Method for predicting battery life
Methods and systems are provided for reliably providing a prognosis of the life-expectancy of a vehicle battery. A state of degradation of the battery is predicted based on a rate of convergence of a metric, that is derived from a sensed vehicle operating parameter, towards a defined threshold, determined based on past history of the metric. The predicted state of degradation is then converted into an estimate of time or distance remaining before the component needs to serviced, and displayed to the vehicle operator. Vehicle control and communication strategies may be defined with respect to the predicted state of degradation.
1. A method for predicting battery health for a vehicle, comprising:
monitoring a battery health parameter in real-time using one or more onboard battery monitoring sensors;
determining a threshold of the monitored battery health parameter based on vehicle operating conditions and battery end-of-life information gathered from each of a plurality of vehicles of a fleet and received via a vehicle communication network, wherein the vehicle is one of the plurality of vehicles in the fleet, and wherein determining the threshold includes determining that the battery end-of-life information indicates that an initial threshold of the monitored battery health parameter is too high or too low and, in response, adapting the initial threshold based on the battery end-of-life information to determine the threshold of the monitored battery health parameter, wherein adapting the initial threshold based on the battery end-of-life information comprises adding an output of a transfer function to the initial threshold, the transfer function mapping, via an input/output relationship, incremental change in the initial threshold to an incremental change to the battery end-of-life information;
defining a battery end-of life prediction algorithm based on a speed of convergence of the monitored battery health parameter to the determined threshold;
estimating an end of life of the battery based on the prediction algorithm; and
limiting one or more autonomous functions or safety-related functions requiring electrical power of the vehicle based on the estimating, or limiting one or more non-essential electrical power requiring systems of the vehicle based on the estimating.
2. The method of claim 1 , wherein limiting the one or more autonomous functions or the safety-related functions requiring the electrical power of the vehicle based on the estimating includes where a degree of the limiting is based on the estimated end of life relative to an end of life threshold.
3. The method of claim 1 , wherein limiting the one or more non-essential electrical power requiring systems of the vehicle based on the estimating includes where a degree of the limiting is based on the estimated end of life relative to an end of life threshold, the one or more non-essential electrical power requiring systems of the vehicle including an electrically-actuated anti-roll control system.
4. The method of claim 1 further comprising displaying a confidence level for the estimated end of life of the battery, wherein the confidence level is determined based on machine learning of data from the plurality of vehicles.
5. The method of claim 1 , wherein the battery end-of-life information includes a batteries in service metric (BISM), wherein determining that the battery end-of-life information indicates that the initial threshold of the monitored battery health parameter is too high or too low comprises comparing the BISM to a performance metric threshold and determining that the BISM exceeds the performance metric threshold, and wherein adapting the initial threshold based on the battery end-of-life information comprises setting the determined threshold to be lower than the initial threshold.
6. The method of claim 1 , wherein the battery end-of-life information includes a false replacement metric (FRM), wherein determining that the battery end-of-life information indicates that the initial threshold of the monitored battery health parameter is too high or too low comprises comparing the FRM to a performance metric threshold and determining that the FRM exceeds the performance metric threshold, and wherein adapting the initial threshold based on the battery end-of-life information comprises setting the determined threshold to be higher that the initial threshold.