatment with a combination of 0.5 mg/L gastrodin and kainic acid also caused the number of differentiated neurons to be significantly increased compared with treatment with kainic acid alone.Experimental findings suggest that gastrodin reduces the excitability of kainic acid and induces neural stem cell differentiation into neurons. | Guifang Sun Zhihao Yuan Boai Zhang Yanjie Jia Yangfei Ji Xingrong Ma Yu Liu Yanru Liu Quanqing Wen Yanling Zhao | 2012 | Neural Regeneration Research2012，7，12： | 4 |
| 2 | Online power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning and driving cycle reconstruction显示文摘This paper proposes a novel power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning algorithm.Three parallel soft actor-critic(SAC)networks are trained for high speed,medium speed,and low-speed conditions respectively;the reward function is designed as minimizing the cost of energy cost and battery aging.During operation,the driving condition is recognized at each moment for the algorithm invoking based on the learning vector quantization(LVQ)neural network.On top of that,a driving cycle reconstruction algorithm is proposed.The historical speed segments that were recorded during the operation are reconstructed into the three categories of high speed,medium speed,and low speed,based on which the algorithms are online updated.The SAC-based control strategy is evaluated based on the standard driving cycles and Shenyang practical data.The results indicate the presented method can obtain the effect close to dynamic programming and can be further improved by up to 6.38%after the online update for uncertain driving conditions. | Zhiyuan Fang Zeyu Chen Quanqing Yu Bo Zhang Ruixin Yang | 2022 | Green Energy and Intelligent Transportation2022，1，2： | 1 |
| 3 | A branch current estimation and correction method for a parallel connected battery system based on dual BP neural networks显示文摘In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery cell.If the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches,there will be a large error between the calculated branch current and the real branch current.Adding current sensors to measure each branch current is not practical because of the high cost.Accurate estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously inconsistent.This paper puts forward a method to estimate and correct branch currents based on dual back propagation(BP)neural networks.In the proposed method,one BP neural network is used to estimate branch currents,the other BP neural network is used to reduce the estimation error cause by current pulse excitations.Furthermore,this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence differences.The effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel. | Quanqing Yu Yukun Liu Shengwen Long Xin Jin Junfu Li Weixiang Shen | 2022 | Green Energy and Intelligent Transportation2022，1，2： | 0 |
| 4 | Applications of AI in advanced energy storage technologies显示文摘The prompt development of renewable energies necessitates advanced energy storage technologies,which can alleviate the intermittency of renewable energy.In this regard,artificial intelligence(AI)is a promising tool that provides new opportunities for advancing innovations in advanced energy storage technologies(AEST).Given this,Energy and AI organizes a special issue entitled“Applications of AI in Advanced Energy Storage Technologies(AEST)”. | Rui Xiong Hailong Li Quanqing Yu Alessandro Romagnoli Jakub Jurasz Xiao-Guang Yang | 2023 | Energy and AI2023，13，3： | 0 |
| 5 | Battery aging-minimal speed control of autonomous heavy-duty electric trucks in adaptation to highway topography and traffic显示文摘The development of battery electric(BE)heavy-duty trucks(HDTs)is highly limited to the short cycling life of batteries.In this paper,we propose a battery aging-conscious control strategy for extended battery life by optimizing the speed trajectory of BE HDT.A state-space model is constructed by connecting the vehicle dynamics and battery state of charge,and a mechanism-based aging model of battery is then introduced to formulate the optimization problem for minimal battery aging and energy consumption.The optimization problem is solved within a model predictive control framework for the real-time speed control of the vehicle.A non-cooperative platooning controller is further developed for the vehicle in adaptation to the traffic,where the intervehicular distance is controlled for reducing the air drag coefficient so that both the energy consumption and battery aging are improved.Simulation results show that for the single-vehicle controller,the battery degradation and energy consumption are,respectively,reduced by up to 25.7%and 3.2%compared with the cruise control strategy.Based on the non-cooperative controller,the HDT is able to follow preceding vehicles with different parameters with battery aging and energy consumption further,respectively,reduced by 2%–5%and 9%–10%compared with those of the single-vehicle controller. | ZHANG YongZhi WANG Chun YU QuanQing ZHENG Ling | 2023 | Science China(Technological Sciences)2023，66，10： | 0 |
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