EV Charge Planner

安装指南

1. ClawHub CLI 从源直接安装技能的最快方式。
   npx clawhub@latest install ev-charge-planner
2. 手动安装 将技能文件夹复制到以下位置之一
   全局模式
   ~/.openclaw/skills/
   工作区
   /skills/
   优先级：工作区 > 本地 > 内置
3. 提示词安装 将此提示词复制到 OpenClaw 即可自动安装。
   请帮我使用 Clawhub 安装 ev-charge-planner。如果尚未安装 Clawhub，请先安装（npm i -g clawhub）。
   更倾向于下载？
   获取包含原生技能文件的ZIP压缩包。
   下载技能 (.zip)

什么是

EV Charge Planner 是一款命令行 Skill，旨在结合您自己的车辆、电价、日程安排和家庭用电负荷，做出实用的家用电动汽车充电决策。它考虑了从插座到电池的充电损耗，将充电桩电流（安培）转换为千瓦，估计时间与成本，并根据 80% 的安全指导准则检查 EVSE 的持续负荷是否超出配电盘容量。
作为 Openclaw Skills 中用于日常能源与成本规划的工具之一，它整合了三个计算器：session 用于单次充电；tou 用于将充电嵌入分时电价（TOU）时间窗；compare 用于月度电动汽车与燃油车成本对比。它使用 Python 标准库离线运行，并基于实际驾驶效率和充电比例进行设计，而非仅仅依靠车辆的标称数据。

应用场景

估算单次充电所需的时间、从电表消耗的总电量以及费用。
检查目标充电任务是否可以在分时电价优惠起点与出发截止时间之间完成，包括预留充完电后的缓冲时间。
计算最晚的安全开始时间；当优惠时间窗过短时，评估替代方案。
使用实际行驶里程、家用与公共充电电价，以及真实世界中的车辆效率，对比月度电动汽车与燃油车的成本。
在评估 EVSE 或充电速度时，将 Level 2 充电桩的安培数转换为近似的千瓦数。
检查提议的充电桩与现有家庭负荷是否接近配电盘或断路器的 80% 持续负荷指导限制。
当电价、季节、通勤需求或公共充电比例发生变化时，重新计算规划方案。

工作原理

收集车辆的可用电池容量、实际每度电行驶里程（miles per kWh）、充电桩功率或电流、电价，以及任何出发截止时间或家庭负荷细节。
对于单次充电任务，使用请求的电池电量、充电桩功率和电价运行 session。规划器将估算充电时长、插座端电量、成本、电池百分比、增加的里程、配电盘负荷，并在提供出发时间时给出最晚开始时间。
对于分时电价规划，使用优惠时间窗、普通电价、出发时间和期望缓冲时间运行 tou。规划器将报告充电是否适用、可用余量、最晚安全开始时间，以及相比普通电价节省的费用。
如果请求的充电任务无法完全容纳，对比报告给出的选项：提高充电桩功率、接受带估计里程的局部充电，或者为剩余电量支付普通电价。
对于月度拥车成本估算，使用月行驶里程、电动汽车效率、家用与公共充电电价及比例、汽油价格和燃油车 MPG 运行 compare。
审查假设条件，并在情况变化时重新运行。充电效率模型设定为 90%；该 Skill 建议使用仪表盘实际效率和出发缓冲时间，而不是仅依赖官方标称数据。

配置指南

该 Skill 被描述为纯 Python 标准库且离线运行；不需要安装外部包或访问网络。只需在您的 agent 工作区中提供 Skill 文件，然后从 Skill 目录中使用 Python 3 调用附带的脚本。文档记录的脚本路径为 scripts/ev_planner.py。
python3 scripts/ev_planner.py session --kwh-needed 32 --kw 7.4
--rate 0.23 --battery 60 --house-amps 28 --panel-amps 100
--departure "07:00" 对于分时电价规划，提供时间窗、优惠及普通电价、出发时间以及可选的缓冲时间：
python3 scripts/ev_planner.py tou --kwh-needed 44 --kw 11
--offpeak "23:00-07:00" --rate 0.11 --regular-rate 0.34
--departure "06:30" --buffer-min 30 对于月度电动汽车与燃油车的对比：
python3 scripts/ev_planner.py compare --miles-month 900
--ev-mi-per-kwh 3.4 --home-rate 0.14 --public-pct 20
--public-rate 0.48 --gas-price 4.10 --gas-mpg 30 请使用您实际的公用事业电价和仪表盘效率以获得更有用的估算。提供的文档未指定包管理器安装命令或持久配置文件。

数据架构

该 Skill 在文档化的工作流中是无状态的：将输入作为命令行参数提供并查看计算结果。未指定数据库、账户或生成的输出文件格式。
Command Main inputs Calculated planning data
session 所需电量、充电桩 kW、电价；可选电池容量、家庭和配电盘安培数、出发时间 充电时长、插座端电量、成本、电池百分比、增加里程、配电盘负荷检查，以及适用的最晚开始时间
tou 所需电量、充电桩 kW、分时优惠时间窗与电价、普通电价、出发时间、可选缓冲时间 是否容纳状态、余量、最晚安全开始时间、节省费用，以及无法完全容纳时的替代方案
compare 月行驶里程、电动汽车 mi/kWh、家用电价、公共充电比例与电价、油价、汽油车 MPG 估算的月度电动汽车与燃油车能源成本及成本差额
模型假设与支撑文件
充电效率建模为 90%，以计入从插座到电池的损耗。
在 240V 下，估计的充电功率为 安培数 × 0.24 kW；文档记载的阶梯涵盖 16A 到 48A。
EVSE 持续负荷按照断路器或配电盘的 80% 指导标准进行检查；这是规划指导，而非电气检验。
Frontmatter 元数据将该 Skill 标识为 ev-charge-planner，版本为 1.0.1，带有电动汽车、充电、电价、能源和个人财务等标签。
references/charging-physics.md 记录了效率、安培数、配电盘负荷管理、电池充电实践以及寒冷天气注意事项。
references/tou-rates-playbook.md 涵盖了分时电价计划、费率价差、签约注册、计量选项，以及固定电价可能更优的情况。

高级特性

三个专注的 CLI 计算器：
session
、
tou
compare
分别解决单次充电规划、电价时间窗调度和月度成本对比。
具备截止时间意识的调度：
计算最晚开始时间，并在出发前预留可配置的缓冲时间。
无法容纳时的替代方案：
当优惠时间窗不足时，评估更快的充电速度、带估计里程的局部充电，或按普通电价支付剩余电量。
用电负荷感知：
包含家庭和配电盘安培数，以标记潜在的持续负荷风险并估算安全充电功率。
真实混合充电比例：
将公共充电与家用充电分开建模，以便在拥车对比中体现昂贵的公共能源成本。
离线且无外部依赖：
使用 Python 标准库运行，使这些 Openclaw Skills 非常适合在没有网络连接的情况下进行本地规划。
实用边界：
适用于家用交流充电与电动汽车对比燃油车的能源成本估算；它不是 DC 快充长途旅行、太阳能容量设计或整个公用事业账单审计工具。

name: ev-charge-planner description: "Use when planning home EV charging — computes charge time and cost for a session (with wall-to-battery losses and breaker load checks), plans overnight charging inside time-of-use off-peak windows (latest safe start, doesn't-fit options), and compares monthly fuel cost EV vs gas car including public-charging mix; handles kW/amps, departure deadlines, and cold-day buffer advice." version: 1.0.1
author: Denis Voronin
license: MIT
tags: [ev, electric-vehicle, charging, electricity-rates, energy, personal-finance]

EV Charge Planner

Overview

Tens of millions of drivers now charge an EV at home, and nearly all of them get the math wrong in the same ways: they forget ~10% wall-to-battery losses (a "60 kWh" fill draws ~67 kWh from the meter), they charge at 7¢-per-kWh worse rates than they could because they never learned their utility's time-of-use windows, they size charger amperage against panel capacity by vibe, and they compare EV vs gas costs using the EPA sticker efficiency instead of their own.

This skill is three calculators:

session — kWh needed + charger kW + rate → hours:minutes to target, wall energy, cost, battery %, miles added, panel/breaker load check (80% continuous guidance), and latest start time for a departure deadline.
tou — off-peak window planning: does the charge fit before your departure (with buffer)? Latest safe start? If not: raise amperage / accept partial / price the regular-rate tail.
compare — monthly EV vs gas cost at YOUR prices and mileage, with the public-charging mix that quietly destroys the economics.

Pure stdlib, offline.

When to Use

"How long to add 40 kWh on my 7.4 kW wallbox?" / "What will it cost?"
"Off-peak is 23:00-07:00 and I leave at 6:30 — can I fit 44 kWh?"
"Is the EV actually cheaper than my 30-mpg car at $4.10 gas?"
"Can my 100A panel handle a 48A charger plus the house?"
"My charger asks for amps — what do 16/32/40/48A mean in kW?"
Sizing a wallbox purchase vs charging needs (power math side)

Don't use for: DC fast-charging road-trip planning (different curve — charge rates fall steeply above 80%), off-grid/solar sizing (needs panel production data), or utility-bill auditing (only the EV slice is modeled).

How It Works — Numbered Steps
Know your inputs: battery usable kWh, charger kW (amps × 0.24 at 240V — 32A ≈ 7.7 kW, 48A ≈ 11.5 kW), your rate schedule (flat vs TOU windows), and your car's real mi/kWh (dashboard average, not the sticker).
Session math:

python3 scripts/ev_planner.py session --kwh-needed 32 --kw 7.4 \

--rate 0.23 --battery 60 --house-amps 28 --panel-amps 100
--departure "07:00"

→ 4h48m, 35.6 kWh from the wall, $8.18, panel at 59% (OK), start 02:11.
TOU planning (where the real savings live):

python3 scripts/ev_planner.py tou --kwh-needed 44 --kw 11 \

--offpeak "23:00-07:00" --rate 0.11 --regular-rate 0.34
--departure "06:30" --buffer-min 30

→ fits with 2h33m slack, latest start 01:33, saves $11.24/session vs charging at the regular rate.
When it does NOT fit, the tool prices all three exits: raise charger kW, accept a partial charge (with miles), or pay the regular-rate tail.
Monthly comparison:

python3 scripts/ev_planner.py compare --miles-month 900 --ev-mi-per-kwh 3.4 \

--home-rate 0.14 --public-pct 20 --public-rate 0.48
--gas-price 4.10 --gas-mpg 30

→ EV $61/mo vs gas $123/mo → $742/yr saved.
Set the car/EVSE departure-and-target once; re-run when rates, seasons, or commute change.
The Physics (short version)
Wall ≠ battery. Charging is ~88-92% efficient; the tool uses 90%. A 60 kWh fill at $0.14/kWh costs $9.33, not $8.40.
kW from amps @240V: kW = amps × 0.24. Level 2 amperage ladder: 16A=3.8, 24A=5.8, 32A=7.7, 40A=9.6, 48A=11.5 kW.
Panel guidance: continuous loads (EVSE) should stay under 80% of breaker/panel rating. 48A charging + evening house load can breach this on a 100A panel — the tool flags it and computes the safe kW.
TOU arbitrage: typical US spreads run 2-3× between off-peak and peak ($0.11 vs $0.34 here). Overnight-only charging is the single highest-ROI habit in EV ownership — worth $300-600/yr for an average driver.
Cold days: batteries condition/warm post-charge; finishing 30 min before departure preserves range. Hence the buffer default.

Common Pitfalls

Charging "to full" every night. Most batteries are happiest in the 20-80% band; nightly 80% + 100% before trips is the standard protocol.
Ignoring TOU enrollment. Many utilities default you to flat rate; enrolling in EV/TOU plans is a 15-minute phone call that halves charging cost. The compare tool shows the spread.
Counting public DC charging as 'basically home charging.' At $0.40-0.60/kWh, DC fast charging costs MORE per mile than a 30-mpg car at $4 gas. The public-mix percentage exists to keep this honest.
Sizing the charger by 'more is better.' A 48A EVSE on a 100A panel with a heat pump and an electric dryer is a load-management problem; 32A covers 40+ miles/hour of charging anyway — most overnight needs fit.
Assuming the sticker efficiency. Cold weather, high speed, and hills move real consumption 10-30%. Use the dashboard's lifetime mi/kWh.
Departing mid-charge in winter. The battery is warm while charging; a 30-minute post-charge buffer lets preconditioning work from grid power instead of battery.
Forgetting vampire loads in compare. If the EV lets you enter mileage only, add 5-10% for climate/sentinel drain on the kWh side.

Example

Owner: 62-kWh EV, new 7.4 kW wallbox, off-peak 23:00-07:00 @ $0.11,
departs 06:30. Drives 900 mi/mo, gas alternative is 30 mpg @ $4.10.

$ session --kwh-needed 32 --kw 7.4 --rate 0.11 --departure "07:00"
→ 4h48m, $4.36 at off-peak (vs $8.18 flat-rate) — start at 02:11.

$ tou --kwh-needed 44 --kw 11 --offpeak "23:00-07:00" --rate 0.11
--regular-rate 0.34 --departure "06:30" → FITS, slack 2h33m, latest start 01:33, saves $11.24/session.

$ compare --miles-month 900 --ev-mi-per-kwh 3.4 --home-rate 0.11
--public-pct 10 --public-rate 0.48 --gas-price 4.10 --gas-mpg 30 → EV ~$40/mo vs gas $123/mo → saves ~$1000/yr charging at home,
but only $260/yr if 50% of charging is public DC.

References

references/charging-physics.md — efficiency losses, amperage/kW tables, panel load management, battery 20-80 protocol, cold-weather effects.
references/tou-rates-playbook.md — how TOU plans work, typical US rate spreads, enrollment steps, metering options, when flat-rate beats TOU.
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