Abstract: A battery diagnosing apparatus according to one embodiment of the present disclosure includes: a profile acquisition unit that acquires a battery profile representing a correspondence between a voltage and a capacity of a battery; and a control unit that divides a capacity section of the battery profile into a plurality of sections, derives a target value for one target index related to a differential capacity peak or a charging capacity ratio among a plurality of diagnosis indices set in advance, from each of the divided sections, compares a correspondence between the derived plurality of target values with a preset reference profile that represents a correspondence between a plurality of target indices, and diagnoses a state of the battery based on a result of the comparison.

Abstract: Methods and apparatus consistent with the present disclosure may use a plurality of force sensing devices that allow a computer of an autonomous vehicle (AV) to identify locations within the AV where particular objects reside. These force sensing devices may allow the AV computer to identify how much a particular object weights and identify whether an object has moved based on a location where a particular amount of weight currently resides versus a location where that weight previously resided in the AV. Weight information combined with other sensor data (e.g. camera or LIDAR data) may allow the AV computer to identify whether a particular object is a person or a package. The AV computer may also validate that certain driving rules are enforced, such that persons or packages are being moved safely and correctly in the AV.

Abstract: A device for simulating suction penetration of suction caisson based on centrifuge model test includes a model box, a suction caisson model in the model box and containing a suction caisson and a connecting rod, a penetration device disposed above the model box and movably connected to the connecting rod, a vacuum system connected to the suction caisson, a lower part of the suction caisson is provided with soil, and an upper part of the soil is provided with a water body. The penetration device includes a guide rail and a drive lever. The drive lever penetrates through the model box and then is connected to an electromagnetic adsorption device, a bottom end of which includes a guide housing, and the connecting rod is inserted into the guide housing. The device can dynamically correct displacement deviation in a suction penetration process, thereby being continuously tested in a centrifugal state.

Abstract: A secondary battery degradation determination apparatus configured to perform degradation determination for a secondary battery as a determination target includes a processor. The processor is configured to calculate an estimated electric capacity of the secondary battery, based on at least a slope between any two points on a charging curve indicating a charging characteristic of the secondary battery; and perform the degradation determination for the secondary battery, based on the estimated electric capacity that is calculated.

Abstract: This load estimating device for a rolling bearing comprises: a vibration sensor for measuring vibrations of the rolling bearing during rotation; a rotational speed sensor for measuring the rotational speed of the rolling bearing during rotation; a deriving means for deriving a vibration value for a predetermined vibration frequency, using vibration information measured by the vibration sensor; and an estimating means for estimating a load acting on the rolling bearing, said load corresponding to the rotational speed measured by the rotational speed sensor and the vibration value derived by the deriving means, using a table defining a correspondence relationship between the load acting on the rolling bearing, the vibration value for the predetermined vibration frequency, and the rotational speed.

Abstract: The subject matter of this specification can be embodied in, among other things, method for testing operation of a thrust reverser actuator lock includes determining, by a controller, that an actuator output of a controllable actuator is at a predetermined first position, locking, by the controller, an actuator lock configured to prevent movement of the actuator output of an actuator away from the predetermined first position, initiating, by the controller, actuation of the actuator output away from the predetermined first position, determining, by the controller based on a position feedback sensor, a second position of the actuator output, determining, by the controller, a nominal condition of the actuator lock based on determining that the second position is within a predetermined threshold distance from the predetermined first position of the actuator output, and providing, by the controller and based on the determined nominal condition, a signal indicative of the nominal condition.

Abstract: Processing circuitry functionally includes: an internal resistance calculation unit; a reference value obtaining unit configured to obtain a reference value for internal resistance; a correction coefficient calculation unit configured to calculate a correction coefficient indicating how great difference between the internal resistance and the reference value is: a correction value calculation unit configured to calculate a correction value by correcting the reference value using the correction coefficient; and an estimation unit configured to calculate estimated internal resistance, based on the correction value and present voltage. The processing circuitry is configured to control charge and/or discharge of the battery, based on the internal resistance or the estimated internal resistance.

Abstract: Circuitry for a resistance of an electrochemical cell comprising at least one first electrode and a second electrode, the circuitry comprising: drive circuitry configured to apply a stimulus to the at least one first electrode of the electrochemical cell; sense circuitry configured to measure a response of the electrochemical cell to the stimulus, the response comprising a faradaic component and a non-faradaic component; and processing circuitry configured to: sample the response at a sample time, the sample time selected to maximise a ratio of the non-faradaic component to the faradaic component; and determine the resistance of the electrochemical cell based on the response at the sample time.

Abstract: A second polymer is prepared through derivatization of a first polymer. Kendrick Mass Defect (KMD) analysis is applied on a mass spectrum of the second polymer, to thereby produce a plot. Meanwhile, a plurality of mass candidates for a non-primary-chain segment are calculated based on a mass spectrum of the first polymer. The KMD analysis is applied on the plurality of mass candidates, to thereby produce reference images. A mass of the non-primary-chain segment is identified through matching of two KMD analysis results.

Abstract: A device and a method for measuring a thermal charging state in a latent heat thermal energy storage (LHTES) system are provided. The device includes an energy storage unit with a phase change material (PCM) and one or more thermistor wires disposed along the length of the PCM. A control circuit is electrically connected to the thermistor wires. The control circuit passes an electric current through the one or more thermistor wires and measures the resulting resistance change. A processor, in communication with the control circuit, is configured to determine the thermal charging state of the PCM based on the measured resistance change. This configuration allows for an integrated thermal measurement along the entire length of the PCM to provide an accurate assessment of the stored energy.

Abstract: An information processing device according to the present application includes a hardware processor functioning as an acquisition unit, a determination unit, and a provision unit. The acquisition unit acquires user information that is information relative to a user. The determination unit determines a recommended battery on the basis of the user information acquired by the acquisition unit. The recommended battery is recommended to the user and used for a moving object. The provision unit provides information relative to the recommended battery determined by the determination unit.

Abstract: A power detector with wide dynamic range. The power detector includes a linear detector, followed by a voltage-to-current-to-voltage converter, which is then followed by an amplification stage. The current-to-voltage conversion in the converter is performed logarithmically. The power detector generates a desired linear-in-dB response at the output. In this power detector, the distribution of gain along the signal path is optimized in order to preserve linearity, and to minimize the impact of offset voltage inherently present in electronic blocks, which would corrupt the output voltage. Further, the topologies in the sub-blocks are designed to provide wide dynamic range, and to mitigate error sources. Moreover, the temperature sensitivity is designed out by either minimizing temperature variation of an individual block such as the v-i-v detector, or using two sub-blocks in tandem to provide overall temperature compensation.

Abstract: Voltage measurement techniques for battery cells in a battery management system (BMS) are described. Raw voltage measurements may include errors caused by ohmic losses due to cable impedances and other elements in the BMS. The techniques include performing a calibration cycle to generate correction values based on measured response with discharge switches in different states. The techniques also include scaling the correction values during runtime to correct for filter response errors in the voltage measurement.

Abstract: An intelligent predictive battery replacement system to increase disaster recovery stability uses training samples created using battery report indexes and vault condition recovery reports to train a linear regression model learning process to learn a recursion between battery post recovery charge and a set of time series battery operational parameters. Once trained, the learning process is used in a predictive manner to predict the post recovery charge state of batteries deployed in storage controllers, to provide a predictive per-battery risk assessment. The per-battery risk assessment identify batteries that may be scheduled to be replaced to increase disaster recovery stability of the storage systems.

Abstract: A method, a computer system, and a computer program product for regionalized climate models is provided. Embodiments of the present invention may include selecting a class of a reduced order model. Embodiments of the present invention may include building a neural network in a reduced order space. Embodiments of the present invention may include recovering full state dynamics. Embodiments of the present invention may include training a model. Embodiments of the present invention may include providing an output.

Abstract: A cytometer includes a plurality of measurement sections, each comprising first and second sidewalls and a base therebetween, the sidewalls and base defining a channel portion extending from an entrance to an exit; an electrode group arranged on the opposite sides of the base and part way between the entrance and exit, the electrode group comprising an upstream electrode, a centre electrode and a downstream electrode in the way of a trajectory between the entrance and exit, and in respective order; the measurement sections being connected together to form at least one measurement channel comprising a plurality of the channel portions connected together in series; and, a lock-in amplifier; the central electrode of each group connected to the excitation signal port of the lock-in amplifier, and the upstream and downstream electrodes connected to the voltage differential input ports of the lock-in amplifier.

Abstract: A battery management system for tracking a state of charge of a battery including multiple cells including a coulomb counter (CC) estimator and an artificial intelligence (AI) estimator. The CC estimator determines a charge difference over time for tracking changes of a state of charge of each cell and updates a corresponding state of charge value. The AI estimator converts a set of sample values of a cell into an estimated state of charge for calibrating the cell. A controller may periodically invoke the AI estimator to calibrate the state of charge of each cell and to update a corresponding state of charge value. Processing by the AI estimator may be minimized by being used only to calibrate each cell before combined error of the CC estimator reaches a predetermined threshold. AI based calibration may use Long Short-Term Memories and may further incorporate voltage mean and voltage variance over time.

Abstract: An automated maintenance, monitoring and diagnostics infrastructure can include small, portable sensor devices, which can be attached to industrial machines and physical equipment. The sensor devices include wireless communication facilities and an accelerometer, capable of measuring vibration signals. Based on received sensor data, the system determines an anomaly associated with a physical equipment and periodically evaluates additional data generated by the sensor device and determines an occurrence in a change of the anomaly.

Abstract: A battery apparatus estimates a surface SOC representing a potential at an electrode surface of the battery, and predicts an output of the battery during a requested time based on the first surface SOC, a cut-off voltage, and the requested time.

Abstract: A system includes a traction battery and a controller. The traction battery includes first and second arrays of battery cells. The controller is programmed to implement first and second filters, such as first and second bar-delta filters, configured to respectively generate output indicative of a state-of-charge (SOC) of the first and second arrays from models of the first and second arrays. The controller is further programmed to charge and/or discharge the traction battery according to power limits defined by the SOC of the first and second arrays.