Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system(BMS),where building an accurate battery model is the first step in model-based estimation algorith...Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system(BMS),where building an accurate battery model is the first step in model-based estimation algorithms.To date,although the comparative studies on different battery models have been performed intensively,little attention is paid to the comparison among different online parameters identification methods regarding model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost.In this paper,based on the Thevenin model,the three most widely used online parameters identification methods,including extended Kalman filter(EKF),particle swarm optimization(PSO),and recursive least square(RLS),are evaluated comprehensively under static and dynamic tests.It is worth noting that,although the built model’s terminal voltage may well follow a measured curve,these identified model parameters may significantly out of reasonable range,which means that the error between measured and predicted terminal voltage cannot be seen as a gist to determine which model is the most accurate.To evaluate model accuracy more rigorously,battery state-of-charge(SOC)is further estimated based on identified model parameters under static and dynamic tests.The SOC prediction results show that EKF and RLS algorithms are more suitable to be used for online model parameters identification under static and dynamic tests,respectively.Moreover,the random offset is added into originally measured data to verify the robustness ability of different methods,whose results indicate EKF and RLS have more satisfactory ability against imprecisely sampled data under static and dynamic tests,respectively.Considering model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost simultaneously,EKF is recommended to be adopted to establish battery model in real application among these three most widely used methods.展开更多
Polarization-based integrated navigation system(PINS)that combines the polarization sensor(PS)and the inertial navigation system(INS)has been widely recognized as an effective solution for acquiring attitude informati...Polarization-based integrated navigation system(PINS)that combines the polarization sensor(PS)and the inertial navigation system(INS)has been widely recognized as an effective solution for acquiring attitude information of unmanned aerial vehicles(UAVs).However,based on the PINS hardware configuration,the accurate acquisition of UAV position information remains a challenge.In this article,we propose an improved PS/INS integrated navigation scheme by incorporating an embedded UAV dynamic model(UDM).Compared with existing PS/INS fusion methods,the presented PINS enables the optimal estimation of the UDM thrust coefficient error along with other system state elements,thus significantly improving the UDM accuracy.On this basis,the UDM and PS are used to fuse with the INS,which improves the estimation accuracy of both the UAV attitude and position.Furthermore,we employ an adaptive fusion strategy to detect the reliability of PS data.Therefore,once the UDM is corrected using reliable PS data,it can further fuse with the INS,thereby improving the environmental adaptability of the PINS.The simulation and flight experiment results verified the effectiveness of the proposed PS/INS/UDM integrated navigation scheme.展开更多
基金supported by the State Grid Company Science and Technology Project(Grant No.5230HQ19000J).
文摘Precise states estimation for the lithium-ion battery is one of the fundamental tasks in the battery management system(BMS),where building an accurate battery model is the first step in model-based estimation algorithms.To date,although the comparative studies on different battery models have been performed intensively,little attention is paid to the comparison among different online parameters identification methods regarding model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost.In this paper,based on the Thevenin model,the three most widely used online parameters identification methods,including extended Kalman filter(EKF),particle swarm optimization(PSO),and recursive least square(RLS),are evaluated comprehensively under static and dynamic tests.It is worth noting that,although the built model’s terminal voltage may well follow a measured curve,these identified model parameters may significantly out of reasonable range,which means that the error between measured and predicted terminal voltage cannot be seen as a gist to determine which model is the most accurate.To evaluate model accuracy more rigorously,battery state-of-charge(SOC)is further estimated based on identified model parameters under static and dynamic tests.The SOC prediction results show that EKF and RLS algorithms are more suitable to be used for online model parameters identification under static and dynamic tests,respectively.Moreover,the random offset is added into originally measured data to verify the robustness ability of different methods,whose results indicate EKF and RLS have more satisfactory ability against imprecisely sampled data under static and dynamic tests,respectively.Considering model accuracy,robustness ability,adaptability to the different battery operating conditions and computation cost simultaneously,EKF is recommended to be adopted to establish battery model in real application among these three most widely used methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.62388101,62425302,62227813,62373033,62403024)the National Key R&D Program of China(Grant No.2020YFA0711200)。
文摘Polarization-based integrated navigation system(PINS)that combines the polarization sensor(PS)and the inertial navigation system(INS)has been widely recognized as an effective solution for acquiring attitude information of unmanned aerial vehicles(UAVs).However,based on the PINS hardware configuration,the accurate acquisition of UAV position information remains a challenge.In this article,we propose an improved PS/INS integrated navigation scheme by incorporating an embedded UAV dynamic model(UDM).Compared with existing PS/INS fusion methods,the presented PINS enables the optimal estimation of the UDM thrust coefficient error along with other system state elements,thus significantly improving the UDM accuracy.On this basis,the UDM and PS are used to fuse with the INS,which improves the estimation accuracy of both the UAV attitude and position.Furthermore,we employ an adaptive fusion strategy to detect the reliability of PS data.Therefore,once the UDM is corrected using reliable PS data,it can further fuse with the INS,thereby improving the environmental adaptability of the PINS.The simulation and flight experiment results verified the effectiveness of the proposed PS/INS/UDM integrated navigation scheme.
