Abstract: A vehicle, system and a method of predicting a thermal runaway event in a battery pack. The vehicle includes a battery pack having a plurality of battery cells. The system includes a sensor and a processor. The measures a parameter of a battery cell of a battery pack of the vehicle. The processor is configured to determine a charging response of the battery cell from the parameter, determine a likelihood of the thermal runaway event from the charging response, and control an operation of the vehicle to prevent the thermal runaway event based on the likelihood.

Abstract: A vehicle operates a system and method of predicting a thermal runaway event in a battery pack of the vehicle. The battery pack includes a battery cell. The sensor obtains measurements of a parameter of the battery cell at a plurality of times. The processor is configured to determine a value of at least one feature of the battery cell from the measurements of the parameter, determine a likelihood of the thermal runaway event from the value of the at least one feature, and take an action to prevent the thermal runaway event from occurring based on the likelihood.

Abstract: A decoding method, a memory storage device and a memory control circuit unit are disclosed. The method includes: activating a decoding circuit which supports a plurality of decoding modes each corresponding to a threshold value, wherein a distribution of the threshold value corresponds to error correction abilities of the decoding modes; reading first data from a rewritable non-volatile memory module; performing, by the decoding circuit, a first decoding operation on the first data; obtaining a decoding parameter according to an execution result of the first decoding operation; and performing, by the decoding circuit, a second decoding operation on the first data based on a first decoding mode among the decoding modes according to a relative numerical relationship between the decoding parameter and the threshold value.

Abstract: A decoding method, a memory storage device and a memory control circuit unit are disclosed. The method includes: activating a decoding circuit which supports a plurality of decoding modes each corresponding to a threshold value, wherein a distribution of the threshold value corresponds to error correction abilities of the decoding modes; reading first data from a rewritable non-volatile memory module; performing, by the decoding circuit, a first decoding operation on the first data; obtaining a decoding parameter according to an execution result of the first decoding operation; and performing, by the decoding circuit, a second decoding operation on the first data based on a first decoding mode among the decoding modes according to a relative numerical relationship between the decoding parameter and the threshold value.

Abstract: A trip energy estimation system includes a memory and a control module. The memory stores average traffic speed, average traffic acceleration, driver speed, and driver acceleration data. The control module executes an algorithm to estimate an amount of energy for an electric vehicle to travel between two locations. The algorithm includes: determining, based on the average traffic speed data and the average traffic acceleration data, a baseline amount of energy for the electric vehicle to travel between the two locations; determining, based on the driver speed data and the driver acceleration data, a dynamic amount of energy corresponding to at least one of stop and go events, over speeding, or under speeding; and determining a total amount of energy based on the baseline amount of energy and the dynamic amount of energy. The control module, based on the total amount of energy, performs an operation including indicating a trip estimate.

Abstract: A system in an electric or hybrid vehicle includes two or more battery packs to power one or more motors of the vehicle to move the vehicle. A controller dynamically activates an operational mode of the vehicle during vehicle operation, the operational mode being one of a plurality of possible operational modes. Each of the possible operational modes defines which of the two or more battery packs are connected to the one or more motors and, based on two or more of the two or more battery packs being connected to the one or more motors, also defines an interconnection among the two or more of the two or more battery packs. The plurality of possible operational modes includes a first operational mode defining a series connection between the two or more battery packs and second operational mode defining a parallel connection between the two or more battery packs.

Abstract: An electric vehicle, an electric drive system and method of operating the electric vehicle. The electric drive system provides a torque to a wheel of the electric vehicle. The processor is configured to obtain a value of the torque at which the electric drive system of the vehicle is operating, determine an optimal speed for the vehicle for the torque based on an efficiency model of the electric drive system, wherein the optimal speed corresponds to an optimal drive efficiency for the electric drive system, and operate the vehicle at the optimal speed.

Abstract: A solar loading-based system includes a memory, a solar load prediction module, and an eco-routing module. The memory is configured to store map information and environment information. The solar load prediction module is configured, based on the map information and the environment information, to (i) determine a route of a host vehicle, (ii) predict solar loading on the host vehicle along the route, and (iii) predict an amount of energy to be consumed by the host vehicle over the route based on the predicted solar loading. The eco-routing module is configured, based on the amount of energy to be consumed by the host vehicle over the route, to at least one of (i) determine whether to follow the route, or (ii) inform a user of the route and the predicted amount of energy to be consumed over the route.

Abstract: A solar loading-based system includes a memory, a disturbance prediction module, a cabin temperature estimation module and a thermal control module. The memory stores a cabin thermal load model of an interior cabin of a host vehicle and a solar load prediction model. The disturbance prediction module: receives signals indicative of states of cabin thermal actuators and comfort metrics; and predicts an effect of solar loading over a known portion of a predicted route including predicting cabin temperatures based on the solar load prediction model, the states of the cabin thermal actuators, and the comfort metrics. The cabin temperature estimation module, based on the cabin thermal load model, determines a first comfort metric based on the predicted cabin temperatures. The thermal control module controls cabin thermal actuators to adjust cabin states, including the first comfort metric, to respective target values based on the predicted effect of solar loading.

Abstract: A trip energy estimation system for a vehicle includes a traffic speed module configured to determine an average traffic speed along a projected route, a path information module configured to output path information indicating route features along the projected route, a perceived speed module configured to output a perceived vehicle speed along the projected route based on the average traffic speed and the path information, and a dynamic driving module configured to calculate and output a predicted driver speed based on the perceived vehicle speed and a feedback input indicative of the predicted driver speed. The dynamic driving module is configured to execute a machine learning algorithm to calculate the predicted driver speed.

Abstract: A distributed learning system of a vehicle includes: a control module configured to: control a plant of the vehicle using a policy; send signals to a learning module including information on an impact of the control on the plant; and selectively control the plant using exploratory control; and the learning module, where the learning module is separate from the control module and is configured to selectively update the policy based on (a) the signals from the control module, (b) state parameters resulting from the control of the plant using the policy, and (c) performance feedback determined based on the control of the plant using the policy and the selective control of the plant using exploratory control, where the control module is configured to receive the exploratory control from the learning module.

Abstract: A computer for an energy management system of an electric vehicle includes a processor. The computer further includes a memory including instructions such that the processor is programmed to determine a value function V based on a plurality of actions U in a plurality of states S. The processor is further programmed to select an action associated with a highest reward value at a current state S. The action U is an HVAC subsystem variable. The state S is a traction power drawn from a rechargeable energy storage system (RESS) to operate a traction subsystem, a base power input drawn from the RESS to operate an HVAC subsystem, a nominal reference cabin heat input set-point determined by the local HVAC processor, an acceleration of the electric vehicle, a current vehicle speed, an average vehicle speed, and a calibrated average vehicle speed estimate.

Abstract: A battery system includes: at least two battery modules, each including at least three strings of battery cells that are configured to, at different times be: connected in series and to a first positive terminal via first switches; connected in parallel and to a second positive terminal via second switches; and disconnected from both of the first and second positive terminals; and a switch control module configured to: during operation in a first power mode: determine periods of phases, respectively; and determine periods for the strings, respectively, to be connected to the second positive terminal during the phases; during operation in a second power mode: set the periods of the phases, respectively, to be equal in length; and set the periods for the strings, respectively, to be equal in length; and selectively actuate the switches based on the periods of the phases and the periods for the strings.

Abstract: A battery system includes: a first positive terminal, a second positive terminal, and a negative terminal; at least two battery modules, each including switches and at least three strings of battery cells configured to, via the switches, at different times be: connected in series and to the first positive terminal; connected in parallel and to the second positive terminal; and disconnected from both of the first and second positive terminals; and a switch control module configured to, when a fault is diagnosed in one of the strings of one of the battery modules: at least one of: actuate the switches and isolate the one of the battery modules from the first and second positive terminals; and actuate the switches and isolate the one of the strings from the first and second positive terminals; and selectively actuate the switches of the remainder of the battery modules using model predictive control.

Abstract: A trip energy estimation system includes a memory and a control module. The memory stores average traffic speed, average traffic acceleration, driver speed, and driver acceleration data. The control module executes an algorithm to estimate an amount of energy for an electric vehicle to travel between two locations. The algorithm includes: determining, based on the average traffic speed data and the average traffic acceleration data, a baseline amount of energy for the electric vehicle to travel between the two locations; determining, based on the driver speed data and the driver acceleration data, a dynamic amount of energy corresponding to at least one of stop and go events, over speeding, or under speeding; and determining a total amount of energy based on the baseline amount of energy and the dynamic amount of energy. The control module, based on the total amount of energy, performs an operation including indicating a trip estimate.

Abstract: A method for generating energy-optimized travel routes for a motor vehicle includes one or more of the following: receiving an origin destination (OD) of the motor vehicle and an encrypted energy consumption database of the motor vehicle; generating N candidate routes for the OD; evaluating encrypted energy consumption over a route using an encrypted energy consumption database; applying at least one of homomorphic addition function or homomorphic multiplication function to the encrypted energy consumption data; and returning N candidate routes and their encrypted energy consumption to a client.

Abstract: A control system of a vehicle includes: a target speed module configured to, using a parametric driver model and based on first driver parameters, second driver parameters, and vehicle parameters, determine a target vehicle speed trajectory for a future predetermined period; a driver parameters module configured to determine the first driver parameters based on conditions within a predetermined distance in front of the vehicle; and a control module configured to adjust at least one actuator of the vehicle based on the target vehicle speed trajectory and a present vehicle speed.

Abstract: A method for generating energy-optimized travel routes for a motor vehicle includes one or more of the following: receiving an origin destination (OD) of the motor vehicle and an encrypted energy consumption database of the motor vehicle; generating N candidate routes for the OD; evaluating encrypted energy consumption over a route using an encrypted energy consumption database; applying at least one of homomorphic addition function or homomorphic multiplication function to the encrypted energy consumption data; and returning N candidate routes and their encrypted energy consumption to a client.

Abstract: A computer for an energy management system of an electric vehicle includes a processor. The computer further includes a memory including instructions such that the processor is programmed to determine a value function V based on a plurality of actions U in a plurality of states S. The processor is further programmed to select an action associated with a highest reward value at a current state S. The action U is an HVAC subsystem variable. The state S is a traction power drawn from a rechargeable energy storage system (RESS) to operate a traction subsystem, a base power input drawn from the RESS to operate an HVAC subsystem, a nominal reference cabin heat input set-point determined by the local HVAC processor, an acceleration of the electric vehicle, a current vehicle speed, an average vehicle speed, and a calibrated average vehicle speed estimate.

Abstract: A method for controlling automated driving operations of a vehicle includes determining vehicle origin and destination data, and generating a graphical representation of a road network with multiple candidate routes between the vehicle's origin and destination. Road-level data, including speed, turn angle, and/or gradient data, is received for each candidate route, and respective total energy uses are estimated for the vehicle to traverse across the candidate routes. Multiple candidate driving strategies, each having respective speed and acceleration profiles, are determined for the candidate route with the lowest estimated total energy use. An optimal candidate driving strategy is selected through a cost evaluation of the associated speed and acceleration profiles and forward movement simulations of the vehicle over a prediction horizon.