EV Fleet Charging Protocol Adaptation for Battery Aging Control
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Solution Overview
Problem
Existing electric vehicle battery charging protocols, designed under lab conditions, may be too conservative or aggressive, leading to suboptimal charging times and battery aging when applied in real-world conditions across a fleet of vehicles.
Innovation Solution
A method that adapts the baseline charging protocol for electric vehicle batteries by modifying it based on real-world performance data from a subset of vehicles, using a cloud backend to generate alternative protocols, measure their impact, and update the baseline protocol to optimize charging times and extend battery lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a static charging protocol is used to maximize charging speed, then charging time is reduced, but battery degradation increases under certain real-world conditions
Solution Approach 1:
The patent transforms the static charging protocol into a dynamic one by continuously adapting charge current based on real-time battery state measurements (temperature, voltage, age) and environmental conditions. The BMS adjusts charging parameters on-the-fly rather than following a fixed protocol, allowing optimization for both speed and battery protection under varying real-world conditions.
Solution Approach 2:
The system implements feedback loops where battery state measurements during charging are fed back to the BMS, which then adjusts the charging protocol accordingly. This closed-loop control enables the system to respond to actual battery conditions, preventing excessive degradation while maintaining efficient charging, unlike open-loop static protocols.
2Reliability
If a conservative charging protocol is used to protect battery life, then battery degradation is reduced, but charging time increases
Solution Approach 1:
The charging protocol dynamically adjusts between conservative and aggressive charging strategies based on real-time battery state. When battery conditions permit, the system increases charge current to reduce charging time; when conditions indicate risk of degradation, it reduces current to protect battery life, achieving both goals adaptively.
Solution Approach 2:
The system changes charging parameters (current, voltage, temperature thresholds) based on battery state of charge, temperature, and estimated battery age. This parameter adaptation allows the protocol to be aggressive when safe and conservative when necessary, optimizing both charging speed and battery protection.
3Ease of manufacture
If lab-derived charging protocols are applied to real-world conditions, then charging protocol implementation is simple, but performance is suboptimal across diverse operating conditions
Solution Approach 1:
The BMS performs self-optimization by automatically adapting the charging protocol based on measurements from its own sensors without requiring external intervention or complex manual configuration. The system learns from its own operational data and environmental conditions, maintaining simplicity while achieving real-world optimization.
Solution Approach 2:
Real-world performance feedback from actual charging operations is used to continuously refine and adapt the charging protocol. The system measures actual battery response and charging outcomes, then adjusts future charging strategies accordingly, enabling continuous improvement beyond lab-derived baseline protocols.
Data Source
AI summary
Systems and methods are disclosed for optimizing charging protocols across the fleet of electric vehicles over time. In particular, a cloud backend generates one or more alternative or experimental charging protocols by perturbing a baseline charging profile of a baseline charging protocol. The ‘perturbed’ charging protocol(s) are deployed to a subset of the fleet of electric vehicles. As the subset of the fleet of electric vehicles charge their batteries using the perturbed charging protocol(s), the cloud backend observes, based on battery data received from the subset of the fleet of electric vehicles, an impact of the perturbed charging protocol(s) on battery aging and charging times. Based on these battery data, the cloud backend revises and improves the baseline charging protocol that is deployed to the entire fleet of electric vehicles.